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At least 253 records · Page 14Linked to original sources
USGS2026

pySATSI: A Python package for computing focal mechanism stress inversions

We introduce pySATSI, a Python package for computing earthquake focal mechanism stress inversions. This algorithm can handle a wide variety of types of stress inversion problems with a single script and can duplicate many capabilities of preceding methodologies. We also add new capabilities that include spatiotemporally variable inversion grids, damped stress estimates for clusters with few or no focal mechanisms, and variable fault‐plane ambiguities that the user can assign to individual events. In addition, we added the ability to use damped stress inversions with fault‐plane ambiguity probabilities that are weighted by fault instabilities. Our algorithm is computationally efficient with faster runtimes than previous algorithms, scales well for large datasets, and can be easily parallelized.

Seismological Research Letters↗
USGS1990

The effect of S-wave arrival times on the accuracy of hypocenter estimation

Well-constrained hypocenters (latitude, longitude, depth, and origin time) are required for nearly all studies that use earthquake data. We have examined the theoretical basis behind some of the widely accepted “rules of thumb” for obtaining accurate hypocenter estimates that pertain to the use of S phases and illustrate, in a variety of ways, why and when these “rules” are applicable. Results of experiments done for this study show that epicentral estimates (latitude and longitude) are typically far more robust with respect to data inadequacies; therefore, only examples illustrating the relationship between S phase arrival time data and focal depth and origin time estimates are presented. Most methods used to determine earthquake hypocenters are based on iterative, linearized, least-squares algorithms. Standard errors associated with hypocenter parameters are calculated assuming the data errors may be correctly described by a Gaussian distribution. We examine the influence of S-phase arrival time data on such algorithms by using the program HYPOINVERSE with synthetic datasets. Least-squares hypocenter determination algorithms have several shortcomings: solutions may be highly dependent on starting hypocenters, linearization and the assumption that data errors follow a Gaussian distribution may not be appropriate, and depth/origin time trade-offs are not readily apparent. These shortcomings can lead to biased hypocenter estimates and standard errors that do not always represent the true error. To illustrate the constraint provided by S-phase data on hypocenters determined without some of these potential problems, we also show examples of hypocenter estimates derived using a probabilistic approach that does not require linearization. We conclude that a correctly timed S phase recorded within about 1.4 focal depth's distance from the epicenter can be a powerful constraint on focal depth. Furthermore, we demonstrate that even a single incorrectly timed S phase can result in depth estimates and associated measures of uncertainty that are significantly incorrect.

