GNGTS 2024 - Atti del 42° Convegno Nazionale

11_Abstract_GNGTS2024.pdf	9
Estimate of seismic fracture surface energy from pseudotachylyte-bearing faults	9
S. Aldrighetti1, G. Di Toro1,2, G. Pennacchioni1	9
1 Dipartimento di Geoscienze, Università degli Studi di Padova, Padua, Italy	9
Offshore fault geometry revealed from earthquake locations using new state-of-art techniques: the case of the 2022 Adriatic Sea earthquake sequence	12
Like An*1, Francesco Grigoli2, Bogdan Enescu1,3, Mauro Buttinelli4, Mario Anselmi4, Irene Molinari4, and Yoshihiro Ito5	12
1Department of Geophysics, Graduate School of Science, Kyoto University, Kyoto, Japan	12
2Department of Earth Sciences, University of Pisa, Pisa, Italy	12
3National Institute for Earth Physics, Magurele, Bucharest, Romania	12
4National Institute of Geophysics and Volcanology, Italy	12
5Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto, Japan	12
1 Istituto Nazionale di Geofisica e Vulcanologia, Roma, Italy	13
2 Istituto Nazionale di Geofisica e Vulcanologia, Pisa, Italy	13
3 Università degli Studi di Roma Tre, Roma, Italy	13
A. Arrighetti1, B. Gelli2, V. Castelli3	16
1 École normale supérieure - Université PSL (AOROC UMR 8546), Paris, France;	16
2 Università degli Studi di Siena, Italia; 3 INGV, Bologna/Ancona, Italia	16
Fig. 1 – The 1467 Siena seismic sequence according to CPTI15 v. 4.0 (Rovida et al., 2022).	17
Three-dimensional magnetotelluric inversion applied in a sector of the Irpinia Fault System (Southern Apennine)	25
1 Istituto di Metodologie per l’Analisi Ambientale, CNR – Tito Scalo, PZ	25
2 Istituto di Geofisica e Vulcanologia INGV, Roma	25
3 Università degli Studi di Bari	25
The harsh life of an earthquake in the region that doesn't exist	27
S. Baranello1,2, R. Camassi1, V. Castelli1	27
1 Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy	27
The historical research on earthquakes often clashes with harsh reality: if the earthquake is not destructive, if it occurs during a particularly complex historical period, dominated by wars, epidemics, and other misfortunes, there is the possibility of its memory being lost. Sometimes, in addition to the scant production of testimonies about the earthquake and its impact, possible problems arise in the preservation of such testimonies. And finally, the obstacle course of the historical seismologist can find many doors closed today. Literally.	27
And this happens especially in the region that doesn't exist...	27
When any of these circumstances (or all of them) occur, research must necessarily pursue not only written testimonies but also simple clues, indirect evidence of the earthquake's occurrence, such as local traditions, the presence of a local earthquake-related cult, etc.	27
Baratta (1901) devotes only a few very generic lines to the Molise earthquake of May 1712. First of all, he says that an earthquake was felt in early May in Naples, and that it caused panic among the Neapolitans. To this news, Baratta adds that an earthquake was also felt in Campobasso where "some houses and churches were ruined." Finally, he mentions several shocks that were felt in Benevento between May and June 15.	27
Baratta's sources are respectively a summary of the Bologna Gazette published by De Rossi (1889) and a brief mention of Campobasso by Sarnelli (1716).	27
In the Postpischl (1985) catalogue, these pieces of information are summarized into an event dated generically to May 1712, located in Bojano, with an epicentral intensity of VIII MCS (Tab. 1).	27
The AMGNDT995 data sheet dedicated to the 1712 earthquake considers various information not clearly attributable to a single event and downgrades the earthquake, dated May 8th, locating it in Campobasso with an epicentral intensity uncertain between VI and VII MCS. The study suggests that the assertion that houses and churches were 'ruined' refers to a level of moderate, non-structural damage. This interpretation has been incorporated into the CPTI catalogue in its various versions.	28
Recently, in the frame of a research project aimed at improving the preliminary AMGNDT995 studies, the case of the 1712 earthquake has been reopened, following the report of the presence of the cult of San Michele in Ripalimosani, connected to the averted danger during an earthquake dated May 1712 [Mascia, 2000].	28
Overall, this is certainly a very interesting and complex situation regarding a certainly important earthquake that affected a very large area of central Italy (Fig. 1).	28
Fig. 1 – Distribution map of the distribution of the effects of the earthquake of 8 May 1712	29
Andrea Brogi1,2, Paola Vannoli3, Martina Zucchi1, Pierfrancesco Burrato3, Umberto Fracassi3, Gianluca Valensise3, Hsun-Ming Hu4,5, Chuan-Chou Shen4,5	31
1 Department of Earth and Geoenvironmental Sciences (University of Bari, Italy)	31
2 CNR-IGG, Institute for Geosciences and Georesources (C.N.R., Italy)	31
3 Istituto Nazionale di Geofisica e Vulcanologia (Italy)	31
4 High-Precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, (National Taiwan University, Taiwan, ROC)	31
Fluid-rock interaction in eclogite-facies meta-peridotites (Erro-Tobbio Unit, Ligurian Alps)	35
S. Cacciari1, G. Pennacchioni1, M. Scambelluri2, E. Cannaò3, G. Toffol1	35
1 Università degli Studi di Padova, Italy	35
2 Università degli Studi di Genova, Italy	35
3 Università degli Studi di Milano - La Statale, Italy	35
Scambelluri M., Tonarini S.; 2012: Boron isotope evidence for shallow fluid transfer across subduction zones by serpentinized mantle. Geology 40, 10, 907–910. doi: 10.1130/G33233.1	38
Stress drop scaling is still a very controversial topic: is it real or apparent?	39
G. Calderoni1	39
1 Istituto Nazionale di Geofisica e Vulcanologia (INGV, Italy)	39
The stress drop scaling is still an unresolved issue and continues to be controversial in the scientific community. However, knowledge of seismic source scaling parameters plays a fundamental role in assessing the seismic forecasting in a given area and in improving ground motion predictions for seismic hazard mitigation. For this reason, this study compares the Brune stress drop of the earthquake sequence that struck the 2010-2014 Pollino area in the southern Apennines with those estimated for other earthquakes that occurred in different areas of the Apennines during the following seismic sequences: 2009 L’Aquila (Calderoni et al., 2013), 2016-2017 Amatrice (Calderoni & Abercrombie 2023), 2013-2014 Sannio-Matese (Calderoni et al., 2023) and 2019 Northern Edge of the Calabrian Arc Subduction Zone (Calderoni et al., 2020). Three different methods are used, and the results are compared with previous studies.In the first procedure (Calderoni et al. 2019) a two-step approach is used to model the attenuation and then estimate the source parameters from individual earthquake spectra. In the second procedure, an EGF approach is applied. In the third procedure, a modified EGF approach is applied using a scaling law derived by Calderoni et al., (2013) for the L’Aquila 2009 seismic sequence. To gain deeper insights into the interpretation of the result, the structural complexities and tectonic barriers that control seismic activity in the Pollino area are considered (Cirillo et al., 2022).	39
A “new” Aeolian event in the 20th century: the 19th June 1916 earthquake in Filicudi Island.	41
C.H. Caracciolo	41
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Italy	41
Conclusions	44
What about the predecessors of the February 2023 earthquakes in Eastern Anatolia?	56
V. Castelli1, K. Sesetyan1,2, A.A. Gomez Capera3, C. Meletti4, M. Stucchi1	56
1Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Bologna, Bologna/Ancona, Italy;	56
2Bogazici University, Kandilli Observatory and Earthquake Research Institute, Dept. of Earthquake Engineering, Istanbul, Turkey;	56
3Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Milano, Milano, Italy;	56
4Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Pisa, Pisa, Italy.	56
«Se dice etiam per teremoti esser sommerso et ruinato tre terre» (How a large historical earthquake was born).	60
V. Castelli 1	60
1 Istituto Nazionale di Geofisica e Vulcanologia-Sezione di Bologna, Bologna/Ancona, Italy	60
Fig. 1 – Excerpt of the report written Maestro Andrea on 10 March 1514, as transcribed in Venice by Marin Sanudo sometime in the second half of 1514 (Biblioteca Nazionale Marciana, Venice).	60
Active Transpressive Faulting Along the High Atlas Mountains: the 8 September 2023, MW 6.8, Morocco Earthquake	63
D. Cheloni1, N. A. Famiglietti2, R. Caputo3, C. Tolomei1, A. Vicari2	63
1 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy	63
2 Istituto Nazionale di Geofisica e Vulcanologia, Sezione Irpina, Italy	63
3 Department of Physics & Earth Sciences, Ferrara University, Italy	63
Geodetic Modelling of the 2023 MW 7.8 and 7.6 Türkiye Earthquake Sequence	66
D. Cheloni1, N. A. Famiglietti2, A. Akinci1, R. Caputo3, A. Vicari2	66
1 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy	66
2 Istituto Nazionale di Geofisica e Vulcanologia, Sezione Irpina, Italy	66
3 Department of Physics & Earth Sciences, Ferrara University, Italy	66
Seismic cycle in bituminous dolostones (Central Apennines, Italy)	70
M. Chinello1, E. Bersan, M. Fondriest1, T. Tesei1, G. Di Toro1,2	70
Estimating the source parameters of a moderate earthquake using the second seismic moments	74
Cuius1,2, A. Saraò3, H. Meng4, G. Costa1	74
1 Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy	74
2 National Institute of Geophysics and Volcanology, INGV, Roma, Italy	74
3 National Institute of Geophysics and Applied Geophysics, OGS, Trieste, Italy	74
4 Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, Guangdong, China	74
Introduction	74
The study of earthquake generation and associated seismic parameters such as seismic moment, rupture size, rupture velocity and direction, and stress drop is crucial for understanding earthquake dynamics and the underlying physics of the seismic process. This information plays an important role in the estimation of ground shaking near the earthquake source and in the assessment of seismic hazard, even for low to moderate magnitude earthquakes.	74
The kinematic properties of small earthquakes are often difficult to determine, and simple models are often used to represent these events, although improved records show that source complexity is common even for small earthquake ruptures (e.g. Calderoni and Abercrombie, 2023 and reference therein).	74
A critical task in determining finite source attributes for moderate and low magnitude earthquakes requires good removal of path and site effects. To address this problem, several methods based on empirical Green's function (EGF) deconvolution have been developed in recent decades. Although the EGF offers several advantages, its application is associated with some difficulties, as there are often no focal mechanisms for small earthquakes and source effects have been observed even for low energy events (Calderoni et al. 2023).	74
