An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNe...An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were compared.It shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be achieved.For the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative training.We expanded a self-built dataset by using transfer learning and data augmentation.Next,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model identification.The results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing dataset.Furthermore,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology identification.This proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.展开更多
Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating d...Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.展开更多
基金support from the National Natural Science Foundation of China(Grant Nos.52022053 and 52009073)the Natural Science Foundation of Shandong Province(Grant No.ZR201910270116).
文摘An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were compared.It shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be achieved.For the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative training.We expanded a self-built dataset by using transfer learning and data augmentation.Next,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model identification.The results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing dataset.Furthermore,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology identification.This proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.
基金This paper is supported by the Engineering Center of Chinese Continental Scientific Drilling (No. CCSD2004-04-01)the Focused Subject Program of Beijing (No. XK104910598).
文摘Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.
