Predicting Missing Seismic Velocity Values Using Self-Organizing Maps to Aid the Interpretation of Seismic Reflection Data from the Kevitsa Ni-Cu-PGE Deposit in Northern Finland

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.Recently, 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. Typically 2D "maps" are used to visualize the underlying statistical relationships between the variables, which makes interpretation of the complex multidimensional data more effective (e.g., [11]). 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]).Extensive seismic reflection data have been collected at the Kevitsa Ni-Cu-PGE deposit that is located in northern Finland (Figure 1). These data include four intersecting 2D seismic reflection profiles acquired in 2007 as a part of High-Resolution Reflection Seismics for Ore Exploration (HIRE) 2007-2010 project that aimed at testing the applicability of seismic exploration method in Kevitsa [16,17] and a 3D seismic reflection survey conducted in 2010 for mine planning and deep mineral exploration purposes [18,19]. Lateral...

Editorial for Special Issue “Seismic Methods in Mineral Exploration”

“…These minerals are constituents of amphibolite facies assemblages that are diagnostic of metamorphosed and intensely-altered footwall of volcanogenic massive sulphide ore lenses. Junno et al [9] use self-organizing map (SOM) analysis to predict missing seismic velocity values from other available borehole data at the Kevitsa Ni-Cu-PGE deposit, Finland. SOM is used to predict missing seismic velocities from other geophysical, geochemical, geological, and geotechnical data to support the interpretation of a series of reflections observed beneath the Kevitsa resource area.…”