Bulletin of the Seismological Society of America↗
USGS2007

USGS Digital Spectral Library splib06a

Introduction We have assembled a digital reflectance spectral library that covers the wavelength range from the ultraviolet to far infrared along with sample documentation. The library includes samples of minerals, rocks, soils, physically constructed as well as mathematically computed mixtures, plants, vegetation communities, microorganisms, and man-made materials. The samples and spectra collected were assembled for the purpose of using spectral features for the remote detection of these and similar materials. Analysis of spectroscopic data from laboratory, aircraft, and spacecraft instrumentation requires a knowledge base. The spectral library discussed here forms a knowledge base for the spectroscopy of minerals and related materials of importance to a variety of research programs being conducted at the U.S. Geological Survey. Much of this library grew out of the need for spectra to support imaging spectroscopy studies of the Earth and planets. Imaging spectrometers, such as the National Aeronautics and Space Administration (NASA) Airborne Visible/Infra Red Imaging Spectrometer (AVIRIS) or the NASA Cassini Visual and Infrared Mapping Spectrometer (VIMS) which is currently orbiting Saturn, have narrow bandwidths in many contiguous spectral channels that permit accurate definition of absorption features in spectra from a variety of materials. Identification of materials from such data requires a comprehensive spectral library of minerals, vegetation, man-made materials, and other subjects in the scene. Our research involves the use of the spectral library to identify the components in a spectrum of an unknown. Therefore, the quality of the library must be very good. However, the quality required in a spectral library to successfully perform an investigation depends on the scientific questions to be answered and the type of algorithms to be used. For example, to map a mineral using imaging spectroscopy and the mapping algorithm of Clark and others (1990a, 2003b), one simply needs a diagnostic absorption band. The mapping system uses continuum-removed reference spectral features fitted to features in observed spectra. Spectral features for such algorithms can be obtained from a spectrum of a sample containing large amounts of contaminants, including those that add other spectral features, as long as the shape of the diagnostic feature of interest is not modified. If, however, the data are needed for radiative transfer models to derive mineral abundances from reflectance spectra, then completely uncontaminated spectra are required. This library contains spectra that span a range of quality, with purity indicators to flag spectra for (or against) particular uses. Acquiring spectral measurements and performing sample characterizations for this library has taken about 15 person-years of effort. Software to manage the library and provide scientific analysis capability is provided (Clark, 1980, 1993). A personal computer (PC) reader for the library is also available (Livo and others, 1993). The program reads specpr binary files (Clark, 1980, 1993) and plots spectra. Another program that reads the specpr format is written in IDL (Kokaly, 2005). In our view, an ideal spectral library consists of samples covering a very wide range of materials, has large wavelength range with very high precision, and has enough sample analyses and documentation to establish the quality of the spectra. Time and available resources limit what can be achieved. Ideally, for each mineral, the sample analysis would include X-ray diffraction (XRD), electron microprobe (EM) or X-ray fluorescence (XRF), and petrographic microscopic analyses. For some minerals, such as iron oxides, additional analyses such as Mossbauer would be helpful. We have found that to make the basic spectral measurements, provide XRD, EM or XRF analyses, and microscopic analyses, document the results, and complete an entry of one spectral library sample, all takes about

Data Series↗
USGS2011

Data for giant constrictors - Biological management profiles and an establishment risk assessment for nine large species of pythons, anacondas, and the boa constrictor

Giant Constrictors' Climate Space The giant constrictors' climate space data set represents the information needed to recreate the climate space and climate matching analyses in Reed and Rodda (2009). A detailed methodology and results are included in that report. The data include locations for nine species of large constrictors including Python molurus, Broghammerus reticulatus, P. sebae, P. natalensis, Boa constrictor, Eunectes notaeus, E. deschauenseei, E. beniensis, and E. murinus. The locations are from published sources. Climate data are included for monthly precipitation and average monthly temperature along with the species locations. The individual spreadsheets of location data match the figures in the Reed and Rodda (2009) report, illustrating areas of the mainland United States that match the climate envelope of the native range. The precipitation and temperature data at each location were used to determine the climate space for each species. Graphs of climate space formed the basis for the algorithms in the data set, and more details can be found in Reed and Rodda (2009). These algorithms were used in ArcGIS to generate maps of areas in the United States that matched the climate space of locations of the snakes in their native range. We discovered a rounding error in ArcGIS in the implementation of the algorithms, which has been corrected here. Therefore the shapefiles are slightly different than those appearing in the risk assessment figures illustrating areas of the United States that match the climate envelope of the species in their native ranges. However, the suitable localities are not different at the scale of intended use for these maps, although there are more noticeable differences between areas classified as 'too cold' and 'too hot'.

Data Series↗
USGS2002

Ice Core Depth-Age Relation for Vostok delta-D and Dome Fuji delta-18O Records Based on the Devils Hole Paleotemperature Chronology

This report presents the data for the Vostok - Devils Hole chronology, termed V-DH chronology, for the Antarctic Vostok ice core record. This depth - age relation is based on a join between the Vostok deuterium profile (D) and the stable oxygen isotope ratio (18O) record of paleotemperature from a calcitic core at Devils Hole, Nevada, using the algorithm developed by Landwehr and Winograd (2001). Both the control points defining the V-DH chronology and the numeric values for the chronology are given. In addition, a plausible chronology for a deformed bottom portion of the Vostok core developed with this algorithm is presented. Landwehr and Winograd (2001) demonstrated the broader utility of their algorithm by applying it to another appropriate Antarctic paleotemperature record, the Antarctic Dome Fuji ice core 18O record. Control points for this chronology are also presented in this report but deemed preliminary because, to date, investigators have published only the visual trace and not the numeric values for the Dome Fuji 18O record. The total uncertainty that can be associated with the assigned ages is also given.

