Degree

Doctor of Philosophy (PhD)

Department

Earth and Energy Sciences

Document Type

Dissertation

Abstract

Pitted mounds are widespread landforms across the northern plains of Mars, yet their origin remains uncertain. These features have been interpreted as possible expressions of subsurface fluid activity, including sedimentary volcanism, magmatic processes, or other fluid-assisted mechanisms. Determining their distribution, morphology, and geology is therefore important for understanding the evolution of subsurface hydrological processes and the planet’s potential for habitability. However, the large spatial extent of mound-bearing terrains makes comprehensive manual mapping impractical. This dissertation presents an automated approach to mound detection using Faster Region-Based Convolutional Neural Network (Faster R-CNN) from high-resolution Context Camera (CTX) images including morphometric and mineralogical analyses to investigate the distribution and origin of pitted mounds in Isidis Planitia. A training dataset of 432 images containing 2,296 manually labeled mound features was used to train the detection model. Application of the trained model across Isidis Planitia resulted in the identification of more than 100,000 candidate mound features. The resulting dataset enables quantitative analysis of mound morphology, size distributions, and spatial clustering patterns at basin scale. Morphometric results using High Resolution Imaging Science Experiment (HiRISE) elevation data indicate that the mounds generally exhibit broad, low-relief geometries and low aspect ratios that are more consistent with landforms produced from fluid-assisted sediment mobilization. Spatial clustering and alignment of mound fields suggest that subsurface fracture systems may have influenced the migration of fluids toward the surface. Spectral analyses using Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data identify regional mafic mineral signatures associated with basaltic plains materials and hydrated mineral detections indicative of past fluid–rock interactions. These observations suggest that fluid-assisted sediment mobilization likely played a significant role in the formation of pitted mounds in Isidis Planitia, although contributions from magmatic processes cannot be entirely excluded. By integrating automated detection with geological interpretation, this work investigates how machine learning approaches can enable large-scale geomorphological mapping on planetary surfaces and provides constraints on the processes shaping these landforms on Martian northern plains.

Date

2-6-2026

DOI

https://proquest.com/docview/3347862372

First Committee Chair

Gabriele Morra

First Committee Member

Chris Okubo

Second Committee Member

Davide Oppo

Third Committee Member

P. Io Ioannidi

Fourth Committee Member

Tolga Karsili

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Geology Commons

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