Publications
Showing 51 results for Author: Mark B. Adams
Mar, 2026
Conference Paper
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are…
Feb, 2026
Journal
Denoising Seismograms in the Time Domain Using a Deep Learning Model
Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spect…
Feb, 2026
Book Chapter
Robust AI Techniques to Support High-consequence Applications in the Cyber Age
Many of today's high-consequence tasks require narrow subject matter expertise (SME), tooling, and thoughtful thorough planning to transform intricate calculations and analysis into subsequent plans and actions. For AI to help tackle these types of problems, we must employ measures that ensure statistical and mathematical robustness every step of the way. This chapter will…
Jun, 2025
Journal
PickerXL, a Large Deep Learning Model to Measure Arrival Times from Noisy Seismic Signals
Precisely measuring seismic arrival times is a labor‐intensive task but is critical for both earthquake monitoring and subsurface imaging. Recently published deep learning models have demonstrated superior performance compared to traditional automatic approaches for picking arrival times. Although existing deep learning models have shown promising results, further advancem…
Dec, 2023
Conference Paper
Transformative Data Analytics Capabilities for Nuclear Forensics and Safeguards