Theses and Dissertations
Advisor
Skarke, Adam
Committee Member
Rodgers III, John
Committee Member
Lalk, Sarah
Committee Member
Mercer, Andrew
Committee Member
Travis, Rick
Date of Degree
5-15-2026
Original embargo terms
Immediate Worldwide Access
Document Type
Graduate Thesis - Open Access
Major
Geoscience (Geology)
Degree Name
Master of Science (M.S.)
College
College of Arts and Sciences
Department
Department of Geosciences
Abstract
This thesis evaluates the capacity to predict subsurface geologic features and submarine landslide susceptibility using surficial geomorphology derived from bathymetric elevation data in the Northern Gulf of Mexico. Quantitative geomorphic variables including slope, curvature, aspect, rugosity, geomorphons, and Bathymetric Position Index were generated from 30-meter digital elevation models and used as explanatory variables in presence-only Maximum Entropy models. Known locations of faults, pockmarks, mud volcanoes, hydrocarbon seeps, and landslides (particularly intact scarps) were used to train and validate predictive models through k-fold cross validation. Model performance was assessed using omission rates and AUC values. Results demonstrate that specific geomorphic signatures, such as slope and geomorphic landforms, correlate strongly with subsurface structures and geohazards (i.e. pit and valley landforms correlated with pockmark features). Spatial statistical models derived from the Gulf were subsequently applied to Jezero Crater on Mars to evaluate transferability to planetary surfaces lacking subsurface data.
Recommended Citation
Wing, Allison L., "Assessing the relationship between subsurface geology and surficial geomorphology: remotely predicting geologic features and geohazards using an elevation-trained machine learning algorithm in the Northern Gulf of Mexico" (2026). Theses and Dissertations. 7010.
https://scholarsjunction.msstate.edu/td/7010
