Global Earthquake Forecasting System (GEFS): The challenges ahead

Abstract

We conclude this special issue on the Global Earthquake Forecasting System (GEFS) by briefly reviewing and analyzing the claims of non-seismic precursors made in the present volume, and by reflecting on the current limitations and future directions to take. We find that most studies presented in this special volume, taken individually, do not provide strong enough evidence of non-seismic precursors to large earthquakes. The majority of the presented results are hampered by the fact that the task at hand is susceptible to potential biases in data selection and possible overfitting. The most encouraging results are obtained for ground-based geoelectric signals, although the probability gain is likely small compared to an earthquake clustering baseline. The only systematic search on satellite data available so far, those of the DEMETER mission, did not find a robust precursory pattern. The conclusion that we can draw is that the overall absence of convincing evidence is likely due to a deficit in systematically applying robust statistical methods and in integrating scientific knowledge of different fields. Most authors are specialists of their field while the study of earthquake precursors requires a system approach combined with the knowledge of many specific characteristics of seismicity. Relating non-seismic precursors to earthquakes remains a challenging multidisciplinary field of investigation. The plausibility of these precursors predicted by models of lithosphere-atmosphere-ionosphere coupling, together with the suggestive evidence collected here, call for further investigations. The primary goal of the GEFS is thus to build a global database of candidate signals, which could potentially improve earthquake predictability (if the weak signals observed are real and false positives sufficiently uncorrelated between different data sources). Such a stacking of disparate and voluminous data will require big data storage and machine learning pipelines, which has become feasible only recently. This special issue compiled an eclectic list of non-seismic precursor candidates, which is in itself a valuable source of information for seismologists, geophysicists and other scientists who may not be familiar with such types of investigations. It also forms the foundation for a coherent, multi-disciplinary collaboration on earthquake prediction.

Initial Foundation for Predicting Individual Earthquake's Location and Magnitude by Using Glass-Box Physics Rule Learner

“…Consistent evolution or automated optimization of many hyper-parameters of the GPRL framework may be done by inheriting the reinforcement learning paradigm (48). In light of the multifaceted nature of earthquake phenomena, enabling imminent individual earthquake predictions will require comprehensible collaborations of geophysics, mechanics, computer science, data science, and so on like a recent multi-disciplinary global collaborations (49). The initial outcome of this study catalyzes such a broad endeavor.…”

Gauss curvature-based unique signatures of individual large earthquakes and its implications for customized data-driven prediction

“…Unique classifications of individual large earthquakes may advance the existing earthquake forecasting methods 5 – 8 , 10 , 12 – 15 , 31 . In light of the multifaceted nature of earthquake phenomena, enabling imminent individual earthquake predictions will require comprehensible collaborations 44 , and the outcome of this study will promote such a broad endeavor.…”

Seismic roulette: Hazards and risks

“…As can be judged after reading the most recent review of “The Global Earthquake Forecasting System: Towards Using Non‐seismic Precursors for the Prediction of Large Earthquakes” (Freund et al, 2021), most, if not all, non‐seismic “low‐hanging fruits” remain earthquake precursor candidates lacking the results of similar credible efforts, which results are required for reliable “operational forecasting” of earthquake hazard (Mignan, Ouillon, Sornette, & Freund, 2021).…”

“…The L'Aquila earthquake scored as a "failure to predict" only because its epicentre was located about 10 km distance outside the alarm territory of the Northern region identified by CN for the corresponding time window (Peresan et al, 2011). When the epicentre of the target earthquake is located just outside the alert region As can be judged after reading the most recent review of "The Global Earthquake Forecasting System: Towards Using Nonseismic Precursors for the Prediction of Large Earthquakes" (Freund et al, 2021), most, if not all, non-seismic "low-hanging fruits" remain earthquake precursor candidates lacking the results of similar credible efforts, which results are required for reliable "operational forecasting" of earthquake hazard (Mignan, Ouillon, Sornette, & Freund, 2021).…”