Abstract
We use self-organizing map (SOM) analysis to predict missing seismic velocity values from other available borehole data. The site of this study is the Kevitsa Ni-Cu-PGE deposit within the mafic-ultramafic Kevitsa intrusion in northern Finland. The site has been the target of extensive seismic reflection surveys, which have revealed a series of reflections beneath the Kevitsa resource area. The interpretation of these reflections has been complicated by disparate borehole data, particularly because of the scarce amount of available sonic borehole logs and the varying practices in logging of borehole lithologies. SOM is an unsupervised data mining method based on vector quantization. In this study, SOM is used to predict missing seismic velocities from other geophysical, geochemical, geological, and geotechnical data. For test boreholes, for which measured seismic velocity logs are also available, the correlation between actual measured and predicted velocities is strong to moderate, depending on the parameters included in the SOM analysis. Predicted reflectivity logs, based on measured densities and predicted velocities, show that some contacts between olivine pyroxenite/olivine websterite-dominant host rocks of the Kevitsa disseminated sulfide mineralization—and metaperidotite—earlier extensively used “lithology” label that essentially describes various degrees of alteration of different olivine pyroxenite variants—are reflective, and thus, alteration can potentially cause reflectivity within the Kevitsa intrusion.
Highlights
Mining and exploration environments are targets of intensive drilling for resource estimation and feasibility studies, and commonly, in addition to lithology, drill hole logging includes geochemical assays and geotechnical analyses and parameters
Data mining approaches, such as the self-organizing map (SOM; [1,2]) analysis employed in this study, have been increasingly used for analyses of the disparate and multivariate geoscientific datasets that are typical for mining and exploration environments (e.g., [3,4,5,6,7,8,9,10])
SOM is an artificial neural network that works on a vector quantization methodology allowing unsupervised analysis, i.e., no prior information is needed on the type or number of groupings within the data, to determine the underlying linear and non-linear relationships amongst different data
Summary
Mining and exploration environments are targets of intensive drilling for resource estimation and feasibility studies, and commonly, in addition to lithology, drill hole logging includes geochemical assays and geotechnical analyses and parameters. Data mining approaches, such as the self-organizing map (SOM; [1,2]) analysis employed in this study, have been increasingly used for analyses of the disparate and multivariate geoscientific datasets that are typical for mining and exploration environments (e.g., [3,4,5,6,7,8,9,10]). Major strengths of the methodology include the robust handling of sparse and disparate input data, labels, incomplete data, and the ability to predict missing data values for such data (e.g., [12,13,14])
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