Publications
Publications from the staff of the Geology, Minerals, Energy, and Geophysics Science Center
Sensitivity testing of marine turbidite age estimates along the Cascadia subduction zone
Defining the hafnium isotopic signature of the Appalachian orogen through analysis of detrital zircons from modern fluvial sediments
Deep structure of Siletzia in the Puget Lowland: Imaging an obducted plateau and accretionary thrust belt with potential fields
A far-traveled basalt lava flow in north-central Oregon, USA
Mafic alkaline magmatism and rare earth element mineralization in the Mojave Desert, California: The Bobcat Hills connection to Mountain Pass
Occurrences of alkaline and carbonatite rocks with high concentrations of rare earth elements (REE) are a defining feature of Precambrian geology in the Mojave Desert of southeastern California. The most economically important occurrence is the carbonatite stock at Mountain Pass, which constitutes the largest REE deposit in the United States. A central scientific goal is to understand the genesis
Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA
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