Search USGSSearch

USGS · 70017638

Prospector II: Towards a knowledge base for mineral deposits

Abstract

What began in the mid-seventies as a research effort in designing an expert system to aid geologists in exploring for hidden mineral deposits has in the late eighties become a full-sized knowledge-based system to aid geologists in conducting regional mineral resource assessments. Prospector II, the successor to Prospector, is interactive-graphics oriented, flexible in its representation of mineral deposit models, and suited to regional mineral resource assessment. In Prospector II, the geologist enters the findings for an area, selects the deposit models or examples of mineral deposits for consideration, and the program compares the findings with the models or the examples selected, noting the similarities, differences, and missing information. The models or the examples selected are ranked according to scores that are based on the comparisons with the findings. Findings can be reassessed and the process repeated if necessary. The results provide the geologist with a rationale for identifying those mineral deposit types that the geology of an area permits. In future, Prospector II can assist in the creation of new models used in regional mineral resource assessment and in striving toward an ultimate classification of mineral deposits. ?? 1994 International Association for Mathematical Geology.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R.B. McCammon. 1994. Prospector II: Towards a knowledge base for mineral deposits. https://doi.org/10.1007/bf02083422

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Declustering of clustered preferential sampling for histogram and semivariogram inference

Measurements of attributes obtained more as a consequence of business ventures than sampling design frequently result in samplings that are preferential both in location and value, typically in the form of clusters along the pay. Preferential sampling requires preprocessing for the purpose of properly inferring characteristics of the parent population, such as the cumulative distribution and the semivariogram. Consideration of the distance to the nearest neighbor allows preparation of resampled sets that produce comparable results to those from previously proposed methods. Clustered sampling of size 140, taken from an exhaustive sampling, is employed to illustrate this approach. ?? International Association for Mathematical Geology 2007.

Mathematical Geology

Comparison of two probability distributions used to model sizes of undiscovered oil and gas accumulations: Does the tail wag the assessment?

Undiscovered oil and gas assessments are commonly reported as aggregate estimates of hydrocarbon volumes. Potential commercial value and discovery costs are, however, determined by accumulation size, so engineers, economists, decision makers, and sometimes policy analysts are most interested in projected discovery sizes. The lognormal and Pareto distributions have been used to model exploration target sizes. This note contrasts the outcomes of applying these alternative distributions to the play level assessments of the U.S. Geological Survey's 1995 National Oil and Gas Assessment. Using the same numbers of undiscovered accumulations and the same minimum, medium, and maximum size estimates, substitution of the shifted truncated lognormal distribution for the shifted truncated Pareto distribution reduced assessed undiscovered oil by 16% and gas by 15%. Nearly all of the volume differences resulted because the lognormal had fewer larger fields relative to the Pareto. The lognormal also resulted in a smaller number of small fields relative to the Pareto. For the Permian Basin case study presented here, reserve addition costs were 20% higher with the lognormal size assumption. ?? 2002 International Association for Mathematical Geology.

Mathematical Geology

Uncertainty estimation for resource assessment-an application to coal

The U.S. Geological Survey is conducting a national assessment of coal resources. As part of that assessment, a geostatistical procedure has been developed to estimate the uncertainty of coal resources for the historical categories of geological assurance: measured, indicated, inferred, and hypothetical coal. Data consist of spatially clustered coal thickness measurements from coal beds and/or zones that cover, in some cases, several thousand square kilometers. Our procedure involved trend removal, an examination of spatial correlation, computation of a sample semivariogram, and fitting a semivariogram model. This model provided standard deviations for the uncertainty estimates. The number of sample points (drill holes) in each historical category also was estimated. Measurement error in the thickness of the coal bed/zone was obtained from the fitted model or supplied exogenously. From this information approximate estimates of uncertainty on the historical categories were computed. We illustrate the methodology using drill hole data from the Harmon coal bed located in southwestern North Dakota. The methodology will be applied to approximately 50 coal data sets.

Mathematical Geology