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At least 1,441 records · Page 80Linked to original sources
USGS2017

Highlighting the complexities of a groundwater pilot study during an avian influenza outbreak: Methods, lessons learned, and select contaminant results

The highly pathogenic avian influenza (H5N2) outbreak in the Midwestern United States (US) in 2015 was historic due to the number of birds and poultry operations impacted and the corresponding economic loss to the poultry industry and was the largest animal health emergency in US history. The U.S. Geological Survey (USGS), with the assistance of several state and federal agencies, aided the response to the outbreak by developing a study to determine the extent of virus transport in the environment. The study goals were to: develop the appropriate sampling methods and protocols for measuring avian influenza virus (AIV) in groundwater, provide the first baseline data on AIV and outbreak- and poultry-related contaminant occurrence and movement into groundwater, and document climatological factors that may have affected both survival and transport of AIV to groundwater during the months of the 2015 outbreak. While site selection was expedient, there were often delays in sample response times due to both relationship building between agencies, groups, and producers and logistical time constraints. This study's design and sampling process highlights the unpredictable nature of disease outbreaks and the corresponding difficulty in environmental sampling of such events. The lessons learned, including field protocols and approaches, can be used to improve future research on AIV in the environment.

Iowa↗
USGS2023

A residual oil zone (ROZ) assessment methodology with application to the central basin platform (Permian Basin, USA) for enhanced oil recovery (EOR) and long-term geologic CO2 storage

Residual oil zones (ROZ) form due to various geologic conditions and are located below the oil/water contact (OWC) of main pay zones (MPZ). Since ROZs usually contain immobile oil, they have not typically been considered commercially attractive for development by conventional primary recovery methods used in the initial phases of oil production. However, during the last decade some operators of these viable fields that also contain ROZs have extended carbon dioxide enhanced oil recovery (CO 2 -EOR) to below the MPZ to commercially recover oil from the associated ROZ. Increased interest in ROZs is also due to the application of anthropogenic CO 2 for oil recovery, leading to the subsurface sequestration of CO 2 , which can be part of the current net-zero carbon oil and climate change objectives. Several detailed studies of selected formations in the Permian Basin of the United States have shown that ROZs can be as common as traditional conventional oil reservoir traps, suggesting significant resources for potential additional hydrocarbon recovery and subsurface CO 2 sequestration via CO 2 -EOR. However, applications of CO 2 -EOR to ROZs have been limited despite the estimation of significant oil resources considered recoverable through CO 2 -EOR, and the benefit of concurrent geologic CO 2 storage that would help offset carbon emissions from the produced oil. The combination of insufficient economic incentives and technical reasons related to data scarcity, such as lack of penetration of wells and well logs, for locating ROZs has limited development of their resource potential when compared to known fields. This paper presents a probabilistic methodology for identifying and evaluating ROZ resources for CO 2 -EOR and CO 2 sequestration potential with the use of public and proprietary data sources. The methodology was developed during a pilot study that focused on the ROZ in the San Andres Formation of a nine-county area in the Permian Basin in West Texas. The pilot study estimated a mean oil in place of 25 × 10 9 barrels (bbl) of oil and a mean potential incremental oil recovery and CO 2 utilization of 2.6 × 10 9 bbl and 28.2 Tcf (1.46 × 10 9 tons), respectively, with 1 hydrocarbon pore volume (HCPV) of injection using the water alternating gas (WAG) method. The results of this pilot study are consistent with reported volumes in the literature for a similar area in the Permian Basin. The pilot study demonstrated that this methodology could be used to identify and assess the recoverable oil and coincident CO 2 storage volumes of ROZs in other formations and regions.

