Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Natural Resources Research”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
USGS2003

Use of a probabilistic neural network to reduce costs of selecting construction rock

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.

Natural Resources Research↗
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↗
USGS2003

Application of artificial neural networks to complex groundwater management problems

As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models.

Natural Resources Research↗
USGS2004

A bootstrap approach to computing uncertainty in inferred oil and gas reserve estimates

This study develops confidence intervals for estimates of inferred oil and gas reserves based on bootstrap procedures. Inferred reserves are expected additions to proved reserves in previously discovered conventional oil and gas fields. Estimates of inferred reserves accounted for 65% of the total oil and 34% of the total gas assessed in the U.S. Geological Survey’s 1995 National Assessment of oil and gas in US onshore and State offshore areas. When the same computational methods used in the 1995 Assessment are applied to more recent data, the 80-year (from 1997 through 2076) inferred reserve estimates for pre-1997 discoveries located in the lower 48 onshore and state offshore areas amounted to a total of 39.7 billion barrels of oil (BBO) and 293 trillion cubic feet (TCF) of gas. The 90% confidence interval about the oil estimate derived from the bootstrap approach is 22.4 BBO to 69.5 BBO. The comparable 90% confidence interval for the inferred gas reserve estimate is 217 TCF to 413 TCF. The 90% confidence interval describes the uncertainty that should be attached to the estimates. It also provides a basis for developing scenarios to explore the implications for energy policy analysis.

Natural Resources Research↗
USGS2004

Commentary: A cautionary tale regarding use of the National Land Cover Dataset 1992

Digital land-cover data are among the most popular data sources used in ecological research and natural resource management. However, processes for accurate land-cover classification over large regions are still evolving. We identified inconsistencies in the National Land Cover Dataset 1992, the most current and available representation of land cover for the conterminous United States. We also report means to address these inconsistencies in a bird-habitat model. We used a Geographic Information System (GIS) to position a regular grid (or lattice) over the upper midwestern United States and summarized the proportion of individual land covers in each cell within the lattice. These proportions were then mapped back onto the lattice, and the resultant lattice was compared to satellite paths, state borders, and regional map classification units. We observed mapping inconsistencies at the borders between mapping regions, states, and Thematic Mapper (TM) mapping paths in the upper midwestern United States, particularly related to grass I and-herbaceous, emergent-herbaceous wetland, and small-grain land covers. We attributed these discrepancies to differences in image dates between mapping regions, suboptimal image dates for distinguishing certain land-cover types, lack of suitable ancillary data for improving discrimination for rare land covers, and possibly differences among image interpreters. To overcome these inconsistencies for the purpose of modeling regional populations of birds, we combined grassland-herbaceous and pasture-hay land-cover classes and excluded the use of emergent-herbaceous and small-grain land covers. We recommend that users of digital land-cover data conduct similar assessments for other regions before using these data for habitat evaluation. Further, caution is advised in using these data in the analysis of regional land-cover change because it is not likely that future digital land-cover maps will repeat the same problems, thus resulting in biased estimates of change.

Wildlife Society Bulletin↗
USGS2022

Social Scientist GS–0101

This broad study field focuses on understanding values, perceptions, attitudes, and knowledge of humans and society as they relate to one another and the world around them. Several Social Science branches use various methodologies to conduct research on natural resources and hazards, climate and land-use change, and other related topics and interactions. Social Science discipline examples include anthropology, political science, psychology, sociology, and human geographers.

General Information Product↗
USGS2013

Ocean climate indicators: A monitoring inventory and plan for tracking climate change in the north-central California coast and ocean region

The impacts of climate change, defined as increasing atmospheric and oceanic carbon dioxide and associated increases in average global temperature and oceanic acidity, have been observed both globally and on regional scales, such as in the North-central California coast and ocean, a region that extends from Point Arena to Point Año Nuevo and includes the Pacific coastline of the San Francisco Bay Area. Because of the high economic and ecological value of the region’s marine environment, the Gulf of the Farallones National Marine Sanctuary (GFNMS) and other agencies and organizations have recognized the need to evaluate and plan for climate change impacts. Climate change indicators can be developed on global, regional, and site-specific spatial scales, and they provide information about the presence and potential impacts of climate change. While indicators exist for the nation and for the state of California as a whole, no system of ocean climate indicators exist that specifically consider the unique characteristics of the California coast and ocean region. To that end, GFNMS collaborated with over 50 regional, federal, and state natural resource managers, research scientists, and other partners to develop a set of 2 ocean climate indicators specific to this region. A smaller working group of 13 regional partners developed monitoring goals, objectives, strategies, and activities for the indicators and recommended selected species for biological indicators, resulting in the Ocean Climate Indicators Monitoring Inventory and Plan. The working group considered current knowledge of ongoing monitoring, feasibility of monitoring, costs, and logistics in selecting monitoring activities and selected species.

