Identifying potentially induced seismicity and assessing statistical significance in Oklahoma and California
Mark McClure, Riley Gibson, Kitkwan Chiu, and Rajesh Ranganath

TL;DR
This paper presents a robust statistical method to identify and assess the significance of induced seismicity related to wastewater disposal, applied to California and Oklahoma, revealing strong evidence in Oklahoma and probable evidence in California.
Contribution
The study introduces a flexible, longitudinal statistical approach for detecting induced seismicity in large datasets, accounting for temporal correlation and high kurtosis in seismic observations.
Findings
Strong evidence of induced seismicity in Oklahoma
Probable induced seismicity in California
Method applicable to other datasets and risk factor analysis
Abstract
We develop a statistical method for identifying induced seismicity from large datasets and apply the method to decades of wastewater disposal and seismicity data in California and Oklahoma. The method is robust against a variety of potential pitfalls. The study regions are divided into gridblocks. We use a longitudinal study design, seeking associations between seismicity and wastewater injection along time-series within each gridblock. The longitudinal design helps control for non-random application of wastewater injection. We define a statistical model that is flexible enough to describe the seismicity observations, which have temporal correlation and high kurtosis. In each gridblock, we find the maximum likelihood estimate for a model parameter that relates induced seismicity hazard to total volume of wastewater injected each year. To assess significance, we compute likelihood ratio…
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