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Geophysical mapping of plume discharge to surface water at a crude oil spill site; inversion versus machine learning

Neil Terry, Frederick D. Day-Lewis, John W. Lane, Jared J. Trost and Barbara A. Bekins
Geophysical mapping of plume discharge to surface water at a crude oil spill site; inversion versus machine learning
Geophysics (September 2019) 84 (5): EN67-EN80

Abstract

The interpretation of geophysical survey results to answer hydrologic, engineering, and geologic questions is critical to diverse problems for management of water, energy, and mineral resources. Although geophysical images provide valuable qualitative insight into subsurface architecture and conditions, translating geophysical images into quantitative information (e.g., saturation, concentration, and hydraulic properties) often involves substantial nonuniqueness and uncertainty owing to the limited resolution of geophysical imaging and uncertainty in petrophysical relations. We have developed a machine-learning approach to address these challenges in the context of a field-based investigation to map zones where a hydrocarbon plume was discharging to surface water at the National Crude Oil Spill Fate and Natural Attenuation Research Site in Bemidji, Minnesota, USA. The two-step approach combines multiple types of geophysical and direct information and effectively bypasses inversion and its associated assumptions. Integrating multifrequency electromagnetic induction, ground-penetrating radar, and fluid-sampling data, we first identify discharge zones and second estimate specific conductance versus depth. Compared with conventional inversion results, the machine-learning results (1) directly address the study objectives (delineating the discharge zones); (2) better extract depth-dependent information from the data, for which sensitivity diminishes rapidly with depth; and (3) quantify the uncertainty of the predictions (i.e., discharge versus nondischarge zones), rather than the uncertainty of the geophysical estimates (i.e., the standard error of estimation for the logarithm of electrical conductivity).


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 84
Serial Issue: 5
Title: Geophysical mapping of plume discharge to surface water at a crude oil spill site; inversion versus machine learning
Affiliation: U. S. Geological Survey, Hydrogeophysics Branch, Storrs, CT, United States
Pages: EN67-EN80
Published: 201909
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 87
Accession Number: 2020-011745
Categories: Applied geophysicsEnvironmental geology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
N43°30'00" - N49°00'00", W97°10'00" - W89°45'00"
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute.
Update Code: 202009
Program Name: USGSOPNon-USGS publications with USGS authors
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