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At least 505 records · Page 28Linked to original sources
USGS2012

Quick-start guide for version 3.0 of EMINERS - Economic Mineral Resource Simulator

Quantitative mineral resource assessment, as developed by the U.S. Geological Survey (USGS), consists of three parts: (1) development of grade and tonnage mineral deposit models; (2) delineation of tracts permissive for each deposit type; and (3) probabilistic estimation of the numbers of undiscovered deposits for each deposit type (Singer and Menzie, 2010). The estimate of the number of undiscovered deposits at different levels of probability is the input to the EMINERS (Economic Mineral Resource Simulator) program. EMINERS uses a Monte Carlo statistical process to combine probabilistic estimates of undiscovered mineral deposits with models of mineral deposit grade and tonnage to estimate mineral resources. It is based upon a simulation program developed by Root and others (1992), who discussed many of the methods and algorithms of the program. Various versions of the original program (called "MARK3" and developed by David H. Root, William A. Scott, and Lawrence J. Drew of the USGS) have been published (Root, Scott, and Selner, 1996; Duval, 2000, 2012). The current version (3.0) of the EMINERS program is available as USGS Open-File Report 2004-1344 (Duval, 2012). Changes from version 2.0 include updating 87 grade and tonnage models, designing new templates to produce graphs showing cumulative distribution and summary tables, and disabling economic filters. The economic filters were disabled because embedded data for costs of labor and materials, mining techniques, and beneficiation methods are out of date. However, the cost algorithms used in the disabled economic filters are still in the program and available for reference for mining methods and milling techniques included in Camm (1991). EMINERS is written in C++ and depends upon the Microsoft Visual C++ 6.0 programming environment. The code depends heavily on the use of Microsoft Foundation Classes (MFC) for implementation of the Windows interface. The program works only on Microsoft Windows XP or newer personal computers. It does not work on Macintosh computers. This report demonstrates how to execute EMINERS software using default settings and existing deposit models. Many options are available when setting up the simulation. Information and explanations addressing these optional parameters can be found in the EMINERS Help files. Help files are available during execution of EMINERS by selecting EMINERS Help from the pull-down menu under Help on the EMINERS menu bar. There are four sections in this report. Part I describes the installation, setup, and application of the EMINERS program, and Part II illustrates how to interpret the text file that is produced. Part III describes the creation of tables and graphs by use of the provided Excel templates. Part IV summarizes grade and tonnage models used in version 3.0 of EMINERS.

Open-File Report↗
USGS2013

Uniform California earthquake rupture forecast, version 3 (UCERF3): the time-independent model

In this report we present the time-independent component of the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), which provides authoritative estimates of the magnitude, location, and time-averaged frequency of potentially damaging earthquakes in California. The primary achievements have been to relax fault segmentation assumptions and to include multifault ruptures, both limitations of the previous model (UCERF2). The rates of all earthquakes are solved for simultaneously, and from a broader range of data, using a system-level "grand inversion" that is both conceptually simple and extensible. The inverse problem is large and underdetermined, so a range of models is sampled using an efficient simulated annealing algorithm. The approach is more derivative than prescriptive (for example, magnitude-frequency distributions are no longer assumed), so new analysis tools were developed for exploring solutions. Epistemic uncertainties were also accounted for using 1,440 alternative logic tree branches, necessitating access to supercomputers. The most influential uncertainties include alternative deformation models (fault slip rates), a new smoothed seismicity algorithm, alternative values for the total rate of M≥5 events, and different scaling relationships, virtually all of which are new. As a notable first, three deformation models are based on kinematically consistent inversions of geodetic and geologic data, also providing slip-rate constraints on faults previously excluded because of lack of geologic data. The grand inversion constitutes a system-level framework for testing hypotheses and balancing the influence of different experts. For example, we demonstrate serious challenges with the Gutenberg-Richter hypothesis for individual faults. UCERF3 is still an approximation of the system, however, and the range of models is limited (for example, constrained to stay close to UCERF2). Nevertheless, UCERF3 removes the apparent UCERF2 overprediction of M6.5–7 earthquake rates and also includes types of multifault ruptures seen in nature. Although UCERF3 fits the data better than UCERF2 overall, there may be areas that warrant further site-specific investigation. Supporting products may be of general interest, and we list key assumptions and avenues for future model improvements.

California↗
USGS2014

The Tetracorder user guide: version 4.4

Imaging spectroscopy mapping software assists in the identification and mapping of materials based on their chemical properties as expressed in spectral measurements of a planet including the solid or liquid surface or atmosphere. Such software can be used to analyze field, aircraft, or spacecraft data; remote sensing datasets; or laboratory spectra. Tetracorder is a set of software algorithms commanded through an expert system to identify materials based on their spectra (Clark and others, 2003). Tetracorder also can be used in traditional remote sensing analyses, because some of the algorithms are a version of a matched filter. Thus, depending on the instructions fed to the Tetracorder system, results can range from simple matched filter output, to spectral feature fitting, to full identification of surface materials (within the limits of the spectral signatures of materials over the spectral range and resolution of the imaging spectroscopy data). A basic understanding of spectroscopy by the user is required for developing an optimum mapping strategy and assessing the results.

