Initial Foundation for Predicting Individual Earthquake's Location and Magnitude by Using Glass-Box Physics Rule Learner

TL;DR

This paper introduces a novel glass-box physics rule learner (GPRL) framework that uncovers hidden rules from seismic data, showing potential for predicting individual earthquake locations and magnitudes without predefined mechanisms.

Contribution

The study presents a new transparent rule-based learning framework that identifies plausible physical rules from seismic data, advancing earthquake prediction methods.

Findings

GPRL identifies rules involving pseudo power and pseudo vorticity of energy.

Independent tests support the rules' potential in predicting earthquake magnitude and location.

The framework demonstrates promising but initial results in data-guided earthquake prediction.

Abstract

Although researchers accumulated knowledge about seismogenesis and decades-long earthquake data, predicting imminent individual earthquakes at a specific time and location remains a long-standing enigma. This study hypothesizes that the observed data conceal the hidden rules which may be unraveled by a novel glass-box (as opposed to black-box) physics rule learner (GPRL) framework. Without any predefined earthquake-related mechanisms or statistical laws, GPRL's two essentials, convolved information index and transparent link function, seek generic expressions of rules directly from data. GPRL's training with 10-years data appears to identify plausible rules, suggesting a combination of the pseudo power and the pseudo vorticity of released energy in the lithosphere. Independent feasibility test supports the promising role of the unraveled rules in predicting earthquakes' magnitudes and their specific locations. The identified rules and GPRL are in their infancy requiring substantial improvement. Still, this study hints at the existence of the data-guided hidden pathway to imminent individual earthquake prediction.