2020/06/18 by Kiani, M.
Computer Science · Earth and Planetary Sciences · Environmental Science · #Earthquake Detection and Analysis #FOS: Electrical engineering #FOS: Physical sciences #Geophysics (physics.geo-ph) #Landslides and related hazards #Seismology and Earthquake Studies #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.10419
openalex publication_date 2020/06/18 · openalex created_date 2020/06/25 · openalex updated_date 2026/07/28
Earthquake prediction is one of the most pursued problems in geoscience. Different geological and seismological approaches exist for the prediction of the earthquake and its subsequent land change. However, in many cases, they fail in their mission. In this paper, we address the well-established earthquake prediction problem by a novel approach. We use a four-dimensional location-time machine learning scheme to estimate the time of earthquake and its land change. We present a study for the Ridgecrest, California 2019 earthquake prediction. We show the accuracy of our method is around 14 centimeters for the land change, and around 2 days for the time of the earthquake, predicted from data more than 3 years before the earthquake.