Open-File Report↗
USGS2016

The effect of suspended sediment and color on ultraviolet spectrophotometric nitrate sensors

Four commercially available ultraviolet nitrate spectrophotometric sensors were evaluated by the U.S. Geological Survey Hydrologic Instrumentation Facility (HIF) to determine the effects of suspended sediment concentration (SSC) and colored dissolved organic matter (CDOM) on sensor accuracy. The evaluated sensors were: the Hach NITRATAX plus sc (5-millimeters (mm) path length), Hach NITRATAX plus sc (2 mm), S::CAN Spectro::lyser (5 mm), and the Satlantic SUNA V2 (5 mm). A National Institute of Standards and Technology-traceable nitrate-free sediment standard was purchased and used to create the turbid environment, and an easily made filtered tea solution was used for the CDOM test. All four sensors performed well in the test that evaluated the effect of suspended sediment on accuracy. The Hach 5 mm, Hach 2 mm, and the SUNA V2 met their respective manufacturer accuracy specifications up to concentrations of 4,500 milligrams per liter (mg/L) SSC. The S::CAN failed to meet its accuracy specifications when the SSC concentrations exceeded 4,000 mg/L. Test results from the effect of CDOM on accuracy indicated a significant skewing of data from all four sensors and showed an artificial elevation of measured nitrate to varying amounts. Of the four sensors tested, the Satlantic SUNA V2’s accuracy was affected the least in the CDOM test. The nitrate concentration measured by the SUNA V2 was approximately 24 percent higher than the actual concentration when estimated total organic carbon values exceeded 44 mg/L. Measured nitrate concentration falsely increased 49 percent when measured by the Hach 5 mm, and 75 percent when measured by the Hach 2 mm. The S::CAN’s reported nitrate concentration increased 96 percent. Path length plays an important role in the sensor’s ability to compensate measurements for matrix interferences, but does not solely determine how well a sensor can handle all interferences. The sensor’s proprietary algorithms also play a key role in matrix interference compensation. The sensors’ ability to compensate for CDOM varied significantly during the tests, even among the three with 5-mm path lengths. Results of this evaluation suggest that the proprietary algorithms of the nitrate analyzers are more effective compensating for suspended sediment, and less effective compensating for CDOM (color) when sensor path length remains constant.

Open-File Report↗
USGS2016

Feasibility study of earthquake early warning (EEW) in Hawaii

The effects of earthquake shaking on the population and infrastructure across the State of Hawaii could be catastrophic, and the high seismic hazard in the region emphasizes the likelihood of such an event. Earthquake early warning (EEW) has the potential to give several seconds of warning before strong shaking starts, and thus reduce loss of life and damage to property. The two approaches to EEW are (1) a network approach (such as ShakeAlert or ElarmS) where the regional seismic network is used to detect the earthquake and distribute the alarm and (2) a local approach where a critical facility has a single seismometer (or small array) and a warning system on the premises. The network approach, also referred to here as ShakeAlert or ElarmS, uses the closest stations within a regional seismic network to detect and characterize an earthquake. Most parameters used for a network approach require observations on multiple stations (typically 3 or 4), which slows down the alarm time slightly, but the alarms are generally more reliable than with single-station EEW approaches. The network approach also benefits from having stations closer to the source of any potentially damaging earthquake, so that alarms can be sent ahead to anyone who subscribes to receive the notification. Thus, a fully implemented ShakeAlert system can provide seconds of warning for both critical facilities and general populations ahead of damaging earthquake shaking. The cost to implement and maintain a fully operational ShakeAlert system is high compared to a local approach or single-station solution, but the benefits of a ShakeAlert system would be felt statewide—the warning times for strong shaking are potentially longer for most sources at most locations. The local approach, referred to herein as “single station,” uses measurements from a single seismometer to assess whether strong earthquake shaking can be expected. Because of the reliance on a single station, false alarms are more common than when using a regional network of seismometers. Given the current network, a single-station approach provides more warning for damaging earthquakes that occur close to the station, but it would have limited benefit compared to a fully implemented ShakeAlert system. For Honolulu, for example, the single-station approach provides an advantage over ShakeAlert only for earthquakes that occur in a narrow zone extending northeast and southwest of O‘ahu. Instrumentation and alarms associated with the single-station approach are typically maintained and assessed within the target facility, and thus no outside connectivity is required. A single-station approach, then, is unlikely to help broader populations beyond the individuals at the target facility, but they have the benefit of being commercially available for relatively little cost. The USGS Hawaiian Volcano Observatory (HVO) is the Advanced National Seismic System (ANSS) regional seismic network responsible for locating and characterizing earthquakes across the State of Hawaii. During 2014 and 2015, HVO tested a network-based EEW algorithm within the current seismic network in order to assess the suitability for building a full EEW system. Using the current seismic instrumentation and processing setup at HVO, it is possible for a network approach to release an alarm a little more than 3 seconds after the earthquake is recorded on the fourth seismometer. Presently, earthquakes having M≥3 detected with the ElarmS algorithm have an average location error of approximately 4.5 km and an average magnitude error of -0.3 compared to the reviewed catalog locations from the HVO. Additional stations and upgrades to existing seismic stations would serve to improve solution precision and warning times and additional staffing would be required to provide support for a robust, network-based EEW system. For a critical facility on the Island of Hawaiʻi, such as the telescopes atop Mauna Kea, one phased approach to mitigate losses could be to immediately install a single station system to establish some level of warning. Subsequently, supporting the implementation of a full network-based EEW system on the Island of Hawaiʻi would provide additional benefit in the form of improved warning times once the system is fully installed and operational, which may take several years. Distributed populations across the Hawaiian Islands, including those outside the major cities and far from the likely earthquake source areas, would likely only benefit from a network approach such as ShakeAlert to provide warnings of strong shaking.

