GNGTS 2021 - Atti del 39° Convegno Nazionale

GNGTS 2021 S essione 3.1 404 coherence with the known geology of the area (Pace, 2020). A deep resistive body ( R2 in Fig. 2) was imaged in agreement with previous 2D models, but new insight emerged about its spatial extension and orientation. At a depth of 3-7 km, the resistivity was higher than 200 Ωm, and the orientation was around N40-50°E. This orientation is quite similar to that observed for the deep 3D structures imaged below the “Lago Boracifero” area in the adjacent Larderello geothermal field (Santilano, 2017). Given the same spatial orientation, the deep structures differ in that the one in the Larderello field is less resistive (< 100 Ωm), while the one imaged in the Travale geothermal field is more resistive than the background (> 200 Ωm). The resistive nature of the deep body was actually not unexpected because it is hosted in a vapor-dominated system in correspondence to the granite units. The final 3D resistivity model was also compared with other subsurface data and models. The outcome of this study provides new insight into the interpretation of the complex geothermal system of Travale. Conclusion This work presented the recent advances in the interpretation of the MT data from the Travale geothermal field. Two different methods were applied for the first time to the Travale data set: 2D stochastic inverse modeling and 3D derivative-based inversion. The former overcame any prior bias on the final resistivity model; the latter revealed the complete spatial extension of the imaged subsurface structures, thus extending previous knowledge. 3D MT inversion was challenging due to computational reasons: it usually requires 100 unknowns more than 2D inversion and the forward modelling calculation is much heavier. Future work should broaden the MT characterization of the LTGA by means of new acquisition campaigns that would enlarge the investigated zone, ideally with a regular space-covering of the sites. The existing data set need to be enriched for all the sites with the acquisition of the geomagnetic transfer function, which is fundamental for 3D inversion. The integration of multiple geophysical data sets would also be beneficial for a comprehensive study and better exploration of the geothermal system. Acknowledgements Professors A. Mart í , P. Queralt and J. Ledo from the University of Barcelona gave scientific support for MT data analysis and inversion. Part of the MT data were kindly provided by ENEL GP. Other MT data were acquired as part of the European projects INTAS and I-GET. Computational resources were provided by hpc@polito (http://hpc.polito.it). Dr. N. Meqbel (Consulting-GEO) kindly provided the 3D MT inversion code ModEM and the software 3D-GRID Academic. References Chave A.D. and Jones A.G.; 2012: The Magnetotelluric Method, Theory and Practice . Cambridge University Press. 604 pp. Krieger L. and Peacock J. R.; 2014: MTpy: A Python toolbox for magnetotellurics. Comput. Geosci., 72 , 167- 175. Kelbert A., Meqbel N., Egbert G.D. and Tandon K.; 2014: ModEM: A modular system for inversion of electromagnetic geophysical data . Comput. Geosci. http://dx.doi.org/10.1016/j.cageo.2014.01.010 Ledo J.; 2005: 2-D versus 3-D Magnetotelluric data interpretation . Surveys in Geophysics, 26 , 511–543. Manzella A., Spichak V., Pushkarev P., Sileva D., Oskooi B., Ruggieri G. and Sizov Y.; 2006: Deep fluid circulation in the Travale geothermal area and its relation with tectonic structure investigated by a magnetotelluric survey. Thirty-First Workshop on Geothermal Reservoir Engineering proceedings, Stanford University, Stanford, California, January 30 – February 1, p. 1-6.