Statistical framework for the evaluation of earthquake forecasting: A case study based on satellite surface temperature anomalies

Abstract

Abstract There are growing observational evidences that various geophysical anomalies precede large earthquakes. However, the reliability of these anomalies for earthquake forecasting is controversial, and therefore more consistent assessment of forecasting ability is required. A framework for investigating pre-seismic anomaly detection using essential statistical indicators before global earthquakes is proposed. Surface temperature (ST) data from the Atmospheric Infrared Sounder (AIRS) sensor were used to realize this framework. First, seismic-related ST anomalies were identified, and then the statistical characteristics of forecasting ability for three indicators (accuracy, missed detection, and false alarm) were calculated in retrospective and prospective ways. The ST anomalies displayed some aggregation effects. Negative anomalies mainly concentrated on epicenters and to the north, while positive anomalies were found on the periphery; neither were strongly dependent on earthquake magnitude. The temporal evolution of forecasting metrics was relatively stable for the period 2010–2018. Mean accuracy, missed detection, and false alarm ratios were 6.01%, 1.60%, and 92.39%, respectively. Accuracy and missed detection ratios showed some spatial correlation and both peaked in the same area (e.g., eastern Japan); however, most areas showed very high false alarm ratios. Based on our findings, the combination of AIRS ST data and the Z-score anomaly detection algorithm to predict short-term earthquakes is currently not practical; the possibility of earthquake forecasting based on satellite thermal infrared measurements remains a huge challenge. However, our results confirmed the efficiency of this framework for quantitatively evaluating earthquake forecasting ability. This approach could be applied to various geophysical parameters and anomaly detection methods.