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USGS2022

Acquisition of Moon measurements by Earth orbiting sensors for lunar calibration

The reflected light from the Moon can be utilized as a reference for radiometric calibration by employing a model to generate reference values corresponding to the Moon observations made by instruments. Using a calibration target that is outside the atmosphere provides a distinct advantage for space-based instruments; however, the lunar irradiance sensed by satellite instruments naturally changes as the host spacecraft traverses its orbit. This article presents a study of the potential impact on lunar radiometric measurements due to their acquisition from an orbiting platform. A simulation of a Sun-synchronous orbit was coupled to the U.S. Geological Survey (USGS) lunar model to generate predicted irradiances for points along orbit passes through several lunations. These irradiance values exhibit variations tied to the spacecraft motion, arising primarily from changes in the Moon-sensor distance and the phase angle. The two effects are similar in overall magnitude, but their respective contributions depend on the time of month and the orbit. Relative changes in irradiance mostly fall within an envelope of ±0.006% per second, except at the smallest phase angles. These studies enable planning space-based Moon observations to minimize the change in the target irradiance, an important consideration for measurements acquired for radiometric characterization of the Moon.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2009

Atmospheric correction at AERONET locations: A new science and validation data set

This paper describes an Aerosol Robotic Network (AERONET)-based Surface Reflectance Validation Network (ASRVN) and its data set of spectral surface bidirectional reflectance and albedo based on Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and AQUA data. The ASRVN is an operational data collection and processing system. It receives 50 ?? 50 km 2 ; subsets of MODIS level 1B (L1B) data from MODIS adaptive processing system and AERONET aerosol and water-vapor information. Then, it performs an atmospheric correction (AC) for about 100 AERONET sites based on accurate radiative-transfer theory with complex quality control of the input data. The ASRVN processing software consists of an L1B data gridding algorithm, a new cloud-mask (CM) algorithm based on a time-series analysis, and an AC algorithm using ancillary AERONET aerosol and water-vapor data. The AC is achieved by fitting the MODIS top-of-atmosphere measurements, accumulated for a 16-day interval, with theoretical reflectance parameterized in terms of the coefficients of the Li SparseRoss Thick (LSRT) model of the bidirectional reflectance factor (BRF). The ASRVN takes several steps to ensure high quality of results: 1) the filtering of opaque clouds by a CM algorithm; 2) the development of an aerosol filter to filter residual semitransparent and subpixel clouds, as well as cases with high inhomogeneity of aerosols in the processing area; 3) imposing the requirement of the consistency of the new solution with previously retrieved BRF and albedo; 4) rapid adjustment of the 16-day retrieval to the surface changes using the last day of measurements; and 5) development of a seasonal backup spectral BRF database to increase data coverage. The ASRVN provides a gapless or near-gapless coverage for the processing area. The gaps, caused by clouds, are filled most naturally with the latest solution for a given pixel. The ASRVN products include three parameters of the LSRT model (kL, kG, and kV), surface albedo, normalized BRF (computed for a standard viewing geometry, VZA = 0, SZA = 45??), and instantaneous BRF (or one-angle BRF value derived from the last day of MODIS measurement for specific viewing geometry) for the MODIS 500-m bands 17. The results are produced daily at a resolution of 1 km in gridded format. We also provide a cloud mask, a quality flag, and a browse bitmap image. The ASRVN data set, including 6 years of MODIS TERRA and 1.5 years of MODIS AQUA data, is available now as a standard MODIS product (MODASRVN) which can be accessed through the Level 1 and Atmosphere Archive and Distribution System website ( http://ladsweb.nascom.nasa.gov/data/search.html). It can be used for a wide range of applications including validation analysis and science research. ?? 2006 IEEE.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2019