Hawaii↗
USGS2017

Time-causal decomposition of geomagnetic time series into secular variation, solar quiet, and disturbance signals

A theoretical basis and prototype numerical algorithm are provided that decompose regular time series of geomagnetic observations into three components: secular variation; solar quiet, and disturbance. Respectively, these three components correspond roughly to slow changes in the Earth’s internal magnetic field, periodic daily variations caused by quasi-stationary (with respect to the sun) electrical current systems in the Earth’s magnetosphere, and episodic perturbations to the geomagnetic baseline that are typically driven by fluctuations in a solar wind that interacts electromagnetically with the Earth’s magnetosphere. In contrast to similar algorithms applied to geomagnetic data in the past, this one addresses the issue of real time data acquisition directly by applying a time-causal, exponential smoother with “seasonal corrections” to the data as soon as they become available.

Open-File Report↗
USGS2017

Integrated wetland management for waterfowl and shorebirds at Mattamuskeet National Wildlife Refuge, North Carolina

Mattamuskeet National Wildlife Refuge (MNWR) offers a mix of open water, marsh, forest, and cropland habitats on 20,307 hectares in coastal North Carolina. In 1934, Federal legislation (Executive Order 6924) established MNWR to benefit wintering waterfowl and other migratory bird species. On an annual basis, the refuge staff decide how to manage 14 impoundments to benefit not only waterfowl during the nonbreeding season, but also shorebirds during fall and spring migration. In making these decisions, the challenge is to select a portfolio, or collection, of management actions for the impoundments that optimizes use by the three groups of birds while respecting budget constraints. In this study, a decision support tool was developed for these annual management decisions. Within the decision framework, there are three different management objectives: shorebird-use days during fall and spring migrations, and waterfowl-use days during the nonbreeding season. Sixteen potential management actions were identified for impoundments; each action represents a combination of hydroperiod and vegetation manipulation. Example hydroperiods include semi-permanent and seasonal drawdowns, and vegetation manipulations include mechanical-chemical treatment, burning, disking, and no action. Expert elicitation was used to build a Bayesian Belief Network (BBN) model that predicts shorebird- and waterfowl-use days for each potential management action. The BBN was parameterized for a representative impoundment, MI-9, and predictions were re-scaled for this impoundment to predict outcomes at other impoundments on the basis of size. Parameter estimates in the BBN model can be updated using observations from ongoing monitoring that is part of the Integrated Waterbird Management and Monitoring (IWMM) program. The optimal portfolio of management actions depends on the importance, that is, weights, assigned to the three objectives, as well as the budget. Five scenarios with a variety of objective weights and budgets were developed. Given the large number of possible portfolios (16 14 ), a heuristic genetic algorithm was used to identify a management action portfolio that maximized use-day objectives while respecting budget constraints. The genetic algorithm identified a portfolio of management actions for each of the five scenarios, enabling refuge staff to explore the sensitivity of their management decisions to objective weights and budget constraints. The decision framework developed here provides a transparent, defensible, and testable foundation for decision making at MNWR. The BBN model explicitly structures and parameterizes a mental model previously used by an expert to assign management actions to the impoundments. With ongoing IWMM monitoring, predictions from the model can be tested, and model parameters updated, to reflect empirical observations. This framework is intended to be a living document that can be updated to reflect changes in the decision context (for example, new objectives or constraints, or new models to compete with the current BBN model). Rather than a mandate to refuge staff, this framework is intended to be a decision support tool; tool outputs can become part of the deliberations of refuge staff when making difficult management decisions for multiple objectives.

