Robust Satellite Techniques for Detecting Preseismic Thermal Anomalies

Tramutoli, Valerio; Filizzola, Carolina; Genzano, Nicola; Lisi, Mariano

doi:10.1002/9781119156949.ch14

Cited by 10 publications

(19 citation statements)

References 71 publications

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“…We evaluated the possible correlations between the SSTA appearances and location, time, and magnitude of the earthquakes belonging to the daily filtered catalog. To this aim, we applied empirical rules which were established taking account of both previous studies (see Tramutoli, Corrado, Filizzola, Genzano, Lisi, & Pergola, 2015; Tramutoli et al., 2018, and references therein) and physical models (e.g., Tramutoli et al., 2013) to date proposed to explain the TA appearances before and after large earthquakes. Furthermore, the defined validation rules, which should drive the retrospective correlation analysis, match those rules used by the CSEP project (Schorlemmer et al., 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Like in previous studies (see Tramutoli, Corrado, Filizzola, Genzano, Lisi, & Pergola, 2015; Tramutoli et al., 2018, and references therein), we carried out a preliminary space–time persistence analysis on the TAs before qualifying them as Significant Thermal Anomalies (STAs). In fact, deriving from a statistical approach, the RETIRA box index can be exposed to a proliferation of signal outliers (see Tramutoli et al., 2005).…”

Section: Methodsmentioning

confidence: 99%

“…Among others, the approach, proposed by Tramutoli (1998, 2007) and named Robust Satellite Techniques (RST), has shown the best performance in discriminating anomalous TIR changes presumably related to seismic activity from TIR signal variability caused by other effects (see Tramutoli et al., 2005). The RST methodology has been applied in different world seismic active areas, analyzing time‐series of satellite imagery collected by TIR sensors on board polar and geostationary platforms (see Tramutoli, Corrado, Filizzola, Genzano, Lisi, & Pergola, 2015; Tramutoli et al., 2018, for a detailed review).…”

Section: Introductionmentioning

confidence: 99%

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Statistical Correlation Analysis Between Thermal Infrared Anomalies Observed From MTSATs and Large Earthquakes Occurred in Japan (2005–2015)

et al. 2021

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Most of the studies devoted to improving our knowledge on the preparative phases of earthquakes have been based, until now, on the study of single (seismological, geochemical, biological, etc.) observables. A large scientific literature documents the occurrence of anomalous space-time transients of such parameters, which are apparently linked to earthquake preparation phases (see for instance Cicerone et al., 2011; Jiao et al., 2018; Tronin, 2006). However, no demonstration is given concerning the possibility of transforming such observations in methodologies that are actually effective (in terms of precision and reliability of the prediction) in the context of an Operational Earthquake Forecast (OEF) system (see also Geller, 1997, and references therein). A multiparametric probabilistic approach, devoted to improving the quality of short-term forecast of seismic hazard (rather than providing exact prediction of future earthquakes), today seems the most promising perspective (e.g., Genzano et al., 2020; Tramutoli & Vallianatos, 2020). To this aim, preliminary studies devoted to qualifying the "predictive" potential of each candidate parameter are of fundamental importance. Starting from long-term correlation analyses, they could provide for each considered parameter the occurrence probability P(Δt, Α, Μ) of an earthquake of magnitude > M within a well-defined region A and period of time Δt. On this basis, the weight to attribute to each parameter within a multiparametric system, devoted to dynamically estimating seismic hazard, could be determined. Such a kind of study should investigate the whole data set of the observations, equally addressing achieved successful rates (i.e., how many anomalies are followed by earthquakes), as well as false-positive rates (i.e., how many anomalies are not followed by earthquakes).

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Correlation Analysis Between Thermal Infrared Anomalies Observed From MTSATs and Large Earthquakes Occurred in Japan (2005–2015)

et al. 2021

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“…Details about the methodology used in this paper and its previous applications are summarized in [17,73]. In this section, we want just to recall that the RST approach rests on the Robust Estimator of TIR Anomalies (RETIRA) index, whose more general expression is the following: The black line defines the area where the RST approach was applied to identify TIR anomalies on SEVIRI satellite images (SEVIRI cut area).…”

Section: The Robust Estimator Of Thermal Infrared Anomalies (Retira)mentioning

confidence: 99%

Section: The Robust Estimator Of Thermal Infrared Anomalies (Retira)mentioning

confidence: 99%

RST Analysis of Anomalous TIR Sequences in Relation with Earthquakes Occurred in Turkey in the Period 2004–2015

et al. 2022

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The paper provides, for the first time, a long-term (>10 years) analysis of anomalous transients in Earth’s emitted radiation over Turkey and neighbouring regions. The RST (Robust Satellite Techniques) approach is used to identify Significant Sequences of Thermal Anomalies (SSTAs) over about 12 years (May 2004 to October 2015) of night-time MSG-SEVIRI satellite images. The correlation analysis is performed with earthquakes with M ≥ 4, which occurred in the investigated period/region within a pre-defined space-time volume around SSTA occurrences. It confirms, also for Turkey, the possibility to qualify SSTAs among the candidate parameters of a multi-parametric system for time-Dependent Assessment of Seismic Hazard (t-DASH). After analysing about 4000 images (about 400 million of single satellite records), just 155 SSTAs (about 4 every 100 images) were isolated; 115 (74% out of the total) resulted in earthquake-related (false-positive rate 26%). Results of the error diagram confirms a non-casual correlation between RST-based SSTAs and earthquake occurrences, with probability gain values up to 2.2 in comparison with the random guess. The analysis, separately performed on Turkish areas characterized by different faults and earthquakes densities, demonstrates the SSTA correlation with a dynamic seismicity more than with static tectonic settings.

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