Most Earth-surface calcites precipitate out of isotopic equilibrium

Oxygen-isotope thermometry played a critical role in the rise of modern geochemistry and remains extensively used in (bio-)geoscience. Its theoretical foundations rest on the assumption that 18 O/ 16 O partitioning among water and carbonate minerals primarily reflects thermodynamic equilibrium. However, after decades of research, there is no consensus on the true equilibrium 18 O/ 16 O fractionation between calcite and water ( 18 α cc/w ). Here, we constrain the equilibrium relations linking temperature, 18 α cc/w , and clumped isotopes ( Δ 47 ) based on the composition of extremely slow-growing calcites from Devils Hole and Laghetto Basso (Corchia Cave). Equilibrium 18 α cc/w values are systematically ~1.5‰ greater than those in biogenic and synthetic calcite traditionally considered to approach oxygen-isotope equilibrium. We further demonstrate that subtle disequilibria also affect Δ 47 in biogenic calcite. These observations provide evidence that most Earth-surface calcites fail to achieve isotopic equilibrium, highlighting the need to improve our quantitative understanding of non-equilibrium isotope fractionation effects instead of relying on phenomenological calibrations.

Nature Communications↗

USGS2007

The question of recharge to the deep thermal reservoir underlying the geysers and hot springs of Yellowstone National Park: Chapter H in Integrated geoscience studies in Integrated geoscience studies in the Greater Yellowstone Area—Volcanic, tectonic, and hydrothermal processes in the Yellowstone geoecosystem

The extraordinary number, size, and unspoiled beauty of the geysers and hot springs of Yellowstone National Park (the Park) make them a national treasure. The hydrology of these special features and their relation to cold waters of the Yellowstone area are poorly known. In the absence of deep drill holes, such information is available only indirectly from isotope studies. The δD-δ18O values of precipitation and cold surface-water and ground-water samples are close to the global meteoric water line (Craig, 1961). δD values of monthly samples of rain and snow collected from 1978 to 1981 at two stations in the Park show strong seasonal variations, with average values for winter months close to those for cold waters near the collection sites. δD values of more than 300 samples from cold springs, cold streams, and rivers collected during the fall from 1967 to 1992 show consistent north-south and east-west patterns throughout and outside of the Park, although values at a given site vary by as much as 8 &permil; from year to year. These data, along with hot-spring data (Truesdell and others, 1977; Pearson and Truesdell, 1978), show that ascending Yellowstone thermal waters are modified isotopically and chemically by a variety of boiling and mixing processes in shallow reservoirs. Near geyser basins, shallow recharge waters from nearby rhyolite plateaus dilute the ascending deep thermal waters, particularly at basin margins, and mix and boil in reservoirs that commonly are interconnected. Deep recharge appears to derive from a major deep thermal-reservoir fluid that supplies steam and hot water to all geyser basins on the west side of the Park and perhaps in the entire Yellowstone caldera. This water (T ≥350°C; δD = –149±1 &permil;) is isotopically lighter than all but the farthest north, highest altitude cold springs and streams and a sinter-producing warm spring (δD = –153 &permil;) north of the Park. Derivation of this deep fluid solely from present-day recharge is problematical. The designation of source areas depends on assumptions about the age of the deep water, which in turn depend on assumptions about the nature of the deep thermal system. Modeling, based on published chloride-flux studies of thermal waters, suggests that for a 0.5- to 4-km-deep reservoir the residence time of most of the thermal water could be less than 1,900 years, for a piston-flow model, to more than 10,000 years, for a well-mixed model. For the piston-flow model, the deep system quickly reaches the isotopic composition of the recharge in response to climate change. For this model, stable-isotope data and geologic considerations suggest that the most likely area of recharge for the deep thermal water is in the northwestern part of the Park, in the Gallatin Range, where major north-south faults connect with the caldera. This possible recharge area for the deep thermal water is at least 20 km, and possibly as much as 70 km, from outflow in the thermal areas, indicating the presence of a hydrothermal system as large as those postulated to have operated around large, ancient igneous intrusions. For this model, the volume of isotopically light water infiltrating in the Gallatin Range during our sampling period is too small to balance the present outflow of deep water. This shortfall suggests that some recharge possibly occurred during a cooler time characterized by greater winter precipitation, such as during the Little Ice Age in the 15th century. However, this scenario requires exceptionally fast flow rates of recharge into the deep system. For the well-mixed model, the composition of the deep reservoir changes slowly in response to climate change, and a significant component of the deep thermal water could have recharged during Pleistocene glaciation. The latter interpretation is consistent with the recent discovery of warm waters in wells and springs in southern Idaho that have δD values 10–20 &permil; lower than the winter snow for their present-day high-level recharge. These waters have been interpreted to be Pleistocene in age (Smith and others, 2002). The well-mixed model permits a significant component of recharge water for the deep system to have δD values less negative than –150 &permil; and consequently for the deep system recharge to be closer to the caldera at a number of possible localities in the Park.

