Журнал геологии и геофизики
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Statistical Framework for the Evaluation of Earthquake Forecasting

Xinjian Shan

Observational evidence is increasing that several geophysical anomalies occur before major earthquakes. However, the accuracy of these anomalies in earthquake forecasting is debatable, necessitating a more uniform assessment of predicting abilities. Before global earthquakes, a methodology for exploring pre-seismic anomaly identification utilising fundamental statistical indicators is provided. This framework was built using surface temperature (ST) data from the Atmospheric Infrared Sounder (AIRS) sensor. The statistical characteristics of forecasting capacity for three indicators (accuracy, missed detection, and false alarm) were calculated retrospectively and prospectively after seismic-related ST abnormalities were found. There were some aggregation effects in the ST anomalies. Negative anomalies were mostly discovered around epicentres and to the north, while positive anomalies were mostly found on the outskirts; neither was highly influenced by earthquake magnitude. For the period 2010–2018, the temporal evolution of predicting measures remained reasonably constant. 6.01 percent, 1.60 percent, and 92.39 percent, respectively, for accuracy, missed detection, and false alarm rates