Long-term implications of new gas estimates

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added.

Nonrenewable Resources↗

USGS1995

Economics of mining law

Modern mining law, by facilitating socially and environmentally acceptable exploration, development, and production of mineral materials, helps secure the benefits of mineral production while minimizing environmental harm and accounting for increasing land-use competition. Mining investments are sunk costs, irreversibly tied to a particular mineral site, and require many years to recoup. Providing security of tenure is the most critical element of a practical mining law. Governments owning mineral rights have a conflict of interest between their roles as a profit-maximizing landowner and as a guardian of public welfare. As a monopoly supplier, governments have considerable power to manipulate mineral-rights markets. To avoid monopoly rent-seeking by governments, a competitive market for government-owned mineral rights must be created by artifice. What mining firms will pay for mineral rights depends on expected exploration success and extraction costs. Landowners and mining firms will negotlate respective shares of anticipated differential rents, usually allowing for some form of risk sharing. Private landowners do not normally account for external benefits or costs of minerals use. Government ownership of mineral rights allows for direct accounting of social prices for mineral-bearing lands and external costs. An equitable and efficient method is to charge an appropriate reservation price for surface land use, net of the value of land after reclamation, and to recover all or part of differential rents through a flat income or resource-rent tax. The traditional royalty on gross value of production, essentially a regressive income tax, cannot recover as much rent as a flat income tax, causes arbitrary mineral-reserve sterilization, and creates a bias toward development on the extensive margin where marginal environmental costs are higher. Mitigating environmental costs and resolving land-use conflicts require local evaluation and planning. National oversight ensures that the relative global avaliability of minerals and other values are considered, and can also promote adaptive efficiency by publicizing creative local solutions, providing technical support, and funding useful research. ?? 1995 Oxford University Press.

Nonrenewable Resources↗