Idaho, Montana, Wyoming↗

USGS2023

City-scale geothermal energy everywhere to support renewable resilience – A transcontinental cooperation

Cities have important and varying incentives to transform their energy sector to all-electric with low carbon emissions. However, they often encounter a number of impediments when attempting to implement such a change. For example, while urban areas have the highest energy demand-density, cities often lack the space for installing additional energy generation and/or long-duration energy storage systems. Cities also have existing environmental issues from energy sources (e.g., pollution from dust, waste heat or noise) that make residents sensitive to energy infrastructure development. Utilizing power from conventional sources, such as natural gas, biomass and hydropower, which usually are distanced from the urban areas, also make cities more vulnerable to supply disruptions. One promising de-carbonizing energy option for cities focuses on their heating and cooling needs, which constitutes around 1/3 of U.S. and 1/2 of European energy consumption (including industrial processes like drying, pasteurization, etc.; Jadun and others, 2017; EU Commission 2022). If heating and cooling loads can be met by geothermal direct-use technologies, then the need for new electric sources can be greatly lessened. Despite the proven efficacy of geothermal energy as a city/community-scale heating and cooling resource, it is currently only a niche resource in the heating and cooling sector, though has significant potential for future growth. Historically, emphasis has been placed on geothermal electricity generation potential that requires higher temperature (greater than 90 °C) resources at drillable depths, but potentially viable areas are geographically limited and typically well removed from urban centers. Key drivers for investments were represented by greater political interest in renewable electricity production, higher revenues and less effort in distributing the produced energy via grids. In contrast, low-temperature (less than 90 °C) geothermal resources can be used directly for heating and cooling almost everywhere and are cost-effective in urban/suburban settings. In addition, the increased prominence of renewable electricity sources, such as wind and solar onto city-scale electric grids, has led to new urgency around questions of energy storage. Underground thermal energy storage (UTES), wherein surplus or waste heat is stored underground for later use, could present a long-duration energy storage solution. From October 2022 through September 2024, a transcontinental consortium consisting of geological surveys, geoscience organizations, industry representatives and universities aims to develop an understanding of the global potential for city-scale geothermal, proposing guidelines to aid in promoting the economic utilization of low temperature geothermal resources. Efforts will focus on providing city managers and other decision makers with the information needed to evaluate and implement suitable city/community-scale geothermal technologies. Funded by the U.S. Geological Survey’s John Wesley Powell Center for Analysis and Synthesis, this interdisciplinary consortium will showcase tools, datasets, and scientific recommendations to accelerate the broader understanding and adoption of renewable energy systems that access geothermal resources. The collaborative research activities include standardization of nomenclatures, resource description and characterization strategies globally. The results from these activities will be combined with a preliminary climate-driven, city-based energy needs related analysis to perform energy supply/demand matching analysis. The identification of city-specific applications that would benefit from the geothermal technologies provides the basis to up-scale city-specific determinations to regional and national assessments of resource estimates. The city-scale geothermal energy research initiative will ultimately provide the synergies and management analysis that can address benefits, environmental impacts, regulatory frameworks, sustainability, and suitability in retrofitted buildings or new as well as existing heating networks.

Conference Paper↗

USGS2006

Introduction to ‘Antarctic climate evolution: View from the margin’

