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 109 records · Page 6Linked to original sources
USGS2000

The origin and evolution of safe-yield policies in the Kansas groundwater management districts

The management of groundwater resources in Kansas continues to evolve. Declines in the High Plains aquifer led to the establishment of groundwater management districts in the mid-1970s and reduced streamflows prompted the enactment of minimum desirable streamflow standards in the mid-1980s. Nonetheless, groundwater levels and streamflows continued to decline, although at reduced rates compared to premid-1980s rates. As a result, "safe-yield" policies were revised to take into account natural groundwater discharge in the form of stream baseflow. These policies, although a step in the right direction, are deficient in several ways. In addition to the need for more accurate recharge data, pumping-induced streamflow depletion, natural stream losses, and groundwater evapotranspiration need to be accounted for in the revised safe-yield policies. Furthermore, the choice of the 90% flow-duration statistic as a measure of baseflow needs to be reevaluated, as it significantly underestimates mean baseflow estimated from baseflow separation computer programs; moreover, baseflow estimation needs to be refined and validated.

Kansas↗
USGS2000

Assessment of nitrate-nitrogen distribution in Kansas groundwater, 1990-1998

Nitrate-nitrogen (nitrate-N) in groundwater is a potential problem in many parts of Kansas. From 1990 to 1998, 747 water samples were collected from domestic, irrigation, monitoring, and public water supply wells primarily from the western two-thirds of the state, and analyzed for nitrate-N by the Kansas Geological Survey. Nitrate-N concentrations of the 747 samples analyzed range from 29% with less than or equal to 3 mg/L, 51% between 3 and 10 mg/L, and 20% greater than or equal to 10 mg/L. Factors that show a statistically significant relationship with the occurrence of nitrate-N in Kansas groundwater in this assessment include geographic area of the state, depth of well, and age of well. Nitrate-N levels of wells screened in the High Plains aquifer in south-central Kansas showed a statistically higher concentration than samples collected from the High Plains aquifer in the western portion of the state. Comparison of nitrate-N with depth of well indicated that shallower wells throughout the state generally have higher nitrate-N values than deeper wells. Irrigation wells older than 1975 showed statistically higher nitrate-N concentration than wells installed during and since 1975, possibly related to changes in well-construction practices and regulations that occurred in 1975.

Kansas↗
USGS2000

Analyzing slug tests in wells screened across the watertable: A field assessment

The slug test is the most widely used technique for the in situ estimation of hydraulic conductivity in confined and unconfined formations. Currently, there are no generally accepted methods in the groundwater literature for the analysis of response data from slug tests performed in wells screened across the watertable. A field study was undertaken in an attempt to develop a set of practical guidelines for tests conducted in such wells. Three wells, screened within unconsolidated material exhibiting a range of hydraulic conductivities (.05-30.0 m/day), were installed to depths of up to 9 m (30 ft) in Kansas River alluvium that ranges in thickness from 15 m to 21 m (50 ft to 70 ft) near Lawrence, Kansas. Intensive well-development efforts removed any drilling debris that could interfere with well-formation hydraulics. Once the wells were developed properly, a series of slug tests was performed at each well. The tests were designed to assess the role of the unsaturated zone and the appropriateness of assuming a fixed hydraulic head upper boundary. The results of this investigation can be summarized as follows: (1) the sufficiency of well development should be based on repeat slug tests and not the clarity of pumped water; (2) the effective screen radius for best model analysis should be based on a mass balance and not nominal screen dimensions; (3) the watertable can be represented as a constant head boundary and flow in the unsaturated zone can be ignored in most situations; (4) conventional techniques for the analysis of slug-test data seem to be reasonable for slug tests conducted in wells screened across the watertable, when used with the appropriate effective screen radius and normalized head range; and (5) fluctuations in the watertable elevation through time can be exploited to obtain some insight into the nature of vertical variation in hydraulic conductivity at a well. The results of this investigation indicate that multiple slug tests should be performed at wells screened across the watertable in order to reliably assess the sufficiency of well development and the appropriateness of conventional theory. ?? 2000 International Association for Mathematical Geology.

