No abstract

A new cross-journal special collection invites contributions on how machine learning can be used for solid Earth observation, modeling and understanding.

Borehole image logs greatly facilitate detailed characterization of rock formations, especially for the highly heterogeneous and anisotropic carbonate rocks. However, interpreting image logs requires massive time and workforce and lacks consistency and repeatability because it relies heavily on a human interpreter's expertise, experience, and alertness. Thus, we propose to train an end-to-end deep neural network (DNN) for instant and consistent facies classification of carbonate rocks from acoustic image logs and gamma ray logs. The DNN is modified from the well-known U-Net for image segmentation. The training data are composed of two datasets: (1) manually labeled field data measured by different imaging tools from the geologically complex Brazilian pre-salt region and (2) noise-free synthetic data. Some short sections of the field data that are challenging for manual labeling due to entangled features and noises or low resolution are left unlabeled for a blind test after training. All labeled data are divided into a training set, a validation set, and a test set to avoid over-fitting. We demonstrate that the trained DNN achieves 77% classification accuracy for the test set and provides reasonable predictions for the challenging unlabeled sets. It is a great achievement given the complexity and variability of the field data. Compared with manual classification, our DNN provides more consistent and higher-resolution predictions in a highly efficient manner and thus dramatically contributes to automatic image log interpretation.