This special issue on “Antarctic Climate Evolution—view from the margin” presents results from modelling studies and reports on geoscience data aimed at improving our understanding of the behaviour of the Antarctic ice sheet and the climate of the region. This research field is of interest because of the sensitivity of the polar regions to global warming, and because of the influence of the Antarctic ice sheet on global sea level and climate through most if not all of the Cenozoic Era. The Antarctic ice sheet both responds to and forces changes on global climate and sea level. We need to be aware of the scale and frequency of these changes if we are to understand past patterns of environmental change elsewhere on earth. It was only three decades ago that we discovered from strata drilled in shelf basins on the Antarctic margin that the Antarctic ice sheet had a history that predated the Quaternary ice ages by over 20 million years ( Hayes et al., 1975 ). Later that year the first interpretation of Antarctic glacial history through the Cenozoic Era from oxygen isotopes, recorded in foraminifera from deep-sea sediment cores, was published ( Shackleton and Kennett, 1975 ). Revisions with a more extensive database have modified the story a little ( Miller et al., 1987 , Zachos et al., 2001 ), and there have been recent attempts to resolve the temperature–ice volume ambiguity ( Lear et al., 2000 ). However, reports on strata drilled on the Antarctic margin have unambiguously shown the character of this huge ice sheet, which was oscillating in the Oligocene ( Barrett et al., 1987 , Barrett, 1999 ) with a period and magnitude comparable with the Northern Hemisphere ice sheets of the Quaternary ( Naish et al., 2001a , Naish et al., 2001b ). In this issue we present further research on the history of the Antarctic ice sheet from Oligocene to recent times, most of them from the Antarctic margin, but with some on the nature of the deep-sea isotope record, and others using recently developed modeling techniques to investigate the influence of atmosphere, ocean and biosphere on past Antarctic climate. This special issue is the third in three years on the theme of Antarctic Climate Evolution. The first followed a workshop in Erice, Sicily, in 2001 to report on results from ANTOSTRAT, a SCAR-sponsored project for gathering and analysing circum-Antarctic seismic data for planning and promoting offshore drilling for climate history. The introduction to that issue (Florindo et al., 2003) provides a review of the recent history of circum-Antarctic drilling by the Ocean Drilling Program (Legs 113, 114, 119, 120, 177, 178, 188 and 189) and the Cape Roberts Project. For a more comprehensive review of earlier drilling in the Ross Sea region (Deep Sea Drilling Project Leg 28, Dry Valley Drilling Project, McMurdo Sound Sediment and Tectonic Studies, Cenozoic Investigations in the western Ross Sea) see Hambrey and Barrett (1993). The first of these issues (Florindo et al., 2003) featured a global plate reconstruction of the Southern Hemisphere through Cenozoic time with emphasis on evolution of Cenozoic seaways (Lawver and Gahagan, 2003) along with a study of the inception and early evolution of the EAIS using a new coupled global climate (GCM)–dynamic ice sheet model (DeConto and Pollard, 2003b), as well as data from recent drilling around the margin covering time period from Cretaceous to the present. A second special issue on the same theme (Florindo et al., 2005) also featured a mix of modelling and data papers with a focus on the Eocene–Oligocene boundary and the initiation of ice sheet growth, including a pioneering attempt to evaluate the relative influence of fluvial versus glacial processes in shaping the landscape of the Prydz Bay sector of Antarctica (Jamieson et al., 2005). The remainder of the issue comprised further papers on seismic stratigraphy and reports from drilling around the margin. The papers to be found in this special issue, like the previous two, maintain the mix of modelling- and data-oriented papers that reflect the range of this research.

Palaeogeography, Palaeoclimatology, Palaeoecology↗

USGS2016

Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2008

What was the groundwater quality before mining in a mineralized region? Lessons from the Questa Project

The U.S. Geological Survey, in cooperation with the New Mexico Environment Department and supported by Molycorp, Inc (currently Chevron Minerals), has completed a 5-year investigation (2001-2006) to determine the pre-mining ground-water quality at Molycorp's Questa molybdenum mine in northern New Mexico. Current mine-site ground waters are often contaminated with mine-waste leachates and no data exists on premining ground-water quality so that pre-mining conditions must be inferred. Ground-water quality undisturbed by mining is often worse than New Mexico standards and data are needed to help establish closure requirements. The key to determining pre-mining conditions was to study the hydrogeochemistry of a proximal natural analog site, the Straight Creek catchment. Main rock types exposed to weathering include a Tertiary andesite and the Tertiary Amalia tuff (rhyolitic composition), both hydrothermally altered to various degrees. Two types of ground water are common in mineralized areas, acidic ground waters in alluvial debris fans with pH 3-4 and bedrock ground waters with pH 6-8. Siderite, ferrihydrite, rhodochrosite, amorphous to microcrystalline Al(OH)3, calcite, gypsum, barite, and amorphous silica mineral solubilities control concentrations of Fe(II), Fe(III), Mn(II), Al, Ca, Ba, and SiO2, depending on pH and solution composition. Concentrations at low pH are governed by element abundance and mineral weathering rates. Concentrations of Zn and Cd range from detection up to about 10 and 0.05 mg/L, respectively, and are derived primarily from sphalerite dissolution. Concentrations of Ni and Co range from detection up to 1 and 0.4 mg/L, respectively, and are derived primarily from pyrite dissolution. Concentrations of Ca and SO4 are derived from secondary gypsum dissolution and weathering of calcite and pyrite. Metal:sulfate concentration ratios are relatively constant for acidic waters, suggesting consistent weathering rates, independent of catchment. These trends, combined with lithology, mineralogy, and mineral solubility controls, provide useful constraints on pre-mining ground-water quality for the mine site where the lithology is known.