North Carolina↗
USGS2017

Analysis of the variability in ground-motion synthesis and inversion

In almost all past inversions of large-earthquake ground motions for rupture behavior, the goal of the inversion is to find the “best fitting” rupture model that predicts ground motions which optimize some function of the difference between predicted and observed ground motions. This type of inversion was pioneered in the linear-inverse sense by Olson and Apsel (1982), who minimized the square of the difference between observed and simulated motions (“least squares”) while simultaneously minimizing the rupture-model norm (by setting the null-space component of the rupture model to zero), and has been extended in many ways, one of which is the use of nonlinear inversion schemes such as simulated annealing algorithms that optimize some other misfit function. For example, the simulated annealing algorithm of Piatanesi and others (2007) finds the rupture model that minimizes a “cost” function which combines a least-squares and a waveform-correlation measure of misfit. All such inversions that look for a unique “best” model have at least three problems. (1) They have removed the null-space component of the rupture model—that is, an infinite family of rupture models that all fit the data equally well have been narrowed down to a single model. Some property of interest in the rupture model might have been discarded in this winnowing process. (2) Smoothing constraints are commonly used to yield a unique “best” model, in which case spatially rough rupture models will have been discarded, even if they provide a good fit to the data. (3) No estimate of confidence in the resulting rupture models can be given because the effects of unknown errors in the Green’s functions (“theory errors”) have not been assessed. In inversion for rupture behavior, these theory errors are generally larger than the data errors caused by ground noise and instrumental limitations, and so overfitting of the data is probably ubiquitous for such inversions. Recently, attention has turned to the inclusion of theory errors in the inversion process. Yagi and Fukahata (2011) made an important contribution by presenting a method to estimate the uncertainties in predicted large-earthquake ground motions due to uncertainties in the Green’s functions. Here we derive their result and compare it with the results of other recent studies that look at theory errors in a Bayesian inversion context particularly those by Bodin and others (2012), Duputel and others (2012), Dettmer and others (2014), and Minson and others (2014). Notably, in all these studies, the estimates of theory error were obtained from theoretical considerations alone; none of the investigators actually measured Green’s function errors. Large earthquakes typically have aftershocks, which, if their rupture surfaces are physically small enough, can be considered point evaluations of the real Green’s functions of the Earth. Here we simulate smallaftershock ground motions with (erroneous) theoretical Green’s functions. Taking differences between aftershock ground motions and simulated motions to be the “theory error,” we derive a statistical model of the sources of discrepancies between the theoretical and real Green’s functions. We use this model with an extended frequency-domain version of the time-domain theory of Yagi and Fukahata (2011) to determine the expected variance 2 τ caused by Green’s function error in ground motions from a larger (nonpoint) earthquake that we seek to model. We also differ from the above-mentioned Bayesian inversions in our handling of the nonuniqueness problem of seismic inversion. We follow the philosophy of Segall and Du (1993), who, instead of looking for a best-fitting model, looked for slip models that answered specific questions about the earthquakes they studied. In their Bayesian inversions, they inductively derived a posterior probability-density function (PDF) for every model parameter. We instead seek to find two extremal rupture models whose ground motions fit the data within the error bounds given by 2 τ , as quantified by using a chi-squared test described below. So, we can ask questions such as, “What are the rupture models with the highest and lowest average rupture speed consistent with the theory errors?” Having found those models, we can then say with confidence that the true rupture speed is somewhere between those values. Although the Bayesian approach gives a complete solution to the inverse problem, it is computationally demanding: Minson and others (2014) needed 1010 forward kinematic simulations to derive their posterior probability distribution. In our approach, only about107 simulations are needed. Moreover, in practical application, only a small set of rupture models may be needed to answer the relevant questions—for example, determining the maximum likelihood solution (achievable through standard inversion techniques) and the two rupture models bounding some property of interest. The specific property that we wish to investigate is the correlation between various rupturemodel parameters, such as peak slip velocity and rupture velocity, in models of real earthquakes. In some simulations of ground motions for hypothetical large earthquakes, such as those by Aagaard and others (2010) and the Southern California Earthquake Center Broadband Simulation Platform (Graves and Pitarka, 2015), rupture speed is assumed to correlate locally with peak slip, although there is evidence that rupture speed should correlate better with peak slip speed, owing to its dependence on local stress drop. We may be able to determine ways to modify Piatanesi and others’s (2007) inversion’s “cost” function to find rupture models with either high or low degrees of correlation between pairs of rupture parameters. We propose a cost function designed to find these two extremal models.