Natural Resources Research↗
USGS2001

The Upper Pennsylvanian Pittsburgh coal bed: Resources and mine models

The U.S. Geological Survey recently completed a digital coal resource assessment model of the Upper Pennsylvanian Pittsburgh coal bed, which indicates that after subtracting mined-out coal, 16 billion short tons (14 billion tonnes) remain of the original 34 billion short tons (31 billion tonnes) of coal. When technical, environmental, and social restrictions are applied to the remaining Pittsburgh coal model, only 12 billion short tons (11 billion tonnes) are available for mining. Our assessment models estimate that up to 0.61 billion short tons (0.55 billion tonnes), 2.7 billion short tons (2.4 billion tonnes), and 8.5 billion short tons (7.7 billion tonnes) could be available for surface mining, continuous mining, and longwall mining, respectively. This analysis is an example of a second-generation regional coal availability study designed to model recoverability characteristics for all the major coal beds in the United States.

Maryland, Ohio, Pennsylvania, West Virginia↗
USGS2001

Some simple guides to finding useful information in exploration geochemical data

Most regional geochemistry data reflect processes that can produce superfluous bits of noise and, perhaps, information about the mineralization process of interest. There are two end-member approaches to finding patterns in geochemical data—unsupervised learning and supervised learning. In unsupervised learning, data are processed and the geochemist is given the task of interpreting and identifying possible sources of any patterns. In supervised learning, data from known subgroups such as rock type, mineralized and nonmineralized, and types of mineralization are used to train the system which then is given unknown samples to classify into these subgroups. To locate patterns of interest, it is helpful to transform the data and to remove unwanted masking patterns. With trace elements use of a logarithmic transformation is recommended. In many situations, missing censored data can be estimated using multiple regression of other uncensored variables on the variable with censored values. In unsupervised learning, transformed values can be standardized, or normalized, to a Z-score by subtracting the subset's mean and dividing by its standard deviation. Subsets include any source of differences that might be related to processes unrelated to the target sought such as different laboratories, regional alteration, analytical procedures, or rock types. Normalization removes effects of different means and measurement scales as well as facilitates comparison of spatial patterns of elements. These adjustments remove effects of different subgroups and hopefully leave on the map the simple and uncluttered pattern(s) related to the mineralization only. Supervised learning methods, such as discriminant analysis and neural networks, offer the promise of consistent and, in certain situations, unbiased estimates of where mineralization might exist. These methods critically rely on being trained with data that encompasses all populations fairly and that can possibly fall into only the identified populations.

Natural Resources Research↗
USGS2001

Characteristics of water-well yields in part of the Blue Ridge Geologic Province in Loudoun County, Virginia

Loudoun County, Virginia, which is located about 50 km to the west of Washington, DC, was the site of intensive suburban development during the 1980s and 1990s. In the western half of the county, the source of water for domestic use has been from wells drilled into the fractured crystalline bedrock of the Blue Ridge Geologic Province. A comprehensive digital database that contains information on initial yield, location, depth, elevation, and other data for 3651 wells drilled in this 825.5-km 2 area was combined with a digital geologic map to form the basis for a study of geologic and temporal controls on water-well yields. Statistical modeling procedures were used to determine that mean yields for the wells were significantly different as a function of structural setting, genetic rock type, and geologic map unit. The Bonferroni procedure then was used to determine which paired comparisons contributed to these significant differences. The data were divided into 15 temporal drilling increments to determine if the time-dependent trends that exist for the Loudoun County data are similar to those discovered in a previous study of water-well yields in the Pinardville 7.5-min quadrangle, New Hampshire. In both regions, trends, which include increasing proportions of very low yield wells and increasing well depths through time, and the counterintuitive result of increasing mean well yields through time, were similar. In addition, a yield-to-depth curve similar tothat discovered in the Pinardville quadrangle was recognized in this study. Thus, the temporal model with a feed-forward-loop mechanism to explain the temporal trends in well characteristics proposed for the New Hampshire study appears to apply to western Loudoun County.

