Statistical Failure Mechanism Analysis of Earthquakes Revealing Time Relationships
Parsa Rastin, Michael LuValle

TL;DR
This paper demonstrates that separating earthquakes based on their statistical tensor structure reveals temporal associations, suggesting potential insights into earthquake mechanisms and forecasting possibilities.
Contribution
It introduces a novel statistical separation method for earthquakes based on tensor moments, uncovering temporal relationships not previously observed.
Findings
Positive temporal association among similar earthquake types
Negative association between different earthquake types
Potential for mechanistic insights and improved forecasting
Abstract
If we assume that earthquakes are chaotic, and influenced locally then chaos theory suggests that there should be a temporal association between earthquakes in a local region that should be revealed with statistical examination. To date no strong relationship has been shown (refs not prediction). However, earthquakes are basically failures of structured material systems, and when multiple failure mechanisms are present, prediction of failure is strongly inhibited without first separating the mechanisms. Here we show that by separating earthquakes statistically, based on their central tensor moment structure, along lines first suggested by a separation into mechanisms according to depth of the earthquake, a strong indication of temporal association appears. We show this in earthquakes above 200 Km along the pacific ring of fire, with a positive association in time between earthquakes of…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Anomaly Detection Techniques and Applications · Earthquake Detection and Analysis