Open-File Report↗
USGS2018

Research to improve ShakeAlert earthquake early warning products and their utility

Earthquake early warning (EEW) is the rapid detection of an earthquake and issuance of an alert or notification to people and vulnerable systems likely to experience potentially damaging ground shaking. The level of ground shaking that is considered damaging is defined by the specific application; for example, manufacturing equipment may experience damage at a lower intensity ground shaking than would cause damage to a building. Along the West Coast of the United States, the warning times for ground shaking could range as high as tens of seconds for moderate levels of ground shaking, or potentially longer, if a lower ground-shaking threshold is used to issue alerts. However, it is not always possible to provide advance warning of ground shaking, particularly for locations close to an earthquake that are most likely to experience very strong ground shaking. EEW alerts may be useful to individuals who can use a few seconds to move to a safe zone and to electromechanical systems that can take automatic actions to reduce damage and injuries. An EEW system, ShakeAlert, has been under development in the United States since 2006. Federal and State governments, as well as the private sector, are now investing in the ShakeAlert prototype system that will, when completed, become an operational public system for the West Coast of the United States. While the current prototype is delivering alerts to test users, improvements to the accuracy, timeliness, and utility of the alerts are needed. For this reason, it is essential that the ShakeAlert system be continuously improved through targeted research, involving not only the current ShakeAlert partner organizations, but also the broader scientific, engineering, and emergencyresponse communities. To this end, this report describes the opportunities for improvement that can be addressed through research and development over the next 5 years. Our recommendations are organized into four areas: (1) understand EEW capabilities and user needs, (2) make alerts as fast and accurate as possible, (3) ensure reliability when it counts, and (4) explore the use of new instrumentation. The first challenge is to understand EEW capabilities and user needs. EEW must deliver actionable information to people and to automated systems to mitigate short- and longterm impacts of damaging ground shaking, so development of EEW must be motivated by the needs of users. Within this challenge, we must study the technical capabilities and limitations of EEW in general, and the ShakeAlert system specifically. This includes development of performance metrics that assess the timeliness and accuracy of alerts to understand the value and utility of the ShakeAlert EEW product(s) for various user groups, including different industry sectors, emergency-management agencies, and the public. Research is needed to define the alerting choices that maximize the utility of the system for users and to determine what the available communication pathways are for providing timely alert information. Additionally, we engage users to assess how alerts will be used by different sectors to mitigate losses and to inform EEW product design. Further, social-science research is needed to develop alert messaging, including what relevant prior and follow-up information are required, to ensure effective use of alerts. The second challenge is to make alerts as fast and as accurate as possible. The timeliness and accuracy of an EEW alert is important because it will set in motion a series of actions and downstream products. An EEW alert will trigger notification across emergency-alert systems and across multiple communication channels to populations in impacted regions. The EEW alert region may grow as the earthquake fault-rupture length increases, and the EEW system’s characterization of it, evolves. We must continue research into new or improved seismic and geodetic waveform-processing methods necessary to rapidly characterize the expected ground shaking and associated uncertainties. It is important to thoroughly evaluate whether new methods improve alerts through more accurate ground-motion estimates and (or) reduced latencies (that is, longer warning times). New methods could include tracking the extent of a large rupture in real time (known as finite-fault algorithms) and ground-motionbased EEW algorithms. Additionally, ground motion predictions could be optimized for each earthquake as the earthquake fault rupture progresses by using, for example, event terms to shift ground-motion curves for more (or less) energetic ruptures. The third challenge is to ensure reliability when it counts. This challenge requires us to explore approaches that assess the expected performance of ShakeAlert across the range of earthquake magnitudes, locations, and depths that may occur within the alerting region. Large, damaging earthquakes and their associated aftershock sequences matter most for hazard and for EEW, but these large-earthquake sequences occur infrequently. We expect ShakeAlert to respond robustly to these large-earthquake sequences despite potentially long periods of relative seismic quiescence in the intervening years, and in spite of inevitable communication challenges that arise during and after a large earthquake. We must develop methods to utilize the broadest available datasets to test EEW performance, including ground-motion data recorded in other parts of the world. The observational period for large, damaging earthquakes in any particular region has been short in comparison to estimated large-earthquake recurrence times. Ground-motion records for very large, damaging western United States events and major aftershock sequences do not yet exist, nor do data exist for all potential sources of noise and spurious signals that ShakeAlert must be “tuned” to reject. In addition, robust synthetic data could provide the flexibility to test a wider range of earthquake magnitude, tectonic-setting, and noise scenarios than are covered by existing observational data. Synthetic ground-motion data must be thoroughly vetted against records of smaller magnitude earthquakes to ensure that they accurately capture both the onset and the amplitude of the ground shaking. The final challenge is to explore the use of new instrumentation. The development of EEW around the world to date has focused on the use of high-quality, scientific-grade seismic and geodetic instrumentation. The use of additional types of instrumentation or information may also improve EEW products by filling gaps in sensor coverage in countries that already have dense seismic networks or enable EEW in countries without such networks. We must keep up with these developments and continuously assess their value in supplementing existing EEW systems, such as ShakeAlert, or enabling EEW where such systems do not exist. Such developments include low-cost instrumentation with microelectromechanical system (MEMS) sensors and global positioning system (GPS)/global navigation satellite system (GNSS) antennas embedded in low-cost consumer electronics, sea-floor seismometers, geodetic instrumentation deployed along the Cascadia and Alaska megathrust margins of western North America, and borehole strainmeters that are already deployed across the region.

