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

Qingyan Meng,Ying Zhang

doi:10.5194/nhess-19-535-2019

Abstract

Abstract. Research in the field of earthquake prediction has a long history, but the inadequacies of traditional approaches to the study of seismic threats have become increasingly evident. Remote sensing and Earth observation technology, an emerging method that can rapidly capture information concerning anomalies associated with seismic activity across a wide geographic area, has for some time been believed to be the key to overcoming the bottleneck in earthquake prediction studies. However, a multi-parametric method appears to be the most promising approach for increasing the reliability and precision of short-term seismic hazard forecasting, and thermal infrared (TIR) anomalies are important earthquake precursors. While several studies have investigated the correlation among TIR anomalies identified by the robust satellite techniques (RSTs) methodology and single earthquakes, few studies have extracted TIR anomalies over a long period within a large study area. Moreover, statistical analyses are required to determine whether TIR anomalies are precursors to earthquakes. In this paper, 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 Sichuan region using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, while the earthquake catalog was used to ascertain the correlation between TIR anomalies and earthquake occurrences. Most TIR anomalies corresponded to earthquakes, and statistical methods were used to verify the correlation between the extracted TIR anomalies and earthquakes. This is the first time that the ability to predict earthquakes has been evaluated based on the positive predictive value (PPV), false discovery rate (FDR), true-positive rate (TPR), and false-negative rate (FNR). The statistical results indicate that the prediction potential of RSTs with use of MODIS is limited with regard to the Sichuan region.

Highlights

Changes in the surface temperature of the Earth’s crust prior to the occurrence of earthquakes have been attested to by numerous observations (Tronin et al, 2002)
The example considered the earthquakes of M ≥ 5.0, and the results indicate that 58 (TP1 plus False positive (FP)) Thermal infrared (TIR) anomalies appeared over the duration of the study period, and 15 (TP1) of these correspond to earthquakes, while the other 43 (FP) do not; as such, the probability of exact correspondence between TIR anomalies and earthquakes is 25.9 % (PPV), while the probability of no correspondence is 74.1 % (FDR)
Statistical analyses of 18 years’ worth of data on the correlation between earthquakes and TIR anomalies indicate that 51.7 % of TIR anomalies correspond to earthquakes of M ≥ 4.0 in the Sichuan region, and the higher the M, the more likely it is that the earthquakes will correspond to TIR anomalies

Summary

Introduction

Changes in the surface temperature of the Earth’s crust prior to the occurrence of earthquakes have been attested to by numerous observations (Tronin et al, 2002). Anomalous TIR emissions have been detected by satellite sensors prior to the occurrence of major earthquakes (Piroddi et al, 2014). The investigation of TIR signals as seismic precursors has gained traction worldwide, in Russia, China, India, the United States, and Italy, while Saraf et al (2009) observed similar short-term anomalies in the epicentral regions of earthquakes in India, Algeria, Iran, China, Pakistan, and Indonesia using National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR), Terra/AquaModerate Resolution Imaging Spectroradiometer (MODIS) and passive microwave Defense Meteorological Satellite Program Special Sensor Microwave/Imager (DMSP-SSM/I) satellite data, applying the term “transient TIR anomalies” (Saraf et al, 2009).

Results

Discussion

Conclusion