GNGTS 2016 - Atti del 35° Convegno Nazionale

328 GNGTS 2016 S essione 2.1 climatological changes and/or season time drifts; - µ Δ T ( x , y ) time average value of Δ T ( x , y , t ) at the location x , y computed on cloud-free records belonging to the selected data set ( t ∈ τ ); - σ Δ T ( x , y ) standard deviation value of Δ T ( x , y , t ) at the location x , y computed on cloud-free records belonging to the selected data set ( t ∈ τ ). Excess Δ T ( x , y , t ) - µ Δ T ( x , y ) represents the Signal (S) to be investigated for its possible relation with seismic activity. It is always evaluated by comparison with the corresponding natural/observational Noise (N), represented by σ Δ T ( x , y ) which describes the overall (local) variability of S including all (natural and observational, known and unknown) sources of its variability as historically observed at the same site in similar observational conditions (sensor, time of day, month, etc.). This way, the relative importance of the measured TIR signal (or the intensity of anomalous TIR transients) can naturally be evaluated in terms of S/N ratio by the RETIRA index. Identification of Significant Sequences of TIRAnomalies (SSTAs). The particular spatial distribution of this kind of TAs and their transitory character in the temporal domain, normally allows to identify them and, in any case, to distinguish them from the spatially and temporally persistent ones possibly related to an impending earthquake, even in the case where they have similar intensity. This is the reason why (together with relative intensity) spatial extension and persistence in time are requirements to be satisfied in order to preliminarily identify what we call significant thermal anomalies (STAs). By this way an operational definition of STA can be given by considering a location x , y affected by a STA at the time t if the following requirements are satisfied: (a) Relative intensity Δ T ( x , y , t ) >K; (b) Control on spurious effects : Absence of known sources of spurious TAs (see above); (c) Spatial persistence It is not isolated being part of a group of TAs covering at least 150 km 2 within an area of 1° x 1°; (d) Temporal persistence Previous conditions (i.e., the existence of a group of TAs covering at least 150 km 2 within an area of 1° x 1° around x , y ) are satisfied at least one more time in the 7 days preceding/following t . After applying the above-mentioned rules to the whole data set collected by using different satellite sensors for various geo-tectonic settings Significant Sequences of Thermal Anomalies (SSTAs) were identified where each one is composed by several STAs spanned on 2 or more TIR Anomaly Maps (TAMs). Long-term correlation analysis: result and discussion. Based on the long-term (more than 15 years) experience on TIRAnomalyMaps (TAMs) analyses (e.g., Tramutoli et al. , 2015a, 2015b, and reference herein) performed by authors for tens of earthquakes occurred in different continents and tectonic settings, empirical rules were applied in order to evaluate the possible correlations existing among the appearance of SSTAs and time, location and magnitude of earthquakes. By this way each single STA observed at the time t in the location ( x , y ) will be considered possibly related to seismic activity if: - It belongs to a previously identified SSTA; - An earthquake of M ≥ 4 occurs 30 days after its appearance or within 15 days before ( temporal window ); - An earthquake with M ≥ 4 occurs within a distance D, from the considered STA, so that 150 km ≤ D ≤ R D being R D = 10 0.43M the Dobrovolsky et al. (1979) distance ( spatial window ). By this way, starting from each STA belonging to an SSTA, different possibly affected areas can be built for different possible magnitudes of future/past earthquakes. The convolution of the contours drawn for all the STAs belonging to the same SSTA, allow to draw the contours of the areas (different for different magnitudes) possibly affected by future/past earthquakes.