← Back to search

Earthquake catalogs from induced seismicity case studies in western Texas and northern Oklahoma

data.gov.uk (National Data Library) no licence stated ↪ webpage — more clicking needed British Geological Survey (BGS) · updated 2026-08-13
Open at publisher ↗
This dataset contains earthquake catalogs from induced seismicity cases in western Texas and northern Oklahoma/Kansas, and the results of earthquake magnitude forecasting methods (using Extreme Value Theory) applied to these datasets (Verdon and Eisner, 2024). The catalogs archived here were curated from regional earthquake datasets: for Oklahoma/Kansas the original data source was Park et al. (2022, DOI 10.1785/0320220020) and for Texas the original source was TexNet (https://catalog.texnet.beg.utexas.edu). Verdon and Eisner (2024) curated subsets of this data (drawn from within 20 x 20 km2 "blocks") in order to apply and test their forecasting methods on localised event catalogs. In this dataset we provide the localised catalogs, and the results of the forecasting models (tracking forecasted earthquake magnitudes over time in comparison with the observed evolution of events). The input data and forecasting results are provided in Matlab format structures (.mat). Matlab scripts and functions (.m files) are also provided to read the data and perform the analyses that are presented in Verdon and Eisner (2024). A README text file is provided that details the content of the Matlab structures. Reference: Verdon, J.P., and L. Eisner, 2024. An empirically constrained forecasting strategy for induced earthquake magnitudes using extreme value theory: Seismological Research Letters 95, 3278-3294. DOI: 10.1785/0220240061

Files & links (2)

ResourceFormatVerifiedSize
Data?↪ webpage — more clicking needed32 KB
Citation Information - Digital Object Identifier (DOI)?unchecked

More from British Geological Survey (BGS)

Contains public sector information licensed under the Open Government Licence v3.0 and other licences as stated per dataset. Metadata collated by the UK Open Data Index.