The simplest general representation of an earthquake that contains information about the rupture extent and directivity is the point-source representation plus the variances or second-degree moments of the moment-release distribution. The hypocenter and the origin time of the earthquake correspond to the spatial and temporal average (first degree moment) of the release moment distribution. The information about the rupture extent, the characteristic duration and the direction of rupture propagation correspond to the variance of the moment distribution in the spatial, temporal and spatio-temporal domain (second-degree moments). Seismic moments are calculated from apparent durations measured from apparent source time functions (ASTF) for each station after removal of path effects. The ASTF is thus the projection of the rupture process onto the seismic ray path, and its properties also depend on the azimuth and take-off angles (e.g. McGuire, 2004). For a unilateral rupture, the ASTF observed from stations in the direction of propagation would be significantly shorter than the ASTF from stations in the opposite direction.	74
A major advantage of the second moments method is that it can theoretically be applied to all earthquakes, regardless of their magnitude and complexity, and without requiring the assumptions of an a priori source model (e.g. McGuire 2004; Meng et al., 2020; Cuius et al., 2023). It is also a consistent tool for evaluating scaling relationships between finite source attributes and earthquake magnitudes for large and small earthquakes and for resolving fault plane ambiguity.	75
However, the elimination of the path effect is crucial, and a biased ASTF calculation would lead to inaccurate calculations of the second seismic moments. However, there may also be other factors that influence the results of the second moments, even if the propagation effects have been correctly removed.	75
The aim of this study is to implement and test an efficient method for estimating source parameters and rupture directivity in near real-time for medium and small earthquakes. To achieve our goal, we implemented an approach developed by McGuire et al. (2004), which consists of calculating the second-degree seismic moments (Meng et al., 2020; Cuius et al., 2023). In this paper, we first perform a study with some synthetic tests to evaluate the influence of uncertainties related to our prior knowledge and observations on the resulting source parameters (Cuius et al. 2023). We then apply the method to a real earthquake in Italy and present the result.	75
Analysis of the sensitivity of the second moments tensor resolutions	75
To evaluate the sensitivity of the second moment solutions, we used synthetic ASTFs computed for a rectangular plane fault discretized by a grid of cells, each assigned a specific slip value. Full details can be found in Cuius et al. 2023. The input parameters used to model the ASTF for a magnitude Mw 4.6 earthquake source are listed in Tab. 1. We assumed that the epicenter was located in central Italy and approximated the fault as a 3.0 km box model (Fig. 1). The rupture area was divided into 12x12 cells, and the slip distribution and rupture time for the unilateral (Fig. 1a; 1b) and bilateral (Fig. 1d; 1e) scenarios were taken from a previous study of a similar magnitude earthquake (SRCMOD database - Mai and Thinbgaijam, 2014), with a focal mechanism of 247° strike, 46° dip and 40° dip. Using the actual station configuration, we calculated the ASTFs with a sampling frequency of 100 Hz and a source time function of 3 seconds. A uniform propagation of the rupture front with a rupture velocity of 2.75 km/s was assumed, which corresponds to 0.9 times the S-wave velocity in the source region. A simplified 1-D velocity model for central Italy was used to model the ASTF (Cuius et al., 2023).	75
Tab. 1. Input parameters used to model the unilateral and bilateral scenarios for the characteristic rupture size ( and ), characteristic rupture duration ( ), centroid rupture velocity ( ) and directivity (dir).	75
Fig 1. Input source for unilateral (A,B) and bilateral (D,E) scenarios. The star represents the hypocenter, the dot represents the centroid location, and the arrow indicates the rupture direction. Panels (C,F) show the ASTFs calculated from the respective models for three different azimuth directions.	76
To investigate how the uncertainties introduced by the input data may affect the solutions of the resolved second seismic moments, we used the bootstrap approach. In this technique, perturbations are introduced for each input parameter to be analyzed by generating 1000 variations around the mean value. An inversion is then performed to assess the impact on the mean and standard deviation of the resulting data. The workflow is summarized in Fig. 2.	76
We investigated the uncertainties associated with the ASTF, the location of the hypocenter, the station distributions around the source, the focal mechanism, and the velocity model used for ray tracing. Some of these tests are interrelated. For example, the uncertainties in the position of the hypocenter and the velocity model affect the calculated ray path, and both the different focal mechanism and station coverage affect the resolution of the fault plane. The uncertainties in the epicenter estimates were not investigated because they have negligible effects on the slowness vectors in the inversion of the second moments.	76
Results of the synthetic tests	76
The sensitivity analysis performed in this study shows that the uncertainties in the input data have different effects on the calculation of the source parameters and that an accurate measurement of the ASTF as well as the velocity model play the most important role in influencing the inversion process. The results of our tests (Tab. 2 and Fig. 3) show that the main source parameters, i.e. fracture size, swelling duration and centroid velocity, are generally well reproduced within the standard deviation. The source duration resulting from the inversion process is strongly influenced by the duration of the input ASTF, and even 10 % influences the inversion of the second moment tensor. In the case of dense instrumentation, the horizontal location of the earthquake can be well resolved, but the resolution of the earthquake depth is largely determined by the velocity model, and an inaccurate earthquake location can lead to uncertainties in the resolved second moments. Care must also be taken to avoid artifacts due to the discretization of the velocity model when the hypocenter is located at an interface between two layers with high velocity contrast.	77
Fig. 2 Flowchart of the perturbation test. For each test, we computed 1000 random station configurations or perturbed input variables (depth, velocity model, focal mechanism, or observed c) with a given standard deviation. Then we performed the inversion and calculated the source parameters and directivity. Finally, we calculated the mean and dispersion of the output variables of the 1000 scenarios.	77
The values of the directivity depend on the ASTF duration, the choice of velocity model and the focal mechanism (Fig. 3). To ensure good resolution of the fault plane, good coverage of the ray path is critical for both upward and downward waves (McGuire, 2004). The component of rupture directivity along the dip can only be well determined if stations directly above the hypocenter are available, as the seismic rays are nearly horizontal at most other stations.	77
Fig. 3 Violin plots showing the Mean values and dispersions of each output variable resulting from each perturbation test given on the x-axis, i.e., focal mechanism (fm), observed τc (oτc ), velocity models (mA and mB, respectively), hypocentral depth (h), and station configuration (sc) for the unilateral scenario. (A–E) represent the solutions for the characteristic length, characteristic width, source duration, directivity and centroid rupture velocity respectively. The y-axis indicates the value of the output variable. The shape of each violin graph reflects the numerical counts of the resulting value. The red line serves as reference, indicating the input value.	78
Application to real case: the Mw 4.6 Central Italy earthquake	80
The method was then applied to study the Mw 4.6 event of March 2023 in central Italy, using data from the Italian seismic network (RSN (Amato et al., 2008) and the Italian accelerometry network (RAN (Costa et al., 2022)). We compute the ASTFs through the EGF deconvolution using the P and S waves.	80
We calculated the second seismic moment to obtain information about the directivity and source parameters. The main parameters calculated with this method are the following = 1.16 km, = 0.615, = 0.14 s, = 1.86 m/s, dir = 64, stress drop = 7.37 MPa). The relatively small value of is possibly due to the poor resolution of the vertical component and can be explained by the interaction of two factors: the vertical rupture plane and the small number of stations in the immediate vicinity of the epicenter (< 5 km).	80
Conclusions	80
The use of second-moment tensors to determine the source parameters, including directivity, of moderate-magnitude earthquakes could be a valuable tool to improve our understanding of the source dynamics in a given area and to the risk mitigation. One possible application of the second-moments method to small earthquakes would be to identify portions of large faults that produce super-shear ruptures and correlate them with the geology of the fault zone. The second moments method also provides lower constraints on rupture velocity, which can be particularly useful for unilateral ruptures. However, before the results can be interpreted, the resolution limits of the method need to be known due to the possible uncertainties of the parameters used as inputs to the computational procedure.	80
To overcome the difficulties related to the analysis of noisy signals in the time domain, which can be an important limitation in the calculation of ASTFs and consequently the source duration for low magnitude events, an experimental approach based on the frequency domain is currently being developed. Although the frequency domain deconvolution-based method is currently more time consuming than time domain deconvolution, it can be used in situations where the determination of reliable ASTFs is difficult due to noise, which is often the case for low magnitude earthquakes.	80
Acknowledgements	80
We are deeply grateful to the Italian Department of Civil Protection – Presidency of the Council of Ministers for funding this research.	80
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Corresponding author: arianna.cuius@ingv.it	81
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1 Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome, Italy	84
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Early results of a systematic revision of Ferrarese seismicity of the 13th-15th centuries.	97
