Abstract
Abstract To solve engineering geological problems, including water conservancy, transportation, and mining, it is necessary to obtain information on rock mass structures, such as slopes, foundation pits, and tunnels, in time. The traditional method for obtaining structural information requires manual measurement, which is time consuming and labor intensive. Because geological information is complicated and diverse, it is not practical for general deep learning methods to obtain full-scale structural surface trace images to prepare training samples. Transfer learning can abstract high-level features from low-level features with a small number of training samples, which can automatically express the inherent characteristics of objects. This article proposed a rock mass structural surface trace extraction method based on the transfer learning technique that considers the attention mechanism and shape constraints. For the general test set, the accuracy of rock mass structural surface trace recognition with the proposed method can reach 87.2%. Experimental results showed that the proposed method has advantages in extracting complicated geological structure information and is valuable for providing technical support for the extraction of geological information in the construction of water conservancy, transportation, mining, and related projects.
Highlights
The timely acquisition of information regarding rock mass structures such as slopes, foundation pits, and tunnels is a necessary basis for evaluating problems in water conservancy, transportation, mining, and other engineering projects
The method presented in this article provides a new approach to rock mass structural surface trace extraction in rock mass engineering fields relating to water conservancy, railways, highways, and mining
Applications of deep learning in geology are mainly focused on lithology identification of rock grinding pieces and field rock samples
Summary
The timely acquisition of information regarding rock mass structures such as slopes, foundation pits, and tunnels is a necessary basis for evaluating problems in water conservancy, transportation, mining, and other engineering projects. Rock trace extraction based on transfer learning 99 rock mass structural surface information from digital images accurately and automatically is a difficult problem to solve. Both deep and shallow features of objects could be well expressed by high-level features abstracted from low-level features using deep learning technology. Feng et al [9] proposed a deep learning lithology automatic recognition method based on the fresh rock surface image and the twin convolutional neural network structure and used the subchannels in the twin convolutional neural network to extract the global and local feature information of the rock to construct a descriptor for lithology identification. As one of the most popular models, residual networks (ResNets) have various applications in computer vision and many other areas [20]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
