Integrated lithology identification based on images and elemental data from rocks

Abstract

In order to realize low-cost, fast and accurate lithology identification in reservoir evaluation, stratum development potential evaluation and underground engineering construction, we propose an intelligent lithology identification method, which can achieve deep fusion of images and elemental data from rocks, and a step-wise model training method. Lithological features are extracted from both images and the rock elemental data using convolution layers and fully connected layers; those features are then concatenated and fed to the fully connected layers to achieve deep fusion. In the model training phase, the image feature extraction module of the fusion model is first trained only with images, and then the entire fusion model is trained using both the images and the rock elemental data. We also conduct reliability assessment and then compare the performance of our fusion identification model to that of an identification model that relies solely on images. The maximum accuracy (94.62%), recall (94.30%), F1-score (93.98%), and Kappa coefficient (0.94) values of the fusion method far exceed that of the image identification method. The step-wise model training method noticeably reduces the amount of rock elemental data required by the fusion model. High image similarity and smaller lithology features degrade the performance of the image identification method, while the fusion method can significantly overcome these challenges and thus greatly improve the accuracy of lithology identification. The fusion identification method is more accurate and robust than the image identification method.