Texas↗
USGS2019

Precision of VS30 values derived from noninvasive surface wave methods at 31 sites in California

We study the inter- and intra-method variability of VS30 results by inverting/forward-modeling individual dispersion data for 31 seismographic stations located in California where combinations of surface-wave methods were applied and the minimum recorded wavelength from each method satisfies the 30-meter depth criteria. These methods consist of noninvasive geophysical (active and passive surface-wave techniques) multi-station approaches, including the Multi-channel Analysis of Surface Waves (MASW; Rayleigh and Love waves), Spectral Analysis of Surface Waves (SASW), Microtremor Array [using Extended Spatial Autocorrelation (ESAC) processing methods], and Refraction Microtremor (ReMi) methods. Depending on the apparent geologic or seismic complexity of the site, field crews applied one or a combination of these methods whenever economically feasible to estimate the one-dimensional shear-wave velocity (VS) profile and calculate VS30, the time-averaged VS to a depth of 30 m. For each of the 31 sites, we find both types of variability in VS30 estimates generally remain insignificant (arithmetic mean of 5 % difference). We also find similar results (3 %) when we evaluate individual-method based VS30 estimates against composite-method based estimates. We note that VS30 values vary insignificantly when using a combination of complementary methods, e.g., active MASW data combined with passive MAM data, and that the most reliable results are also based on close fitting of the theoretical dispersion data to the representative (experimental) dispersion data.

California↗
USGS2023

The 3D Elevation Program—Supporting Montana’s economy

Introduction Montana, America’s fourth largest State with an area of 147,040 square miles, is defined by its diverse terrain. The western two-fifths of the State falls within the Rocky Mountains and the eastern three-fifths is in the Great Plains. Because of its location along the Continental Divide, the rivers in Montana drain into either the Pacific Ocean or the Gulf of Mexico. Montana is often called the Treasure State due to its mineral wealth, which includes oil, gas, and coal, but the State’s primary economic activity is agriculture. Other economic activities include natural resources conservation, water supply and quality, infrastructure and construction management, flood risk management, and geologic resource assessment and hazard mitigation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Montana↗
USGS2003

Economic drivers of mineral supply

The debate over the adequacy of future supplies of mineral resources continues in light of the growing use of mineral-based materials in the United States. According to the U.S. Geological Survey, the quantity of new materials utilized each year has dramatically increased from 161 million tons2 in 1900 to 3.2 billion tons in 2000. Of all the materials used during the 20th century in the United States, more than half were used in the last 25 years. With the Earth?s endowment of natural resources remaining constant, and increased demand for resources, economic theory states that as depletion approaches, prices rise. This study shows that many economic drivers (conditions that create an economic incentive for producers to act in a particular way) such as the impact of globalization, technological improvements, productivity increases, and efficient materials usage are at work simultaneously to impact minerals markets and supply. As a result of these economic drivers, the historical price trend of mineral prices3 in constant dollars has declined as demand has risen. When price is measured by the cost in human effort, the price trend also has been almost steadily downward. Although the United States economy continues its increasing mineral consumption trend, the supply of minerals has been able to keep pace. This study shows that in general supply has grown faster than demand, causing a declining trend in mineral prices.

Open-File Report↗
USGS1999

Earthquake probabilities in the San Francisco Bay region: 2000 to 2030 - A summary of findings

The San Francisco Bay region sits astride a dangerous “earthquake machine,” the tectonic boundary between the Pacific and North American Plates. The region has experienced major and destructive earthquakes in 1838, 1868, 1906, and 1989, and future large earthquakes are a certainty. The ability to prepare for large earthquakes is critical to saving lives and reducing damage to property and infrastructure. An increased understanding of the timing, size, location, and effects of these likely earthquakes is a necessary component in any effective program of preparedness. This study reports on the probabilities of occurrence of major earthquakes in the San Francisco Bay region (SFBR) for the three decades 2000 to 2030. The SFBR extends from Healdsberg on the northwest to Salinas on the southeast and encloses the entire metropolitan area, including its most rapidly expanding urban and suburban areas. In this study a “major” earthquake is defined as one with M≥6.7 (where M is moment magnitude). As experience from the Northridge, California (M6.7, 1994) and Kobe, Japan (M6.9, 1995) earthquakes has shown us, earthquakes of this size can have a disastrous impact on the social and economic fabric of densely urbanized areas. To reevaluate the probability of large earthquakes striking the SFBR, the U.S. Geological Survey solicited data, interpretations, and analyses from dozens of scientists representing a wide crosssection of the Earth-science community (Appendix A). The primary approach of this new Working Group (WG99) was to develop a comprehensive, regional model for the long-term occurrence of earthquakes, founded on geologic and geophysical observations and constrained by plate tectonics. The model considers a broad range of observations and their possible interpretations. Using this model, we estimate the rates of occurrence of earthquakes and 30-year earthquake probabilities. Our study considers a range of magnitudes for earthquakes on the major faults in the region—an innovation over previous studies of the SFBR that considered only a small number of potential earthquakes of fixed magnitude.

