Publications
Publications from the staff of the Geology, Minerals, Energy, and Geophysics Science Center
Vegetation loss following vertical drowning of Mississippi River deltaic wetlands leads to faster microbial decomposition and decreases in soil carbon
Late Triassic paleogeography of southern Laurentia and its fringing arcs: Insights from detrital zircon U-Pb geochronology and Hf isotope geochemistry, Auld Lang Syne basin (Nevada, USA)
Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range
Updated three-dimensional temperature maps for the Great Basin, USA
Polyphase stratabound scheelite-ferberite mineralization at Mallnock, Eastern Alps, Austria
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
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