38 Geoscience and Hydrogeology in the AI Era: From Predictive Models to Real-Time Applications

This session offers a unique platform to showcase advancements in Artificial Intelligence (AI), Machine Learning (ML), and physics-informed methodologies, focusing on topics in hydrogeology, subsurface flow and transport, real-time geophysical inversion, critical minerals, and energy applications.

Sunday, 19 October 2025
1:30 PM - 5:30 PM
HBGCC, 210AB

Predicting Groundwater Hydrochemical Facies in Three Dimensions across the Conterminous United States with Random Forest Classification

Paper number: 38-4
Program: Technical Sessions
Time: 2:35 PM – 2:50 PM

Authors

Abstract

A random forest classification (RFC) model was developed to predict hydrochemical facies (HCFs) of groundwater in three dimensions across the conterminous United States (CONUS). Major-ion data from 152,673 sites were used to categorize groundwater into one of six HCFs based on predominant cations and anions (CaMg-HCO3, NaK-HCO3, CaMg-SO4, NaK-SO4, Cl, or Mixed). These six HCFs were used as prediction targets for RFC modeling. Model features that represent relevant geochemical processes and(or) physical conditions were derived from readily available, CONUS-scale data. Additional model features were specifically engineered to support this analysis and include the elevation of well bottoms relative to the base of drinking water (ERDW) and six binary flags that relate the prevalence of each HCF to units of the geologic map of North America. The most important model feature overall was ERDW. The model was used to map HCFs at a 1-km2 resolution across the CONUS and to depths of 400 m below the base of drinking water (which varies from 22 m to 2 km). Model predictions are consistent with the expected evolution of HCFs along groundwater flow paths. CaMg-HCO3 is predicted to occur near the water table in the humid east, and near the water table in areas of the west underlain by crystalline rocks, volcanics, and unconsolidated sands and gravels. At depths below the base of drinking-water supplies, the model predicts a rapid transition from HCO3 HCFs to Cl. Model predictions are reliable based on point data, and data averaged across hydrogeologic regions and with depth. Model predictions of HCFs could be used for mapping salinity and other groundwater characteristics across the CONUS. Predictions of HCFs can also be used – in conjunction with measurements of specific conductance – to provide reliable estimates of salinity or total dissolved solids.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-412915

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