Field evaluation of the strength properties of jointed rock masses is a challenging task in geotechnical engineering. Typically, laboratory tests using small jointed specimens have difficu...

Deep Convolutional Neural Network-Based Method for Strength Parameter Prediction of Jointed Rock Mass Using Drilling Logging Data

Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application

Microseismic technology has widely been used in many rock engineering applications to shield workers from engineering hazards and monitor underground construction. To avoid the heavy workloads imposed by the manual recognition of many microseismic signals, this study proposes a new end-to-end training network architecture to automatically identify microseismic events. A dataset including not only easily identifiable microseismic signals but also barely distinguishable nontypical data has been collected from a practical rock engineering project for training and testing the network model. The applicability of various networks for this task is discussed to select the best method for microseismic recognition. We modify the residual skip connections to make them more suitable for the signal classification task. Then, the novel depthwise spatial and channel attention (DSCA) module is proposed. This module can autonomously learn how to weight information with different levels of importance, similar to human attention, which greatly improves the network performance without incurring additional computational costs. Theoretically, it can be a useful tool to replace traditional denoising algorithms and model the interdependencies between the different channels of a multichannel signal. Furthermore, the DSCA module and the modified residual connections are combined with a traditional convolutional network to obtain a novel network architecture named ResSCA and the results of comparative experiments are presented. Finally, single- and multichannel models are constructed based on ResSCA, which achieved improved accuracy rates. Their advantages and drawbacks are analyzed. This study presents a modified network architecture suitable for identifying and classifying complex signals to enable intelligent microseismic monitoring, which is valuable for various rock engineering applications.

Identification of Microseismic Events in Rock Engineering by a Convolutional Neural Network Combined with an Attention Mechanism

Complex-valued neural networks for machine learning on non-stationary physical data

Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines. Although machine learning has been widely applied in seismic data processing, feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated. In this research, two groups of seismic events with a minimum local magnitude (ML) of −3 were observed in an underground coal mine. They were respectively located around a dyke and the longwall face. Additionally, two types of undesired signals were also recorded. Four machine learning methods, i.e. random forest (RF), support vector machine (SVM), deep convolutional neural network (DCNN), and residual neural network (ResNN), were used for classifying these signals. The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy. The DCNN with seismogram images as the input reached the best performance with more than 94% accuracy. As mining is a dynamic progress that could change the characteristics of seismic signals, the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining. A cascaded workflow consisting of database update, model training, signal prediction, and results review was established. By progressively calibrating the DCNN model, it achieved up to 99% prediction accuracy. The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.

Yi Duan

Papers

Identifying microseismic events in a mining scenario using a convolutional neural network