Application of guided fuzzy C-means clustering algorithm in joint inversion comprehensive interpretation
There are differences in the inversion results of different geophysical methods,and the key to obtain ing accurate underground knowledge is a final reasonable interpretation based on the joint inversion results of dif-ferent methods.A guided fuzzy C-means(FCM)clustering algorithm is proposed for this purpose,and based on the fuzzy C-means(FCM)clustering algorithm,this paper includes the existing geologic understanding,in-troduces prior constraint information to guide the determination of the clustering centers,and provides a compre-hensive quantitative interpretation of the results of the geophysical joint inversion,aiming at reducing the subjec-tivity and limitations of traditional manual interpretation.The model test shows that the guided FCM clustering technology is more effective than the traditional FCM clustering technologies,especially its ability to effectively distinguish different geological bodies when processing inversion data of complex geological structures.The re-sults of practical data applications demonstrate the great potential of the guided FCM clustering technology in the comprehensive interpretation of multi-attribute geophysical joint inversion results.This technology not only makes geophysical data interpretation more scientific but also provides a more reliable and accurate tool for un-derground resource exploration.
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