Geosciences Journal↗

USGS2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

Nature Reviews Earth & Environment↗

USGS2007

The geology of asbestos in the United States and its practical applications

Recently, naturally occurring asbestos (NOA) has drawn the attention of numerous health and regulatory agencies and citizen groups. NOA can be released airborne by (1) the disturbance of asbestos-bearing bedrocks through human activities or natural weathering, and (2) the mining and milling of some mineral deposits in which asbestos occurs as an accessory mineral(s). Because asbestos forms in specific rock types and geologic conditions, this information can be used to focus on areas with the potential to contain asbestos, rather than devoting effort to areas with minimal NOA potential. All asbestos minerals contain magnesium, silica, and water as essential constituents, and some also contain major iron and/or calcium. Predictably, the geologic environments that host asbestos are enriched in these components. Most asbestos deposits form by metasomatic replacement of magnesium-rich rocks. Asbestos-forming environments typically display shear or evidence for a significant influx of silica-rich hydrothermal fluids. Asbestos-forming processes can be driven by regional metamorphism, contact metamorphism, or magmatic hydrothermal systems. Thus, asbestos deposits of all sizes and styles are typically hosted by magnesium-rich rocks (often also iron-rich) that were altered by a metamorphic or magmatic process. Rock types known to host asbestos include serpentinites, altered ultramafic and some mafic rocks, dolomitic marbles and metamorphosed dolostones, metamorphosed iron formations, and alkalic intrusions and carbonatites. Other rock types appear unlikely to contain asbestos. These geologic insights can be used by the mining industry, regulators, land managers, and others to focus attention on the critical locales most likely to contain asbestos.

Environmental & Engineering Geoscience↗

USGS2013

History and evaluation of national-scale geochemical data sets for the United States

Six national-scale, or near national-scale, geochemical data sets for soils or stream sediments exist for the United States. The earliest of these, here termed the ‘Shacklette’ data set, was generated by a U.S. Geological Survey (USGS) project conducted from 1961 to 1975. This project used soil collected from a depth of about 20 cm as the sampling medium at 1323 sites throughout the conterminous U.S. The National Uranium Resource Evaluation Hydrogeochemical and Stream Sediment Reconnaissance (NURE-HSSR) Program of the U.S. Department of Energy was conducted from 1975 to 1984 and collected either stream sediments, lake sediments, or soils at more than 378,000 sites in both the conterminous U.S. and Alaska. The sampled area represented about 65% of the nation. The Natural Resources Conservation Service (NRCS), from 1978 to 1982, collected samples from multiple soil horizons at sites within the major crop-growing regions of the conterminous U.S. This data set contains analyses of more than 3000 samples. The National Geochemical Survey, a USGS project conducted from 1997 to 2009, used a subset of the NURE-HSSR archival samples as its starting point and then collected primarily stream sediments, with occasional soils, in the parts of the U.S. not covered by the NURE-HSSR Program. This data set contains chemical analyses for more than 70,000 samples. The USGS, in collaboration with the Mexican Geological Survey and the Geological Survey of Canada, initiated soil sampling for the North American Soil Geochemical Landscapes Project in 2007. Sampling of three horizons or depths at more than 4800 sites in the U.S. was completed in 2010, and chemical analyses are currently ongoing. The NRCS initiated a project in the 1990s to analyze the various soil horizons from selected pedons throughout the U.S. This data set currently contains data from more than 1400 sites. This paper (1) discusses each data set in terms of its purpose, sample collection protocols, and analytical methods; and (2) evaluates each data set in terms of its appropriateness as a national-scale geochemical database and its usefulness for national-scale geochemical mapping.

