Intelligent Acquisition of the Cluster Centers for Inversion of Gravity Data

Clustering constraint can effectively establish the relationship between petrophysical information and inversion results, thus playing an important role in lithology identification. In practical application, the number of cluster centers should be predetermined based on available rock physical data of the study area, which is often difficult. To solve this problem, this letter innovatively introduces a method for intelligently obtaining the number of cluster centers. The presented method is based on the Dense-Net architecture, which strengthens feature propagation using dense connectivity to achieve the reuse of input data. Also, Bayesian optimization is adopted for unsupervised iteration to determine the suitable combination of hyperparameters. With the abovementioned operations, the loss curve converges effectively and efficiently. The trained network is validated on theoretical cases that do not exist in the training set. The obtained result is very close to the true value. Finally, the gravity field data from the San Nicolas deposit are adopted for real data test. The results are basically consistent with the geological conditions of the study area, proving the practicability of the presented method.