Scaling of Seismic Memory with Earthquake Size

TL;DR

This study investigates long-term seismic memory and its dependence on earthquake size using waveform analysis, revealing size-dependent autocorrelation behaviors and scale-invariant properties across different earthquake depths and distances.

Contribution

It demonstrates that earthquake size influences long-range autocorrelations in seismic data, providing new insights into seismic memory and scale-invariance.

Findings

Long-range power-law anticorrelations in waveform sign series.

Size-dependent strengthening of autocorrelations with earthquake magnitude.

DFA scaling exponent independent of earthquake depth and distance.

Abstract

It has been observed that the earthquake events possess short-term memory, i.e. that events occurring in a particular location are dependent on the short history of that location. We conduct an analysis to see whether real-time earthquake data also possess long-term memory and, if so, whether such autocorrelations depend on the size of earthquakes within close spatiotemporal proximity. We analyze the seismic waveform database recorded by 64 stations in Japan, including the 2011 "Great East Japan Earthquake", one of the five most powerful earthquakes ever recorded which resulted in a tsunami and devastating nuclear accidents. We explore the question of seismic memory through use of mean conditional intervals and detrended fluctuation analysis (DFA). We find that the waveform sign series show long-range power-law anticorrelations while the interval series show long-range power-law correlations. We find size-dependence in earthquake auto-correlations---as earthquake size increases, both of these correlation behaviors strengthen. We also find that the DFA scaling exponent $\alpha$ has no dependence on earthquake hypocenter depth or epicentral distance.