Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California’s Lost Hills and Belridge oil fields
Growing water stress has led to emerging interest in protecting fresh and brackish groundwater as a potential supplement to water supplies and raised questions about factors that could affect the future quality of fresh and brackish aquifers. Limited well infrastructure, particularly in regions where elevated salinity has led to limited historical groundwater development, hinders traditional mapping of salinity distributions through groundwater sampling. This paper presents a quantitative salinity mapping approach of the upper 300 m using high‐resolution, regionally comprehensive resistivity models derived from Bayesian inversion of an airborne electromagnetic survey adjacent to the Lost Hills and Belridge oil fields in the southwestern San Joaquin Valley of California. Using local water quality observations as an interpretational foundation, a probabilistic approach yields maps of fresh, saline, and brackish groundwater while quantifying joint uncertainty inherited from the geophysical data and interpretational relations. Saline and fresh regions are mapped with relatively high confidence in many locations, while areas of lower confidence, particularly at depth, can be mapped as their most probable salinity category while reflecting the relative uncertainty in the interpretation. These maps identify a stratified salinity structure, where saline water commonly occurs in the surficial aquifer overlying fresher groundwater in the Tulare aquifer, separated by regional confining clay layers. Downgradient of unlined surface water diversions, recharge of imported surface water results in relatively fresh groundwater throughout the depth of investigation.
|Publication Subtype||Journal Article|
|Title||Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California’s Lost Hills and Belridge oil fields|
|Series title||Water Resources Research|
|Contributing office(s)||Geology, Geophysics, and Geochemistry Science Center|
|Description||e2019WR026273, 20 p.|
|Google Analytic Metrics||Metrics page|