W. David Menzie

Computer Monte Carlo simulation in quantitative resource estimation

The method of making quantitative assessments of mineral resources sufficiently detailed for economic analysis is outlined in three steps. The steps are (1) determination of types of deposits that may be present in an area, (2) estimation of the numbers of deposits of the permissible deposit types, and (3) combination by Monte Carlo simulation of the estimated numbers of deposits with the historical grades and tonnages of these deposits to produce a probability distribution of the quantities of contained metal.Two examples of the estimation of the number of deposits (step 2) are given. The first example is for mercury deposits in southwestern Alaska and the second is for lode tin deposits in the Seward Peninsula.The flow of the Monte Carlo simulation program is presented with particular attention to the dependencies between grades and tonnages of deposits and between grades of different metals in the same deposit.

Is there a metric for mineral deposit occurrence probabilities

Traditionally, mineral resource assessments have been used to estimate the physical inventory of critical and strategic mineral commodities that occur in pieces of land and to assess the consequences of supply disruptions of these commodities. More recently, these assessments have been used to estimate the undiscovered mineral wealth in such pieces of land to assess the opportunity cost of using the land for purposes other than mineral production. The field of mineral resource assessment is an interdisciplinary field that draws elements from the disciplines of geology, economic geology (descriptive models), statistics and management science (grade and tonnage models), mineral economics, and operations research (computer simulation models). The purpose of this study is to assert that an occurrenceprobability metric exists that is useful in “filling out” an assessment both for areas in which only a trivial probability exists that a new mining district could be present and for areas where nontrivial probabilities exist for such districts.

Public attitudes and policies toward mineral resources on the brink of the 21st century

In this issue, we feature an article by W. David Menzie, a research geologist with the U.S. Geological Survey, Reston, Virginia. Dr. Menzie is a leading expert on quantitative mineral-resource assessment. He has made significant contributions to quantitative assessment methodologies through the development of spatial mineral deposit density models, grade and tonnage models, and the design of metrics for describing mineral deposit occurrences. He has also studied the geology and mineral resources of the Circle quadrangle, Alaska. Dr. Menzie earned a B.S. degree in geology from Dickinson College, an M.S. in geology, an M.A. in statistics, and a Ph.D. in Geology from the Pennsylvania State University.

Comment on “Metallic Mineral Resources in the Twenty-First Century: I. Historical Extraction Trends and Expected Demand” by Alberto E. Patino Douce, in Natural Resources Research DOI: 10.1007/s11053-015-9266-z

We agree with the author that metal consumption cannot be described as a logistic function of time, but disagree that we ever said it should be so modeled. Whether one uses a linear or a logistic function, direct use of time as a predictor of metal consumption does not make sense and provides little confidence in the predictions. The main purpose of this paper was to generate estimates of likely future demand of metallic raw materials. Many studies show that metal consumption per capita is well predicted by GDP per capita because consumption has been shown to increase with increasing income, but levels off, or even declines with high income (Radetzki and Tilton 1990; Menzie et al. 2005; Singer and Menzie 2009, 2010; Jaunky 2012; Puchkov 2005). Thus, a short-term linear function of global consumption might seem to be a good predictor, but it ignores the leveling off or decline in consumption that occurs in the long term as per capita income increases. A logistic function of per capita national metal consumption based on GDP per capita is desirable for predicting long-term consumption. There is persuasive evidence that per capita consumption of minerals levels off with higher per capita income, which is significant if one attempts to predict future demand of minerals.