California↗
USGS2011

U.S. Geological Survey circum-arctic resource appraisal

Among the greatest uncertainties in future energy supply is the amount of oil and gas yet to be found in the Arctic. Using a probabilistic geology-based methodology, the U.S. Geological Survey has assessed the area north of the Arctic Circle. The Circum-Arctic Resource Appraisal (CARA) consists of three parts: (1) Mapping the sedimentary sequences of the Arctic (Grantz and others 2009), (2) Geologically based estimation of undiscovered technically recoverable petroleum (Gautier and others 2009, discussed in this presentation) and (3) Economic appraisal of the cost of delivering the undiscovered resources to major markets (also reported at this conference by White and others). We estimate that about 30% of the world's undiscovered gas and about 13% of the world's undiscovered oil may be present in the Arctic, mostly offshore under less than 500m of water. Billion BOE-plus accumulations of gas and oil are predicted at a 50% probability in the Kara Sea, Barents Sea, offshore East and West Greenland, Canada, and Alaska. On a BOE basis, undiscovered natural gas is three times more abundant than oil in the Arctic and is concentrated in Russian territory. Oil resources, while critically important to the interests of Arctic countries, are probably not sufficient to significantly shift the current geographic patterns of world oil production. Copyright 2011, Offshore Technology Conference.

Conference Paper↗
USGS2005

Guiding principles of USGS methodology for assessment of undiscovered conventional oil and gas resources

During the last 30 years, the methodology for assessment of undiscovered conventional oil and gas resources used by the Geological Survey has undergone considerable change. This evolution has been based on five major principles. First, the U.S. Geological Survey has responsibility for a wide range of U.S. and world assessments and requires a robust methodology suitable for immaturely explored as well as maturely explored areas. Second, the assessments should be based on as comprehensive a set of geological and exploration history data as possible. Third, the perils of methods that solely use statistical methods without geological analysis are recognized. Fourth, the methodology and course of the assessment should be documented as transparently as possible, within the limits imposed by the inevitable use of subjective judgement. Fifth, the multiple uses of the assessments require a continuing effort to provide the documentation in such ways as to increase utility to the many types of users. Undiscovered conventional oil and gas resources are those recoverable volumes in undiscovered, discrete, conventional structural or stratigraphic traps. The USGS 2000 methodology for these resources is based on a framework of assessing numbers and sizes of undiscovered oil and gas accumulations and the associated risks. The input is standardized on a form termed the Seventh Approximation Data Form for Conventional Assessment Units. Volumes of resource are then calculated using a Monte Carlo program named Emc2, but an alternative analytic (non-Monte Carlo) program named ASSESS also can be used. The resource assessment methodology continues to change. Accumulation-size distributions are being examined to determine how sensitive the results are to size-distribution assumptions. The resource assessment output is changing to provide better applicability for economic analysis. The separate methodology for assessing continuous (unconventional) resources also has been evolving. Further studies of the relationship between geologic models of conventional and continuous resources will likely impact the respective resource assessment methodologies. ?? 2005 International Association for Mathematical Geology.

Conference Paper↗
USGS2019

Employing an ecosystem services framework to deliver decision ready science

Public land managers have limited information to allow for the integration and balancing of multiple objectives in land management decisions including the social (cultural and health), economic (monetary and nonmonetary), and environmental aspects. In this article, we document an approach to consider the many facets of decision making by incorporating them into a decision context using an ecosystem services framework. This analysis is based on a multi-partner project led by the US Geological Survey and the US Fish and Wildlife Service to provide land management decision support for the Great Dismal Swamp National Wildlife Refuge. It is an integrated ecologic-economic analysis of baseline (current) and potential future quantities, qualities, and values of selected ecosystem services from the Refuge. Alternative management scenarios are modeled to consider the impact of specific management actions or natural disturbances on priority ecosystem services. We examine the benefits and challenges of using this framework. Key lessons learned from this effort include the mismatch in timing between physical and social science; the challenge of integrating methods from multiple disciplines; the importance of frequent communication to overcome siloed research; and the utility of an integrating framework for ecosystem services and supporting tools such as the dynamic ecosystem model.