Conterminous United States↗

USGS1996

The 1993 Mississippi river flood: A one hundred or a one thousand year event?

Power-law (fractal) extreme-value statistics are applicable to many natural phenomena under a wide variety of circumstances. Data from a hydrologic station in Keokuk, Iowa, shows the great flood of the Mississippi River in 1993 has a recurrence interval on the order of 100 years using power-law statistics applied to partial-duration flood series and on the order of 1,000 years using a log-Pearson type 3 (LP3) distribution applied to annual series. The LP3 analysis is the federally adopted probability distribution for flood-frequency estimation of extreme events. We suggest that power-law statistics are preferable to LP3 analysis. As a further test of the power-law approach we consider paleoflood data from the Colorado River. We compare power-law and LP3 extrapolations of historical data with these paleo-floods. The results are remarkably similar to those obtained for the Mississippi River: Recurrence intervals from power-law statistics applied to Lees Ferry discharge data are generally consistent with inferred 100- and 1,000-year paleofloods, whereas LP3 analysis gives recurrence intervals that are orders of magnitude longer. For both the Keokuk and Lees Ferry gauges, the use of an annual series introduces an artificial curvature in log-log space that leads to an underestimate of severe floods. Power-law statistics are predicting much shorter recurrence intervals than the federally adopted LP3 statistics. We suggest that if power-law behavior is applicable, then the likelihood of severe floods is much higher. More conservative dam designs and land-use restrictions Nay be required.

Environmental & Engineering Geoscience↗

USGS2016

Climate change and future fire regimes: Examples from California

Climate and weather have long been noted as playing key roles in wildfire activity, and global warming is expected to exacerbate fire impacts on natural and urban ecosystems. Predicting future fire regimes requires an understanding of how temperature and precipitation interact to control fire activity. Inevitably this requires historical analyses that relate annual burning to climate variation. Fuel structure plays a critical role in determining which climatic parameters are most influential on fire activity, and here, by focusing on the diversity of ecosystems in California, we illustrate some principles that need to be recognized in predicting future fire regimes. Spatial scale of analysis is important in that large heterogeneous landscapes may not fully capture accurate relationships between climate and fires. Within climatically homogeneous subregions, montane forested landscapes show strong relationships between annual fluctuations in temperature and precipitation with area burned; however, this is strongly seasonal dependent; e.g., winter temperatures have very little or no effect but spring and summer temperatures are critical. Climate models that predict future seasonal temperature changes are needed to improve fire regime projections. Climate does not appear to be a major determinant of fire activity on all landscapes. Lower elevations and lower latitudes show little or no increase in fire activity with hotter and drier conditions. On these landscapes climate is not usually limiting to fires but these vegetation types are ignition-limited. Moreover, because they are closely juxtaposed with human habitations, fire regimes are more strongly controlled by other direct anthropogenic impacts. Predicting future fire regimes is not rocket science; it is far more complicated than that. Climate change is not relevant to some landscapes, but where climate is relevant, the relationship will change due to direct climate effects on vegetation trajectories, as well as by feedback processes of fire effects on vegetation distribution, plus policy changes in how we manage ecosystems.

Geoscience Canada↗

USGS2019

Assessment experimental semivariogram uncertainty in the presence of a polynomial drift

The semivariogram, which measures the spatial variability between experimental data, is generally used as a structural input in all two-point geostatistical procedures. However, in most geoscience applications, experimental semivariograms are usually computed from a limited number of sparsely spaced measurements, which results in uncertainty associated with the semivariance values estimated for a specified number of lags. More importantly, considering a spatial variable modelled by a nonstationary random field, uncertainty is not only in the experimental semivariogram of the residuals, but also in the coefficients of the drift model estimated from the available experimental data. Therefore, when assessing the reliability of an experimental semivariogram (or estimated semivariances) in the nonstationary case, both aforementioned uncertainties should be taken into account. The aim of this paper is to extend the “Generalised Bootstrap” procedure to the nonstationary model by propagating the uncertainty associated with the estimated drift coefficients into the uncertainty in the experimental semivariogram of the residuals. The proposed methodology is demonstrated in a case study using abundant geophysical measurements characterised by a nonstationary random function. Two scenarios are evaluated in the case study: (1) it is assumed that the drift coefficients can be estimated without any uncertainty, and (2) uncertainty of the drift coefficients is taken into account. We have explained the methodology that allows to assess the uncertainty of the semivariogram lag estimates in the presence of the drift in the mean. Considering the second scenario, uncertainty is obviously larger than the case where uncertainty of the drift in the mean is ignored. This evaluation should be considered in applications where the data is often rather limited, such as subsurface hydrology (i.e. porosity, transmissivity), soil science (i.e. heavy metal content, soil moisture) and mining (i.e. scoping or pre-feasibility stage of the project). In fact, modern geostatistics should provide not only the semivariogram estimates but also estimation of its uncertainty.