Open-File Report↗
USGS2003

Processing large remote sensing image data sets on Beowulf clusters

High-performance computing is often concerned with the speed at which floating- point calculations can be performed. The architectures of many parallel computers and/or their network topologies are based on these investigations. Often, benchmarks resulting from these investigations are compiled with little regard to how a large dataset would move about in these systems. This part of the Beowulf study addresses that concern by looking at specific applications software and system-level modifications. Applications include an implementation of a smoothing filter for time-series data, a parallel implementation of the decision tree algorithm used in the Landcover Characterization project, a parallel Kriging algorithm used to fit point data collected in the field on invasive species to a regular grid, and modifications to the Beowulf project's resampling algorithm to handle larger, higher resolution datasets at a national scale. Systems-level investigations include a feasibility study on Flat Neighborhood Networks and modifications of that concept with Parallel File Systems.

Open-File Report↗
USGS2005

GWM-a ground-water management process for the U.S. Geological Survey modular ground-water model (MODFLOW-2000)

GWM is a Ground?Water Management Process for the U.S. Geological Survey modular three?dimensional ground?water model, MODFLOW?2000. GWM uses a response?matrix approach to solve several types of linear, nonlinear, and mixed?binary linear ground?water management formulations. Each management formulation consists of a set of decision variables, an objective function, and a set of constraints. Three types of decision variables are supported by GWM: flow?rate decision variables, which are withdrawal or injection rates at well sites; external decision variables, which are sources or sinks of water that are external to the flow model and do not directly affect the state variables of the simulated ground?water system (heads, streamflows, and so forth); and binary variables, which have values of 0 or 1 and are used to define the status of flow?rate or external decision variables. Flow?rate decision variables can represent wells that extend over one or more model cells and be active during one or more model stress periods; external variables also can be active during one or more stress periods. A single objective function is supported by GWM, which can be specified to either minimize or maximize the weighted sum of the three types of decision variables. Four types of constraints can be specified in a GWM formulation: upper and lower bounds on the flow?rate and external decision variables; linear summations of the three types of decision variables; hydraulic?head based constraints, including drawdowns, head differences, and head gradients; and streamflow and streamflow?depletion constraints. The Response Matrix Solution (RMS) Package of GWM uses the Ground?Water Flow Process of MODFLOW to calculate the change in head at each constraint location that results from a perturbation of a flow?rate variable; these changes are used to calculate the response coefficients. For linear management formulations, the resulting matrix of response coefficients is then combined with other components of the linear management formulation to form a complete linear formulation; the formulation is then solved by use of the simplex algorithm, which is incorporated into the RMS Package. Nonlinear formulations arise for simulated conditions that include water?table (unconfined) aquifers or head?dependent boundary conditions (such as streams, drains, or evapotranspiration from the water table). Nonlinear formulations are solved by sequential linear programming; that is, repeated linearization of the nonlinear features of the management problem. In this approach, response coefficients are recalculated for each iteration of the solution process. Mixed?binary linear (or mildly nonlinear) formulations are solved by use of the branch and bound algorithm, which is also incorporated into the RMS Package. Three sample problems are provided to demonstrate the use of GWM for typical ground?water flow management problems. These sample problems provide examples of how GWM input files are constructed to specify the decision variables, objective function, constraints, and solution process for a GWM run. The GWM Process runs with the MODFLOW?2000 Global and Ground?Water Flow Processes, but in its current form GWM cannot be used with the Observation, Sensitivity, Parameter?Estimation, or Ground?Water Transport Processes. The GWM Process is written with a modular structure so that new objective functions, constraint types, and solution algorithms can be added.