A. Faoro1, R. Camassi1, V. Castelli2	97
1 Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Bologna, Bologna, Italy,	97
2Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Bologna, Bologna/Ancona, Italy	97
Fig. 1 – Seismic history of Ferrara (1000-2020) from Locati et al. (2022). The dated earthquakes are local ones (i.e. with epicentral location “in” Ferrara or in the “Ferrarese”).	98
H. Fernandez1,2, G. D. Chiappetta1, A. Schibuola1,3, M. La Rocca4, S. Gentili1, L. Peruzza1	100
1 OGS, Trieste, Italy	100
2 Università degli Studi "G. d'Annunzio" Chieti - Pescara, Italy	100
3 Université Gustave Eiffel, Marne-la-vallée, France	100
Fig. 1 - Seismic activity on the Esaro valley, Northern Calabria, Italy (1985-2023)	101
Linking Seismicity, Geochemical And Environmental Data: The New Frontier Of Multiparametric Networks	105
E. Ferrari1, M. Massa1, A.L. Rizzo2,1, S. Lovati1, F. Di Michele1	105
1 National Institute of Geophysics and Volcanology (INGV), Milano, Italy	105
2 Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano, Italy	105
A. Ferreri1, A. Romeo2, R. Giannuzzi1, G. Cecere2, S. de Lorenzo1, L. Falco2, M. Filippucci1,2, M. Michele2, G. Selvaggi2, A. Tallarico1,2	107
1 Dipartimento di Scienze della Terra e Geoambientali, Università degli Studi di Bari Aldo Moro, Bari, Italy	107
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R. Fonzetti 1, 2, L. Valoroso 1, A. Govoni 1, P. De Gori 1, C. Chiarabba 1	112
1 Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome (RM)	112
2 Università degli Studi Roma Tre , Rome (RM)	112
Fig.1 – a) The entire 2009 QF seismicity with at least 6 P- and 4 S arrival times (blue dots) relocated by using Hypoellipse with an optimised 1D-velocity model. The best locations (selecting criteria are specified in the upper left corner of the map) are shown. Red triangles are the seismic stations used by QF for the locations. Dark grey lines represent the main faults of the area. Pink lines are the evidence of surface rupture during the L’Aquila 2009 seismic sequence. Black straight lines are traces of vertical cross sections; b) some of the most interesting vertical sections (strike N°50E) showing the depth distribution of the seismic events occurred within ± 0.8 km distance from the sections; c) above, the magnitude vs time diagram and histogram of magnitude values; bottom) histograms showing the number of P- and S- waves, RMS (s), ERH (km), ERZ (km) and GAP (°).	113
Introduction	124
Previous seismic source models for the 1783 seismic sequence	125
Structural data	126
Seismological data: 3D fault modelling from earthquakes distribution	126
Slip Tendency	129
Conclusion	129
References	131
F. Grigoli1, C. Rossi2, C. Cocorullo2	132
1 University of Pisa, Italy	132
2 Seismix s.r.l., Italy	132
Supervised and unsupervised machine learning approaches for identifying the preparatory process of moderate earthquakes at The Geysers, California	142
A.G. Iaccarino1, M. Picozzi1,2	142
1Università degli Studi di Napoli “Federico II”, Dipartimento di Fisica “Ettore Pancini”, Napoli, Italy	142
2National Institute of Oceanography and Applied Geophysics, OGS, Sgonico, Italy	142
Fig. 1 – Maps of the M3.5 events compared to the areal well density (contour plot). The events with a preparatory phase are shown as red squares; yellow squares refer to the events with an unclear preparatory phase; the other M3.5 events as grey squares.	143
G. Lavecchia1,2, C. Andrenacci1,2, S. Bello1,2, F. Pietrolungo1,2, D. Cirillo1,2, A. Carducci1,2, F. Ferrarini1,2, F. Brozzetti1,2, R. de Nardis1,2	152
1 DiSPuTer, Università degli Studi “G. d’Annunzio” Chieti-Pescara, Chieti, Italy	152
2 CRUST - Centro inteRUniversitario per l’analisi Sismotettonica Tridimensionale, Chieti, Italy	152
Analysis and preliminary results of the Mw 4.9, Marradi seismic sequence (September 18th, 2023), in the northern Apennines, carried out by the BSI working group.	154
A. Lisi, L. Arcoraci, P. Battelli, M. Berardi, B. Castello, D. Latorre, A. Marchetti, M. Michele, V. Misiti, A. Nardi, D. Piccinini, A. Rossi, Gruppo di lavoro del Bollettino Sismico Italiano*	154
Fig.1 – Comparison between hypocentral parameters (a,b,c) and used time readings (d) for the same 352 earthquakes detected, during the first 3 days of the sequence, from seismic monitoring room and revised from BSI.	155
Fig.2 – Map view: Earthquakes recorded in the first 3 days of the sequence and revised by the BSI working group. Events are initially relocated using the NonLinLoc code (Lomax et al., 2000) and the local 1D velocity model from Pastori et al. (2019). Subsequently, a double-difference code (Waldhauser and Schaff, 2008) is applied to improve the geometries of the activated structures. The two vertical cross-sections are oriented perpendicular to the strike of the computed TDMT solution of the Mw=4.9 event (Scognamiglio et al., 2006).	156
Fig.3 – Cumulative number of events over the time and relative magnitude distribution until October 10th. (a)The colours represent the average cross-correlation (CC) of each detected event. (b) New detected events are shown in red colour while the templates are in grey. (c ) The Gutenberg - Richter relationship. (d) The magnitude of matched events (in red with their respective error bars) and that of the templates (in black).	157
Studying the Viability of Kinematic Rupture Models and Source Time Functions with Dynamic Constraints	158
M.E. Locchi1, F. Mosconi1, M. Supino2, E. Casarotti2, E. Tinti1,2	158
1 Sapienza Università di Roma, Rome, Italy;	158
2 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy;	158
Fig. 1: Workflow for one model: results from spontaneous rupture of a bidirectivity model on the left; kinematic inversions results using ground-motion from dynamic model. For each source time function different rise time and rupture velocity were tested. The slip distribution from best models of each source time function.	159
Seismic attenuation and stress on the San Andreas Fault at Parkfield: are we critical yet?	168
L. Malagnini*1,2, R. M. Nadeau2, and T. Parsons3	168
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Density values in the shallow crust: analysis and comparison of deep well data in the Adriatic region (Italy)	177
M.T. Mariucci1, P. Montone1, P. Balossino2	177
1 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy	177
2 Former ENI S.p.A. Natural Resources, Italy	177
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3D Hypocenters relocation in high-resolution central Mediterranean velocity model	179
I. Menichelli1*, P. De Gori1, C. Chiarabba1	179
1 Istituto Nazionale di Geofisica e Vulcanologia (Rome, Italy)	179
In this study, a new 3D relocated hypocenters catalogue has been built for the Italian region using an updated 3D velocity model computed for the central Mediterranean area (Menichelli et al., 2023). Classical one-dimensional velocity models, due to their limitation in recovering lateral heterogeneous variations in the velocity structure, offer only a simplified depiction of the reality. For this reason, the necessity to use 3-D velocity models in the location of hypocenters, which consider the inhomogeneous structure of the different layers that constitute the earth's interior, has been emerging in recent years.	179
The 3D tomographic model used has been computed inverting P- and S- arrival times recorded between 2014-2021 by the RSN (Italian Seismic Network) and AlpArray (AlpArray 2015; Hetenyi et al., 2018) seismic network. In particular, the seismic data set includes Pg, Pn, Sg, and Sn, and the related arrival times were manually picked within a maximum epicentral distance of 1000 km.	179
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Exploring Italy's Present-Day Stress Field Complexity through Utilisation of Geophysical, Geological, and In Situ Drilling Data	182
P. Montone and M. T. Mariucci	182
Istituto Nazionale di Geofisica e Vulcanologia, Roma, Italy	182
Fig. 1 – New data from 21 earthquake focal mechanisms and 9 breakout directions. Further 10 wells with no ovalization are highlighted with open circles.	183
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Modeling dynamic ruptures on extended faults for microearthquakes induced by fluid injection	185
F. Mosconi1, E. Tinti1,2, E. Casarotti2, A. A. Gabriel3, R. Dorozhinskii4, L. Dal Zilio5, A. P. Rinaldi5, and M. Cocco2	185
1 Università la Sapienza, Rome, Italy	185
Fig. 1 Snapshot of the slip rate evolution during the rupture propagation of a microearthquakes (Mw = 0.71) in the context of fluid induced seismicity; for the model with Dc = 0.6mm.	186
L. Passarelli 1, S. Cesca2, L. Mizrahi3, G. Petersen2	187
M. Picozzi1,2, A.G. Iaccarino2, D. Spallarossa3	188
ML-based workflow for earthquake detection and location: preliminary results from the northern Apennines with a model trained on local waveforms	189
G. Poggiali1, S. Bagh2, L. Chiaraluce2, C. J. Marone1, Z. E. Ross3, E. Tinti1, W. Zhu4	189
1 La Sapienza Università di Roma, Rome, Italy	189
2 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy	189
3 Seismological Laboratory, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA	189
4 University of California, Department of Earth & Planetary Science, Berkeley, CA, USA	189
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3Dipartimento di Scienze Agroalimentari, Ambientali e Animali, University of Udine, Italy	210
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2 Istituto Nazionale di Geofisica e Vulcanologia, Milano, Italy	244
3 Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy	244
4 Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, Catania, Italy	244
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Rovida A., Locati M., Camassi R., Lolli B., Gasperini P., Antonucci A.; 2022: Catalogo Parametrico dei Terremoti Italiani (CPTI15), versione 4.0. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/CPTI/CPTI15.4	246
Guidoboni E., Ferrari G., Mariotti D., Comastri A., Tarabusi G., Sgattoni G., Valensise G.; 2018: CFTI5Med, Catalogo dei Forti Terremoti in Italia (461 a.C.-1997) e nell’area Mediterranea (760 a.C.-1500). Istituto Nazionale di Geofisica e Vulcanologia (INGV). doi: https://doi.org/10.6092/ingv.it-cfti5	247
Rovida, A., M. Locati, R. Camassi, B. Lolli, and P. Gasperini; 2020: The Italian earthquake catalogue CPTI15, Bull. Earthq. Eng. 18, 2953–2984, doi: 10.1007/s10518-020-00818-y	247
Rovida A., Locati M., Camassi R., Lolli B., Gasperini P., Antonucci A.; 2022: Catalogo Parametrico dei Terremoti Italiani (CPTI15), versione 4.0. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/CPTI/CPTI15.4	247
Recent activity of the Stradella fault (Emilia Arc, northern Italy) by a multi-scale approach	248
Acknowledgments	249
Integrated analysis of geophysical data: a case study from Central Italy	250
M.M. Tiberti1, F.E. Maesano1, M. Buttinelli1, P. De Gori1, F. Ferri2, L. Minelli1, M. Di Nezza1, C. D'Ambrogi2	250
1 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy	250
