Stochastic Inversions for Source, Path, and Site Parameters From West Texas Earthquakes

This dataset and python scripts are associated with a study that investigates the source, path, and site effects of earthquakes in the Delaware Basin of West Texas using recorded ground motions. By applying a Bayesian parametric inversion technique, we analyze 6,479 ground motion records from 354 events recorded at 49 stations. The study models Fourier amplitude spectra as a combination of source, path, and site effects, extracting key parameters such as moment magnitude, stress parameter, quality factor, geometrical spreading, and site-specific amplification and diminution (kappa) parameters.

The inversion-derived parameters offer insights for both seismological research and engineering applications, particularly in developing ground motion predictive models for larger, more hazardous events (M > 5.0). The primary audience includes seismologists, earthquake engineers, geophysicists, and hazard modelers interested in seismic source characterization, ground motion prediction, and regional seismic risk assessment.

The published dataset includes the input and output data from the stochastic inversions for West Texas, as well as the python scripts used to process the ground motions and perform the stochastic inversions. Publishing these scripts and data allows this research to be reproduced and allows the developed workflow to be applied to other datasets.