ArticleScientific reports2023
Sharpen data-driven prediction rules of individual large earthquakes with aid of Fourier and Gauss.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Scalable intermediate-term earthquake forecasting with multimodal fusion neural networks.Scientific reports · 2025Article
- Self-evolving artificial intelligence framework to better decipher short-term large earthquakes.Scientific reports · 2024Article
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1 author.
Funding
Abstract
Predicting individual large earthquakes (EQs)' locations, magnitudes, and timing remains unreachable. The author's prior study shows that individual large EQs have unique signatures obtained from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs ([Formula: see text] 6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.
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