Author ORCID Identifier
https://orcid.org/0000-0001-5633-426X
Date of Award
Spring 5-6-2026
Document Type
Thesis (Ph.D.)
Department or Program
Engineering Sciences
First Advisor
Dr. Fridon Shubitidze
Second Advisor
Dr. Benjamin Barrowes
Abstract
Remote sensing of the subsurface is a critical need, including searching for targets such as buried infrastructure, unexploded ordnance (UXO), and even geological formations and structures, encompassing a humanitarian need as well as environmental and ecological ones. The reliable identification of the 20 million acres of land considered UXO-contaminated in Ukraine since 2022 or the coming overhaul of US drinking water pipelines, as examples, highlight the importance of not only having a reliable method to identify targets of interest in the subsurface, but also to go from data collection to decision-making in a streamlined and quick manner.
This thesis presents work done to address these problems and provide a cost-effective and safe method to perform real-time detection and classification of buried targets.
We built, tested, and integrated multi-modal lightweight electromagnetic induction (EMI) sensors for autonomous platforms. These systems were operated in handheld setup, as well as being deployed on unmanned ground (UGVs) and aerial vehicles (UAVs). These systems were tested in field conditions for detection and classification of UXO, as well as for detecting and differentiating between buried lead, copper and galvanized steel pipes. In addition to the hardware development, machine learning algorithms and training methodologies were implemented for improved classification performance.
To further enhance subsurface investigations, the EMI sensors were directly paired with total-field magnetometers. These lightweight magnetic sensors have a very small form factor and high sensitivity, allowing them to detect small-magnitude magnetic signals, though only of ferromagnetic objects. By pairing them with the EMI sensor, we show deeper detection capability than by EMI alone, as well as detecting non-ferrous materials with the magnetometers.
Next, we take advantage of the small size of the total-field magnetometers and their scalability in array formations. We developed closed-form solutions to perform target localization from magnetic data sets- this allows the magnetometers to be used not only for surveys and anomaly detections, but also to specifically provide target location and depth information from an initial pass.
Finally, we examine the applicability of machine learning techniques for target classification. Specifically, combining machine learning with synthetic data generation to improve the classification performance for deep targets and noisy data.
Recommended Citation
Orman-Kollmar, Maximilian Albert Joshua, "Ultralight Electromagnetic Systems on Moving Platforms with Advanced Signal Processing for Subsurface Target Detection, Localization, and Identification" (2026). Dartmouth College Ph.D Dissertations. 499.
https://digitalcommons.dartmouth.edu/dissertations/499
