Optimizing Earthquake Nowcasting With Machine Learning: The Role of Strain Hardening in the Earthquake Cycle.

Autor: Rundle JB; Department of Physics University of California Davis CA USA.; Santa Fe Institute Santa Fe NM USA.; Department of Earth and Planetary Science University of California Davis CA USA.; Program in Public Health University of California Irvine CA USA., Yazbeck J; Department of Physics University of California Davis CA USA., Donnellan A; Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA., Fox G; University of Virginia Charlottesville VA USA., Ludwig LG; Program in Public Health University of California Irvine CA USA., Heflin M; Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA., Crutchfield J; Department of Physics University of California Davis CA USA.
Jazyk: angličtina
Zdroj: Earth and space science (Hoboken, N.J.) [Earth Space Sci] 2022 Nov; Vol. 9 (11), pp. e2022EA002343. Date of Electronic Publication: 2022 Oct 26.
DOI: 10.1029/2022EA002343
Abstrakt: Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two-parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain-hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude-longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.
(© 2022 The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje