Lithogeochemical Prospecting: Signals Processing Applied to Segmentation of Geochemical Borehole Profiles

Large volumes of quantitative geochemical data, presenting a formidable interpretation task to geologists, are generated in the course of exploration drilling for new ore deposits. The incorporation of geological expertise into computer programs developed for the interpretation of this data would improve the speed and accuracy with which it is interpreted. A detailed analysis of the geochemical log interpretation problem, together with a survey of relevant research in Artificial Intelligence and petroleum well- logging techniques, confirms, that artificial intelligence, particularly as developed in the fields of expert systems and pattern recognition, provides techniques for implementing such data interpretation programs. In addition, it provides data analysis techniques significantly more powerful than those currently applied to geochemical data, and very appropriate to the hypothesise and test' character of mineral exploration problem-solving. The conversion of raw borehole log data to meaningful higher level geochemical entities, in terms of which geological reasoning may take place, is a critical step in applying these techniques to log data analysis. A method of achieving this conversion as an exercise in signal-to-symbol transformation is presented.1