Integration of Borehole Data in Geophysical Inversion Using Fuzzy Clustering

Abstract

Borehole data is critical information to constrain geophysical inversions. Borehole information is usually used as a petrophysical constraint of the inversion scheme. Therefore, borehole information is only valuable if the borehole features are the same as the physical parameters of geophysical models. In fact, we may have many drilled holes, but the physical parameter of the geophysical model is only available in a few holes. Thus, the question is how we can exploit other borehole features to assist the inversion. We present an application of fuzzy clustering to incorporate multiple borehole features such as lithological, assay and wireline logs in the geophysical inversion. The integration of this extra information assist inversion process to build a model that fit surface geophysical data and simultaneously honour the prior information of borehole data. We apply this approach to the case study over the Kevitsa deposit within the Kevitsa ultramafic intrusion in northern Finland. The inversion of seismic reflection data with assistance from borehole information produces a more geologically interpretable image than seismic reflection data. The integration of both petrophysical and spatial attribute of the borehole enable the inversion to build a better model than only petrophysical constraint.