2Istituto Superiore per la Protezione e la Ricerca Ambientale, Servizio Geologico d’Italia, Rome, Italy	250
G. Toffol1, G. Pennacchioni1, L. Menegon2, D. Wallis3, M. Faccenda1, A. Camacho4, M. Bestmann5	251
1 Department of Geosciences, University of Padova, Padva, Italy	251
2 Department of Geosciences, University of Oslo, Oslo, Norway	251
3 Department of Earth Sciences, University of Cambridge, Cambridge, UK	251
4 Department of Geological Sciences, University of Manitoba, Winnipeg, Canada	251
5 Department of Geology, University of Vienna, Wien, Austria	251
G. Valensise1, F. Donda2, A. Tamaro2, S. Parolai3	252
1 INGV, Rome, Italy	252
2 OGS, Trieste, Italy	252
3 University of Trieste, Italy	252
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Mai, P. Martin; Aspiotis, Theodoros; Aquib, Tariq Anwar; Cano, Eduardo Valero; Castro‐Cruz, David; Espindola‐Carmona, Armando; Li, Bo; Li, Xing; Liu, Jihong; Matrau, Rémi; Nobile, Adriano; Palgunadi, Kadek Hendrawan; Ribot, Matthieu; Parisi, Laura; Suhendi, Cahli; Tang, Yuxiang; Yalcin, Bora; Klinger, Yann; Jónsson, Sigurjón (2023). "The Destructive Earthquake Doublet of 6 February 2023 in South‐Central Türkiye and Northwestern Syria: Initial Observations and Analyses". The Seismic Record. 3 (2): 105–115. doi:10.1785/0320230007	276
Reitman, Nadine G, Richard W. Briggs, William D. Barnhart, Jessica A. Thompson Jobe, Christopher B. DuRoss, Alexandra E. Hatem, Ryan D. Gold, John D. Mejstrik, and Sinan Akçiz (2023) Preliminary fault rupture mapping of the 2023 M7.8 and M7.5 Türkiye Earthquakes. DOI: https://doi.org/10.5066/P985I7U2	276
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Neely J. S., Salditch L., Spencer B. D. and Stein S.; 2023: A More Realistic Earthquake Probability Model Using Long‐Term Fault Memory. Bull. Seismol. Soc. Am., 113(2), 843-855.	279
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Ross Z. E., Hauksson E. and Ben-Zion, Y.; 2017: Abundant off-fault seismicity and orthogonal structures in the San Jacinto fault zone. Sci. Adv., 3(3), e1601946.	279
Tan Y. J., Waldhauser F., Ellsworth W. L., Zhang M., Zhu W., Michele M., ... and Segou M.; 2021: Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016–2017 Central Italy sequence. The Seismic Record, 1(1), 11-19.	279
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The 1783 Calabria earthquake sequence: a review of the coseismic effects on the natural environment	280
C. Zei1,2, C. Ciuccarelli1, M.G. Bianchi1, G. Tarabusi1, D. Mariotti1	280
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Zei C., Tarabusi G., Ciuccarelli C., Mariotti D., Baranello S., Sgattoni G., Burrato P., CFTI working Group; 2023: A new database of historical earthquake-induced landslides in Italy, 41st National Conference of the GNGTS, Bologna https://gngts.ogs.it/atti/GNGTS2023/HTML/212/	283
12_Abstract_GNGTS2024.pdf	289
Imaging the North-South deformation through the application of potential theory to InSAR measurements	289
Barone1, P. Mastro1, A. Pepe1, M. Fedi2, P. Tizzani1, R. Castaldo1	289
1 Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), Consiglio Nazionale delle Ricerche (CNR), Napoli, Italia.	289
2 Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse (DiSTAR), Università degli Studi di Napoli Federico II, Napoli, Italia.	289
Synthetic Aperture Radar Interferometry (InSAR) is a well-established technique for monitoring and modeling the ground deformation field in volcanic areas and geothermal fields. Specifically, when SAR images are acquired along both the ascending and descending satellites orbits, the retrieval of the East-West (E-W) and vertical components of the related three-dimensional (3D) ground deformation field is conceivable; the North-South (N-S) one is usually not available and different techniques have been proposed to solve this task. However, the resolutions and accuracies of these retrieved measurements are not always satisfactory.	289
Here, we show a new approach for the retrieval of the N-S component and the reconstruction of the 3D ground deformation field in volcanic frameworks. The proposed methodology is based on the theory of the potential functions and the integral transforms of potential fields. We test our workflow on synthetic deformation datasets computed according to the commonly used analytic volcanic deformation sources (i.e., Mogi’s, Okada’s and Yang’s models). The results show that the proposed technique allows the retrieval of the N-S deformation with negligible errors with respect to the expected one.	289
We then consider this approach to reconstruct the 3D ground deformation field that occurred at Sierra Negra volcano (Galapagos Islands, Ecuador) during the 2017 – 2018.5 unrest, which has led to the eruption. The comparison with GNSS data shows that we are able to image the pre-eruptive N-S deformation for this volcano with a mean error of about 5%, which is a surprising result for this kind of application.	289
The next step of this study is the modeling of the volcanic deformation sources through the use of the retrieved 3D ground deformation field and showing the impact in the framework of the ambiguity solving.	289
Introduction	290
Overview of the methods	292
Results	292
Concluding remarks	294
Acknowledgements	295
References	295
1 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Roma1, Arezzo, Italy	296
2 Università degli Studi “Roma Tre”, Roma, Italy	296
3 Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, Roma, Italy	296
4 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Roma1, Roma, Italy	296
5 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Palermo, Milazzo, Italy	296
Fig. 1 – Example of an earthquake and rumble located on 22/07/2020 at 19:36 in the area of Montecassino (FR). Note the occurrence of three small seismic events (coloured traces) prior to the infrasound signal (black).	297
1 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Napoli, Osservatorio Vesuviano, Napoli, Italy	313
Trans-dimensional Mt. Etna P-wave anisotropic imaging	315
G. Del Piccolo1, R. Lo Bue1, B.P. VanderBeek1, M. Faccenda1, O. Cocina2, M. Firetto Carlino2, E. Giampiccolo2, A. Morelli3, J.S. Byrnes4	315
1 Dipartimento di Geoscienze, Università degli Studi di Padova	315
Nonlinear convective motion of the asthenosphere and the lithosphere melting. A model for the birth of a volcano	328
1 Department of Mathematics and Physics, Università della Campania "Luigi Vanvitelli", Caserta, Italy.	328
1 National Institute of Geophysics and Volcanology – Palermo (INGV, Italy)	349
Fig. 1 – Maps showing the position of Panarea Island in Southeastern Tyrrhenian Sea (red marker in left panel) and the position of the investigated vent (cyan marker in right panel).	350
Fig. 2 – Power Spectral Density (PSD) (panel a) and Pressure Power Spectrum (panel b) of 04/10/2022.	351
Fig. 3 – Charts showing the Spectrograms recorded on 04/10/2022 on which the inversion range is indicated (red lines).	351
b value tomography at Campi Flegrei: enlightening different rheological behaviour in volcanic areas	357
2 Department of Mathematics and Physics, Università della Campania "Luigi Vanvitelli", Caserta, Italy.	357
13_Abstract_GNGTS2024.pdf	365
1 Department of Physics, University of Salerno, Fisciano, Italy	365
Amoruso A. and Crescentini L.; 2022: Clues of Ongoing Deep Magma Inflation at Campi Flegrei Caldera (Italy) from Empirical Orthogonal Function Analysis of SAR Data. Remote Sens., 14, 5698, DOI 10.3390/rs14225698.	365
C. Braitenberg	366
Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy,	366
1 Università degli Studi di Catania, Catania, Italy	368
2 Università degli Studi di Ferrara, Ferrara, Italy	368
3 Istituto Nazionale di Oceanografia e di Geofisica Sperimentale-OGS, Trieste, Italy	368
Fig. 1 – IGM95 points reoccupied during the 2023 campaigns	368
Fig. 2 – Velocity field obtained by IGM95 points.	370
Fig. 1 - Main working screen of the open-source SAR-TOOL software. In particular, the figure shows a work session related to the interactive selection and addition of input data required by the SISTEM algorithm, featuring a terminal on the right showing both the parameters entered and those calculated by the algorithm.	380
Fig. 2 - Examples of products obtained through SAR-TOOL. (a) Modelling of a synthetic DEM and a spherical Mogi source located at a depth of 2000 metres above sea level; (b) Visualisation of a phase interferogram relative to a ground deformation period at Etna volcano (Italy); (c) Output of the displacements generated by a spherical Mogi source and calculated by the SISTEM algorithm through interaction with 60 synthetic GPS points arranged in a random geometry within the grid.	381
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MCMTpy waveform inversion package: testing a new method for moment tensor estimation	400
T. Mancuso1, C. Totaro1, B. Orecchio1	400
1 Department of Mathematical and Computer Sciences, Physical and Earth Sciences (University of Messina, Italy)	400
A. M. Marotta1, R. Barzaghi2, A. Bollino1, A. Regorda1, R. Sabadini1	402
1 Department of Earth Sciences Ardito Desio, Universit`a degli Studi di Milano, Milano, Italy	402
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Physical models for the Solid Earth and integration between modeling and data of different nature	404
1 INGV, Dept.Palermo, Palermo, Italy	404
2 CNR-IGG, Pisa, Italy	404
1. Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy,	405
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T. Ninivaggi1, G. Selvaggi2, S. Mazza2, M. Filippucci2,2,3, F. Tursi4, W. Czuba5	413
1 Istituto Nazionale di Geofisica e Vulcanologia, Sezione Irpinia, Grottaminarda, Italy	413
2 Istituto Nazionale di Geofisica e Vulcanologia, ONT, Roma, Italy	413
3 Dipartimento di Scienze della Terra e Geoambientali, Università degli Studi di Bari	413
“Aldo Moro”, Bari, Italy	413
4 Dipartimento di Scienze della Terra, Università degli Studi di Torino, Torino, Italy	413
5 Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland	413
Fig. 1 – Observation of the later seismic phase. a) Stations which recorded the x-phase (green triangles). Seismograms of stations in black do not show the later arrival. Section trace AB of the Fig. 3, passing from the 2011 earthquake (n. 37). The red lines delineate the old (30 Ma) and the present subduction signature. The two thin black lines delineate the azimuths -60° and 30° starting from the epicentre. b) Time-distance vertical seismograms of the 2011 event aligned with P arrival time at 20 s. The red line marks the later phase arrivals.	414
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1 Università degli studi di Trieste	421
2 Istituto di Oceanografia e di Geofisica Sperimentale, OGS, Trieste	421
3 EvK2cnr, Bergamo	421
Gibbons, S.J., Ringdal, F.; 2006: The detection of low magnitude seismic events using array-based waveform correlation. Geophys. J. Int. 165 (1), 149–166.	421