Advances in Ecological Research↗
USGS2011

Summaries of important areas for mineral investment and production opportunities of nonfuel minerals in Afghanistan

The U.S. Geological Survey (USGS) and the U.S. Department of Defense Task Force for Business and Stability Operations (TFBSO) entered into an agreement with the Afghanistan Geological Survey to study and assess the fuel and nonfuel mineral resources of Afghanistan from October 2009 to September 2011 so that these resources could be economically extracted to expand the economy of Afghanistan. This report summarizes the results of joint studies on 24 important areas of interest (AOIs) of nonfuel mineral resources that were identified for mineral investment and production opportunities in Afghanistan. This report is supported by digital data and archival and non-USGS reports on each AOI, and these data are available from the Afghanistan Geological Survey Data Center in Kabul (http://mom.gov.af/en/ and http://www.bgs.ac.uk/afghanminerals/) and for viewing and download on the USGS public Web site and in a separate viewer at http://mapdss2.er.usgs.gov/.

Open-File Report↗
USGS2012

Natural hazards science strategy

The mission of the U.S. Geological Survey (USGS) in natural hazards is to develop and apply hazard science to help protect the safety, security, and economic well-being of the Nation. The costs and consequences of natural hazards can be enormous, and each year more people and infrastructure are at risk. USGS scientific research—founded on detailed observations and improved understanding of the responsible physical processes—can help to understand and reduce natural hazard risks and to make and effectively communicate reliable statements about hazard characteristics, such as frequency, magnitude, extent, onset, consequences, and where possible, the time of future events. To accomplish its broad hazard mission, the USGS maintains an expert workforce of scientists and technicians in the earth sciences, hydrology, biology, geography, social and behavioral sciences, and other fields, and engages cooperatively with numerous agencies, research institutions, and organizations in the public and private sectors, across the Nation and around the world. The scientific expertise required to accomplish the USGS mission in natural hazards includes a wide range of disciplines that this report refers to, in aggregate, as hazard science. In October 2010, the Natural Hazards Science Strategy Planning Team (H–SSPT) was charged with developing a long-term (10-year) Science Strategy for the USGS mission in natural hazards. This report fulfills that charge, with a document hereinafter referred to as the Strategy, to provide scientific observations, analyses, and research that are critical for the Nation to become more resilient to natural hazards. Science provides the information that decisionmakers need to determine whether risk management activities are worthwhile. Moreover, as the agency with the perspective of geologic time, the USGS is uniquely positioned to extend the collective experience of society to prepare for events outside current memory. The USGS has critical statutory and nonstatutory roles regarding floods, earthquakes, tsunamis, landslides, coastal erosion, volcanic eruptions, wildfires, and magnetic storms—the hazards considered in this plan. There are numerous other hazards of societal importance that are considered either only peripherally or not at all in this Strategy because they are either in another of the USGS strategic science plans (such as drought) or not in the overall mission of the USGS (such as tornados).

Open-File Report↗
USGS1977

Conversion factors: SI metric and U.S. customary units

The policy of the U.S. Geological Survey is to foster use of the International System of Units (SI) which was defined by the 11th General Conference of Weights and Measures in 1960. This modernized metric system constitutes an international "language" by means of which communications throughout the world's scientific and economic communities may be improved. This publication is designed to familiarize the reader with the SI units of measurement that correspond to the common units frequently used in programs of the Geological Survey. In the near future, SI units will be used exclusively in most publications of the Survey; the conversion factors provided herein will help readers to obtain a "feel" for each unit and to "think metric."

