Data-Driven, Automated Mapping of High-Impact Regions for Seismic Station Deployment

ABSTRACT Seismic networks must adapt as activity evolves, but practical and transferable methods for deciding where new stations add the most value are limited. We present a data-driven, automated workflow that integrates recency-weighted seismicity, network geometry, and contextual layers to map high-impact regions for station deployment. The workflow is released as an open-source Python package. The method computes four grid-based metrics under user-defined thresholds: near-field event coverage at two radii (S4, S10), azimuthal-gap improvement (G), and a contextual variable (here, cumulative saltwater disposal [SWD] volume). A user-weighted composite index summarizes combined benefit, and unsupervised k-means clustering translates hotspots into discrete priority regions. We applied the workflow to the Midland basin (Texas) using 2017–2025 TexNet data and SWD volumes. We identified four priority regions where additional stations would reduce near-field gaps, improve azimuthal coverage, and align with operational goals. The framework is reproducible, objective, and readily transferable: the contextual layer (e.g., SWD) can be replaced by alternatives such as fault proximity, Global Navigation Satellite System deformation, industry operations indicators, or population, enabling rapid reapplication across diverse monitoring settings.