Machine learning of identifying the redox transitional zone by SIP data in sandstone-type uranium deposits
In the metallogenic process of sandstone-type uranium deposits,redox reaction is one of the significant factors to enrich uranium.Accurately delimiting the range of the redox transition zone can provide crucial clues for deep ore prospecting.Based on the difference of electrochemical properties between rocks in oxidation-reduction zones,Spectral Induced Polarization(SIP)method can invert and obtain multiple complex resistivity parameters of underground lithologies with Cole-Cole model.By applying the combination of these parameters to an enhanced Kmeans clustering,SIP data can automatically cluster the litholgy of different complex resistivity parameters through unsupervised machine learning algorithms,and reach the goal of distinguishing the oxidation-reduction layers in sandstone-type uranium deposits.With the dilling verification,the SIP data identified stratigraphic zones were of good consistency with the geological outcome,indicating that the Kmeans++method can effectively identify the redox zoning information from SIP result data.The study provided reference for cutting down the prospecting area and orientating the next prospecting direction in uranium exploration.
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