Open-File Report↗
USGS2018

Modeling managed flows in the Sacramento/San Joaquin watershed, California, under scenarios of future change for CASCaDE2

Projections of managed flows from the Sacramento River/San Joaquin River watershed, California, into the San Francisco Bay and Sacramento-San Joaquin Delta under scenarios of future climate change are needed for evaluations of potential impacts on water supply and estuarine ecosystems. A new, multiple-model approach for achieving this is described. First, downscaled global climate model outputs are used to drive an existing Variable Infiltration Capacity/Variable Infiltration Capacity Routing (VIC/RVIC) model of Sacramento/San Joaquin hydrology, resulting in projections of daily, unimpaired flows throughout the watershed. A management model, Computational Assessments of Scenarios of Change for the Delta Ecosystem phase 2 (CASCaDE2) modified CalSim (C2-CalSim), uses these projections as inputs and produces monthly estimates of reservoir and other infrastructure operations and resulting downstream managed flows. A historical resampling algorithm, CASCaDE2 resampling algorithm (CRESPI), also uses the projected daily unimpaired flows, along with historical managed flows, to estimate the daily variability in managed flows throughout the watershed. The monthly and daily managed-flow estimates are combined in a way that preserves the multi-decadal variability and century-scale trends produced by the C2-CalSim model and the day-to-day variability produced by the CRESPI algorithm. The performance of the new modeling approach is evaluated at major inflows to the Bay-Delta estuary using multiple metrics and found to be satisfactory for the purposes of future scenario evaluation.

California↗
USGS2025

Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska

Executive Summary The conference report accompanying the fiscal year (FY) 2022 Consolidated Appropriations Act (Public Law 117–103) for the U.S. Department of the Interior and related agencies directed the U.S. Geological Survey (USGS) to “work with the State of Alaska to develop an implementation plan to be completed within two years in order to put ShakeAlert/Earthquake Early Warning in Alaska” (p. 29). Congress included $1 million in the FY 2022 appropriation to conduct this effort. The USGS Earthquake Hazards Program, along with partner organizations, has developed the ShakeAlert earthquake early warning (EEW) system for the West Coast, which currently operates in California, Oregon, and Washington. The purpose of the system and its alert delivery partners is to reduce the impact of earthquakes and save lives and property by delivering ShakeAlert-powered alerts that are transmitted to the public via mass notification technologies, and by providing more detailed data streams to institutional users and commercial service providers to trigger automated, user-specific, protective actions. ShakeAlert was designed in such a way that it could be expanded to other U.S. regions with high earthquake risk, after the build-out of seismic and geodetic networks to support ShakeAlert in a specified region is completed and the necessary funding is secured for long-term operation and maintenance. When an earthquake occurs, seismic waves radiate from the rupturing fault like waves on a pond. It is these waves that people feel as earthquake shaking and that can cause damage to structures. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Early warnings of impending shaking are then sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking. There will almost always be a region near the earthquake epicenter where alerts arrive after damaging shaking has begun. The ShakeAlert system updates its ground-motion estimates as an earthquake grows larger. In response to the FY 2022 congressional direction, the USGS worked with the State of Alaska to devise this implementation plan for ShakeAlert expansion to Alaska. The USGS engaged with the Alaska Division of Homeland Security and Emergency Management (DHS&EM) and the Alaska Division of Geological and Geophysical Surveys (DGGS). A cooperative agreement was awarded to the Alaska Earthquake Center (AEC) at the University of Alaska Fairbanks (UAF) for their contributions to the plan and their work coordinating with other networks in Alaska. The USGS engaged with the Alaska Seismic Hazards Safety Commission (ASHSC) throughout the process. The USGS also held a series of Alaska stakeholder engagements. The process of developing