A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data

摘要

Research in the field of earthquakeprediction has a long history, but the inadequacies of traditional approachesto the study of seismic threats have become increasingly evident. Remotesensing and Earth observation technology, an emerging method that can rapidlycapture information concerning anomalies associated with seismic activityacross a wide geographic area, has for some time been believed to be the keyto overcoming the bottleneck in earthquake prediction studies. However, amulti-parametric method appears to be the most promising approach forincreasing the reliability and precision of short-term seismic hazardforecasting, and thermal infrared (TIR) anomalies are important earthquakeprecursors. While several studies have investigated the correlation among TIRanomalies identified by the robust satellite techniques (RSTs) methodology andsingle earthquakes, few studies have extracted TIR anomalies over a longperiod within a large study area. Moreover, statistical analyses are requiredto determine whether TIR anomalies are precursors to earthquakes. In thispaper, RST data analysis and the Robust Estimator of TIR Anomalies (RETIRA)index were used to extract the TIR anomalies from 2002 to 2018 in the Sichuanregion using Moderate Resolution Imaging Spectroradiometer (MODIS) landsurface temperature (LST) data, while the earthquake catalog was used toascertain the correlation between TIR anomalies and earthquake occurrences.Most TIR anomalies corresponded to earthquakes, and statistical methods wereused to verify the correlation between the extracted TIR anomalies andearthquakes. This is the first time that the ability to predict earthquakeshas been evaluated based on the positive predictive value (PPV), falsediscovery rate (FDR), true-positive rate (TPR), and false-negative rate (FNR).The statistical results indicate that the prediction potential of RSTs withuse of MODIS is limited with regard to the Sichuan region.