Pettenati F., C. Cravos, T. Dawa Sherpa, L. Adhikari Sherpa, M. P. Plasencia Linares, M. Romanelli, G. Verza; 2014: The installation of a new broadband seismometer to the EVK2-CNR Pyramid International Laboratory-Observatory (Everest, Nepal). 33° Convegno Nazionale Gruppo Nazionale di Geofisica della Terra Solida GNGTS. Bologna, 25-27 Novembre. 2014. Abstract.	421
Vuan A., Sugan M., Amati G., Kato A.; 2018: Improving the Detection of Low‐Magnitude Seismicity Preceding the Mw 6.3 L’Aquila Earthquake: Development of a Scalable Code Based on the Cross Correlation of Template Earthquakes. Bull Seismol Soc Am 2018; 108 (1), 471–480. doi: https://doi.org/10.1785/0120170106	421
1 Dipartimento di Scienze della Terra e Geoambientali, Università degli Studi di Bari, "Aldo Moro", Bari, Italy	422
2 Istituto Nazionale di Geofisica e Vulcanologia, Roma, Italy	422
Kelbert, A. (2020). The role of global/regional Earth conductivity models in natural geomagnetic hazard mitigation. Surveys in Geophysics,41, 115–166.https://doi.org/10.1007/s10712-019-09579-z	423
1.Department of Mathematics, Computer Sciences, Physics, and Earth Sciences, University of Messina, Italy	424
2.Institut Cartogràfic i Geològic de Catalunya, Barcelona, Spain;	424
3.Instituto Andaluz de Geofísica, Universidad de Granada, Spain	424
Orecchio B., Scolaro S., Batlló J., Neri G., Presti D., Stich D., and Totaro C., (2021). New Results for the 1968 Belice, South Italy, Seismic Sequence: Solving the Long‐Lasting Ambiguity on Causative Source, Seismol. Res. Lett., 92(4), 2364-2381. Doi:10.1785/0220200277	425
Rovida A., Locati M., Camassi R., Lolli B., Gasperini P., and Antonucci A. (2022). Italian Parametric Earthquake Catalogue (CPTI15), version 4.0. Istituto Nazionale di Geofisica e Vulcanologia (INGV). doi:10.13127/CPTI/CPTI15.4	425
Stich D., Batlló J., Macià R., Teves-Costa P., and Morales J. (2005). Moment tensor inversion with single-component historical seismograms: The 1909 Benavente (Portugal) and Lambesc (France) earthquakes, Geophys. J. Int., 162(3), 850–858. Doi: 10.1111/j.1365-246X.2005.02680.x	425
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Federico C., Cocina O., Gambino S., Paonita A., Branca S., Coltelli M., Italiano F., Bruno V., Caltabiano T., Camarda M., Capasso G., De Gregorio S., Diliberto I. S., Di Martino R. M. R., Falsaperla S., Greco F., Pecoraino G., Salerno G., Sciotto M., Bellomo S., Di Grazia G., Ferrari F., Gattuso A., La Pica L., Mattia M., Pisciotta A. F., Pruiti L., Sortino F.; 2023: Inferences on the 2021 ongoing volcanic unrest at vulcano island (Italy) through a comprehensive multidisciplinary surveillance network. Remote Sens. 15, 1405.	429
Hemmings B., Gottsmann J., Whitaker F., Coco A.; 2016: Investigating hydrological contributions to volcano monitoring signals: A time-lapse gravity example. Geophys. J. Int. 207 (1), 259–273.	429
Rinaldi A. P., Todesco M., Vandemeulebrouck J., Revil A., Bonafede M.; 2011: Electrical conductivity, ground displacement, gravity changes, and gas flow at solfatara crater (Campi Flegrei caldera, Italy): results from numerical modeling. J. Volcanol. Geother. Res. 207 (3-4), 93–105.	429
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1Department of Psychological Sciences, Health and Territory, University of the Studies “G. d'Annunzio”, Chieti, Italy	430
2CRUST-Interuniversity Center for 3D Seismotectonics with Territorial Applications, Chieti, Italy	430
3Dipartimento di Fisica e Astronomia (DIFA), Alma Mater Studiorum-Università di Bologna, Bologna, Italy	430
Bagh, S., Chiaraluce, L., De Gori, P., Moretti, M., Govoni, A., Chiarabba, C., Di Bartolomeo, P., Romanelli, M., 2007. Background seismicity in the Central Apennines of Italy: The Abruzzo region case study. Tectonophysics 444, 80–92. https://doi.org/10.1016/j.tecto.2007.08.009	432
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1National Institute of Oceanography and Applied Geophysics - OGS - Italy	434
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1Dipartimento di Geoscienze, Università degli Studi di Padova, Padova, Italia	436
22_Abstract_GNGTS2024.pdf	522
DISASTER RISK ANALYSIS AND REDUCTION	522
M. Ariano1, P.L. Fantozzi1, D. Albarello1	524
1Department of Physics Sciences, Earth and Environment, University of Siena, Siena, Italy	524
D. Attolico1,2, G. Cultrera1, V. De Rubeis1, N. Theodoulidis1	534
1 Istituto Nazionale di Geofisica e Vulcanologia (INGV, Italy)	534
2 Asset - Agenzia regionale Strategica per lo Sviluppo Ecosostenibile del Territorio (Regione Puglia, Italy)	534
S. Azhideh, S. Barani, G. Ferretti, D. Scafidi, G. Pepe	535
1 Università degli Studi di Genova, Genova, Italy	535
Fig. 1 Example distribution of M and R with indication of the mean and modal scenarios.	536
Fig. 2 Comparison of the mean and modal M-R scenarios shown in Figure 1 with the upper-bound curve for disrupted slides and falls proposed by Keefer (1984). The preferred M-R pair is displayed in red.	537
Fig. 3 Map of preferred magnitude (a) and distance (b) associated with the PGA hazard for a 475-yr return period and corresponding earthquake-induced landslide triggering map (c). In the latter, red points are nodes for which the triggering of earthquake-induced landslides can not be excluded.	538
1 National Institute of Oceanography and Applied Geophysics –OGS, Italy	540
2 National Institute of Geophysics and Volcanology –INGV, Italy	540
3 University of Ferrara - Italy	540
Fig. 1: The study area in the Po plain, near the city of Ferrara: on the left, noise measurements and seismic station position; on the right, Vs profiles available.	541
P.L. Bragato1, J. Boaga2, G. Capotosti1, P. Comelli1, S. Parolai1,3, G. Rossi1, H. Siracusa1, P. Ziani1, D. Zuliani1	543
1 National Institute of Oceanography and Applied Geophysics – OGS, Italy	543
2 University of Padova, Italy	543
3 University of Trieste, Italy	543
Fig. 1 – Stations composing the dense accelerometric network in Veneto: green, organizations of volunteers and other public edifices; red, telephone exchange buildings of TIM s.p.a.; yellow, post offices of Poste Italiane s.p.a.	544
G. Caielli1, R. de Franco1, I. Gaudiosi2, A. Mendicelli2, M. Moscatelli2, G. Norini1, D. Rusconi1, M. Simionato2.	545
1 ISTITUTO DI GEOLOGIA AMBIENTALE E GEOINGEGNERIA – CNR - Milano - Italy	545
2 ISTITUTO DI GEOLOGIA AMBIENTALE E GEOINGEGNERIA – CNR - Montelibretti - Italy	545
This work was carried out as part of the projects:	547
N. Carfagna1, P. Pieruccini2, P. Fantozzi1, D. Albarello2,3	548
1 Department of Physical Science, Earth and Environment, University of Siena, Siena, Italy	548
N. Cella, C. Bedon	550
University of Trieste, Department of Engineering and Architecture, Trieste, Italy	550
C. Comina 1, G.M. Adinolfi1, A. Bertea2, C. Bertok1, V. Giraud2, P. Pieruccini1	559
1 Università degli studi di Torino, Department of Earth Sciences; Torino, Italy.	559
2 Seismic Sector, Piedmont Region; Pinerolo, Italy.	559
Figure 1 – Map of the Geological Domains within the Piedmont Region, black dots represent the available geophysical information in terms of shear wave velocity profiles from Regional repository database, on purpose implemented information and specific field tests executed.	560
Figure 3 – Average Vs,z profiles for the non-bedrock units for each GD.	562
G. Cultrera1 , A. Mercuri1	564
(1) Istituto Nazionale di Geofisica e Vulcanologia, Roma - Italy	564
Fig. 1 – Data distribution for lithological classification grouped by rock categories with similar genesis(left) and Site classification from NTC18 (right).	565
Fig. 3 – Distribution of f0 from HVnoise, with respect to the magnitude residuals.	566
A. D’Agostino1, A. Porchia2, G. Cavuoto3, F. Pavano3, M. Moscatelli2, G. Tortorici2, S. Catalano1,2	567
1 Department of Biological, Geological and Environmental Sciences (DBGES) – Section of Earth Science, University of Catania.	567
2 IGAG-CNR - Institute of Environmental Geology and Geoengineering of the Italian National Research Council, Area Della Ricerca di Roma 1.	567
3 ISPC-CNR - Institute of Heritage Science of the Italian National Research Council, Napoli.	567
M. Fasan1, C. Bedon1, F. Romanelli2	572
1 University of Trieste, Department of Engineering and Architecture	572
2 University of Trieste, Department of Mathematics and Geosciences	572
Fig. 1 – Example of different realizations of the rupture process	574
Fig. 2 – Cloud data (indicated with NC) and regressions obtained for the four studied configurations (PGA)	576
Fig. 3 - Fragility curves obtained for the four studied configurations (PGA)	577
S. F. Fornasari , V. Pazzi , G. Costa	579
Dipartimento di Matematica, Informatica e Geoscienze (MIGE - Università degli Studi di Trieste, Italia)	579
I. Gaudiosi1, G. Acunzo2, D. Albarello3, M. Moscatelli1	594
1 Istituto di Geologia Ambientale e Geoingegneria (CNR, Italy)	594
2 Theta Group (Italy)	594
3 Dipartimento di Scienze Fisiche, della Terra e dell’Ambiente (Università degli Studi di Siena, Italy)	594
S. Hailemikael1, G. Cultrera1, C. Barnaba2, G. Laurenzano2, G. Martini1,3, A. Peloso3, F. Cara1, G. Di Giulio1, D. Famiani1	596
1 Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome, Italy.	596
2 Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS), Trieste, Italy.	596
3 Agenzia Nazionale per le Nuove Tecnologie, l’Energia e lo Sviluppo Economico Sostenibile (ENEA), Frascati, Italy.	596
Fig. 1 - Distribution of residuals between median FA_exp and Fa_syn values (in natural logarithmic units) as a function of period: short (FA1 blue), intermediate (red) and long periods (green).	597
G. Laurenzano1, N. Tragni2, P. Klin1, TA Stabile2, MR Gallipoli2 & PRIN SERENA WP06 WG	599
A. Mazelli1,2, C. Bedon2, A. Morassi1	603
1 University of Udine, Polytechnic Department of Engineering and Architecture, Udine, Italy	603
2 University of Trieste, Department of Engineering and Architecture, Trieste, Italy	603
A. Mendicelli1, F. Mori1, C. Varone1, M. Simionato1, M. Moscatelli1	611
At present, the most detailed geological map covering the entire Italian national territory is the 1:100,000 scale geological map of Italy created by ISPRA. To estimate the stratigraphic amplification of seismic motion at the surface over a large area, it is crucial to better define the geological and lithotechnical characteristics of covering soils and geological bedrocks. This work is aimed at improving the definition of recent alluvial covers (Holocene and Upper Pleistocene deposits) compared to the 1:100,000 geological map of Italy. For this purpose, a methodology based on machine learning models has been developed. It considers both categorical and numerical variables to predict the presence/absence of recent flood coverage with good accuracy.	611
To train the machine learning model, both geomorphometric parameters and geological databases at different scales were used. Initially, the methodology was tested in the Calabria Region and in the Marche Region, for which promising results were obtained with good performances in the external test. The next step, still in the development phase, consists in the application of the methodology in a wider area which includes not only the Calabria and Marche regions but also Tuscany, Emilia-Romagna and Umbria. The model thus obtained will be tested across the entire national territory.	611