General Information Product↗
USGS2021

Initial estimates of net infiltration and irrigation from a soil-water-balance model of the Mississippi Embayment Regional Aquifer Study Area

The Mississippi embayment encompasses about 100,000 square miles and covers parts of eight States. In 2016, the U.S. Geological Survey began updating previous work for a part of the embayment known as the Mississippi Alluvial Plain to support informed water use and agricultural policy in the region. Groundwater, water use, economic, and other related models are being combined with field surveys and observations to create a quantitative framework for evaluating regional groundwater withdrawals and their effects on long-term water availability in the Mississippi Alluvial Plain. As part of this effort, the U.S. Geological Survey’s Soil-Water-Balance code (version 2.0) is being used to model potential groundwater recharge and irrigation water use, as necessary inputs to the long-term groundwater modeling efforts. The Soil-Water-Balance code is designed to estimate the distribution and timing of net infiltration leaving the root zone. Soil-Water-Balance makes use of gridded datasets of elevation, soils, land use (including specific crop types), and daily weather datasets to calculate other components of the root-zone water balance, including soil moisture, reference, actual evapotranspiration, snowfall, snowmelt, and canopy interception. Parameters on plant height and growing-season water needs are used to estimate crop-water demand and potential irrigation water use. This report documents the initial construction, calibration, and application of a Soil-Water-Balance model of the Mississippi Embayment Regional Aquifer Study area for simulations running from 1915 to 2017. Further refinements of the model calibration for an expanded model area are planned.

Alabama, Arkansas, Illinois, Kentucky, Louisiana, ↗
USGS2025

U.S. Geological Survey science strategy to address highly pathogenic avian influenza and its effects on wildlife health 2025–29

Executive Summary Highly pathogenic avian influenza (HPAI) is an ecologically and economically important animal disease that can also directly affect humans (a “zoonotic” disease). HPAI was once limited almost exclusively to domestic poultry but has rapidly adapted to diverse animal hosts. Viruses causing HPAI now appear to be maintained and dispersed by wild birds largely independent of poultry, though HPAI continues to cause considerable economic losses and supply chain disruptions in the domestic poultry trade. Coincident with the adaptation of HPAI viruses to wild birds, particularly waterfowl and gulls, increasingly diverse wild bird hosts are becoming exposed to HPAI, often resulting in disease and death. More sporadically, HPAI has caused mass mortality events, particularly among seabirds. Furthermore, viral spillover to wild and domestic mammals has become more common. Spillover to wild mammals has resulted in mortality among diverse terrestrial and marine taxa, including episodic losses of such scale as to represent potential conservation challenges. Since approximately March 2024, HPAI has also affected dairy cows, which represents a new threat to the agricultural economy. Lastly, HPAI has increasingly affected humans through domestic animal exposures, exemplifying the considerable implications of this disease beyond animal health. Rapid changes in the ecology of HPAI are currently outpacing research efforts. For example, it is not entirely clear which newly established hosts may become reservoirs for HPAI viruses (in other words, capable of maintaining HPAI viruses within a broad population indefinitely) and how this may influence viral evolution and dissemination. As a result, there are considerable information gaps regarding HPAI in wildlife that, if filled, would improve the ability of scientists, managers, agricultural industry representatives, and healthcare professionals to understand and to anticipate the effects of HPAI on wild animal, domestic animal, environmental, and human health (“One Health”). The U.S. Geological Survey (USGS) is the lead Federal agency providing scientific research on avian influenza viruses (AIVs), including HPAI viruses, that affect wildlife for which the Department of the Interior (DOI) has management authority. States have jurisdiction over wildlife on Federal lands within their borders (43 CFR § 24.3), so the USGS Ecosystems Mission Area (EMA) coordinates with State natural resource management agencies. The EMA focuses its research on HPAI through priorities identified by the USGS Avian Influenza Science Team ( app. 1 ). Priorities identified by the USGS Avian Influenza Science Team are based on Administration priorities, Congressional direction, and discussions with State, Federal, and Tribal natural resource management agencies that identify specific scientific gaps that need to be filled to inform sound wildlife management decisions. Notable non-DOI Federal partners include the U.S. Department of Agriculture, the lead for the HPAI regulatory response in poultry and livestock, and the Centers for Disease Control and Prevention (CDC), the lead agency for the HPAI response pertaining to human health. The USGS offers unique expertise and capacity pertaining to research on diseases affecting free-ranging wildlife populations. This expertise has been critical to interjurisdictional surveillance and capacity-building efforts, including programs administered by the U.S. Department of Agriculture and the CDC. The USGS also provides resources, guidance, and tools to inform surveillance and interventions conducted by natural resource management agencies. More specifically, the USGS EMA provides objective and rigorous scientific data for inferring (1) the utility of new methods to detect and characterize AIVs, including those maintained in wildlife and the environment; (2) effects of HPAI on wildlife; (3) spatiotemporal patterns of wildlife host and AIV dispersal; (4) the presence and persistence of AIVs in the environment; (5) how HPAI in wildlife influences consumptive and nonconsumptive utilization of wildlife; (6) how new tools and scientific methods may promote sound management decisions for HPAI-affected wildlife, particularly species of conservation concern; and (7) the combined effects of HPAI and other stressors on ecosystem health and resiliency. This science strategy builds upon research outlined in a previous USGS science strategy for HPAI (2016–20) by Harris and others (2016) . This strategy also details research priorities identified by the Administration (for example, U.S. Department of Agriculture, 2025 ) and others based on USGS Avian Influenza Science Team discussions with natural resource management agencies to address HPAI and wildlife health over the next 5 years (2025–29). This strategy presents 7 goals and 26 objectives that focus USGS and partner efforts on priorities that will fill data gaps regarding the effects of HPAI on wildlife managed by or co-managed with the U.S. Department of the Interior such that agencies and partners might anticipate or limit adverse effects on public resources. This strategy also identifies research priorities intended to address HPAI in wildlife and wildlife habitat that are anticipated to support interjurisdictional One Health efforts.