Virginia↗
USGS2001

Geologic coal assessment: The interface with economics

Geologic resource assessments describe the location, general characteristics, and estimated volumes of resources, whether in situ or technically recoverable. Such compilations are only an initial step in economic resource evaluation. This paper identifies, by examples from the Illinois and Appalachian basins, the salient features of a geologic assessment that assure its usefulness to downstream economic analysis. Assessments should be in sufficient detail to allocate resources to production units (mines or wells). Coal assessments should include the spatial distribution of coal bed characteristics and the ability to allocate parts of the resource to specific mining technologies. For coal bed gas assessment, the production well recoveries and well deliverability characteristics must be preserved and the risk structure should be specified so dryholes and noncommercial well costs are recovered by commercially successful wells.

Natural Resources Research↗
USGS2001

Lithodiversity and its spatial association with metallic mineral sites, Great Basin of Nevada

Geographical information system (GIS) techniques were used to investigate the spatial association between metallic mineral sites and lithodiversity in Nevada. Mineral site data sets include various size and type subsets of about 5,500 metal-bearing occurrences and deposits. Lithodiversity was calculated by counting the number of unique geological map units within four sizes of square-shaped sample neighborhoods (2.5-by-2.5, 5-by-5, 10-by-10, and 20-by-20 km) on three different scales of geological maps (national, 1:2,500,000; state, 1:500,000; county, 1:250,000). The spatial association between mineral sites and lithodiversity was observed to increase with increasing lithodiversity. This relationship is consistent for (1) both basin-range and range-only regions, (2) four sizes of sample neighborhoods, (3) various mineral site subsets, (4) the three scales of geological maps, and (5) areas not covered by large-scale maps. A map scale of 1:500,000 and lithodiversity sampling neighborhood of 5-by-5 km was determined to best describe the association. Positive associations occurred for areas having >3 geological map units per neighborhood, with the strongest observed at approximately >7 units. Areas in Nevada with more than three geological map units per 5-by-5 km neighborhood contain more mineral sites than would be expected resulting from chance. High lithodiversity likely reflects the occurrence of complex structural, stratigraphic, and intrusive relationships that are thought to control, focus, localize, or expose mineralization. The application of lithodiversity measurements to areas that are not well explored may help delineate regional-scale exploration targets and provide GIS-supported mineral resource assessment and exploration activity another method that makes use of widely available geological map data.

Nevada↗
USGS2002

Environmentalism and natural aggregate mining

Sustaining a developed economy and expanding a developing one require the use of large volumes of natural aggregate. Almost all human activity (commercial, recreational, or leisure) is transacted in or on facilities constructed from natural aggregate. In our urban and suburban worlds, we are almost totally dependent on supplies of water collected behind dams and transported through aqueducts made from concrete. Natural aggregate is essential to the facilities that produce energy—hydroelectric dams and coal-fired powerplants. Ironically, the utility created for mankind by the use of natural aggregate is rarely compared favorably with the environmental impacts of mining it. Instead, the empty quarries and pits are seen as large negative environmental consequences. At the root of this disassociation is the philosophy of environmentalism, which flavors our perceptions of the excavation, processing, and distribution of natural aggregate. The two end-member ideas in this philosophy are ecocentrism and anthropocentrism. Ecocentrism takes the position that the natural world is a organism whose arteries are the rivers—their flow must not be altered. The soil is another vital organ and must not be covered with concrete and asphalt. The motto of the ecocentrist is “man must live more lightly on the land.” The anthropocentrist wants clean water and air and an uncluttered landscape for human use. Mining is allowed and even encouraged, but dust and noise from quarry and pit operations must be minimized. The large volume of truck traffic is viewed as a real menace to human life and should be regulated and isolated. The environmental problems that the producers of natural aggregate (crushed stone and sand and gravel) face today are mostly difficult social and political concerns associated with the large holes dug in the ground and the large volume of heavy truck traffic associated with quarry and pit operations. These concerns have increased in recent years as society's demand for living space has encroached on the sites of production; in other words, the act of production has engendered condemnation. Many other environmental problems that are associated with dust and noise and blasting from quarry and pit operations have been reduced through the efficient use of technology. Recycling concrete in buildings, bridges, and roads and asphaltic pavements will ultimately reduce the demand for virgin natural aggregate. The impact created by the large holes in the ground required for the mining of natural aggregate can be greatly reduced by planning their reclamation before mining begins.

