Global models for short-term earthquake forecasting and predictive skill assessment

Nandan, Shyam; Kamer, Yavor; Ouillon, Guy; Hiemer, Stefan; Sornette, Didier

doi:10.1140/epjst/e2020-000259-3

Cited by 29 publications

(32 citation statements)

References 55 publications

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“…the slope of the Gutenberg-Richter law) or the accelerating moment release [38]. They also describe the Load/Unload Response Ratio (LURR) algorithm [93] and the Epidemic-Type Aftershock Sequence (ETAS) model [68,[94][95][96] as methods to identify criticality. They also mention gravity and electromagnetic field perturbations being possible in the critical region as a consequence of changes in the stress and related strain fields.…”

Section: Review and Critique Of Each Contributionmentioning

confidence: 99%

“…(20) Nandan et al [68] present rigorous tests of state-of-the-art statistical seismicity models, based on the Epidemic-Type Aftershock Sequence (ETAS) model. The best model is derived from the nonlinear and physics-based multifractal stress activation (MSA) model [97,98].…”

Section: Review and Critique Of Each Contributionmentioning

confidence: 99%

“…Chen et al [52] did a systematic search, used reasonable statistical tests, and independent test sets. Zhuang et al [54] reviewed studies based on such robust statistical tests but noticed the weakness of non-seismic precursors compared to seismic ones and it is here not clear what the probability gain of the Chen et al [52] model is compared to an ETAS-clustering baseline for instance [68]. Both Chen et al [52] and Zhuang et al [54] however, emphasized the importance of probabilistic forecasts (instead of deterministic predictions) due to the stochasticity of the earthquake generating process.…”

Section: Common Pitfalls Of Pattern Recognition In Earthquake Prediction Researchmentioning

confidence: 99%

“…Importantly, the GEFS is initiating a new and much needed phase of cross-disciplinary collaboration [8,50]. The signals addressed in the GEFS issue include: ground-based geoelectric and magnetic anomalies [51][52][53][54][55], radon activity [56], thermal infrared anomalies [57][58][59][60] and longwave radiation [57], combined thermal and gas anomalies [61], (sub)ionospheric signals [53,62,63], total electron content [57], groundwater changes [56,64], precipitation [65], low-frequency "stress waves" [66], solar activity coupling [67], as well as combinations of various signals due to lithosphere-atmosphere-ionosphere coupling [56,57] and comparisons of non-seismic signals to seismic precursors [54,68]. The introductory article of this volume [69] provides a review of the solid-state processes that are proposed to form the basis of the non-seismic precursors and others (in the same volume [70]).…”

Section: Introductionmentioning

confidence: 99%

“…The introductory article of this volume [69] provides a review of the solid-state processes that are proposed to form the basis of the non-seismic precursors and others (in the same volume [70]). While seismic precursors per se have not been considered in this special issue except for their comparison with non-seismic precursors by [54] and an earthquake clustering null hypothesis [68,71], they should of course be a crucial part of any all-encompassing strategy in GEFS. The next step, hints of which are given in the preface to the special issue [72], will be to assemble all types of signals -geodesic, geoelectric, geomagnetic, seismic, etc.…”

Section: Introductionmentioning

confidence: 99%

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Global Earthquake Forecasting System (GEFS): The challenges ahead

Mignan

Ouillon

Sornette

et al. 2021

Eur. Phys. J. Spec. Top.

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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.

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