Publications
Browse more than 160,000 publications authored by our scientists over the past 100+ year history of the USGS. Publications available are: USGS-authored journal articles, series reports, book chapters, other government publications, and more.
Mission Area Publications
Mission Area Publications
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Predicting large hydrothermal systems 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...
Authors
Stanley Paul Mordensky, Erick R. Burns, Jacob DeAngelo, John Lipor
Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models 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...
Authors
Stanley Paul Mordensky, Erick R. Burns, John Lipor, Jacob DeAngelo
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 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...
Authors
Pascal D. Caraccioli, Stanley Paul Mordensky, Cary R. Lindsey, Jacob DeAngelo, Erick R. Burns, John Lipor
Overview of the Cenozoic geology of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia 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...
Authors
Joel E. Robinson, Drew T. Downs
Probabilistic seismic-hazard analysis for the western Kingdom of Saudi Arabia 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)...
Authors
Ryota Kiuchi, Walter D. Mooney, Hani M. Zahran
Seismic hazard assessment for areas of volcanic activity in western Kingdom of Saudi Arabia Seismic hazard assessment for areas of volcanic activity in western Kingdom of Saudi Arabia
Earthquake swarms caused by volcanic activity, tectonic stresses, or industrial operations (oil and gas production) can pose considerable risk for nearby settlements. As a rule, a probabilistic seismic hazard assessment (PSHA) that is based on time-independent earthquakes does not take into account earthquake swarms because of their statistically time-dependent nature. We describe the...
Authors
Hani M. Zahran, Vladimir Sokolov, Ian C. F. Stewart
Ground-motion prediction equations for the western Kingdom of Saudi Arabia Ground-motion prediction equations for the western Kingdom of Saudi Arabia
Ground-motion prediction equations (GMPEs) for the western Kingdom of Saudi Arabia are developed by employing a mixed-effects regression model to modify the Boore and others (2014) Next Generation Attenuation-West2 (NGA-West2) project GMPEs. NGA-West2 addressed several key issues concerning GMPEs for shallow crustal earthquakes in active tectonic regions. However, the NGA-West2 input...
Authors
Ryota Kiuchi, Walter D. Mooney, Hani M. Zahran
Ambient seismic noise tomography of the Kingdom of Saudi Arabia Ambient seismic noise tomography of the Kingdom of Saudi Arabia
Harrat Rahat is a Cenozoic volcanic field in the west-central part of the Kingdom of Saudi Arabia, 150 kilometers east of the Red Sea, and is the site of the most recent eruption in the country (1256 C.E.; 654 in the year of the Hijra). The city of Al Madīnah lies at the north end of Harrat Rahat, and its volcanic and seismic risks are frequently reassessed. In 2009 C.E. an earthquake...
Authors
Francesco Civilini, Walter D. Mooney, Martha K. Savage, John Townend
Thickness of the Saudi Arabian crust Thickness of the Saudi Arabian crust
As part of a joint Saudi Geological Survey (SGS) and U.S. Geological Survey (USGS) project, we analyzed P-wave receiver functions from seismic stations covering most of the Kingdom of Saudi Arabia to map the thickness of the crust across the Arabia Plate. We present an update of crustal-thickness estimates and fill in gaps for the western Arabian Shield and the rifted margin at the Red...
Authors
Alexander R. Blanchette, Simon L. Klemperer, Walter D. Mooney, Hani M. Zahran
Magnetotelluric investigation of northern Harrat Rahat, Kingdom of Saudi Arabia Magnetotelluric investigation of northern Harrat Rahat, Kingdom of Saudi Arabia
Volcanism within the harrats (Arabic for “volcanic field”) of the Kingdom of Saudi Arabia includes at least one historical eruption occurring close to the holy city of Al Madīnah in 1256 C.E. As part of a volcanic- and seismic-hazard assessment of northern Harrat Rahat, magnetotelluric (MT) data were collected to investigate the structural setting of the area, the presence or absence of...
Authors
Jared R. Peacock, Paul A. Bedrosian, Maher K. Al-Dhahry, Adel Shareef, Daniel W. Feucht, Cliff D. Taylor, Benjamin Bloss, Hani M. Zahran
Depth to basement and crustal structure of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia, from gravity and aeromagnetic data Depth to basement and crustal structure of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia, from gravity and aeromagnetic data
New gravity data reveal a prominent negative anomaly along the main vent axis of the northern Harrat Rahat volcanic field in the Kingdom of Saudi Arabia. The gravity low continues north of the volcanic field onto exposures of Proterozoic rocks, indicating that the low is caused not only by the volcanic field (and possibly underlying Cenozoic sediments), but also the underlying...
Authors
Victoria E. Langenheim, Brent T. Ritzinger, Hani M. Zahran, Adel Shareef, Maher K. Al-Dhahry
Isotopic and geochemical evidence for the source of volcanism at Harrat Rahat, Kingdom of Saudi Arabia Isotopic and geochemical evidence for the source of volcanism at Harrat Rahat, Kingdom of Saudi Arabia
Pleistocene and Holocene basalts, hawaiites, mugearites, benmoreites, and trachytes from the northern part of the Harrat Rahat volcanic field, Kingdom of Saudi Arabia, were analyzed for Sr, Nd, Hf, and Pb isotopic compositions. Evolved trachytes with Mg number 0.1, consisting chiefly of alkali basalts but encompassing hawaiites, mugearites, and benmoreites, show a limited range in Hf, Nd...
Authors
Vincent J.M. Salters, Afi Sachi-Kocher, Drew T. Downs, Mark E. Stelten, Thomas W. Sisson