A Novel Method of Multitype Hybrid Rock Lithology Classification Based on Convolutional Neural Networks

Abstract

Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it difficult to ensure the accurate and effective identification of rock lithology. Additionally, multitype hybrid rock lithology identification is challenging, and few studies on this issue are available. In this paper, a novel multitype hybrid rock lithology detection method was proposed based on convolutional neural network (CNN), and neural network model compression technology was adopted to guarantee the model inference efficiency. Four fundamental single class rock datasets: sandstone, shale, monzogranite, and tuff were collected. At the same time, multitype hybrid rock lithologies datasets were obtained based on data augmentation method. The proposed model was then trained on multitype hybrid rock lithologies datasets. Besides, for comparison purposes, the other three algorithms, were trained and evaluated. Experimental results revealed that our method exhibited the best performance in terms of precision, recall, and efficiency compared with the other three algorithms. Furthermore, the inference time of the proposed model is twice as fast as the other three methods. It only needs 11 milliseconds for single image detection, making it possible to be applied to the industry by transforming the algorithm to an embedded hardware device or Android platform.

Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

“…The optimized model obtained significant improvement compared to existing works. Li et al [26] proposed an intelligent model for rockburst prediction using BOA for hyperparameter optimization. Lahmiri et al [52] used BOA to obtain the optimal parameters of models for house price prediction.…”

“…They are recommended for solving other complex problems, such as earth pressure calculation and rock profile reconstruction [21,22]. Research reviews have revealed that AI-based methods have been successfully applied in areas such as tunnel squeezing analysis [23,24], rock mass failure mode classification [25], rock lithology classification [26,27], and rock burst prediction and assessment [28][29][30]. The outstanding results achieved by AI-based methods have garnered significant attention in the prediction of rock mechanical parameters.…”

Object Detection Using Various Camera System

“…The benefit of human vision is that it has a contextual lifecycle that allows it to learn how to identify objects, how far away they are, if they are moving, and if there is a problem with the image. Computer vision teaches machines to execute these tasks, but it must do it faster than the retina and optic nerve [1][2][3][4][5].…”

Multichannel Sandstone Thin Sections Identification Based on Improved DeepLab V3 Plus Neural Network

“…Li et al demonstrated that the models obtained from Adam and RMSprop optimizers outperformed SGD in classifying images of rock sheets using various neural network models. Li et al optimized the YOLO-V3 and SPP structures and, in turn, proposed a new convolutional neural network called RDNet for automated detection of the lithology of multiple types of mixed rocks. Zhou and Wang et al , employed Faster R-CNN for multitarget identification of rock thin sections.…”