Open-File Report↗
USGS2020

Forecasting future beach width- A case study along the Florida Atlantic coast

Historical cross-shore positions of the shoreline and dune base were used as inputs for a Kalman filter algorithm to forecast the positions of these features in the year 2028. The beach width was also computed as the cross-shore distance between the forecasted 2028 shoreline and dune-base positions. While it does not evaluate the suitability of a nesting beach or identify optimal nesting habitat, the beach width can be used as a proxy for habitat availability. An analysis was conducted along the Florida Atlantic coast with an initial goal of demonstrating a method that combines available data for shoreline and dune positions with a Kalman Filter algorithm developed to predict decadal-scale shoreline evolution and then uses these features to define future beach width. This section of the southeastern United States hosts the largest assemblage of nesting loggerhead sea turtles ( Caretta caretta ) in the world, in addition to other species, and critical habitat is designated as part of the species’ listing package under the Endangered Species Act of 1973 (16 U.S.C. ch. 35 § 1531 et seq) for most of the nesting beaches within the study area. This work introduces an approach to inform ecosystem services assessments using data typically derived for shoreline change and storm vulnerability models.

Florida↗
USGS2020

Adjusted geomagnetic data—Theoretical basis and validation

Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are “ground truth” observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce “Definitive” data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an “Adjusted” geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.

Open-File Report↗
USGS1989

A general method for generating bathymetric data for hydrodynamic computer models

To generate water depth data from randomly distributed bathymetric data for numerical hydrodymamic models, raw input data from field surveys, water depth data digitized from nautical charts, or a combination of the two are sorted to given an ordered data set on which a search algorithm is used to isolate data for interpolation. Water depths at locations required by hydrodynamic models are interpolated from the bathymetric data base using linear or cubic shape functions used in the finite-element method. The bathymetric database organization and preprocessing, the search algorithm used in finding the bounding points for interpolation, the mathematics of the interpolation formulae, and the features of the automatic generation of water depths at hydrodynamic model grid points are included in the analysis. This report includes documentation of two computer programs which are used to: (1) organize the input bathymetric data; and (2) to interpolate depths for hydrodynamic models. An example of computer program operation is drawn from a realistic application to the San Francisco Bay estuarine system. (Author 's abstract)

Open-File Report↗
USGS2010

A two-step procedure for calculating earthquake hypocenters at Augustine Volcano: Chapter 7 in The 2006 Eruption of Augustine Volcano, Alaska

This chapter describes a two-step technique for determining earthquake hypocenters at Augustine Volcano. The algorithm, which was originally developed in the mid-1970s, was designed both to overcome limitations in the standard earthquake-location programs available at the time and to take advantage of the detailed seismic-velocity information obtained at Augustine Volcano. Hypocenters are calculated on the basis of a two-dimensional (2D) ray-tracing procedure that accounts for in plane lateral discontinuities within the seismic velocity structure. This algorithm calculates the minimum P- and S-wave travel time between theoretical grid points embedded in the velocity structure to each station in the seismic network. Station corrections that account for the differences between the model and actual velocity structure are derived from a time-term analysis of the 1975 active-source seismic experiment. Each relocated hypocenter is assigned to the grid point with the lowest rms residual between observed and calculated arrival times. Statistical techniques are used to assess the effect of random errors in P-wave-arrival determination on hypocentral location. These tests suggest that the 2D ray-tracing procedure presented here is able to resolve earthquake hypocenter depths to within 0.25 km between the volcano's summit and sea level and within 0.5 km from sea level to depths of 2 km below sea level.

