Earthquake Nowcasting with Deep Learning
| Autor: | Geoffrey Charles Fox, John B. Rundle, Andrea Donnellan, Bo Feng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Physics - Geophysics Computer Science - Machine Learning FOS: Physical sciences deep learning earthquake nowcasting Southern California LSTM transformer attention time series Nash Sutcliffe efficiency AI for science Geophysics (physics.geo-ph) Machine Learning (cs.LG) |
| Zdroj: | GeoHazards; Volume 3; Issue 2; Pages: 199-226 |
| Popis: | We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950–2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open source together with the preprocessed data from the USGS. |
| Databáze: | OpenAIRE |
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