Report↗
USGS2005

Decision making with environmental indices

Since Ott's seminal book on environmental indices (1978), the use of indices has expanded into several natural resource disciplines, including ecological studies, environmental policymaking, and agricultural economics. However, despite their increasing use in natural resource disciplines, researchers and public decision makers continue to express concern about validity of these instruments to capture and communicate multidimensional, and sometimes disparate, characteristics of research data and stakeholder interests. Our purpose is to demonstrate how useful indices can be for communicating environmental information to decision makers. We discuss how environmental indices have evolved over four stages: 1) simple; 2) compound multicriteria; 3) the impact matrix and 4) disparate stakeholder management. We provide examples of simple and compound indices that were used by policy decision makers. We then build a framework, called an Impact Matrix (IM), that comprehensively accounts for multiple indices but lets the user decide how to integrate them. The IM was shaped from the concept of a financial risk payoff matrix and applied to ecosystem risk. While the IM offers flexibility, it does not address stakeholder preferences about which index to use. Therefore, the last phase in our evolutionary ladder includes stakeholder indices to specifically address disparate stakeholder preferences. Finally, we assert that an environmental index has the potential to increase resource efficiency, since the number of decision making resources may be reduced, and hence improve upon resource productivity

Book chapter↗
USGS2001

Abstracts from "Coastal Marsh Dieback in the Northern Gulf of Mexico: Extent, Causes, Consequences, and Remedies

In the spring of 2000, scientists discovered a new and unprecedented loss of salt marsh vegetation in coastal Louisiana and other areas along the northern coast of the Gulf of Mexico. This dieback of salt marsh vegetation, sometimes called the brown marsh phenomenon', primarily involved the rapid browning and dieback of smooth cordgrass (Spanina alterniflora). Coastal Louisiana has already undergone huge, historical losses of coastal marsh due to both human-induced and natural factors, and the current overall rate of wetland loss (25-35 sq mi 65-91 SQ KM each year) stands to threaten Louisiana's coastal ecosystem, infrastructure, and economy. On January 11-12, 2001, individuals from Federal and State agencies, universities, and the private sector met at the conference 'Coastal Marsh Dieback in the Northern Gulf of Mexico: Extent, Causes, Consequences, and Remedies' to discuss and share information shout the marsh dieback. Presentations discussed trends in the progress of dieback during the summer of 2000 and in environmental conditions occurring at field study sites, possible causes including drought and Mississippi low flow' conditions, changes in soil conditions (salinity, the bioavailability of metals, pathogens, etc.), the potential for wetland loss that could occur if above and below normality occurs and is sustained over an extended period, advanced techniques for tracking the dieback via aerial photography and remote sensing, linkages of marsh hydrology to the dieback, and mechanisms of modeling dieback and recovery. In addition, presentations were made regarding development of a web site to facilitate information sharing and progress in preparation for requests for proposals based on an emergency appropriation by the U.S. Congress. All findings tended to support the idea that the dieback constituted a continuing environmental emergency and research and natural resource management efforts should be expended accordingly.

Information and Technology Report↗
USGS2007

U.S. Geological Survey Activities Related to American Indians and Alaska Natives: Fiscal Year 2005