Alaska↗
USGS2009

A Tidally Averaged Sediment-Transport Model for San Francisco Bay, California

A tidally averaged sediment-transport model of San Francisco Bay was incorporated into a tidally averaged salinity box model previously developed and calibrated using salinity, a conservative tracer (Uncles and Peterson, 1995; Knowles, 1996). The Bay is represented in the model by 50 segments composed of two layers: one representing the channel (>5-meter depth) and the other the shallows (0- to 5-meter depth). Calculations are made using a daily time step and simulations can be made on the decadal time scale. The sediment-transport model includes an erosion-deposition algorithm, a bed-sediment algorithm, and sediment boundary conditions. Erosion and deposition of bed sediments are calculated explicitly, and suspended sediment is transported by implicitly solving the advection-dispersion equation. The bed-sediment model simulates the increase in bed strength with depth, owing to consolidation of fine sediments that make up San Francisco Bay mud. The model is calibrated to either net sedimentation calculated from bathymetric-change data or measured suspended-sediment concentration. Specified boundary conditions are the tributary fluxes of suspended sediment and suspended-sediment concentration in the Pacific Ocean. Results of model calibration and validation show that the model simulates the trends in suspended-sediment concentration associated with tidal fluctuations, residual velocity, and wind stress well, although the spring neap tidal suspended-sediment concentration variability was consistently underestimated. Model validation also showed poor simulation of seasonal sediment pulses from the Sacramento-San Joaquin River Delta at Point San Pablo because the pulses enter the Bay over only a few days and the fate of the pulses is determined by intra-tidal deposition and resuspension that are not included in this tidally averaged model. The model was calibrated to net-basin sedimentation to calculate budgets of sediment and sediment-associated contaminants. While simulated net sedimentation in the four basins that comprise San Francisco Bay was correct, the simulations incorrectly eroded shallows while channels deposited because model surface-layer boxes span both shallows and channels, and neglect lateral variability of suspended-sediment concentration. Validation with recent (1983-2005) net sedimentation in South San Francisco Bay was poor, perhaps owing to poorly quantified sediment supply, and to invasive species that altered erosion and deposition processes. This demonstrates that deterministically predicting future sedimentation is difficult in this or any estuary for which boundary conditions are not stationary. The model would best be used as a tool for developing past and present sediment budgets, and for creating scenarios of future sedimentation that are compared to one another rather than considered a deterministic prediction.

Scientific Investigations Report↗
USGS2010

Empirical models of wind conditions on Upper Klamath Lake, Oregon

Upper Klamath Lake is a large (230 square kilometers), shallow (mean depth 2.8 meters at full pool) lake in southern Oregon. Lake circulation patterns are driven largely by wind, and the resulting currents affect the water quality and ecology of the lake. To support hydrodynamic modeling of the lake and statistical investigations of the relation between wind and lake water-quality measurements, the U.S. Geological Survey has monitored wind conditions along the lakeshore and at floating raft sites in the middle of the lake since 2005. In order to make the existing wind archive more useful, this report summarizes the development of empirical wind models that serve two purposes: (1) to fill short (on the order of hours or days) wind data gaps at raft sites in the middle of the lake, and (2) to reconstruct, on a daily basis, over periods of months to years, historical wind conditions at U.S. Geological Survey sites prior to 2005. Empirical wind models based on Artificial Neural Network (ANN) and Multivariate-Adaptive Regressive Splines (MARS) algorithms were compared. ANNs were better suited to simulating the 10-minute wind data that are the dependent variables of the gap-filling models, but the simpler MARS algorithm may be adequate to accurately simulate the daily wind data that are the dependent variables of the historical wind models. To further test the accuracy of the gap-filling models, the resulting simulated winds were used to force the hydrodynamic model of the lake, and the resulting simulated currents were compared to measurements from an acoustic Doppler current profiler. The error statistics indicated that the simulation of currents was degraded as compared to when the model was forced with observed winds, but probably is adequate for short gaps in the data of a few days or less. Transport seems to be less affected by the use of the simulated winds in place of observed winds. The simulated tracer concentration was similar between model results when simulated winds were used to force the model, and when observed winds were used to force the model, and differences between the two results did not accumulate over time.

Scientific Investigations Report↗
USGS2012

Development of regional skews for selected flood durations for the Central Valley Region, California, based on data through water year 2008

Flood-frequency information is important in the Central Valley region of California because of the high risk of catastrophic flooding. Most traditional flood-frequency studies focus on peak flows, but for the assessment of the adequacy of reservoirs, levees, other flood control structures, sustained flood flow (flood duration) frequency data are needed. This study focuses on rainfall or rain-on-snow floods, rather than the annual maximum, because rain events produce the largest floods in the region. A key to estimating flood-duration frequency is determining the regional skew for such data. Of the 50 sites used in this study to determine regional skew, 28 sites were considered to have little to no significant regulated flows, and for the 22 sites considered significantly regulated, unregulated daily flow data were synthesized by using reservoir storage changes and diversion records. The unregulated, annual maximum rainfall flood flows for selected durations (1-day, 3-day, 7-day, 15-day, and 30-day) for all 50 sites were furnished by the U.S. Army Corps of Engineers. Station skew was determined by using the expected moments algorithm program for fitting the Pearson Type 3 flood-frequency distribution to the logarithms of annual flood-duration data. Bayesian generalized least squares regression procedures used in earlier studies were modified to address problems caused by large cross correlations among concurrent rainfall floods in California and to address the extensive censoring of low outliers at some sites, by using the new expected moments algorithm for fitting the LP3 distribution to rainfall flood-duration data. To properly account for these problems and to develop suitable regional-skew regression models and regression diagnostics, a combination of ordinary least squares, weighted least squares, and Bayesian generalized least squares regressions were adopted. This new methodology determined that a nonlinear model relating regional skew to mean basin elevation was the best model for each flood duration. The regional-skew values ranged from -0.74 for a flood duration of 1-day and a mean basin elevation less than 2,500 feet to values near 0 for a flood duration of 7-days and a mean basin elevation greater than 4,500 feet. This relation between skew and elevation reflects the interaction of snow and rain, which increases with increased elevation. The regional skews are more accurate, and the mean squared errors are less than in the Interagency Advisory Committee on Water Data's National skew map of Bulletin 17B.

