Earthquake Forecasting Using Hidden Markov Models

2011/05/18 by Daniel W. Chambers, Jenny A. Baglivo, John E. Ebel +2 · 31 citations
Computer Science · Earth and Planetary Sciences · Mathematics · #Artificial intelligence #Computer science #Earthquake prediction #Event (particle physics) #Geology #Hidden Markov model #Machine learning #Markov chain #Seismology #Seismology and Earthquake Studies #Time Series Analysis and Forecasting #earthquake and tectonic studies #msc:62P12 #stat.AP

paper · pdf · doi:10.1007/s00024-011-0315-1

published in Pure and Applied Geophysics 169(4), 625-639 (Birkhäuser)

openalex publication_date 2011/05/18 · arxiv created 2014/11/20 · arxiv updated 2014/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

This paper develops a novel method, based on hidden Markov models, to forecast earthquakes and applies the method to mainshock seismic activity in southern California and western Nevada. The forecasts are of the probability of a mainshock within one, five, and ten days in the entire study region or in specific subregions and are based on the observations available at the forecast time, namely the inter event times and locations of the previous mainshocks and the elapsed time since the most recent one. Hidden Markov models have been applied to many problems, including earthquake classification; this is the first application to earthquake forecasting.

Citations

• Statistical Inference for Probabilistic Functions of Finite State Markov Chains
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
• A tutorial on hidden Markov models and selected applications in speech recognition
• Biological Sequence Analysis