GNGTS 2016 - Atti del 35° Convegno Nazionale

GNGTS 2016 S essione 2.1 327 Since 2001, the general change detention approach, named Robust Satellite Technique (RST), has been applied to explore the fluctuations of Earth’s thermally emitted radiation - observed by satellite sensors operating in the Thermal InfraRed (TIR) spectral range - in possible relationship with the preparation phases of major earthquakes. Thanks to its intrinsic exportability it was used to study the preparation phases of earthquakes within a wide range of magnitude (from 4.0 to 7.9) in different geo-tectonic contexts (compressive, extensional and transcurrent) in four different continents. It showed good ability to isolate anomalous space- time TIR transients possibly associated to seismic activity, from the normal variability of TIR signal due to other causes (e.g. meteorological). In this paper the space-time correlation between TIR anomalies, identified by the RST methodology, and earthquakes (with M≥4) is evaluated by retrospective long-term analyses carried out in different seismic areas of the world, by using different satellite systems. More than 10.000 satellite images (for more than 10 billions of records) where analyzed in order to identify, by using the RST approach, Significant TIR Anomalies (STAs). Prescriptions on STA’s relative intensity - measured by the RETIRA (Robust Estimator of TIR Anomalies) index - and space-time persistence, were used to identify Significant Sequences of TIR Anomalies (SSTAs). A correlation analysis among the appearance of SSTAs and time, location and magnitude of earthquakes, was performed by applying predefined space-temporal and magnitude constraints. Preliminary results highlight that, depending on the considered geographic region, the occurrence of SSTAs falling out of the pre-fixed space-time correlation window range between 7% (Greece) and 39% (Italy). Molchan error diagram analysis gives a clear indication of non-casualty of such a result with a probability gain (compared with a random guess) ranging from 1,5 up to 3,5. Such a result confirm a positive informative contribution that the use of RST-TIR analysis could give in the framework of a multi-parametric system for a time-Dependent Assessment of Seismic Hazard (t-DASH). RST methodology. The RST approach is based on a multi-temporal analysis of historical data set of satellite observations acquired in similar observational conditions (e.g., same month of the year, same hour of the day, same sensor, etc.). The methodology is based on the general approach Robust AVHRR Technique (RAT; Tramutoli, 1998), which, being exclusively based on satellite data at hand (do not require whatever ancillary data), is intrinsically exportable on different satellite packages, the reason why the original name RAT was changed in the more general Robust Satellite Techniques (RST, Tramutoli, 2005, 2007). Since the first RST application to the thermal monitoring of earthquake prone areas, TIR fluctuations were identified using the RETIRA index (Robust Estimator of TIR Anomalies, Tramutoli, 2005), which can be computed as follows: where: - x,y represent the coordinates of the center of the ground resolution cell corresponding to the pixel under consideration on a satellite image; - t is the time of the measurement acquisition with t ∈ τ , where τ defines the homogeneous domain of multi-annual satellite imagery collected in the same time slot of the day and period (month) of the year; - Δ T ( x , y , t ) = T ( x , y , t ) – T (t) is the value of the difference between the punctual value of TIR brightness temperature T ( x , y , t ) measured at the location x , y , acquisition time t , and its spatial average T ( t ) computed on the investigated area considering only cloud-free locations, all belonging to the same, land or sea, class (i.e., considering only sea pixels if x , y is located on the sea and only land pixels if x , y is located on the land). Note that the choice of such a differential variable Δ T ( x , y , t ) instead of T ( x , y , t ) is expected to reduce possible contributions (e.g., occasional warming) due to day-to-day and/or year-to-year