Title Information
Title
Composition and Impact-Generated Structure of Planetary Crusts Revealed by Dimensionality Reduction: Moon & Mars
Type of Resource (primo)
dissertations
Name: Personal
Name Part
Hundal, Carol Beate
Role
Role Term: Text
creator
Name: Personal
Name Part
Mustard, John
Role
Role Term: Text
Advisor
Name: Personal
Name Part
Evans, Alexander
Role
Role Term: Text
Advisor
Name: Personal
Name Part
Daubar, Ingrid
Role
Role Term: Text
Reader
Name: Personal
Name Part
Dalton, Colleen
Role
Role Term: Text
Reader
Name: Personal
Name Part
Soderblom, Jason
Role
Role Term: Text
Reader
Name: Corporate
Name Part
Brown University. Department of Earth, Environmental, and Planetary Sciences
Role
Role Term: Text
sponsor
Origin Information
Copyright Date
2026
Physical Description
Extent
19, 202 p.
digitalOrigin
born digital
Note: thesis
Thesis (Ph. D.)--Brown University, 2026
Genre (aat)
theses
Abstract
The increasing size and resolution of planetary datasets calls for analytical approaches that can resolve subtle patterns within complex, multidimensional data. Dimensionality-reduction methods, a branch of unsupervised machine learning, meet this challenge by transforming high-dimensional data into a compact set of axes that capture the dominant modes of variation. Although widely used in planetary spectroscopy (Chapter 1), such approaches remain underutilized in other domains of planetary science. This dissertation introduces Guided Endmember Extraction (GEEX), a framework that extends Principal Component Analysis (PCA) through treatment of correlated data and strategic subsetting of geophysical data. We validate GEEX using observations from NASA’s Gravity Recovery and Interior Laboratory (GRAIL) mission to analyze impact-induced signatures within the lunar crust. In Chapter 2, we establish the foundations of GEEX and show that it reproduces key results from prior studies with an order-of-magnitude greater precision, including the transition associated with the lunar depth of pore closure, previously detectable only through Bayesian modeling. GEEX also reveals distinct eigenvector “shapes” for positive and negative gravity anomalies and identifies a critical porosity of ~16.5%, where craters are equally likely to exhibit positive or negative anomalies, indicating that the porosity of the shallow lunar crust is not in equilibrium. In Chapter 3, we apply GEEX to explore, for the first time, how crustal thickness influences crater gravity signatures. We find that thicker crusts produce a wider range of gravity profile shapes favoring negative anomalies, particularly for craters ≥70±10 km in diameter, and identify a ~30 million km³ domain of low-porosity (10-16%) crust at depths of ~5-20 km within intermediate-thickness (40-48 km) regions. In Chapter 4, GEEX enables the first high-resolution analysis of deep porosity around seven farside impact basins. Using the fraction of low-Dot Product Value (DPV < -0.5) complex craters as a proxy for deep crustal porosity, we find that basin rims preserve deep porosity, while basin centers compact over time from Imbrium-aged to Pre-Nectarian basins. Collectively, these findings inform our understanding of lunar crustal evolution and demonstrate GEEX’s potential as a generalizable framework for revealing meaningful structure in large, multidimensional planetary datasets.
Subject (fast) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01749911")
Topic
Dimension reduction (Statistics)
Subject (fast) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01748989")
Topic
Impact craters
Subject (fast) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01240375")
Topic
Moon
Subject (fast) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01065123")
Topic
Planetary science
Language
Language Term (ISO639-2B)
English
Record Information
Record Content Source (marcorg)
RPB
Record Creation Date (encoding="iso8601")
20260427