A Technology for Seismogenic Process Monitoring and Systematic Earthquake Forecasting

Abstract

Earthquakes are a severe natural phenomenon that require continuous monitoring, analysis, and forecasting to mitigate their risks. Seismological data have long been used for this purpose, but geodynamic data from remote sensing of surface displacements have become available in recent decades. In this paper, we present a novel information technology for monitoring, analyzing seismogenic fields, and predicting earthquakes using Earth remote sensing data presented as a time series of surface displacement points for systematic regional earthquake prediction. We demonstrate, for the first time, the successful application of this technology and discuss the method of the minimum area of alarm, which was developed for machine learning and systematic earthquake prediction, as well as the architecture and tools of the GIS platform. Our technology is implemented as a network platform consisting of two GISs. The first GIS automatically loads earthquake catalog data and GPS time series, calculates spatiotemporal fields, performs systematic earthquake prediction in multiple seismically active regions, and provides intuitive mapping tools to analyze seismic processes. The second GIS is designed for scientific research of spatiotemporal processes, including those related to earthquake forecasting. We demonstrate the effectiveness of platform analysis tools that are intuitive and accessible to a wide range of users in solving problems of systematic earthquake prediction. Additionally, we provide examples of scientific research on earthquake prediction using the second GIS, including the effectiveness of using GPS data for forecasting earthquakes in California, estimating the density fields of earthquake epicenters using the adaptive weighted smoothing (AWS) method for predicting earthquakes in Kamchatka, and studying earthquake forecasts in the island part of the territory of Japan using the earthquake catalog and GPS. Our examples demonstrate that the method of the minimum area of alarm used for machine learning is effective for forecasting both catalog and remote sensing data.