# Pre-earthquake State Identification by Micro-earthquake Spike Trains Dissimilarity Analysis

## TL;DR

This study investigates whether analyzing micro-earthquake spike train dissimilarities can identify pre-earthquake states, potentially serving as precursors for larger seismic events by examining statistical independence and physical model compatibility.

## Contribution

It introduces a novel statistical approach using spike train dissimilarity measures to detect pre-earthquake states and assesses its relation to physical earthquake models.

## Key findings

- Increased spike train dissimilarity correlates with upcoming earthquakes.
- The method shows compatibility with the Burridge-Knopoff physical model.
- Pre-earthquake states can be statistically identified through this approach.

## Abstract

The exact mechanisms leading to an earthquake are not fully understood and the space-time structural features are non-trivial. Previous studies suggest the seismicity of very low intensity earthquakes, known as micro-earthquakes, may contain information about the source process before major earthquakes, as they can quantify modifications to stress or strain across time that finally lead to a major earthquake. This work uses the history of seismic activity of micro-earthquakes to analyze the spatio-temporal statistical independence among the monitoring stations of a seismic network. Using point process distance measures applied to the micro-earthquakes' spike trains recorded in these stations, a pre-earthquake state is defined statistically with the aim of finding a relation between the level of dissimilarity among stations' readings and the future occurrence of larger earthquakes in the region. This paper also addresses the compatibility of this statistical approach with the Burridge-Knopoff spring-block physical model for earthquakes. Based on the results, there is evidence for an earthquake precursory state associated with an increase in spike train dissimilarity as evaluated by a statistical surrogate test.