Natural Resources Research↗
USGS2002

Some aspects of resource uncertainty and their economic consequences in assessment of the 1002 Area of the Arctic National Wildlife Refuge

Exploration ventures in frontier areas have high risks. Before committing to them, firms prepare regional resource assessments to evaluate the potential payoffs. With no historical basis for directly estimating size distribution of undiscovered accumulations, reservoir attribute probability distributions can be assessed subjectively and used to project undiscovered accumulation sizes. Three questions considered here are: (1) what distributions should be used to characterize the subjective assessments of reservoir attributes, (2) how parsimonious can the analyst be when eliciting subjective information from the assessment geologist, and (3) what are consequences of ignoring dependencies among reservoir attributes? The standard or norm used for comparing outcomes is the computed cost function describing costs of finding, developing, and producing undiscovered oil accumulations. These questions are examined in the context of the US Geological Survey's recently published regional assessment of the 1002 Area of the Arctic National Wildlife Refuge, Alaska. We study effects of using the various common distributions to characterize the geologist's subjective distributions representing reservoir attributes. Specific findings show that triangular distributions result in substantial bias in economic forecasts when used to characterize skewed distributions. Moreover, some forms of the lognormal distribution also result in biased economic inferences. Alternatively, we generally determined four fractiles (100, 50, 5, 0) to be sufficient to capture essential economic characteristics of the underlying attribute distributions. Ignoring actual dependencies among reservoir attributes biases the economic evaluation.

Alaska↗
USGS2002

Preliminary cellular-automata forecast of permit activity from 1998 to 2010, Idaho and Western Montana

For public land management in Idaho and western Montana, the U.S. Forest Service (USFS) has requested that the U.S. Geological Survey (USGS) predict where mineral-related activity will occur in the next decade. Cellular automata provide an approach to simulation of this human activity. Cellular automata (CA) are defined by an array of cells , which evolve by a simple transition rule, the automaton . Based on exploration trends, we assume that future exploration will focus in areas of past exploration. Spatial-temporal information about mineral-related activity, that is permits issued by USFS and Bureau of Land Management (BLM) in the last decade, and spatial information about undiscovered resources, provide a basis to calibrate a CA. The CA implemented is a modified annealed voting rule that simulates mineral-related activity with spatial and temporal resolution of 1 mi 2 and 1 year based on activity from 1989 to 1998. For this CA, the state of the economy and exploration technology is assumed constant for the next decade. The calibrated CA reproduces the 1989–1998-permit activity with an agreement of 94%, which increases to 98% within one year. Analysis of the confusion matrix and kappa correlation statistics indicates that the CA underestimates high activity and overestimates low activity. Spatially, the major differences between the actual and calculated activity are that the calculated activity occurs in a slightly larger number of small patches and is slightly more uneven than the actual activity. Using the calibrated CA in a Monte Carlo simulation projecting from 1998 to 2010, an estimate of the probability of mineral activity shows high levels of activity in Boise, Caribou, Elmore, Lincoln, and western Valley counties in Idaho and Beaverhead, Madison, and Stillwater counties in Montana, and generally low activity elsewhere.

Idaho, Montana↗
USGS2002

Worldwide estimates of deep natural gas resources based on the U.S. Geological Survey World Petroleum Assessment 2000

