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Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

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Earthquake correlations and networks: a comparative study.

T R Krishna Mohan¹, P G Revathi

¹CSIR Centre for Mathematical Modelling and Computer Simulation, Bangalore 560017, India. kmohan@cmmacs.ernet.in

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|May 24, 2011

Summary

This study quantifies earthquake correlations to identify causally linked pairs. A robust network model reveals universal patterns in earthquake recurrences across different regions, distinguishing aftershocks from spatial events.

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Area of Science:

Geophysics
Complex Systems
Network Science

Background:

Understanding earthquake triggering and recurrence patterns is crucial for seismic hazard assessment.
Previous studies have explored earthquake correlations, but a comprehensive network model for causal analysis is lacking.

Purpose of the Study:

To quantify earthquake correlations and extract causally connected earthquake pairs.
To construct and analyze a network model of earthquakes based on their correlations.
To investigate universal aspects of earthquake recurrence patterns across different seismic regions.

Main Methods:

Developed a correlation metric, a variation of the Baiesi-Paczuski metric.
Constructed a time-ordered earthquake network with links representing correlations.
Identified earthquake recurrences using correlation thresholds within clusters.
Analyzed recurrence length and time distributions in California, Japan, and the Himalayas.

Main Results:

Found a robust unimodal recurrence length distribution, linking rupture length to earthquake magnitude across regions.
Observed a hub structure in network out-degree, dominated by large magnitude earthquakes.
Determined in-degree distribution is influenced by local event density.
Identified two power-law regimes in recurrence time distribution, confirming Omori's law and revealing power-law spatial recurrences.

Conclusions:

The network model provides a universal framework for analyzing earthquake causality and recurrence.
The crossover in recurrence time distribution objectively signals the end of the aftershock regime.
Earthquake networks exhibit scale-invariant properties, with implications for seismic forecasting.