This research was supported with funds from the PNRR, from the project: “National Center for HPC, Big Data and Quantum Computing – HPC – SPOKE 5” – CN00000013.	611
L. Minarelli1, M. Stefani2, S. Amoroso3-1, G. Tarabusi1	612
1Istituto Nazionale di Geofisica e Vulcanologia, Italy	612
2University of Ferrara, Italy	612
3 University of Chieti-Pescara, Italy	612
1 Istituto Nazionale di Geofisica e Vulcanologia – INGV, Milan, Italy	621
2 CNR Istituto di Matematica Applicata e Tecnologie Informatiche “Enrico Magenes”, Milan, Italy	621
3 Istituto Nazionale di Geofisica e Vulcanologia – INGV, Catania, Italy	621
1 Istituto Nazionale di Geofisica e Vulcanologia (Italia)	627
Fig. 1. Schematic representation of the current structure and activities of the SISMIKO Operational Group.	628
1 University of Molise, Dept. of Biosciences and Territory, Campobasso, Italy	629
2 Institute for Construction Technologies ITC-CNR, National Research Council, L’Aquila, Italy	629
3 S2X S.r.l., Campobasso, Italy	629
4 University of Molise, Dept. of Medicine and Health Sciences, Campobasso, Italy	629
Fig. 1 – Multi-level framework for safety management of health facilities structures	630
Fig. 2 – HF-INSPECT software	631
Fig. 3 – HF-ALL RISKS software (the figure illustrates the TFM for the hospital of Campobasso)	633
GIS spatial modelling for seismic exposure assessment: a case study over Central Asia.	639
A. Tamaro, C. Scaini	639
National Institute of Oceanography and Applied Geophysics - OGS, Trieste, Italy	639
G. Tarchini1, D. Spallarossa1, D. Scafidi1, S. Parolai2, M. Picozzi3, D. Bindi4	648
1 DISTAV, University of Genoa, Genoa, Italy	648
2 Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy	648
3 National Institute of Oceanography and Applied Geophysics – OGS, Udine, Italy	648
4 Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany	648
Fig. 1 – STATION main webpage.	649
Fig. 2 – Example of specific webpage for the station IV.AQU.	650
Machine Learning-based modelling for Near Real-Time prediction of liquefaction	653
C. Varone1, F. Mori1, A. Mendicelli1, G. Ciotoli1, G. Acunzo2, G. Naso3, M. Moscatelli1	653
1 CNR Italian National Research Council, Institute of Environmental Geology and Geoengineering (IGAG), Montelibretti, Italy	653
2 Theta Group Srls, Rome, Italy	653
23_Abstract_GNGTS2024.pdf	655
GNGTS 2024	655
DISASTER RISK ANALYSIS AND REDUCTION	655
Towards the IT-ALERT implementation. Early warning and cell-broadcast systems in the context of risk and crisis communication	656
Tsunami Ready: some steps for a people-centred tsunami risk approach	661
“A Scuola di Terremoto”: a targeted risk education project in Calabria (South Italy) to promote behavioural change	666
Insights into risk communication from the analysis of earthquake light phenomena reports in Turkey and Morocco	672
E se… (What if…) a game to learn about risk perception	677
Fig. 1 – Poster of the Activity at Futuro Remoto 2023	678
Preparing for disasters through games: a worth taking bet?	680
From risk to safety for a resilient governance	682
Tsunami risk perception of the touristic population of Stromboli Island: towards effective risk communication strategies	689
Abstract	689
Methodology	689
Results	689
References	690
Risk education and communication: the experience of serious games and Situated Learning Episodes (ELS) in Pandemic	692
Real-time seismicity on your smartphone	695
M. Pignone1, E. Casarotti2, V. Lauciani1, C. Nostro1, C. Meletti3, A. Amato1	695
1 Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, Roma, Italy	695
2 Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italy	695
3 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Pisa, Italy	695
References	699
Trust in authorities and experts as shaping factor of risk perception	700
R. Russo1, M. V. Gargiulo1, R. Iorio2, G. Cavalca2, P. Capuano1	700
1 Università degli Studi di Salerno, Department of Physics “E.R. Caianiello”, Fisciano (SA), Italy	700
In September 2019, the United Nations Secretary-General, António Guterres, remarked that our global community is experiencing a significant challenge known as 'Trust Deficit Disorder'. He noted a decline in people's trust in political institutions, a growing polarization, and the increasing prevalence of populism.	700
Seismic risk communication in Europe over the last two decades	702
G. Musacchio1, A. Saraò2, S. Falsaperla3, A. Scolobig4	702
1Sezione di Milano, Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy	702
2Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS), Italy	702
3Sezione di Catania, Osservatorio Etneo, Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy	702
4Institute for Environmental Sciences, University of Geneva, Switzerland; Equity and Justice Group, International Institute for Applied Systems Analysis, Austria	702
Fig. 1 – a) Publications on seismic risk communication over time. Raw data from Google Scholar database searches according to the strings listed in the text are plotted for all risks (right y-axis) and seismic risk communication (left y-axis) in Europe and worldwide; b) publications shortlisted for this review study.	703
Schools-tailored activities to communicate seismic risk	708
Seismic risk perception in italian hospitals: The role of non-structural elements	717
31_Abstract_GNGTS2024.pdf	722
GNGTS 2024	722
APPLIED GEOPHYSICS FOR ENERGY, ENVIRONMENT AND NEW TECHNOLOGIES	722
O. Amoroso1, V. Giampaolo2, M. Balasco2, M. Blasone1, D. Bubbico3, P. Capuano1, G. De Martino2, M.V. Gargiulo1, F. Napolitano1, A. Perrone2, S. Panebianco2, R. Russo1, V. Serlenga2, T.A. Stabile2	723
C. Bellezza, E. Barison, F. Poletto, A. Schleifer, F. Meneghini, G. Böhm, B. Farina	726
Acknowledgements	727
Salt domes modelling through magnetic data: an unconventional tool for challenging scenarios	728
L. Bianco1, M. Abbas1, L. Speranza2, B. Garcea2, M. Fedi1	728
1 Department of Earth, Environmental and Resources, University of Naples “Federico II”, Naples, Italy.	728
2 Energean, Milan, Italy.	728
T. Braun1, S. Danesi2	729
1 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Roma1, Arezzo, Italy	729
2 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy	729
Fig. 1 – Monitoring domains DE, DI and DR defined for the monitoring of the hydrocarbon exploitation in VA (after Danesi et al., 2021)	731
Fig. 2 – Example screenshot of the seismic activity in VA recorded in 2023 by INGV.	731
Comparison and calibration of Traffic Light Protocols applied in different countries, in the framework of the ENSURE-project	733
T. Braun1, S. Danesi 2	733
1 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Roma1, Arezzo, Italy	733
2 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Italy	733
Fig. 1 – Operation principle of a Traffic Light Protocol	734
The MARE (MARine Energy) project Assessment of energy production potential from marine waves and currents: a case study from Aegadian archipelago	736
A. D'Alessandro1, A. Sulli2, M. Agate2, P. Capizzi2, C. Caruso1, L. Cocchi2, R. D'Anna2, A. Di Benedetto2, A. Figlioli1, M. Gasparo Morticelli2, A. Mandiello1, R. Martorana2, A. Pisciotta1, S. Speciale1, S. Sciré Scappuzzo1, S. Scudero1, G. Vitale1	736
1 Istituto Nazionale di Geofisisica e Vulcanologia, Italy	736
2 Università degli Studi di Palermo, Italy	736
Airborne and Ground IP: an integrated approach for exploration	743
F. Dauti1, A. Viezzoli2, G. Fiandaca1	743
1 The EEM Team for Hydro & eXploration, Dep. of Earth Sciences A. Desio, Università degli Studi di Milano, Via Botticelli 23, Milano (Italy)	743
2 Emergo s.r.l., Via XX Settembre 12, Cascina (Pisa), Italy	743
UAS photogrammetry analysis for coastal hazard assessment: the case study of Maronti landslide (Ischia, 2022)	750
1 Istituto Nazionale di Geofisica e Vulcanologia- Sezione Irpinia, Italy	750
2 Istituto Nazionale di Geofisica e Vulcanologia- Osservatorio Vesuviano, Italy	750
Fig. 1 – Pre and post 3D models, DSMs and section of the investigated area.	751
Time-lapse Gravity Monitoring at surface and Excess Mass Estimation of CO2 Stored in Deep Saline Aquifers	756
M. Milano1, M. Fedi1	756
1 Department of Earth, Environmental and Resources Sciences, University of Naples Federico II, Napoli, Italy.	756
This study regards the assessment of surface gravity surveying for CO2 plume monitoring in a deep saline aquifer (Milano and Fedi, 2023). We simulated surface gravity monitoring of CO2 storage for the injection and post-injection phases and using different injection rates. We show that time lapse gravity data can be used to successfully estimate the CO2 stored mass by means of DEXP multiscale analysis, even when the anomaly is incompletely defined, due to a not proper areal coverage of the survey. The DEXP method has proven to be very stable with respect to noise and to be an efficient technique for simultaneously determining the CO2 plume depth, its geometrical features and stored mass.	756
SpiderTherm: Optimizing Geothermal Extraction for Sustainable Energy Transition	758
Molossi1, G. Gola2, A. Manzella2, M. Pipan1	758
S. Panebianco1,2, C. Satriano3, G. Vivone2, M. Picozzi4, A. Strollo5, T.A. Stabile2	763
1 Dipartimento di Fisica e Scienze della Terra, Università di Ferrara, Italy	763
2 Consiglio Nazionale delle Ricerche (CNR-IMAA), Italy	763
3 Université Paris Cité, Institut de physique du globe de Paris, France.	763
4 Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Italy.	763
5 GFZ German Research Centre for Geoscience, Potsdam, Germany.	763
Sabatini A.1, C. Pauselli1, S. Fuchs2, M. Ercoli1, P. Mancinelli1	764
1 Università degli Studi di Perugia, Dip. Fisica e Geologia, Italy.	764
2 GFZ German Research Centre for Geosciences, Germany.	764
Fig. 1 – Simplified scheme of the adopted approach	773
Seismic attribute analyses for geothermal applications: a case study from the geothermal potential assessment in the Valle Latina area (Central Italy).	775
G. Vico1,2, R. Maffucci2, S. Bigi1	775
1 Dipartimento di Scienze della Terra, Università di Roma La Sapienza, Italy	775
2Istituto Nazionale di Geofisica e Vulcanologia, Roma, Italy	775
Fig. 1 - An example of vectorization results from the VIDEPI dataset, using the process described in this study. a) Scanned image of line FR-309-80. b) SEG-Y file extracted from the scanned image in a), plotted with similar parameters to the original, c) same SEG-Y data as in b) plotted with a variable density display. d) application of seismic attribute “semblance”; e) application of seismic attribute “pseudo-relief”.	776
This approach of seismic attribute analysis is extremely useful for geothermal exploration of similar fields and reservoirs with a strong AI contrast and the construction of more predictive static and dynamic reservoir models based on discontinuity detected by attribute analysis and seismic interpretation. Seismic attributes are very useful in characterizing faults and fractures also in 2D seismic data volumes. The pseudo-relief attribute application made the interpretation of the main unconformities and structural features possible.	777