The U.S. Geological Survey recently assessed undiscovered conventional gas and oil resources in eight regions of the world outside the U.S. The resources assessed were those estimated to have the potential to be added to reserves within the next thirty years. This study is a worldwide analysis of the estimated volumes and distribution of deep (>4.5 km or about 15,000 ft), undiscovered conventional natural gas resources based on this assessment. Two hundred forty-six assessment units in 128 priority geologic provinces, 96 countries, and two jointly held areas were assessed using a probabilistic Total Petroleum System approach. Priority geologic provinces were selected from a ranking of 937 provinces worldwide. The U.S. Geological Survey World Petroleum Assessment Team did not assess undiscovered petroleum resources in the U.S. For this report, mean estimated volumes of deep conventional undiscovered gas resources in the U.S. are taken from estimates of 101 deep plays (out of a total of 550 conventional plays in the U.S.) from the U.S. Geological Survey's 1995 National Assessment of Oil and Gas Resources. A probabilistic method was designed to subdivide gas resources into depth slices using a median-based triangular probability distribution as a model for drilling depth to estimate the percentages of estimated gas resources below various depths. For both the World Petroleum Assessment 2000 and the 1995 National Assessment of Oil and Gas Resources, minimum, median, and maximum depths were assigned to each assessment unit and play; these depths were used in our analysis. Two-hundred seventy-four deep assessment units and plays in 124 petroleum provinces were identified for the U.S. and the world. These assessment units and plays contain a mean undiscovered conventional gas resource of 844 trillion cubic ft (Tcf) occuring at depths below 4.5 km. The deep undiscovered conventional gas resource (844 Tcf) is about 17% of the total world gas resource (4,928 Tcf) based on the provinces assessed and includes a mean estimate of 259 Tcf of U.S. gas from the U.S. 1995 National Assessment. Of the eight regions, the Former Soviet Union (Region 1) contains the largest estimated volume of undiscovered deep gas with a mean resource of343 Tcf.

Natural Resources Research↗
USGS2002

A reconnaissance method for delineation of tracts for regional-scale mineral-resource assessment based on geologic-map data

The U.S. Geological Survey (USGS) is proposing to conduct a global mineral-resource assessment using geologic maps, significant deposits, and exploration history as minimal data requirements. Using a geologic map and locations of significant pluton-related deposits, the pluton-related-deposit tract maps from the USGS national mineral-resource assessment have been reproduced with GIS-based analysis and modeling techniques. Agreement, kappa, and Jaccard's C correlation statistics between the expert USGS and calculated tract maps of 87%, 40%, and 28%, respectively, have been achieved using a combination of weights-of-evidence and weighted logistic regression methods. Between the experts' and calculated maps, the ranking of states measured by total permissive area correlates at 84%. The disagreement between the experts and calculated results can be explained primarily by tracts defined by geophysical evidence not considered in the calculations, generalization of tracts by the experts, differences in map scales, and the experts' inclusion of large tracts that are arguably not permissive. This analysis shows that tracts for regional mineral-resource assessment approximating those delineated by USGS experts can be calculated using weights of evidence and weighted logistic regression, a geologic map, and the location of significant deposits. Weights of evidence and weighted logistic regression applied to a global geologic map could provide quickly a useful reconnaissance definition of tracts for mineral assessment that is tied to the data and is reproducible.

Natural Resources Research↗
USGS2002

Statistical characteristics of xenoliths in the Antioch kimberlite pipe, Marshall County, northeastern Kansas

Geometrical characteristics of xenoliths in the Antioch kimberlite pipe have been considered in statistical terms. A method of conversion of 2D intersections to 3D dimensions was used. It has been shown that the Rosin-Rammler distribution of mass leads to the Weibull distribution of sizes, whereas a fractal distribution of sizes can be expressed as the Pareto distribution. Lognormal, Weibull, and Pareto distributions have been tested as model distributions. The Pareto distribution could be the most appropriate model for the distribution of xenoliths. This conclusion is in agreement with the general concept that the xenoliths formed as a result of an underground explosion without additional breakage occurring during magma transport. The final distribution may be shifted from the initial model as a result of processes of redistribution and sorting of xenoliths in liquid-crystalline flows.