the implementation plan was facilitated by contracted staff from Corner Alliance, which is a government consulting firm. This implementation plan describes the details and estimates the costs for a Phase 1 expansion of the ShakeAlert system to Alaska. A geographically limited Phase 1 goal was chosen that covers the highest risk and most populated areas of Alaska. The areas proposed encompass the State’s main population centers and 90 percent of the State’s population. This Phase 1 design is considered very challenging and ambitious from the viewpoint of network operators. The lessons learned if this plan is implemented could be used to consider subsequent phases to expand EEW beyond Phase 1 in Alaska in the future. ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System (ANSS). This implementation plan calls for a total of 450 high-quality, real-time EEW-capable ANSS seismic stations in Alaska: 270 new stations, 160 upgraded stations, and 20 existing stations. These seismic station numbers are based on a station spacing of 10 kilometers (km) in urban areas, 20 km in seismic source areas that endanger population centers, and 40 km in other areas. The associated costs also include support for some EEW-capable global navigation satellite system (GNSS) stations, with a focus on improving warnings for large subduction zone earthquakes. For effective EEW, ShakeAlert requires low-latency, high-availability, robust telemetry links to deliver continuous, real-time data from field stations to the data centers. The Alaska data processing hardware infrastructure would follow the general design for fail-safe operation that is used for the ShakeAlert system on the West Coast. The ShakeAlert architecture uses two independent layers: the production layer for earthquake processing and the alert layer to make alerting decisions and serve alerts to users. This implementation plan includes two geographically separated data centers in Alaska, each with two fully independent production and alert layers using the same system design developed for the West Coast. As of March 2024, the ShakeAlert system is at version 3.0.1, with more advanced versions in the development and testing pipeline. ShakeAlert originally used two algorithms to determine the location and magnitude of earthquakes using seismic data. A third algorithm that can calculate very large magnitudes of very large earthquakes with geodetic data was added in March 2024. ShakeAlert publishes several data and alert products to meet the needs of different users. All messages include the location of the earthquake, either as a point or a line, and its magnitude. Ground-shaking estimates are published in two forms, as ground-motion contours and a map grid. Providing adequate warning time for strong shaking (the “target threshold”) requires sending alerts at a threshold lower than that strong shaking level (the “alert threshold”). The thresholds for public alerting in Alaska would be a joint USGS and State decision. To have the greatest benefit, ShakeAlert-powered alerts would be delivered to institutional users and individuals by all practical pathways. The USGS alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission nor the infrastructure and expertise to perform mass notifications to the public or implement automatic actions for end users of the alerts. To meet this need, ShakeAlert recruits private sector “technology enablers” that have the necessary expertise to develop end-user implementations using EEW alerts with the goal of stimulating an EEW industry. Earthquake early warning alerts are useless if people do not know how to respond to them. Although the alert messages include instructions about what to do (drop, cover, and hold on), alerts are more effective if people have been trained in advance. Messages about ShakeAlert’s capabilities, limitations, and benefits could be integrated with existing earthquake education programs, including State-run programs. Therefore, ShakeAlert would coordinate with both public and private partners and stakeholders through various partnerships and agreements to accomplish consistent and ongoing public earthquake hazard education. The estimated capital cost of completing the computing infrastructure and sensor networks for the Phase 1 ShakeAlert expansion to Alaska is approximately $66 million in 2024 dollars. The annual operation and maintenance cost of the completed system is estimated to be $12 million per year in 2024 dollars when fully built out.

