15 Water, Water Everywhere: Hydrology and Water Quality in the South-Central U.S.

Tuesday, 11 March 2025
8:00 AM - 12:00 PM
112

DEVELOPING A STRATIGRAPHIC DATABASE FOR BENTON COUNTY, ARKANSAS: CHALLENGES IN DATA PRESERVATION AND UTILIZATION

Paper number: 15-1
Program: Theme Sessions
Time: 8:00 AM – 8:20 AM

Authors

  • Adam Wilson Presenting Author
    School of Physical Sciences - Geology Program, Little Rock, AR
  • Adam Wilson Presenting Author
    Office of the State Geologist, North Little Rock, AR

Abstract

This presentation outlines the development of a shallow stratigraphic database for Benton County, AR, created as part of a STATEMAP project under the Office of the State Geologist, as well as models created using similar datasets.

The Benton county database forms the basis for mapping the shallow subsurface across the state. My approach involved transcribing water well data, georeferencing locations using GIS, and interpreting stratigraphic intervals. The resulting database, built in Excel, comprises 3,394 municipal and residential water well records, including landowner and driller identities, locations, stratigraphic interval data, and water-related point data such as depth, yield, and well use.

While the data is now preserved in a more accessible and usable format, several limitations must be acknowledged. Many records were created by non-geologists, leading to potential misinterpretations in the stratigraphic data. Furthermore, location accuracy is often compromised, with 1,136 records (33.5%) classified as “Center-section,” meaning their spatial accuracy is within 1 square mile. Additionally, time constraints limited the interpretation of stratigraphic intervals to 1,649 records (48.6%).

These limitations impact the quality and accuracy of geological models created with this type of data. My approach to using water well data for model creation involved exporting well point data to ArcGIS Pro and generating isopach and structure contour maps. Without addressing the inherent data limitations, model reliability can be significantly compromised.

A brief discussion will also cover the potential of AI transcription models to address these limitations, increasing data throughput and improving dataset completeness. Even partial automation of well data extraction could facilitate a more robust and comprehensive dataset, ultimately enabling more accurate geological modeling.

Geological Society of America Abstracts with Programs. Vol. 57, No. 1

doi: 10.1130/abs/2025SC-406849

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