Induced Seismicity Completeness Analysis for Improved Data Mining

Abstract

The study of induced seismicity at sites of fluid injection is paramount to assess the seismic response of the earth’s crust and to mitigate the potential seismic risk. However statistical analysis is limited to events above the completeness magnitude mc, which estimation may significantly vary depending on the employed method. To avoid potential biases and optimize the data exploitable for analysis, a better understanding of completeness, detection capacity and censored data characteristics is needed. We apply various methods previously developed for natural seismicity on 16 underground stimulation experiments. We verify that different techniques yield different mc values and we suggest using the 90% quantile of the mc distribution obtained from high-resolution mapping, with mc defined from the mode of local magnitude frequency distributions (MFD). We show that this distribution can be described by an asymmetrical Laplace distribution and the bulk MFD by an asymmetric Laplace mixture model. We obtain an averaged Gutenberg-Richter parameter b=1.03±0.48 and a detection parameter k=3.18±1.97 from mapping, with values subject to high uncertainties across stimulations. We transfer Bayesian mc mapping developed for natural seismicity to the context of induced seismicity, here adapted to local three-dimensional seismicity clouds. We obtain the new prior parameterization mc,pred=1.64log10(d3)−1.83, with d3 the distance to the 3rd nearest seismic station. The potential use of censored data and of mc prediction is finally discussed in terms of data mining to improve the monitoring, modeling and managing of induced seismicity.