Alaska↗
USGS1996

The Modular Modeling System (MMS): User's Manual

The Modular Modeling System (MMS) is an integrated system of computer software that has been developed to provide the research and operational framework needed to support development, testing, and evaluation of physical-process algorithms and to facilitate integration of user-selected sets of algorithms into operational physical-process models. MMS uses a module library that contains modules for simulating a variety of water, energy, and biogeochemical processes. A model is created by selectively coupling the most appropriate modules from the library to create a 'suitable' model for the desired application. Where existing modules do not provide appropriate process algorithms, new modules can be developed. The MMS user's manual provides installation instructions and a detailed discussion of system concepts, module development, and model development and application using the MMS graphical user interface.

Open-File Report↗
USGS2007

Simulation of Surface-Water Conditions in the Nontidal Passaic River Basin, New Jersey

The Passaic River Basin, the third largest drainage basin in New Jersey, encompasses 950 mi2 (square miles) in the highly urbanized area outside New York City, with a population of 2 million. Water quality in the basin is affected by many natural and anthropogenic factors. Nutrient loading to the Wanaque Reservoir in the northern part of the basin is of particular concern and is caused partly by the diversion of water at two downstream intakes that is transferred back upstream to refill the reservoir. The larger of these diversions, Wanaque South intake, is on the lower Pompton River near Two Bridges, New Jersey. To support the development of a Total Maximum Daily Load (TMDL) for nutrients in the nontidal part of the basin (805 mi2), a water-quality transport model was needed. The U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection and New Jersey EcoComplex, developed a flow-routing model to provide the hydraulic inputs to the water-quality model. The Diffusion Analogy Flow model (DAFLOW) described herein was designed for integration with the Water Quality Analysis Simulation Program (WASP) watershed water-quality model. The flow routing model was used to simulate flow in 108 miles of the Passaic River and major tributaries. Flow data from U.S. Geological Survey streamflow-gaging stations represent most of the model's upstream boundaries. Other model inputs include estimated flows for ungaged tributaries and unchanneled drainage along the mainstem, and reported flows for major point-source discharges and diversions. The former flows were calibrated using the drainage-area ratio method. The simulation extended over a 4+ year period representing a range in flow conditions. Simulated channel cross-sectional geometry in the DAFLOW model was calibrated using several different approaches by adjusting area and top width parameters. The model also was calibrated to observed flows for water year 2001 (low flow) at five mainstem gaging stations and one station at which flow was estimated. The model's target range was medium to low flows--the range of typical intake operations. Simulated flow mass balance, hydrographs (flood-wave speed, attenuation, and spread), flow-duration curves, and velocity and depth values were compared to observed counterparts. Mass balance and hydrograph fit were evaluated quantitatively. Simulation results generally were within the accuracy of the flow data at the measurement stations. The model was validated to observed flows for water years 2000 (average flow), 2002 (extreme low flow), and 2003 (high flow). Results for 19 of 20 comparisons indicate average mass-balance and model-fit errors of 6.6 and 15.7 percent, respectively, indicating that the model reasonably represents the time variation of streamflow in the nontidal Passaic River Basin. An algorithm (subroutine) also was developed for DAFLOW to simulate the hydraulic mixing that occurs near the Wanaque South intake upstream from the confluence of the Pompton and Passaic Rivers. The intake draws water from multiple sources, including effluent from a nearby wastewater-treatment plant, all of which have different phosphorus loads. The algorithm determines the proportion of flow from each source and operates within a narrow flow range. The equations used in the algorithm are based on the theory of diffusion and lateral mixing in rivers. Parameters used in the equations were estimated from limited available local flow and water-quality data. As expected, simulation results for water years 2000, 2001, and 2003 indicate that most of the water drawn to the intake comes from the Pompton River; however, during many short periods of low flow and high diversion, particularly in water year 2002, entrainment of the other flow sources compensated for the insufficient flow in the Pompton River. As additional verification of the flow model used in the water-quality model, a Branched Lagrangian Transport Model (B

Scientific Investigations Report↗
USGS2009

Development and Implementation of a Transport Method for the Transport and Reaction Simulation Engine (TaRSE) based on the Godunov-Mixed Finite Element Method

A model to simulate transport of materials in surface water and ground water has been developed to numerically approximate solutions to the advection-dispersion equation. This model, known as the Transport and Reaction Simulation Engine (TaRSE), uses an algorithm that incorporates a time-splitting technique where the advective part of the equation is solved separately from the dispersive part. An explicit finite-volume Godunov method is used to approximate the advective part, while a mixed-finite element technique is used to approximate the dispersive part. The dispersive part uses an implicit discretization, which allows it to run stably with a larger time step than the explicit advective step. The potential exists to develop algorithms that run several advective steps, and then one dispersive step that encompasses the time interval of the advective steps. Because the dispersive step is computationally most expensive, schemes can be implemented that are more computationally efficient than non-time-split algorithms. This technique enables scientists to solve problems with high grid Peclet numbers, such as transport problems with sharp solute fronts, without spurious oscillations in the numerical approximation to the solution and with virtually no artificial diffusion.

