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.

[1]  Donald A. Singer,et al.  Use of a neural network to integrate geoscience information in the classification of mineral deposits and occurrences , 1997 .

[2]  Tom Gedeon,et al.  Artificial neural networks: A new method for mineral prospectivity mapping , 2000 .

[3]  R. A. Crovelli,et al.  An objective replacement method for censored geochemical data , 1993 .

[4]  D. Singer,et al.  Classification of mineral deposits into types using mineralogy with a probabilistic neural network , 1997 .

[5]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[6]  D. Singer,et al.  A Comparison of the Weights-of-Evidence Method and Probabilistic Neural Networks , 1999 .

[7]  R. Howarth Statistics and data analysis in geochemical prospecting , 1983 .

[8]  E. C. Grunsky,et al.  Techniques for analysis and visualization of lithogeochemical data with applications to the Swayze greenstone belt, Ontario , 1999 .

[9]  E. Grunsky Recognition of alteration in volcanic rocks using statistical analysis of lithogeochemical data , 1986 .

[10]  G. Bonham-Carter Geographic Information Systems for Geoscientists: Modelling with GIS , 1995 .

[11]  D. Singer,et al.  Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan , 1996 .

[12]  D. Singer,et al.  Implications of stream-sediment geochemistry in the Northern Carlin trend, Nevada , 2000 .

[13]  D. Singer,et al.  Application of the FINDER System to the Search for Epithermal Vein Gold-Silver Deposits : Kushikino, Japan, A Case Study , 1991 .

[14]  D. Singer,et al.  Geochemistry of stream-sediment samples from the Santa Renia Fields and Beaver Peak quadrangles, northern Carlin Trend, Nevada , 1999 .

[15]  Chapter 9 - Geochemical Characterization of Tin Granites in Northern Thailand , 1983 .

[16]  Q. Cheng,et al.  Integrated Spatial and Spectrum Method for Geochemical Anomaly Separation , 2000 .

[17]  D. Singer,et al.  Integrating spatial and frequency information in the search for kuroko deposits of the Hokuroku District, Japan , 1988 .