An intelligent lithology identification method is proposed based on the deep learning of rock images. The lithology information and position information in rock images can be predicted using the Faster R–CNN architecture through the RPN proposal generation algorithm and the Fast R–CNN detector. To obtain more rock features, the rock detection model is built on the ResNet structure, and the residual learning is used to retain as much as possible detailed information in the original input image. The four-step alternating training is used to fine-tuned end-to-end, and the prediction results are optimized by the cross-entropy loss and the regression loss. To speed up the model and improve the identification accuracy, data augmentation and pre-training are used to train the model. The mAP, P, R and $F_1$ score are used as evaluation indexes of the accuracy, and the Faster R–CNN model is compared with the YOLO v4 model. Results indicate that the mAP of the rock detection model based on the Faster R–CNN is 99.19% and the $F_1$ score is 96.6%. Compared with the YOLO v4 model, the accuracy is higher and the identification ability is more stable. The proposed rock detection model has good identification ability for different rocks in rock images, and the model is of good robustness and generalization performance, which is suitable for rapid intelligent lithology identification in practical geological and logging engineering.

提出了一种基于岩石图像深度学习的智能岩性识别方法。岩石图像中的岩性信息和位置信息可以通过RPN建议生成算法和Fast R-CNN检测器使用Faster R-CNN体系结构进行预测。为了获得更多的岩石特征，岩石检测模型基于ResNet结构构建，并且残差学习用于在原始输入图像中保留尽可能多的详细信息。采用四步交替训练对端到端进行微调，并通过交叉熵损失和回归损失来优化预测结果。为了加速模型并提高识别精度，使用数据增强和预训练来训练模型。的地图，P，R和$F_{\mathrm{1个}}$分数用作准确性的评估指标，并将Faster R–CNN模型与YOLO v4模型进行比较。结果表明，基于Faster R–CNN的岩石检测模型的mAP为99.19％，而$F_{\mathrm{1个}}$得分是96.6％。与YOLO v4模型相比，精度更高，识别能力更稳定。提出的岩石探测模型对岩石图像中的不同岩石具有良好的识别能力，具有良好的鲁棒性和泛化性能，适用于实际地质和测井工程中的快速智能岩性识别。