Kansas↗
USGS1999

Evaluation of weights of evidence to predict epithermal-gold deposits in the Great Basin of the western United States

The weights-of-evidence method provides a simple approach to the integration of diverse geologic information. The application addressed is to construct a model that predicts the locations of epithermal-gold mineral deposits in the Great Basin of the western United States. Weights of evidence is a data-driven method requiring known deposits and occurrences that are used as training sites in the evaluated area. Four hundred and fifteen known hot spring gold–silver, Comstock vein, hot spring mercury, epithermal manganese, and volcanogenic uranium deposits and occurrences in Nevada were used to define an area of 327.4 km 2 as training sites to develop the model. The model consists of nine weighted-map patterns that are combined to produce a favorability map predicting the distribution of epithermal-gold deposits. Using a measure of the association of training sites with predictor features (or patterns), the patterns can be ranked from best to worst predictors. Based on proximity analysis, the strongest predictor is the area within 8 km of volcanic rocks younger than 43 Ma. Being close to volcanic rocks is not highly weighted, but being far from volcanic rocks causes a strong negative weight. These weights suggest that proximity to volcanic rocks define where deposits do not occur. The second best pattern is the area within 1 km of hydrothermally altered areas. The next best pattern is the area within 1 km of known placer-gold sites. The proximity analysis for gold placers weights this pattern as useful when close to known placer sites, but unimportant where placers do not exist. The remaining patterns are significantly weaker predictors. In order of decreasing correlation, they are: proximity to volcanic vents, proximity to east-west to northwest faults, elevated airborne radiometric uranium, proximity to northwest to west and north-northwest linear features, elevated aeromagnetics, and anomalous geochemistry. This ordering of the patterns is a function of the quality, applicability, and use of the data. The nine-pattern favorability map can be evaluated by comparison with the USGS National Assessment for hot spring gold–silver deposits. The Spearman's ranked correlation coefficient between the favorability and the National Assessment permissive tracts is 0.5. Tabulations of the areas of agreement and disagreement between the two maps show 74% agreement for the Great Basin. The posterior probabilities for 51 significant deposits in the Great Basin, both used and not used in the model, show the following: 26 classified as favorable; 25 classified as permissive; and 1, Florida Canyon, classified as nonpermissive.The Florida Canyon deposit has a low favorability because there are no volcanic rocks near the deposit on the Nevada geologic map used. The largest areas of disagreement are caused by the USGS National Assessment team concluding that volcanic rocks older than 27 Ma in Nevada are not permissive, which was not assumed in this model. The weights-of-evidence model is evaluated as reasonable and delineates permissive areas for epithermal deposits comparable to expert's delineation. The weights-of-evidence model has the additional characteristics that it is well defined, reproducible, objective, and provides a quantitative measure of confidence.

California, Nevada↗
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↗
USGS1999

Examining risk in mineral exploration

Successful mineral exploration strategy requires identification of some of the risk sources and considering them in the decision-making process so that controllable risk can be reduced. Risk is defined as chance of failure or loss. Exploration is an economic activity involving risk and uncertainty, so risk also must be defined in an economic context. Risk reduction can be addressed in three fundamental ways: (1) increasing the number of examinations; (2) increasing success probabilities; and (3) changing success probabilities per test by learning. These provide the framework for examining exploration risk. First, the number of prospects examined is increased, such as by joint venturing, thereby reducing chance of gambler's ruin . Second, success probability is increased by exploring for deposit types more likely to be economic, such as those with a high proportion of world-class deposits. For example, in looking for 100+ ton (>3 million oz) Au deposits, porphyry Cu-Au, or epithermal quartz alunite Au types require examining fewer deposits than Comstock epithermal vein and most other deposit types. For porphyry copper exploration, a strong positive relationship between area of sulfide minerals and deposits' contained Cu can be used to reduce exploration risk by only examining large sulfide systems. In some situations, success probabilities can be increased by examining certain geologic environments. Only 8% of kuroko massive sulfide deposits are world class, but success chances can be increased to about 15% by looking in settings containing sediments and rhyolitic rocks. It is possible to reduce risk of loss during mining by sequentially developing and expanding a mine—thus reducing capital exposed at early stages and reducing present value of risked capital. Because this strategy is easier to apply in some deposit types than in others, the strategy can affect deposit types sought. Third, risk is reduced by using prior information and by changing the independence of trials assumption, that is, by learning. Bayes' formula is used to change the probability of existence of the deposit sought on the basis of successive exploration stages. Perhaps the most important way to reduce exploration risk is to employ personnel with the appropriate experience and yet who are learning.

Natural Resources Research↗