Incorporating prior information into seismic impedance inversion using fuzzy clustering technique

Abstract

In this research we use the fuzzy c-means (FCM) clustering technique to add petrophysical information from borehole data to model-based seismic impedance inversion. Model based inversion is a common seismic impedance inversion algorithm because it integrates low frequency data from boreholes and is robust. However, beyond the borehole the solutions are as non-unique as many general geophysical inversion problems and output depends greatly on the initial model. Our approach incorporates prior information from well log or core measurement to build a more realizable earth model by using FCM clustering on the petrophysical measurements. This approach tends to produce earth models with less parameter variation and is well suited for crystalline, or hard rock, inversion where there are only a few distinctive rocks units, but considerable structural complexity. Using synthetic examples we show that our method can effectively recover the true model despite structural complexity. The application to real data from the Kevitsa Ni-Cu-PGE (platinum group elements) deposit in northern Finland shows that our inversion results are consistent with well log data and produces impedance models that are more interpretable than the seismic image alone.