A Novel Method for Evaluating Earthquake Forecast Model Performance and Its Implications for Refining Seismic Likelihood Model

Abstract

Summary Utilizing statistical tests to evaluate earthquake forecasting models is crucial to improve forecasting strategies for seismic hazard assessment. We develop a novel evaluation method for alarm-based earthquake forecast, taking into account the magnitude of seismic energy and the impact area of earthquakes, instead of using solely seismic event number and epicenter locations in conventional approaches. First, we derive a scale-law of Seismic Area by statistically analyzing coseismal maps of past M ≥ 7.0 earthquakes. Second, we proportionally allocate Seismic Moment to surrounding cells based on corresponding seismic area within each cell (SASM-test). Compared to the Molchan test which is conventionally applied for models that forecast the epicenter location, our proposed SASM-test can be applied to the evaluation of forecasting models that focus on the whole earthquake rupture (source area). Third, we apply the SASM-test method to the time-independent probabilistic earthquake forecasting model for the southeastern Tibetan Plateau (RELM-TibetSE) and compare it with other evaluation methods. The retrospective testing shows that the SASM-test demonstrate relatively higher sensitivity, enabling to detect subtle differences between similar models that conventional methods may overlook. Additionally, retrospective test results indicate that: (a) Earthquake forecasting models using GNSS data performed better in forecasting the ‘source area’ than the ‘epicenter location’; (b) Forecasting models based on principal strain rate outperformed the models based on maximum shear strain rate in forecasting both the epicenter location and the source area; (c) Incorporating spatially varying seismogenic layer thickness and rigidity into seismic forecasting models could improve their ability to forecast the ‘source area’ compared to using uniform seismogenic layer properties. The newly proposed SASM-test method can provide a more sensitive and comprehensive approach for the evaluation of earthquake forecasting models, contributing to the refinement of seismic hazard assessments.