California↗
USGS2018

Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015

This report documents the methods for peak-flow frequency (hereinafter “frequency”) analysis and reporting for streamgages in and near Montana following implementation of the Bulletin 17C guidelines. The methods are used to provide estimates of peak-flow quantiles for 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for selected streamgages operated by the U.S. Geological Survey Wyoming-Montana Water Science Center (WY–MT WSC). These annual exceedance probabilities correspond to 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively. Standard procedures specific to the WY–MT WSC for implementing the Bulletin 17C guidelines include (1) the use of the Expected Moments Algorithm analysis for fitting the log-Pearson Type III distribution, incorporating historical information where applicable; (2) the use of weighted skew coefficients (based on weighting at-site station skew coefficients with generalized skew coefficients from the Bulletin 17B national skew map); and (3) the use of the Multiple Grubbs-Beck Test for identifying potentially influential low flows. For some streamgages, the peak-flow records are not well represented by the standard procedures and require user-specified adjustments informed by hydrologic judgement. The specific characteristics of peak-flow records addressed by the informed-user adjustments include (1) regulated peak-flow records, (2) atypical upper-tail peak-flow records, and (3) atypical lower-tail peak-flow records. In all cases, the informed-user adjustments use the Expected Moments Algorithm fit of the log-Pearson Type III distribution using the at-site station skew coefficient, a manual potentially influential low flow threshold, or both. Appropriate methods can be applied to at-site frequency estimates to provide improved representation of long-term hydroclimatic conditions. The methods for improving at-site frequency estimates by weighting with regional regression equations and by Maintenance of Variance Extension Type III record extension are described. Frequency analyses were conducted for 99 example streamgages to indicate various aspects of the frequency- analysis methods described in this report. The frequency analyses and results for the example streamgages are presented in a separate data release associated with this report consisting of tables and graphical plots that are structured to include information concerning the interpretive decisions involved in the frequency analyses. Further, the separate data release includes the input files to the PeakFQ program, version 7.1, including the peak-flow data file and the analysis specification file that were used in the peak-flow frequency analyses. Peak-flow frequencies are also reported in separate data releases for selected streamgages in the Beaverhead River and Clark Fork Basins and also for selected streamgages in the Ruby, Jefferson, and Madison River Basins.

Montana↗
USGS2021

Optimization of the Idaho National Laboratory water-quality aquifer monitoring network, southeastern Idaho

Long-term monitoring of water-quality data collected from wells at the Idaho National Laboratory (INL) has provided essential information for delineating the movement of radiochemical and chemical wastes in the eastern Snake River Plain aquifer, southeastern Idaho. Since 1949, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, has maintained as many as 200 wells in the INL water-quality monitoring network. A network design tool, distributed as an R package, was developed to evaluate and optimize groundwater monitoring in the existing network based on water-quality data collected at 153 sampling sites since January 1, 1989. The objective of the optimization design tool is to reduce well monitoring redundancy while retaining sufficient data to reliably characterize water-quality conditions in the aquifer. A spatial optimization was used to identify a set of wells whose removal leads to the smallest increase in the deviation between interpolated concentration maps using the existing and reduced monitoring networks while preserving significant long-term trends and seasonal components in the data. Additionally, a temporal optimization was used to identify reductions in sampling frequencies by minimizing the redundancy in sampling events. Spatial optimization uses an islands genetic algorithm to identify near-optimal network designs removing 10, 20, 30, 40, and 50 wells from the existing monitoring network. With this method, choosing a greater number of wells to remove results in greater cost savings and decreased accuracy of the average relative difference between interpolated maps of the reduced-dataset and the full-dataset. The genetic search algorithm identified reduced networks that best capture the spatial patterns of the average concentration plume while preserving long-term temporal trends at individual wells. Concentration data for 10 analyte types are integrated in a single optimization so that all datasets may be evaluated simultaneously. A constituent was selected for inclusion in the spatial optimization problem when the observations were sufficient to (1) establish a two-range variability model, (2) classify at least one concentration time series as a continuous record block, and (3) make a prediction using the quantile-kriging interpolation method. The selected constituents include sodium, chloride, sulfate, nitrate, carbon tetrachloride, 1,1-dichloroethylene, 1,1,1-trichloroethane, trichloroethylene, tritium, strontium-90, and plutonium-238. In temporal optimization, an iterative-thinning method was used to find an optimal sampling frequency for each analyte-well pair. Optimal frequencies indicate that for many of the wells, samples may be collected less frequently and still be able to characterize the concentration over time. The optimization results indicated that the sample-collection interval may be increased by an of average of 273 days owing to temporal redundancy.

Idaho↗