Scientific Investigations Report↗
USGS2014

Regional mapping of hydrothermally altered igneous rocks along the Urumieh-Dokhtar, Chagai, and Alborz Belts of western Asia using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and Interactive Data Language (IDL) logical operators: a tool for porphyry copper exploration and assessment

Regional maps of phyllic and argillic hydrothermal alteration were compiled using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. The area mapped extends from northwestern Iran to southeastern Pakistan and includes volcanic and magmatic arcs that make up the Urumieh-Dokhtar volcanic belt (UDVB), the Chagai volcanic belt (CVB), and the central part of the Alborz magmatic belt (AMB). The volcanic belts span the Zagros-Makran transform zone and the present day Baluchistan (Makran) volcanic arc. ASTER visible near infrared (VNIR) data contain three bands between 0.52 and 0.86 micrometers (μm) and the short-wave infrared (SWIR) data consist of six bands spanning 1.6 to 2.43 μm with 15-meter (m), and 30-m resolution, respectively. During the 8-year project, three different calibration methods were used to correct ASTER SWIR anomalies and produce ASTER calibrated reflectance data, which were then used to map hydrothermally altered rocks. Logical operators, used to map hydrothermally altered rocks, perform multiple band ratios and thresholds that can be applied to multiple ASTER scenes using a single algorithm, thus eliminating separate production and application of vegetation and dark pixel masks. Argillic and phyllic band ratio logical operators use band ratios that define the 2.17- and 2.20-μm absorption features to map kaolinite and alunite, typical in argillized rocks, and muscovite, which is a common mineral in phyllic alteration. Band thresholds for ratios in argillic and phyllic logical operator algorithms were determined by mapping known argillic and phyllic rocks at a calibration test site in Cuprite, Nevada, in the United States. The regional argillic and phyllic hydrothermal alteration map and geologic and deposit maps of the study area illustrate that distinct patterns of altered rocks are typically associated with certain types of mineral deposits. The central part of the UDVB contains numerous circular to elliptical patterns (1 to 5 kilometers in diameter) of mapped phyllic- and argillic-altered rocks associated with Eocene to Miocene intrusive igneous rocks, some of which host known porphyry copper deposits such as at Meiduk, Sar Cheshmeh, and Seridune in Iran. The Zagros-Makran transform zone and areas adjacent to the Saindak porphyry deposit and Taftan Volcano contain primarily phyllic-altered rocks that form linear patterns associated with extensive faulting and fractures indicative of epithermal and (or) polymetallic vein deposits. The ASTER alteration map and corresponding geologic maps were used to select circular to elliptical patterns of argillic- and phyllic-altered volcanic and intrusive rocks as potential porphyry copper sites. One hundred and seventy eight potential porphyry copper sites were mapped along the UDVB, and 23 sites were mapped along the CVB. The potential sites were selected to assist in further exploration and assessments of undiscovered porphyry copper deposits.

Scientific Investigations Report↗
USGS2011

Regional skew for California, and flood frequency for selected sites in the Sacramento-San Joaquin River Basin, based on data through water year 2006

Improved flood-frequency information is important throughout California in general and in the Sacramento-San Joaquin River Basin in particular, because of an extensive network of flood-control levees and the risk of catastrophic flooding. A key first step in updating flood-frequency information is determining regional skew. A Bayesian generalized least squares (GLS) regression method was used to derive a regional-skew model based on annual peak-discharge data for 158 long-term (30 or more years of record) stations throughout most of California. The desert areas in southeastern California had too few long-term stations to reliably determine regional skew for that hydrologically distinct region; therefore, the desert areas were excluded from the regional skew analysis for California. Of the 158 long-term stations used to determine regional skew, 145 have minimally regulated annual-peak discharges, and 13 stations are dam sites for which unregulated peak discharges were estimated from unregulated daily maximum discharge data furnished by the U.S. Army Corp of Engineers. Station skew was determined by using an expected moments algorithm (EMA) program for fitting the Pearson Type 3 flood-frequency distribution to the logarithms of annual peak-discharge data. The Bayesian GLS regression method previously developed was modified because of the large cross correlations among concurrent recorded peak discharges in California and the use of censored data and historical flood information with the new expected moments algorithm. In particular, to properly account for these cross-correlation problems and develop a suitable regression model and regression diagnostics, a combination of Bayesian weighted least squares and generalized least squares regression was adopted. This new methodology identified a nonlinear function relating regional skew to mean basin elevation. The regional skew values ranged from -0.62 for a mean basin elevation of zero to 0.61 for a mean basin elevation of 11,000 feet. This relation between skew and elevation reflects the interaction of snow with rain, which increases with increased elevation. The equivalent record length for the new regional skew ranges from 52 to 65 years of record, depending upon mean basin elevation. The old regional skew map in Bulletin 17B, published by the Hydrology Subcommittee of the Interagency Advisory Committee on Water Data (1982), reported an equivalent record length of only 17 years. The newly developed regional skew relation for California was used to update flood frequency for the 158 sites used in the regional skew analysis as well as 206 selected sites in the Sacramento-San Joaquin River Basin. For these sites, annual-peak discharges having recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years were determined on the basis of data through water year 2006. The expected moments algorithm was used for determining the magnitude and frequency of floods at gaged sites by using regional skew values and using the basic approach outlined in Bulletin

