An adaptive clustering constraint inversion method for gravity data

Clustering constraint is one of the effective ways to improve reliability of the inversion results by incorporating petrophysical information based on some mathematical techniques, such as the Fuzzy C-means clustering (FCM) algorithm. However, for most practical geophysical works, enough reliable petrophysical information is not available. In this way, the FCM inversion method is difficult to be performed. In order to solve this problem to some extent, we proposed an adaptive FCM inversion method (AFCM) in this study, with which the cluster information contained in the gravity data can be extracted adaptively and automatically, and then the FCM inversion can be executed without prior petrophysical information. We presented the detailed steps of the proposed method and illustrated its effectiveness on synthetic and field example tests. The obtained inversion results demonstrate that the AFCM method yields much better results compared with the conventional inversion methods without clustering information, as well as those obtained by using the FCM methods with wrong petrophysical information.