Introduction This report describes the activities that the U.S. Geological Survey (USGS) conducted with American Indian and Alaska Native governments, educational institutions, and individuals during Federal fiscal year (FY) 2005. Most of these USGS activities were collaborations with Tribes, Tribal organizations, or professional societies. Others were conducted cooperatively with the Bureau of Indian Affairs (BIA) or other Federal entities. The USGS is the earth and natural science bureau within the U.S. Department of the Interior (DOI). The USGS does not have regulatory or land management responsibilities. As described in this report, there are many USGS activities that are directly relevant to American Indians, Alaska Natives, and to Native lands. A USGS website, dedicated to making USGS more accessible to American Indians, Alaska Natives, their governments, and institutions, is available at www.usgs.gov/indian. This website includes information on how to contact USGS American Indian/Alaska Native Liaisons, training opportunities, and links to other information resources. This report and previous editions are also available through the website. The USGS realizes that Native knowledge and cultural traditions of living in harmony with nature result in unique Native perspectives that enrich USGS studies. USGS seeks to increase the sensitivity and openness of its scientists to the breadth of Native knowledge, expanding the information on which their research is based. USGS scientific studies include data collection, mapping, natural resource modeling, and research projects. These projects typically last 2 or 3 years, although some are parts of longer-term activities. Some projects are funded cooperatively, with USGS funds matched or supplemented by individual Tribal governments, or by the BIA. These projects may also receive funding from the U.S. Environmental Protection Agency (USEPA), the Indian Health Service (part of the Department of Health and Human Services), or other Federal agencies. The USGS routinely works with its sister bureaus in the Department of the Interior to provide the scientific information and expertise needed to meet the Department's science priorities. Some USGS activities described in this report are conducted as collateral tasks that result from USGS employees identifying and responding to perceived needs. These endeavors are usually prompted by employee interests and frequently involve educational activities. The education is often a reciprocal learning and teaching experience for USGS employees and for Native participants. Through these activities, USGS employees help to fulfill a mission of the USGS - to demonstrate scientific relevance - while helping their fellow citizens. Increasingly, some of the educational activities are becoming parts of formal USGS projects. USGS employees also take initiative in assisting American Indians and Alaska Natives by participating in several organizations that promote awareness of science career opportunities among Native peoples and help build support and communication networks. One such group is the American Indian Science and Engineering Society (AISES). USGS employees join this organization on a voluntary basis, bringing the benefits of this expanded network to the USGS, as many employees do with other professional organizations. The studies briefly described in this report span subsistence issues, wildlife health, water quality, mineral resources, monitoring and modeling to gather information and predict what may happen in the future. Although each project description relates to Native Americans in some way, the projects vary widely, including who conducted the work, the goals and products, the duration of the study, and whether it was local or covered a broad area. Each major organizational unit of the USGS has identified an American Indian/Alaska Native liaison. The USGS has a regional organizational structure, with Western, Central,

Circular↗
USGS2021

Navigating ecological transformation: Resist-accept-direct as a path to a new resource management paradigm

Natural resource managers worldwide face a growing challenge: Intensifying global change increasingly propels ecosystems toward irreversible ecological transformations. This nonstationarity challenges traditional conservation goals and human well-being. It also confounds a longstanding management paradigm that assumes a future that reflects the past. As once-familiar ecological conditions disappear, managers need a new approach to guide decision-making. The resist–accept–direct (RAD) framework, designed for and by managers, identifies the options managers have for responding and helps them make informed, purposeful, and strategic choices in this context. Moving beyond the diversity and complexity of myriad emerging frameworks, RAD is a simple, flexible, decision-making tool that encompasses the entire decision space for stewarding transforming ecosystems. Through shared application of a common approach, the RAD framework can help the wider natural resource management and research community build the robust, shared habits of mind necessary for a new, twenty-first-century natural resource management paradigm.

BioScience↗
USGS2006

Negotiation Training Courses for Natural Resource Professionals

FORT's Policy Analysis and Science Assistance Branch (PASA) has been conducting and publishing research on multi-party natural resource negotiation since the 1980s. This research has led to the development of basic and advanced negotiation training courses. Each course is two-and-a-half days. Both courses are a mix of lecture, hands-on training, and discussion. Please join us and other natural resource professionals facing similar problems and share your experiences. Come prepared to candidly discuss examples of successes to embrace, stalemates to recognize, and pitfalls to avoid in natural resource negotiations.

Fact Sheet↗
USGS2017

Northern Prairie Wildlife Research Center—Celebrating 50 years of science

The Northern Prairie Wildlife Research Center (NPWRC) celebrated its 50-year anniversary in 2015. This report is written in support of that observance. We document why and how the NPWRC came to be and describe some of its many accomplishments and the influence the Center’s research program has had on natural resource management. The history is organized by major research themes, proceeds somewhat chronologically within each theme, and covers the Center’s first 50 years of research. During that period, Center scientists authored more than 1,700 publications and reports. More than 1,000 seasonal or temporary field personnel, and more than 100 graduate students, contributed to the Center’s success; many went on to have exemplary careers in natural resource management, conservation, and education. The mission of the Northern Prairie Wildlife Research Center today remains true to the original vision: to provide the knowledge needed to understand, conserve, and manage the Nation’s natural resources for current and future generations, with an emphasis on species and ecosystems of the northern Great Plains. The Center’s first 50 years of applied biological research provides a deep scientific foundation on which to address emerging issues for the natural resources in the northern Great Plains and beyond.