Scientific Investigations Report↗
USGS2015

Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models

The PEST++ Version 1 object-oriented parameter estimation code is here extended to Version 3 to incorporate additional algorithms and tools to further improve support for large and complex environmental modeling problems. PEST++ Version 3 includes the Gauss-Marquardt-Levenberg (GML) algorithm for nonlinear parameter estimation, Tikhonov regularization, integrated linear-based uncertainty quantification, options of integrated TCP/IP based parallel run management or external independent run management by use of a Version 2 update of the GENIE Version 1 software code, and utilities for global sensitivity analyses. The Version 3 code design is consistent with PEST++ Version 1 and continues to be designed to lower the barriers of entry for users as well as developers while providing efficient and optimized algorithms capable of accommodating large, highly parameterized inverse problems. As such, this effort continues the original focus of (1) implementing the most popular and powerful features of the PEST software suite in a fashion that is easy for novice or experienced modelers to use and (2) developing a software framework that is easy to extend. The PEST++ Version 3 software suite can be compiled for Microsoft Windows®4 and Linux®5 operating systems; the source code is available in a Microsoft Visual Studio®6 2013 solution; Linux Makefiles are also provided. PEST++ Version 3 continues to build a foundation for an open-source framework capable of producing robust and efficient parameter estimation tools for large environmental models.

Techniques and Methods↗
USGS2025

Bayesian mapping of regionally grouped, sparse, univariate earth science data

Some earth science data are naturally grouped by region, and it is often desirable to map these data by region. However, if there are only a few samples within each region, then the map should be smoothed in an appropriate way to mitigate the problems that arise from having only a few samples. A smoothing algorithm based on a Bayesian hierarchical model is developed and presented in this report. This algorithm has several features that make it especially suitable for mapping earth science data: it can account for measurements that are censored, it can process multiple datasets with different measurement errors and different censoring thresholds, and it can calculate the uncertainty in any statistic that is mapped. The algorithm is demonstrated by mapping gold concentrations that are measured in streambed sediments in the Taylor Mountains quadrangle in southwestern Alaska.

Alaska↗
USGS2019

Not so normal normals: Species distribution model results are sensitive to choice of climate normals and model type

Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.

Climate↗
USGS2021

Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat 8 (2016-2019)

Prescribed fires and wildfires are common in wetland ecosystems across the Southeastern United States. However, the wetland burned area has been chronically underestimated across the region due to (1) spectral confusion between open water and burned area, (2) rapid post-fire vegetation regrowth, and (3) high annual precipitation limiting clear-sky satellite observations. We developed a machine learning algorithm specifically for burned area in wetlands, and applied the algorithm to the Sentinel-2 archive (2016–2019) across the Southeastern US (>290,000 km 2 ). Combining Landsat-8 imagery with Sentinel-2 increased the annual clear-sky observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validated with WorldView imagery, the Sentinel-2 burned area had a 29% and 30% omission and commission rates of error for burned area, respectively, compared to the US Geological Survey Landsat-8 Burned Area Product (L8 BA), which had a 47% and 8% omission and commission rate of error, respectively. The Sentinel-2 algorithm and the L8 BA mapped burned area within 78% and 60% of wetland fire perimeters ( n = 555) compiled from state and federal agencies, respectively. This analysis demonstrated the potential of Sentinel-2 to support efforts to track the burned area, especially across challenging ecosystem types, such as wetlands.

Florida, Montana, Oregon↗