Bayesian identification of multiple seismic change points and varying seismic rates caused by induced seismicity

Abstract

The Central and Eastern United States (CEUS) has experienced an abnormal increase in seismic activity, which is believed to be related to anthropogenic activities. The U.S. Geological Survey has acknowledged this situation and developed the CEUS 2016 1 year seismic hazard model using the catalog of 2015 by assuming stationary seismicity in that period. However, due to the nonstationary nature of induced seismicity, it is essential to identify change points for accurate probabilistic seismic hazard analysis (PSHA). We present a Bayesian procedure to identify the most probable change points in seismicity and define their respective seismic rates. It uses prior distributions in agreement with conventional PSHA and updates them with recent data to identify seismicity changes. It can determine the change points in a regional scale and may incorporate different types of information in an objective manner. It is first successfully tested with simulated data, and then it is used to evaluate Oklahoma's regional seismicity.

Pore-pressure diffusion, enhanced by poroelastic stresses, controls induced seismicity in Oklahoma

“…1 C ). Focusing on the mainshocks, we followed the declustering approach of Reasenberg (29) recently applied to seismicity catalog in Oklahoma (16, 30, 31) to remove the dependent earthquakes and identify the events directly linked to deep fluid injection ( SI Appendix , Fig. S2).…”

“…Conversely, the amplitude of the seismicity rate showed a larger sensitivity to D than to t crit . Noteworthy, the half-year uncertainty chosen for the t crit sensitivity test is reasonably well-constrained by observation (31). Table 1 summarizes the overall conclusions reached following the sensitivity tests on model parameters.…”

Determining Histories of Slip on Normal Faults With Bedrock Scarps Using Cosmogenic Nuclide Exposure Data

“…The number of slip rate changes is limited by a reversible‐jump algorithm that favors simple solutions (Sambridge et al., 2006). Bayesian techniques are often applied to deal with uncertainty associated with limited data (Amey et al., 2019; Bronk Ramsey, 2009; Montoya‐Noguera & Wang, 2017). Several different Bayesian MCMC approaches have been developed for modeling cosmogenic data from fault scarps (Beck et al., 2018; Tesson & Benedetti, 2019; Tikhomirov et al., 2011).…”

“…In order to model slip rate and the pattern of exhumation through time, we use a modified version of the Bayesian MCMC approach developed by Cowie et al (2017) to explore the age-slip relationships that adequately explain the observed 36 Cl measurements within uncertainties (further described later in this section, the supporting information, and available online, github.com/lcgregory/SimpleSlips). Bayesian statistical methods are widely applied in earth science and geochronology in order to incorporate prior information and calculate the posterior distribution for a set of parameters given quantitative measurements, using a mathematical model (Bronk Ramsey, 2009;Montoya-Noguera & Wang, 2017). Bayesian inversions can also be transdimensional, meaning that the number of model parameters ("unknowns") for which we solve is allowed to vary, increasing or decreasing the complexity of the model depending on what is required by the data (Amey et al, 2019;Bodin & Sambridge, 2009;Dettmer et al, 2010;Green, 1995;Sambridge et al, 2006).…”

Earthquake Arrival Association with Backprojection and Graph Theory

“…Along with these improvements the number of detected arrivals generally increases approximately by an order of magnitude per decreasing magnitude unit of sensitivity, introducing new challenges into the earthquake detection pipeline. Converting high rates of arrival picks into an accurate earthquake catalog can be invaluable in seismology, since dense catalogs improve our understanding of seismogenic processes occurring at plate boundaries (Kato and Nakagawa, 2014;Delorey et al, 2015), allow for monitoring rate changes of seismicity (Montoya-Noguera and Wang, 2017;Fiedler et al, 2018), improve resolution of tomographic images (Peng and Ben-Zion, 2005;Watkins et al, 2018), reveal dynamic triggering and anthropogenic induced seismicity (Shapiro et al, 2006;Hill and Prejean, 2007;Peng et al, 2009;Ellsworth, 2013), and may contain information regarding the timing of future earthquakes (Rouet-Leduc et al, 2017;Lubbers et al, 2018). Dense catalogs also provide new datasets that can be incorporated into increasingly popular machine learning approaches for a variety of applications in seismology (DeVries et al, 2018;Perol et al, 2018;Ross et al, 2018;.…”