Research on Automatic Classification of Coal Mine Microseismic Events Based on Data Enhancement and FCN-LSTM Network

Abstract

Efficient and accurate classification of the microseismic data obtained in coal mine production is of great significance for the guidance of coal mine production safety, disaster prevention and early warning. In the early stage, the classification of microseismic events relies on human experiences, which is not only inefficient but also often causes some misclassifications. In recent years, the neural network-based classification method has become more favored by people because of its advantages in modeling procedures. A microseismic signal is a kind of time-series signal and the application of the classification method is widely optimistic. The number and the balance of the training data samples have an important impact on the accuracy of the classification result. However, the quality of the training data set obtained from the production cannot be guaranteed. A long short-term memory (LSTM) network can analyze the time-series input data, where the image classification at the pixel level can be achieved by the fully convolutional network (FCN). The two structures in the network can not only use the advantages of the FCN for extracting signal details but also use the characteristics of LSTM for conveying and expressing the long time-series information effectively. In this paper, a time-series data enhancement combination process is proposed for the actual poor microseismic data. A hybrid FCN-LSTM network structure was built, the optimal network parameters were obtained by experiments, and finally a reasonable microseismic data classifier was obtained.

Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation

“…Recently, deep learning (DL) has shown excellent capabilities for nonlinear mapping function approximation in computer vision, especially in the tasks of reconstructing models and high-resolution images [15,16]. The development of DL has also brought new opportunities to seismic and microseismic data processing and inversion [17], such as signal denoising [18], signal identification and classification [19,20], first-arrival picking [21][22][23], source location [24], and velocity model building and calibration [25]. Using seismic waveforms as the feature input and velocity models as the labels, the trained models with the nonlinear mapping capability of neural networks can effectively predict velocity models from seismic waveforms.…”

Fault diagnosis of control valves based on small-sample hybrid physics improved Resnet

“…The field of data augmentation has seen substantial contributions from numerous scholars, with a focus on developing algorithms to enhance the quality and utility of datasets, Fan et al [12] introduced a novel approach with their TCVAE-GAN model, which integrates a time convolutional network module within the encoder and decoder framework to augment the reconstruction of time-series data, thereby improving feature extraction capabilities and leveraging generative adversarial networks to further enhance the variational autoencoder's (VAEs) reconstruction ability. Shang et al [13] applied up-sampling to enhance microseismic data collected from coal mine production. They developed a fully convolutional network combined with long and short-term memory network (FCN-LSTM) architecture, validating the algorithm's efficacy through experimental testing.…”