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
Based on the identification and enhancive processing of information about strata, structure, magmatite, and alteration in ore-concentrated area in the eastern Tianshan, an exploration mode of remote sensing geology is...Based on the identification and enhancive processing of information about strata, structure, magmatite, and alteration in ore-concentrated area in the eastern Tianshan, an exploration mode of remote sensing geology is established. The mode covers basic images composed of TM (7, 4, 1), Munsell space transformation for recognizing rock type, directional matched filtering for enhancing structures, multi-layer separating and extracting weak alteration information. It will provide a rapid and effective method for geological mapping and metallogenic prediction in this region.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.
文摘Based on the identification and enhancive processing of information about strata, structure, magmatite, and alteration in ore-concentrated area in the eastern Tianshan, an exploration mode of remote sensing geology is established. The mode covers basic images composed of TM (7, 4, 1), Munsell space transformation for recognizing rock type, directional matched filtering for enhancing structures, multi-layer separating and extracting weak alteration information. It will provide a rapid and effective method for geological mapping and metallogenic prediction in this region.