Electromagnetic Induction Data Inversion: Realistic Prior Models and Spatial Regularization with Respect to Known Structures	779
N. Zaru1, M. Rossi2, G. Vacca1, G. Vignoli1,3	779
1 DICAAR - University of Cagliari, Italy	779
2 Engineering Geology Division – Lund University, Sweden	779
3 Near Surface Land and Marine Geology Department - GEUS, Denmark	779
Probabilistic Petrophysical Inversion of Ground-Based FDEM Data for Alpine Peatland Characterization: A Case Study in the Italian Dolomites	784
N. Zaru1, S. Silvestri2, M. Assiri3, P. Bai4, T.M. Hansen5, G. Vignoli1,6	784
1 DICAAR - University of Cagliari, Italy	784
2 University of Bologna, Italy	784
3 University of Padua, Italy	784
4 SINOPEC, China	784
5 Aarhus University, Denmark	784
6 Near Surface Land and Marine Geology Department, GEUS, Denmark	784
Fig. 1 – Horizontal slices presenting the inversion outcomes derived from the Frequency-Domain Electromagnetic Induction (FDEM) data collected across the Danta peatland in Italy. Each row shows the resistivity model with maximum likelihood and the probability of encountering peat or clay. Rows (a-b) depict these models and probabilities at depths of 3.0 m and 7.0 m respectively. In the third column, above the peat probability, the figure displays the profile's location shown in Figure 2 (highlighted in bright green), along with the positions of associated boreholes marked by red X.	786
Fig. 2 – Vertical profile presenting the chi-squared value of the deterministic (blue) and maximum likelihood (red) models in the top panel. The deterministic model obtained with the standard Occam’s inversion is shown in the middle panel; the maximum likelihood model obtained with the probabilistic inversion in the bottom panel. The horizontal blue and red lines in the fist panel represent the average chi-squared values associated with the two models.	787
Fig. 3 – Vertical profile presenting the inversion outcomes derived from the Frequency-Domain Electromagnetic Induction (FDEM) data collected across the Danta peatland in Italy.	788
Reference	789
32_Abstract_GNGTS2024.pdf	791
GNGTS 2024	791
APPLIED GEOPHYSICS FOR ENERGY, ENVIRONMENT AND NEW TECHNOLOGIES	791
F. Accomando1 and G. Florio1	792
1 Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse – Università di Napoli “Federico II”, Italia.	792
In recent years, there was a notable technological advancement in geophysical sensors. In the case of magnetometry, several sensors were used having the common feature to be miniaturized and lightweight, thus idoneous to be carried by UAV in drone-borne magnetometric surveys. Moreover, such sensors have the common feature to be very cheap, so that it is in principle very easy to have the resources to combine two or three of them to form gradiometers. Nonetheless, another common feature is that their sensitivity ranges from 0.1 to about 200 nT, thus not comparable to that of alkali vapor, standard flux-gate or even proton magnetometers. However, their low-cost, small volume and weight remain as very interesting features of these sensors. In this communication, we want to explore the range of applications of small tri-axial magnetometers commonly used for attitude determination in several devices. We compare the results of ground-based surveys performed with conventional geophysical instruments with those obtained using these sensors.	792
G.M. Adinolfi 1 , C. Comina 1, S.C. Vinciguerra 1	793
1 Department of Earth Sciences, University of Turin, Turin, Italy	793
Combined 3D surface wave and refraction analysis around the Scrovegni Chapel in Padua, Italy	795
I. Barone1, M. Pavoni1, J. T. F. Ting1, J. Boaga1,3, G. Cassiani1,3, D. Dupuy4, R. Deiana2,3	795
1 Università degli Studi di Padova, Dipartimento di Geoscienze, Padova	795
Fig. 1 – Acquisition scheme for the dense 3D seismic survey. 1C and 3D seismic nodes are represented as blue and yellow triangles, respectively, while active source locations are represented as red stars.	796
P-and S-velocity 3D model for the characterisation of the subsurface beneath the village of Arquata del Tronto	798
G. Böhm1, A. Affatato1, L. Baradello1, G. Brancatelli1, E. Forlin1, F. Meneghini1	798
1 National Institute of Oceanography and Applied Geophysics – OGS (Italy)	798
Fig. 2 – Complete 3D Vp model from the travel time tomography of the three lines. a) 3D view. b) Vertical sections extracted along the east-west direction and spaced 25 m apart (see map top right). c) Vertical sections extracted along the north-south direction and spaced 25 m apart (see map top right).	800
Fig. 3 – Horizontal slices corresponding to different depths of the final 3D tomographic P velocity model. The Z coordinate denotes the depth in relation to the corresponding topographic elevation.	801
4. Results and conclusions	801
Monitoring of the saline wedge in the Po di Goro river.	803
P. Boldrin1, E. Ferrari1, F. Droghetti1, A. Bondesan2, E. Rizzo1	803
1 University of Ferrara (Dipartimento di Fisica e Scienze della Terra, Ferrara, Italy )	803
2 Consorzio di Bonifica di Ferrara (Italy )	803
Fig.1: The map shows the pathway of the acquisitions. The acquisitions carried out in two different times of the day along part of Po di Goro (15 Km). During the morning (8.00 AM) when there was low tide and during the afternoon (13.00 PM) when there was a peak of maximum tide. The two paths have an overlap of about 1.5 Km. The different EMs were performed using different frequency of Profiler. It was located on a inflatable boat pulled by a kayak.	804
Deep Electric Resistivity Tomography (DERT) on the Cazzaso Landslide	805
1 OGS – Istituto Nazionale di Oceanografia e di geofisica Sperimentale, Trieste, Italia.	805
2DISAT – Dipartimento di Scienze dell’Ambiente e della terra, Università degli Studi Milano – Bicocca, Milano, Italia.	805
3SCVSA - Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parma, Italia.	805
4Servizio Geologico, Regione Autonoma Friuli Venezia Giulia, Trieste, Italia.	805
Fig 1 Layout of the MS and FW profiles in the area of the Cazzaso landslide.	807
Fig 2 Resistivity model of the ERT3 line, in the transverse direction respect to the landslide.	809
Fig 3 Resistivity volume (20 - 120 Ωm) extracted from the FullWaver cube showing the whole conductive pelitic complex and the contact between this and the dolomite.	810
What does the seismic record of a World War II bomb-explosion look like? observations from a controlled detonation in 26 november 2023 near Ferrara, Italy	812
1Dipartimento di Fisica e Scienze della Terra, Università degli Studi di Ferrara, Italy	812
2Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy	812
Figure 1: SNet4Fer 2.0/3.0 seismic networks (green triangles). The red star represents the point where the WWII bomb was detonated; the circles represent the internal (red) and external (orange) monitoring domains of the Casaglia geothermal field.	813
Figure 2. Examples of the bomb explosion seismograms recorded in stations from the SNet4Fer network. The first two panels show the 3-component records in the SNet4Fer 3.0 stations FEM0 and FEM1 (the seismograms are from the borehole sensors). The third and fourth panels show the records of two NetFer2.0 stations: PONT (3 components) and FORN stations (1 component).	814
L. Capozzoli1, G. De Martino1, S. Imperatore2, F. Nerilli2, L. Telesca1, E. Vasanelli3	822
Fig. 1 - The innovative multi-scale and multi-sensor based approach of ICARUS	824
A Dynamic and Multi-Source Hydrogeophysical Model to Remediate a Complex Hydrocarbon-Contaminated Site	826
P.Ciampi1, G.Cassiani2, G.P.Deidda3, C.Esposito1, G.Scarascia Mugnozza1, M. Petrangeli Papini4	826
1 Department of Earth Science, Sapienza University of Rome	826
2 Department of Geosciences, University of Padua	826
3 Department of Civil, Environmental Engineering and Architecture, University of Cagliari	826
4 Department of Chemistry, Sapienza University of Rome	826
G. Cianchini1, C. Fidani1,2, A. Piscini1, M. Soldani1, A. De Santis1, L. Perrone1 , M. Orlando1, D. Sabbagh1	828
1 Istituto Nazionale di Geofisica e Vulcanologia, Roma, Italy	828
2 Central Italy Electromagnetic Network, Fermo, Italy	828
Fig. 1 – Spectrograms recorded on December 22, 2022, from 19:52 to 22:19 LT; the time is marked every 5 minutes by the vertical dotted lines. Frequency was reported in the logarithm scale on the y-axis and power of signals in dB by a colour legend. Two bands are related to the electrode E-W on the top, and to the microphone recording below. Labels on the left and in the middle referred to the power spectra intensities and times of the spectrograms.	829
Fig. 2 – The acoustic spectrogram recorded on April 18 and the morning of 19, 2023, includes the moment of a small seismic event that occurred at Mirabella, M=2.1, about 20 km from Mefite. The Mirabella earthquake time is indicated by a vertical cyan arrow. Main eigenfrequencies and sudden puffs on the power spectra are indicated.	831
Fig. 3 – Details of the power spectrum relative to three moments (of less than 10 minutes to one hour) hours before the Mirabella M=2.1 earthquake. Periodic modulations of the noise of less than 10 minutes were evidenced between 200 and 800 Hz, on the left. The entire series of resonances from 19 to 170 Hz appeared during the damping interval at the centre. A detail of one of the three puffs is reported on the right where the sudden increase followed by a gradual fading that lasted two minutes is shown. The right plot also shows an unexpected growth of sound in a well-defined range between 55 and 80 Hz.	832
F. D’Ajello Caracciolo1, I. Nicolosi1, V. Sapia1, V. Materni1, G. Tusa1, M. Paratore1, R. Azzaro1	833
1 Istituto Nazionale di Geofisica e Vulcanologia (INGV)	833
Combining active and passive methods to understand the seismic velocity distribution in a thick Quaternary succession of the Po Plain. (Terre del Reno, Ferrara, Italy)	837
G. Di Giulio1, L. Minarelli1, M. Stefani2, G. Milana1, G. Tarabusi1, M. Vassallo1, S. Amoroso1,3, A. Affatato4, L. Baradello4, L. Petronio4	837
Inductive Induced Polarization Effects: the Loupe EM synthetic case study	842
F. Dauti1, A. Viezzoli2, G. Fiandaca1	842
1 The EEM Team for Hydro & eXploration, Dep. of Earth Sciences A. Desio, Università degli Studi di Milano, Milano (Italy)	842
2 Emergo s.r.l., Cascina (Italy)	842
M. Ercoli1*, N. Cavalagli2, M. Barchi1, C. Pauselli1, M. Porreca1, R. Lupi3.	851
Mapping surface/ground water interactions and embarkment composition along the Po river with transient electromagnetics	853
G. Fiandaca1, A. Signora1, S. Galli1, J. Chen1, C. Compostella1, M. Gisolo2, A. Viezzoli3	853
Acknowledgments	855
References	855
S. Galli1, A. Signora1, J. Chen1, F. Schaars2, M. Grohen3, G. Fiandaca1	856
1 The EEM Team for Hydro and eXploration, Dep. of Earth Sciences A. Desio, Università degli Studi di Milano, Milano (Italy)	856