Circular↗
USGS2016

The National Wind Erosion Research Network: Building a standardized long-term data resource for aeolian research, modeling and land management

The National Wind Erosion Research Network was established in 2014 as a collaborative effort led by the United States Department of Agriculture’s Agricultural Research Service and Natural Resources Conservation Service, and the United States Department of the Interior’s Bureau of Land Management, to address the need for a long-term research program to meet critical challenges in wind erosion research and management in the United States. The Network has three aims: (1) provide data to support understanding of basic aeolian processes across land use types, land cover types, and management practices, (2) support development and application of models to assess wind erosion and dust emission and their impacts on human and environmental systems, and (3) encourage collaboration among the aeolian research community and resource managers for the transfer of wind erosion technologies. The Network currently consists of thirteen intensively instrumented sites providing measurements of aeolian sediment transport rates, meteorological conditions, and soil and vegetation properties that influence wind erosion. Network sites are located across rangelands, croplands, and deserts of the western US. In support of Network activities, http://winderosionnetwork.org was developed as a portal for information about the Network, providing site descriptions, measurement protocols, and data visualization tools to facilitate collaboration with scientists and managers interested in the Network and accessing Network products. The Network provides a mechanism for engaging national and international partners in a wind erosion research program that addresses the need for improved understanding and prediction of aeolian processes across complex and diverse land use types and management practices.

Arizona, California, Colorado, Idaho, North Dakota↗
USGS2011

Gas shale/oil shale

The production of natural gas from shales continues to increase in North America, and shale gas exploration is on the rise in other parts of the world since the previous report by this committee was published by American Association of Petroleum Geologists, Energy Minerals Division (2009). For the United States, the volume of proved reserves of natural gas increased 11% from 2008 to 2009, the increase driven largely by shale gas development (Energy Information Administration 2010c). Furthermore, shales have increasingly become targets of exploration for oil and condensate as well as gas, which has served to greatly expand their significance as ‘‘unconventional’’ petroleum reservoirs. This report provides information about specific shales across North America and Europe from which gas (biogenic or thermogenic), oil, or natural gas liquids are produced or is actively being explored. The intent is to reflect the recently expanded mission of the Energy Minerals Division (EMD) Gas Shales Committee to serve as a single point of access to technical information on shales regardless of the type of hydrocarbon produced from them. The contents of this report were drawn largely from contributions by numerous members of the EMD Gas Shales Advisory Committee, with much of the data being available from public websites such as state or provincial geological surveys or other public institutions. Shales from which gas or oil is being produced in the United States are listed in alphabetical order by shale name. Information for Canada is presented by province, whereas for Europe, it is presented by country.

Natural Resources Research↗
USGS2020

Drivers of landscape change in the northwest boreal region

The northwest boreal region (NWB) of North America is a land of extremes. Extending more than 1.3 million square kilometers (330 million acres), it encompasses the entire spectrum between inundated wetlands below sea level to the tallest peak in North America. Permafrost gradients span from nearly continuous to absent. Boreal ecosystems are inherently dynamic and continually change over decades to millennia. The braided rivers that shape the valleys and wetlands continually change course, creating and removing vast wetlands and peatlands. Glacial melt, erosion, fires, permafrost dynamics, and wind-blown loess are among the shaping forces of the landscape. As a result, species interactions and ecosystem processes are shifting across time. The NWB is a data-poor region, and the intention of the NWB Landscape Conservation Cooperative is to determine what data are not available and what data are available. For instance, historical baseline data describing the economic and social relationships in association with the ecological condition of the NWB landscape are often lacking. Likewise, the size and remoteness of this region make it challenging to measure basic biological information, such as species population sizes or trends. The paucity of weather and climate monitoring stations also compound the ability to model future climate trends and impacts, which is part of the nature of working in the north. The purpose of this volume is to create a resource for regional land and resource managers and researchers by synthesizing the latest research on the historical and current status of landscape-scale drivers (including anthropogenic activities) and ecosystem processes, future projected changes of each, and the effects of changes on important resources. Generally, each chapter is coauthored by researchers and land and natural resource managers from the United States and Canada.

Northwest Boreal Region↗