Circular↗
USGS2013

U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration

Executive Summary Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policymakers are faced with countless decisions each year at local, regional, and national levels on issues as diverse as renewable and nonrenewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decisionmaking is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and wellbeing of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decisionmakers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that U.S. Geological Survey (USGS) scientists and partners can play in building scientific understanding and providing timely information to decisionmakers. The strategy underscores the connection between scientific discoveries and the application of new knowledge, and it integrates ecosystem science and decisionmaking, producing new scientific outcomes to assist resource managers and providing public benefits. We envision the USGS as a leader in integrating scientific information into decisionmaking processes that affect the Nation’s natural resources and human well-being. The USGS is uniquely positioned to play a pivotal role in ecosystem science. With its wide range of expertise, the Bureau can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the Department of the Interior (DOI) and other partners in the natural sciences, in both conducting science and applying the results. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected components that reinforce our vision of the USGS providing science that is at the forefront of decisionmaking: Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. Develop tools, technologies, and capacities to inform decisionmaking about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. Support decisionmaking. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the four strategic approaches are a set of proposed actions that represent a sampling of specific USGS activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role in guiding sound decisionmaking during the next decade by advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Circular↗
USGS2001

Some suggested future directions of quantitative resource assessments

Future quantitative assessments will be expected to estimate quantities, values, and locations of undiscovered mineral resources in a form that conveys both economic viability and uncertainty associated with the resources. Historically, declining metal prices point to the need for larger deposits over time. Sensitivity analysis demonstrates that the greatest opportunity for reducing uncertainty in assessments lies in lowering uncertainty associated with tonnage estimates. Of all errors possible in assessments, those affecting tonnage estimates are by far the most important. Selecting the correct deposit model is the most important way of controlling errors because the dominance of tonnage-deposit models are the best known predictor of tonnage. Much of the surface is covered with apparently barren rocks and sediments in many large regions. Because many exposed mineral deposits are believed to have been found, a prime concern is the presence of possible mineralized rock under cover. Assessments of areas with resources under cover must rely on extrapolation from surrounding areas, new geologic maps of rocks under cover, or analogy with other well-explored areas that can be considered training tracts. Cover has a profound effect on uncertainty and on methods and procedures of assessments because geology is seldom known and geophysical methods typically have attenuated responses. Many earlier assessment methods were based on relationships of geochemical and geophysical variables to deposits learned from deposits exposed on the surface-these will need to be relearned based on covered deposits. Mineral-deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different, and (2) deposit types are present in different geologic settings that can be identified from geologic maps. Mineral-deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Grade and tonnage models and development of quantitative descriptive, economic, and deposit density models will help reduce the uncertainty of these new assessments.

Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Sc↗
USGS2003

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Natural Resources Research↗