The map of permanent natural electric field of Russia

The article describes a direct connection between the natural electric potential and the dynamics of the temperature of volcanoes using the examples of observation of the natural electric potential on the surfaces of volcanoes. If the upper part of the volcano is hotter, positive potential anomalies are recorded, and conversely, if the lower part of the volcanoes is hotter, negative anomalies of the same potential are recorded. At the same time, the temperature processes occurring at great depths, as a rule, are closely related to long-lived deep faults. Therefore, observations of the natural potential over these faults will allow controlling the dynamics of deep temperature processes. Given this new direction of the natural potential method and its effective application in the search for non-ferrous metal ores, there is a need to create the map of the natural electrical potential of Russia. As a result, small-scale map would allow us more precise limitation of ore fields and purposefully search for previously undiscovered deposits of metal ores. In addition, a small-scale map would make it possible to most accurately track the development of deep tectonic fault zones and to study them in relation to volcanic activity and seismic events. In this regard, the method of natural electric potential hodograph is considered as one of the possible ways to predict seismic events.

Porphyry Copper Potential of the United States Southern Basin and Range Using ASTER Data Integrated with Geochemical and Geologic Datasets to Assess Potential Near-Surface Deposits in Well-Explored Permissive Tracts

ArcGIS was used to spatially assess and rank potential porphyry copper deposits using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data together with geochemical and geologic datasets in order to estimate undiscovered deposits in the southern Basin and Range Province in the southwestern United States. The assessment was done using a traditional expert opinion three-part method and a prospectivity model developed using weights of evidence and logistic regression techniques to determine if ASTER data integrated with other geologic datasets can be used to find additional areas of prospectivity in well-explored permissive tracts. ASTER hydrothermal alteration data were expressed as 457 alteration polygons defined from a low-pass filtered alteration density map of combined argillic, phyllic, and propylitic rock units. Sediment stream samples were plotted as map grid data and used as spatial information in ASTER polygons. Gravity and magnetic data were also used to define basins greater than 1 km in depth. Each ASTER alteration polygon was ranked for porphyry copper potential using alteration types, spatial amounts of alteration, stream sediment geochemistry, lithology, polygon shape, proximity to other alteration polygons, and deposit and prospects data. Permissive tracts defined for the assessment in the southern Basin and Range Province include the Laramide Northwest, Laramide Southeast, Jurassic, and Tertiary tracts. Expert opinion estimates using the three-part assessment method resulted in a mean estimate of 17 undiscovered porphyry copper deposits, whereas the prospectivity modeling predicted a mean estimate of nine undiscovered deposits. In the well-explored Laramide Southeast tract, which contains the most deposits and has been explored for over 100 years, an average of 4.3 undiscovered deposits was estimated using ASTER alteration polygon data versus 2.8 undiscovered deposits without ASTER data. The Tertiary tract, which contains the largest number of ASTER alteration polygons not associated with known Tertiary deposits, was predicted to contain the most undiscovered resources in the southern Basin and Range Province.

Grade-and-Tonnage Models

Mineral deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different (Singer, Cox, and Drew, 1975; Singer and Kouda, 2003), and (2) deposit types occur in different geologic settings that can be identified from geologic maps. If assessments were only conducted to estimate amounts of undiscovered metals, we would need contained metal models, but determining whether the metals might be economic to recover is an important quality of most assessments, and grades and tonnages are necessary to estimate economic viability of mineral deposits (see chapter 5). In this chapter, we focus on the first part of three-part assessments: grade-and-tonnage models. Too few thoroughly explored mineral deposits are available in most areas being assessed for reliable identification of the important geoscience variables or for robust estimation of undiscovered deposits, so we need mineral-deposit models that are generalized. Well-designed and well-constructed grade-and-tonnage models allow mineral economists to determine the possible economic viability of the resources in the region and provide the foundation for planning. Thus, mineral deposit models play the central role in transforming geoscience information to a form useful to policy-makers. Grade-and-tonnage models are fundamental in the development of other kinds of models such as deposit-density and economic filters. Frequency distributions of tonnages and average grades of well-explored deposits of each type are employed as models for grades and tonnages of undiscovered deposits of the same type in geologically similar settings. Grade-and-tonnage models (Cox and Singer, 1986; Mosier and Page, 1988; Bliss, 1992a, 1992b; Cox et al., 2003; Singer, Berger, and Moring, 2008) combined with estimates of number of undiscovered deposits are the fundamental means of translating geologists’ resource assessments into a language that decision-makers can use. For example, creation of a grade-and-tonnage model for rhyolite-hosted Sn deposits in 1986 demonstrated for the first time that 90 percent of such deposits contain less than 4,200 tons of ore. This made it clear that an ongoing research project by the U.S. Geological Survey on this deposit type could have no effect on domestic supplies of tin, and the project was cancelled.

Integration of Grades, Tonnages, Number of Deposits, and Economic Effects

Now that all of the fundamental parts of a quantitative mineral resource assessment have been discussed, it is useful to reflect on why all of the work has been done. As mentioned in chapter 1, it is quite easy to generate an assessment of the “potential” for undiscovered mineral resources. Aside from the question of what, if anything, “potential” means, there is the more serious question of whether a decision-maker has any use for it. The three-part form of assessment is part of a system designed to respond to the needs of decision-makers. Although many challenging ideas are presented in this book, it has a different purpose than most academic reports. This book has the same goal as Allais (1957)—to provide information useful to decision makers. Unfortunately, handing a decision-maker a map with some tracts outlined and frequency distributions of some tonnages and grades along with estimates of the number of deposits that might exist along with their associated probabilities is not really being helpful—these need to be converted to a language understandable to others. This chapter summarizes how these various estimates can be combined and put in more useful forms. If assessments were conducted only to estimate amounts of undiscovered metals, we would need contained metal models and estimates of the number of undiscovered deposits. Grades are simply the ratio of contained metal to tons of ore (chapter 6), so contained metal estimates are available for each deposit. In the simplest of all cases, one could estimate the expected number of deposits with equation 8.1 (see chapter 8) and multiply it by the expected amount of metal per deposit, such as the 27,770 tons of copper in table 9.1, to make an estimate of the expected amount of undiscovered metal. As pointed out in chapter 1, expected amounts of resources or their values can be very misleading because they provide no information about how uncommon the expected value can be with skewed frequency distributions that are common in mineral resources; that is, uncertainty is ignored.

