Modification of the pattern informatics method for forecasting large earthquake events using complex eigenvectors

TL;DR

This paper enhances earthquake forecasting by modifying the Pattern Informatics method to incorporate complex eigenvectors derived from seismic data, leveraging wave-like stress properties for improved short-term hot-spot predictions.

Contribution

It introduces a novel modification of the Pattern Informatics method using complex principal component analysis and the Hilbert transform for better earthquake forecast accuracy.

Findings

Complex eigenvector analysis produces distinct hot-spot maps.

The method reveals differences in forecast accuracy compared to real-valued analysis.

Potential for improved short-term earthquake prediction.

Abstract

Recent studies have shown that real-valued principal component analysis can be applied to earthquake fault systems for forecasting and prediction. In addition, theoretical analysis indicates that earthquake stresses may obey a wave-like equation, having solutions with inverse frequencies for a given fault similar to those that characterize the time intervals between the largest events on the fault. It is therefore desirable to apply complex principal component analysis to develop earthquake forecast algorithms. In this paper we modify the Pattern Informatics method of earthquake forecasting to take advantage of the wave-like properties of seismic stresses and utilize the Hilbert transform to create complex eigenvectors out of measured time series. We show that Pattern Informatics analyses using complex eigenvectors create short-term forecast hot-spot maps that differ from hot-spot maps created using only real-valued data and suggest methods of analyzing the differences and calculating the information gain.