Theses and Dissertations
Advisor
Skarke, Adam
Committee Member
Lalk, Sarah
Committee Member
Ambinakudige, Shrinidhi
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 study evaluated whether seafloor geomorphology can be used to predict subsurface fault locations along the Cascadia continental margin and explored whether such relationships can be transferred to Mars. Geomorphological classification schemas such as geomorphons, bathymetric position index (BPI), slope, rugosity, aspect, and curvature were derived from seafloor bathymetry data and used as predictor variables in maximum entropy (MaxEnt) models developed separately for Pre-Pleistocene, Pleistocene, Quaternary, and active fault datasets. Model performance ranged from AUC = 0.62 to 0.67, showing moderate predictive skill, with younger fault systems exhibiting stronger geomorphic associations. A secondary analysis showed a weaker but detectable relationship between faults and methane seep occurrence. The best performing model was applied to partially analogous Martian terrains in Valles Marineris, Claritas Fossae, and southern Utopia Planitia. Results suggest that surface morphology can provide meaningful constraints on subsurface structure where direct geological data is limited.
Recommended Citation
Morgan, Emma, "Using seafloor geomorphology, classification schema, and spatial analysis to predict geologic structures, with applications for Mars" (2026). Theses and Dissertations. 6944.
https://scholarsjunction.msstate.edu/td/6944