Estimating the Number of Undiscovered Deposits

The third part of three-part assessments is the estimate of some fixed but unknown number of deposits of each type that exist in the delineated tracts. Until the area being considered is thoroughly and extensively drilled, this fixed number of undiscovered deposits, which could be any number including 0, will not be known with certainty. This number of deposits has meaning only in terms of a grade-and-tonnage model. If this requirement did not exist, any wisp of minerals could be considered worthy of estimation, and even in small regions, we would need to estimate millions of “deposits.” For example, it is not difficult to imagine tens of thousands of fist-sized skarn copper “deposits” in parts of western United States—even in this example, we have used “deposit” size to provide important information. In another example, Wilson et al. (1996) estimated five or more epithermal gold vein deposits at the 90 percent level but provided no grade-and-tonnage model, so these estimated deposits could be any size. To provide critical information to decision-makers, the grade-and-tonnage model is key, and the estimated number of deposits that might exist must be from the grade-and-tonnage frequency distributions. In three-part assessments, the parts and estimates are internally consistent in that delineated tracts are consistent with descriptive models, grade-and-tonnage models are consistent with descriptive models and with known deposits in the area, and estimates of number of deposits are consistent with grade-and-tonnage models. Considerable care must be exercised in quantitative resource assessments to prevent the introduction of biased estimates of undiscovered resources. Biases can be introduced into these estimates either by a flawed grade-and-tonnage model or by the lack of consistency of the grade-and-tonnage model with the number-of-deposit estimates. For this reason, consistency of estimates of number of deposits with the grade-and-tonnage models is the most important guideline. Issues about consistency of mineral deposit models are discussed in chapters 3 through 6. Grade-and-tonnage models (chapter 6), which are the first part of three-part assessments, are of particular concern. In this chapter, the focus is on making unbiased estimates of the number of undiscovered deposits.

Introduction

Every day, somewhere in the world, decisions are made about how public lands that might contain undiscovered resources should be used or whether to invest in exploration for minerals. Less frequently, decisions are made concerning mineral resource adequacy, national policy, and regional development. Naturally, the people making the decisions would like to know the exact consequences of the decisions before the decisions are made. Unfortunately, it is not possible to inform these decision-makers, with any certainty, about amounts, discoverability, or economics of undiscovered mineral resources. The kind of assessment recommended in this book is founded in decision analysis in order to provide a normative framework for making decisions concerning mineral resources under conditions of uncertainty. Our goal is to make explicit the factors that can affect a mineral-related decision so that the decision-maker can clearly see the possible consequences of the decision. This means that we start with the question of what kinds of issues decision-makers are trying to resolve and what types and forms of information would aid in resolving these issues. This book has a different purpose than academic reports common to many assessments, and it is not designed to help select sites for exploration. The audience for products of assessments discussed here comprises governmental and industrial policy-makers, managers of exploration, planners of regional development, and similar decision-makers. Some of the tools and models presented here are useful for selection of exploration sites, but that is a side benefit. The focus of this book is on the practical integration of the fundamental kinds of information needed by the decision-maker. The integrated approach to assessment presented in this book focuses on three assessment parts and the models that support them. The first part uses models of tonnages and grades to estimate possible tonnages and grades of undiscovered deposits. The second part develops mineral resource maps that explore whether an area’s geology permits the existence of one or more types of mineral deposits. The product of this part of the assessment is identification of so-called permissive tracts of land.

Deposit-Density Models

A key function of many forms of quantitative mineral resource assessments is estimation of the number of undiscovered deposits. In any given region, there is some fixed but, in most cases, unknown number of undiscovered deposits of a given type—the number could be zero or a larger integer. Many quantitative resource assessments that are based on a common three-part form of assessment (Singer, 1993a) have used expert judgment to estimate the number of deposits. Estimates of this unknown number are presented in a probabilistic form to reflect the uncertainty associated with the estimate. Ideally, estimates of number of deposits should rely on analogies with similar well-explored geologic settings, just as grades and tonnages of well explored deposits serve as analogs of the qualities and sizes of undiscovered deposits. Estimates of the number of undiscovered deposits can be derived from counts of known deposits per unit area in explored control regions. Number of deposits per unit area of the control regions can be used in histograms to show variation of densities by deposit type. Some research has been conducted on densities of several deposit types so that these ratios can be more widely used as a guide for number-of-deposit estimates (Bliss, Orris, and Menzie, 1987; Bliss, Menzie, Orris, and Page, 1987; Bliss and Menzie, 1993; Bliss, 1992b; Root, Menzie, and Scott, 1992). Most of these studies provide point (i.e., single) estimates of the number of deposits per unit area. Singer et al. (2001) summarize the ideas behind these mineral deposit density models and provide individual estimates for twenty-seven combinations of deposit types and control locations. Many of the specially selected areas they describe provide standards to identify what should be considered high estimates of number of undiscovered deposits in most situations. Thus, many published mineral-deposit densities provide guides that suggest upper limits to estimates but are not necessarily useful in providing estimation guides for more likely situations.