36 From Data to Decision: Integrating Geophysics, Data Systems, and Modeling for Aquifer Characterization in Data-Sparse Regions

Advances in data acquisition, management, and analytics are transforming water-resources research in semi-arid, data-sparse regions. This session highlights the integration of geophysics, data systems, and modeling, including AEM, legacy data digitization, and cloud platforms. Contributions demonstrate how advances in data collection, management, analysis, and modeling  approaches improve the characterization of fresh and brackish groundwater to support management, recharge, and long-term planning.

Wednesday, 20 May 2026
8:00 AM - 12:00 PM
Alvarado F

Geologic Framework Modeling for Aquifer Characterization in Data-Sparse Regions: new datasets promote different modeling techniques

Paper number: 36-9
Program: Technical Sessions
Time: 11:00 AM – 11:20 AM

Authors

Abstract

The New Mexico Bureau of Geology and Mineral Resources Aquifer Mapping and Monitoring Program develops three-dimensional (3D) geologic framework models to support groundwater-resource evaluation across New Mexico. In basins with dense well control, high-confidence stratigraphic and structural surfaces can be interpolated using borehole picks and conventional gridding approaches. In data-sparse regions, widely-spaced wells and uncertain data quality limit the reliability of well-derived data for defining basin geometry, depth to basement, and fault-controlled compartmentalization. In such settings, integration of regional geophysical datasets becomes essential.

We evaluate strategies for integrating sparse borehole control with potential-field data, including ground-based gravimetry and regional aeromagnetic surveys, to constrain basin configuration. Sparse-data methods emphasize structural surface conditioning, resolution-aware gridding, and uncertainty-informed interpolation to reconcile differences in scale among point-based well observations and smoothed geophysical grids. Particular attention is given to mitigating bias introduced by clustered well control and to preserving geologically-realistic fault geometries during framework-surface construction.

The Salt Basin of south-central New Mexico and west Texas provides an example of the challenges inherent to construct subsurface framework models in data-limited basins. Existing gravity data are used to delineate first-order basin geometry and major fault zones, and widely-spaced boreholes provide localized lithologic control. Preliminary framework modeling results show that multi-dataset integration yields more consistent surfaces and improved representation of structural features. Adopting data-adaptive workflows establishes a scalable framework for geologic modeling in data-sparse basins that supports groundwater-resource assessments in regions where well control alone is insufficient to resolve subsurface structure.

Geological Society of America Abstracts with Programs. Vol. 58, No. 4

doi: 10.1130/abs/2026RM-410587

© Copyright 2026 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.