2 Artesia Water, Schoonhoven (The Netherlands)	856
3 Wiertsema & partners, Tolbert (The Netherlands)	856
Figure 2: Comparison of standard inversion and bathymetric inversion of FloaTEM data. Top right: bathymetry incorporated in the inversion; Bottom left: standard inversion, without bathymetry incorporation; Top left: ratio between inversion with/without bathymetry incorporation; Bottom right: histogram of the ratio of the resistivity values of the two inversions.	858
Seismic noise surveys in the area of Etna volcano (southern Italy).	861
S. Hailemikael1, D. Famiani1, G. Milana1, G. Tusa2, M. Paratore2, G. Brunelli3, R. Azzaro2	861
1 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy.	861
2 Istituto Nazionale di Geofisica e Vulcanologia, Catania, Italy.	861
3 Istituto Nazionale di Geofisica e Vulcanologia, Milan, Italy.	861
Fig. 1 Map of investigated sites	863
Fig. 2 Azimuthal variation of H/V functions at selected nodal stations of the passive array survey carried out at Nicolosi (ENIC) monitoring site.	863
Fig. 3 Rayleigh and Love wave dispersion curves from RTBF analysis of the passive array data collected at Nicolosi (ENIC) seismic monitoring site.	864
S. Imposa1, S. Grassi1, G. Morreale1, C. Pirrotta1, L. Cavalier2, A. Gilotti3, D. Giuliano4, E. Cayre5, L.M. Caliò6	865
1 Department of Biological, Geological and Environmental Sciences, University of Catania, Catania, Italy	865
2 Unité mixte de Recherche Ausonius (UMR 5607), Université Bordeaux-Montaigne, Pessac, France 3 Greensol S.R.L., Syracuse, Italy 4 Department of Cultures and Society, University of Palermo, Palermo, Italy 5 Post-doc researcher, Grand Programme de Recherche Human Past, Université de Bordeaux, Talence (France)	865
6 Department of Human Science, University of Catania, Catania, Italy	865
Characterizing groundwater springs in the Italian Alps: an integrated geological, geophysical, and hydrogeological approach	871
Acknowledgments	873
D. Melegari1, G. De Donno1, E. Piegari2	875
1 DICEA (Sapienza - University of Rome, Rome, Italy)	875
2 DISTAR (Federico II - University of Naples, Naples, Italy)	875
Fig. 1 – Aerial image (a) and plan (b) of the municipal solid waste landfill in Central Italy, with the location of the four investigated ERT/IP lines (L1-L4) and of the five piezometers (P1-P5)	876
M. Pavoni 1, J. Boaga 1, A. Bast 2,3, Lichtenegger 2,3, J. Buckel 4	883
1Department of Geosciences, University of Padova, Padova, Italy.	883
2WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland.	883
3Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland.	883
4Institute for Geophysics and Extraterrestrial Physics, Technische Universität Braunschweig, Braunschweig, Germany.	883
G. Penta de Peppo1, M. Cercato1, G. De Donno1	888
1 “Sapienza” University of Rome – DICEA	888
A. Perrone1, J. Bellanova1, G. Calamita1, F. Falabella2, M.R. Gallipoli1, E. Gueguen1, A. Pepe2, S. Piscitelli1, V. Serlenga1, T.A. Stabile1	895
Fig. 1 – Map of Gorgoglione test site (Basilicata region, southern Italy) with location of the in-situ geophysical measurements carried out in the urban area.	896
G. Romano1, L. Capozzoli2, V. Lapenna2, M. Polemio3	898
Fig. 1 - The proposed geophysical approach for characterizing coastal areas of SUBGEO	900
Integrated GPR and FDEM to detect brines pockets in Continental Antarctica	901
I. Santin1, E. Forte1, M. Guglielmin2	901
1 Department of Mathematics, Informatics and Geosciences (University of Trieste, Italy)	901
2 Department of Theoretical and Applied Science (University of Insubria, Italy)	901
Fig. 1 – (A) Location map of CALM grid and Lake n.16 (light blue square) in Boulder Clay Glacier area (MZS: Mario Zucchelli Italian Station). (B) Ortophoto with superimposed the GPR profiles, in white, and the FDEM survey, in red. Yellow lines mark the position of the gravel landing strip.	902
Fig. 2 – Apparent conductivity map down to a nominal depth of 2.5 m on Lake n.16. Red dotted line marks the path of the FDEM survey, while the blue triangle the location of borehole BC1 (taken from Azzaro et al., 2021).	903
Fig. 3 – Exemplary GPR profiles on Lake n.16 (A, B) and on CALM grid (C, C’, C’’), both represented with white lines. C’ and C’’ display dominant frequency and chaos attributes, respectively, of profile C. Yellow dot marks the crossing point between A and B. Location of each GPR profile is highlighted by a coloured line: C, C’, C’’ with a blue line, while A and B with pink and magenta lines, respectively. Pink triangle marks the location of BC1.	904
A. Signora1, S. Galli1, F. Dauti1, A. L. Sullivan1, A. Lucchelli1, M. Gisolo2, G. Fiandaca1	909
1 The EEM Team for Hydro & eXploration, Department Of Earth Sciences “Ardito Desio”, Università degli Studi di Milano, Milano (Italy)	909
2 A2A Ciclo Idrico S.p.a. , Brescia (Italy)	909
EEMstudio: processing and modelling of electric and electromagnetic data in a QGIS plugin	916
N.A.L. Sullivan1, A. Viezzoli2, G. Fiandaca1	916
Introduction	916
QGIS Widget	917
Fig. 1 – QGIS main window with EEMstudio widget on the right, used for management of processing and modelling files. Once uploaded, the coordinates of the acquisition points are automatically added to the QGIS layer, among eventual other layers in the QGIS project. In this figure, two types of data are shown: galvanic and inductive (airborne). White dots are the electrodes, red circles and blue points are the positions of the quadrupoles used in the soundings selected in the galvanic processing app (Figure 2a). Black dots are the inductive soundings and yellow points are the soundings highlighted in red in the inductive processing app (Figure 2b).	917
Processing	917
Fig.2 – Processing window supporting a) galvanic data visualization b) inductive data visualization. In galvanic window, first section: electrode position; second section: data pseudosection; third section: model of rho0; fourth section: model of phi; right panel: IP decay for the selected quadrupoles in the pseudosection. In inductive window, first section: flight altitude; second section: data (blue dots); third section: model of rho0; first left panel: decay in correspondence of the red highlights in the sections; models of rho0 in correspondence of the red highlights in the sections.	918
Modelling	919
Fig3 – Modelling windows. a) Interface to gather all necessary files to launch easily inversions with EEMverter (Fiandaca et al. 2024). b) Model Builder, to build synthetic models. From left to right: table with the parameters and the associated colors, widgets to change the grid, grid where it’s possible to select the cells and assign a color, 1D model of the row marked in blue on the bottom of the grid.	919
Conclusions	919
Acknowledgments	920
References	920
Denser is better? Spatial sampling vs trace stacking in multichannel GPR data to improve sections and depth-slices readability for archaeological prospections.	926
Vergnano1, C. Comina1	926
1 Università degli Studi di Torino, department of Earth Sciences; Torino, Italy	926
Fig. 1 – Comparison between 1.25 m depth-slices from 3D data volumes created using 6 different subsets of the total 32 channels of the Stream C: : upper panels) all the 32 channels, VV+HH (spacing about 3 cm), only the VV channels (spacing about 4 cm), only the HH channels (spacing about 10 cm); lower panels) different subsets of the VV channels with spacing of about 12, 25 and 50 cm respectively.	928
Fig. 2 – Comparison between GPR sections before and after stacking. a1): after dewow, move startime, and background removal. a2): a1) + stacking of adjacent 5 channels. An energy decay gain filter was then applied to both sections to allow for meaningful visual readability.	929
Fig. 3 – a) Comparison between a 60 cm depth-slice from Tindari test site, a1): non stacked, a2) stacked with 5 channels. b) as a), but at 40 cm depth.	930
33_Abstract_GNGTS2024.pdf	934
GNGTS 2024	934
APPLIED GEOPHYSICS FOR ENERGY, ENVIRONMENT AND NEW TECHNOLOGIES	934
S. Berti1,2, M. Aleardi1, E. Stucchi1	936
1 Department of Earth Sciences (University of Pisa, Italy)	936
2 Department of Earth Sciences (University of Florence, Italy)	936
R. Carluccio1, I. Nicolosi1, F. D’Ajello Caracciolo1, L. Minelli1	948
1 Istituto Nazionale di Geofisica e Vulcanologia (INGV)	948
J. Chen1, G. Fiandaca1	953
1The EEM Team for Hydro & eXploration, Department of Earth Sciences "Ardito Desio", University of Milano, Milano (Italy).	953
Acknowledgments	957
G. Fiandaca1, B. Zhang2, J. Chen1, A. Signora1, F. Dauti1, S. Galli1, N.A.L. Sullivan1, A. Bollino1, A. Viezzoli3	961
Introduction	961
Method and results	961
Acknowledgments	966
References	966
S. Galli1, F. Schaars2, F. Smits3,4, L. Borst5, A. Rapiti6, G. Fiandaca1	968
1 The EEM Team for Hydro and eXploration, Dep. of Earth Sciences A. Desio, Università degli Studi di Milano, Milano (Italy)	968
2 Artesia Water, 2871 BP Schoonhoven (The Netherlands)	968
3 Waternet, 1096 AC Amsterdam (The Netherlands)	968
4 Technical University of Delft, 2628 CD Delft (The Netherlands)	968
5 PWN, 1991 AS Velserbroek, (The Netherlands)	968
Figure 3. Comparison between Borehole#8 log (yellow star in Fig. 3) and inversion model. Left – AGMS joint inversion; right – AEM-only inversion. Blue lines – inversion model; black lines – resistivity logs; red lines – rejected data in resistivity log in the joint AGMS inversion.	973
F. Macelloni1, M. H. Altaf1, M. Aleardi1, E.M. Stucchi1	978
1 Department of Earth Sciences, University of Pisa, Pisa, Italy	978
Fig. 3 – a) Observed seismogram; b) seismogram computed from the mean model of the prior distribution; c) predicted seismogram; d) difference between observed and predicted seismograms.	983
a Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo	986
Fig. 1 – Schematic arrangement of the whole instrumentation deposited at the bottom of the c.p. D (a). In (b) and (c) a focus on the centering disc supporting the digitizer.	988
Fig. 2 – Seasonal and whole period PSD’s for the three components. All the curves are reported in grey, while the mean one is color-coded. Peterson reference curves in black.	989
Fig. 3 – “On demand” interface. In (a) the dialog box. In (b) the spectrograms (on the left) and the Power Spectral Density (on the right) for the three components. In (c) the signal polarization polar histogram and in (d) the amplitude spectra (on the top) and the horizontal-to-vertical spectral ratio (on the bottom).	990
J. B. May1, P. Bird2,1, M. M. C. Carafa1	993
1 Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy	993
2 Department of Earth, Planetary, and Space Sciences, UCLA, U.S.A	993
Table 1: Searched models with improved Geometric mean score when compared to Earth5-049.	996
Felipe Rincón1, Sean Berti1,2, Mattia Aleardi1, Eusebio Stucchi1	1000
1 University of Pisa, 2 University of Florence	1000
A. Signora1, G. Fiandaca1	1013
1 The EEM Team for Hydro & eXploration Dep. Of Earth Sciences A. Desio, Università degli Studi di Milano, Via Botticelli 23, Milano (Italy)	1013

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