All Publications
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GeoAI for spatial image processing
Advancing subsurface investigations beyond the borehole with passive seismic horizontal-to-vertical spectral ratio and electromagnetic geophysical methods at transportation infrastructure sites in New Hampshire
A characterization of the deep-sea coral and sponge community along the Oregon Coast using a remotely operated vehicle on the EXPRESS 2022 expedition
Hawaiian volcanic ash, an airborne fomite for nontuberculous mycobacteria
Variable climate-growth relationships of quaking aspen (Populus tremuloides) among Sky Island mountain ranges in the Great Basin, Nevada, USA
The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy
Predicting large hydrothermal systems
We train five models using two machine learning (ML) regression algorithms (i.e., linear regression and XGBoost) to predict hydrothermal upflow in the Great Basin. Feature data are extracted from datasets supporting the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS). The label data (the reported convective signals) are extracted from meas
Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models
Recent advances in machine learning (ML) identifying areas favorable to hydrothermal systems indicate that the resolution of feature data remains a subject of necessary improvement before ML can reliably produce better models. Herein, we consider the value of adding new features or replacing other, low-value features with new input features in existing ML pipelines. Our previous work identified st
Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions
Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify area
Overview of the Cenozoic geology of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia
The Harrat Rahat volcanic field, located in the west-central part of the Kingdom of Saudi Arabia, is one of the larger Cenozoic harrats among the more than 17 harrats situated upon the Arabia Plate. The map plate contained herein shows, at a scale of 1:100,000, the mapped volcanic geology of northern Harrat Rahat, which consists of the northernmost one-fifth of Harrat Rahat. Northern Harrat Rahat
Probabilistic seismic-hazard analysis for the western Kingdom of Saudi Arabia
We present a probabilistic seismic-hazard analysis (PSHA) for the west-central part of the Arabian Peninsula. Our study area includes the northern Harrat Rahat volcanic field and the nearby city of Al Madīnah, Kingdom of Saudi Arabia. This young, active volcanic field experienced one historical eruption in 1256 C.E. (654 in the year of the Hijra) that vented 20 to 22 kilometers (km) southeast of t