Natural Resources Research↗

USGS2014

Porphyry copper assessment of East and Southeast Asia: Philippines, Taiwan (Republic of China), Republic of Korea (South Korea), and Japan

The U.S. Geological Survey collaborated with member countries of the Coordinating Committee for Geoscience Programmes in East and Southeast Asia (CCOP) on an assessment of the porphyry copper resources of East and Southeast Asia as part of a global mineral resource assessment. The assessment covers the Philippines in Southeast Asia, and the Republic of Korea (South Korea), Taiwan (Province of China), and Japan in East Asia. The Philippines host world class porphyry copper deposits, such as the Tampakan and Atlas deposits. No porphyry copper deposits have been discovered in the Republic of Korea (South Korea), Taiwan (Province of China), or Japan. Thirteen geographic areas were delineated as tracts that are permissive for porphyry copper deposits in the assessed area. Individual tracts range from about 3,000 to 100,000 square kilometers in area. Permissive tracts are delineated on the basis of mapped distributions of igneous rocks of specific age ranges that define subduction-related magmatic arcs or magmatic belts that might contain porphyry copper deposits. Most of these magmatic arcs are subduction related, although some porphyry deposits and prospects are present in back-arc or poorly understood tectonic settings. Maps at various scales were used in the compilation; however, the final tract boundaries are intended for use at a scale of 1:1,000,000. Numbers of undiscovered deposits were estimated at different levels of confidence for 10 permissive tracts in the Philippines including one area that extends to eastern Taiwan (Republic of China); permissive tracts in South Korea and Japan are discussed qualitatively. Estimates of numbers of undiscovered deposits were combined with grade and tonnage models using Monte Carlo simulation to estimate amounts of undiscovered resources. Grades and tonnages of known porphyry copper deposits in the study area were compared with global grade and tonnage models to determine the appropriate model for simulation of undiscovered resources. Most of the known deposits are best described as copper-gold subtypes of porphyry copper deposits. For some permissive tracts, a general porphyry copper-gold-molybdenum model was used. Thirty-eight porphyry copper deposits are known in the Philippines; the mean number of undiscovered deposits was estimated to be 28. Mean (arithmetic) resources that could be associated with the undiscovered deposits are 90 million metric tons of copper and 5,800 metric tons of gold, as well as byproduct molybdenum and silver. Additional resources that could be discovered in extensions to known deposits were not evaluated. Assessment results, presented in tables and graphs, indicate expected amounts of total contained metal and mineralized rock in undiscovered deposits at different quantile levels, as well as the arithmetic mean for each tract. The Philippines have a long history of porphyry exploration cycles and mine development, interrupted at times by political and social unrest, environmental concerns, and natural disasters. Changes in mining laws within the region and the recent high price of gold on the world market have prompted renewed interest in porphyry copper deposits in the region. South Korea and Japan have been thoroughly explored for many types of mineral deposits. Available data suggest that the permissive rocks in South Korea typically are too deeply eroded to preserve porphyry copper deposits. Porphyry copper systems may be present in Japan, but are likely to lie at depths greater than the 1 kilometer from the surface protocol adopted for this study. Descriptions of the geologic basis for delineating each tract, the data used, the geologic criteria and rationale for the assessment, and results of the assessment are included in appendixes along with the description of a geographic information system (GIS) that includes tract boundaries, known porphyry copper deposits and significant prospects, and assessment results.

Scientific Investigations Report↗

USGS2013

Porphyry copper assessment of Southeast Asia and Melanesia: Chapter D in Global mineral resource assessment

The U.S. Geological Survey collaborated with member countries of the Coordinating Committee for Geoscience Programmes in East and Southeast Asia (CCOP) on an assessment of the porphyry copper resources of Southeast Asia and Melanesia as part of a global mineral resource assessment. The region hosts world-class porphyry copper deposits and underexplored areas that are likely to contain undiscovered deposits. Examples of known porphyry copper deposits include Batu Hijau and Grasberg in Indonesia; Panguna, Frieda River, and Ok Tedi in Papua New Guinea; and Namosi in Fiji. This assessment covers the countries of Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Myanmar, Papua New Guinea, Singapore, Thailand, and parts of southeastern China, India, the Solomon Islands, Vanuatu, and Fiji. Twenty-two geographic areas were delineated as tracts that are permissive for porphyry copper deposits in Southeast Asia. Permissive tracts are grouped into four broadly defined geographic/geologic areas, as follows: (1) the Indochina Peninsula area, (2) Indonesian and Malaysian Islands, (3) New Guinea Island and Papuan New Guinea islands, and (4) Melanesia. Individual tracts range from less than 1,000 to more than 350,000 square kilometers in area. Permissive tracts are based on mapped and inferred subsurface (<1 kilometer depth) distributions of igneous rocks of specific age ranges that define magmatic arcs and magmatic belts that are likely to contain porphyry copper deposits. Most of these magmatic arcs are subduction-related, although some have porphyry-style deposits occurring in postcollisional and (or) poorly understood tectonic settings. Although maps at a variety of different scales were used in the compilation, the final tract boundaries are intended for use at a scale of 1:1,000,000. Global grade and tonnage models for porphyry copper deposits were evaluated. Most of the known deposits are best described as fitting the copper-gold (Cu-Au) subtype of porphyry copper deposit. For some permissive tracts, a general porphyry copper-gold-molybdenum (Cu-Au-Mo) model was used. Assessment participants estimated numbers of undiscovered deposits at different levels of confidence for most of the permissive tracts. These estimates were combined with grade and tonnage models using a Monte Carlo simulation to estimate undiscovered resources. Additional resources in extensions of deposits with identified resources were not evaluated. Assessment results, presented in tables and graphs, show mean amounts of metal and mineralized rock in undiscovered deposits at different quantile levels, as well as the arithmetic mean for each tract. This assessment estimated a mean of 89 undiscovered porphyry copper deposits for the assessed permissive tracts in Southeast Asia and Melanesia. About 288 million metric tons (Mt) of copper and 18,000 metric tons (t) of gold, as well as byproduct molybdenum and silver, could be associated with undiscovered deposits. This represents about four times the number of deposits with identified resources (23) already discovered in Southeast Asia; reliable reported identified resources for those 23 deposits total 84 Mt of copper and 6,000 t of gold. Eleven permissive tracts have no known porphyry copper deposits with reported resources. Three of those 11 tracts lacked sufficient information for a probabilistic assessment and are discussed in qualitative terms. On a regional basis, both the Indochina Peninsula area and the Indonesian-Malaysian Islands area are estimated to contain about 10 times as much in place copper in undiscovered porphyry copper deposits as has been identified to date. For the New Guinea Island areas, the ratio of undiscovered to identified copper resources is about 2. Some parts of the region have a long history of porphyry exploration cycles and mine development, interrupted at times by political and social unrest, environmental concerns, and natural disasters. Changes in mining laws within the region and the recent high price of gold on the world market have prompted renewed interest in porphyry copper deposits in Southeast Asia and Melanesia. However, predicted undiscovered deposits may not be found, and if found, may not be developed. This assessment includes an overview of the assessment results with summary tables. Detailed descriptions of each tract are included in appendixes, with estimates of numbers of undiscovered deposits, and probabilistic estimates of amounts of copper, molybdenum, gold, and silver that could be contained in undiscovered deposits for each permissive tract. A geographic information system (GIS) that accompanies the report includes tract boundaries and a database of known porphyry copper deposits and significant prospects.

Scientific Investigations Report↗

USGS1999

A comparison of the weights-of-evidence method and probabilistic neural networks

The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of −1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.

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↗