Microseismic records classification using capsule network with limited training samples in underground mining

Abstract

The identification of suspicious microseismic events is the first crucial step in microseismic data processing. Existing automatic classification methods are based on the training of a large data set, which is challenging to apply in mines without a long-term manual data processing. In this paper, we present a method to automatically classify microseismic records with limited samples in underground mines based on capsule networks (CapsNet). We divide each microseismic record into 33 frames, then extract 21 commonly used features in time and frequency from each frame. Consequently, a 21 × 33 feature matrix is utilized as the input of CapsNet. On this basis, we use different sizes of training sets to train the classification models separately. The trained model is tested using the same test set containing 3,200 microseismic records and compared to convolutional neural networks (CNN) and traditional machine learning methods. Results show that the accuracy of our proposed method is 99.2% with limited training samples. It is superior to CNN and traditional machine learning methods in terms of Accuracy, Precision, Recall, F1-Measure, and reliability. Underground engineering causes disturbances in the stress state of the rock mass, leading to a large number of microseismic events 1. By post-processing these records (e.g., P-wave arrival picking 2 , event location 3 , and source parameter calculation 4-6), the mechanical state of the corresponding rock mass can be adequately reflected, which is beneficial especially for disaster early warning in underground mining 7-9. However, in the underground mining process, the microseismic monitoring system often receives interference from blasting operations, ore extraction, mechanical operations, high voltage cables, and magnetic fields 10. Therefore, quickly and accurately identifying microseismic records from a large number of suspicious records is a crucial task. Currently, the classification of suspicious microseismic records depends on the visual scanning of waveforms by experienced analysts 11. However, manual classification of microseismic records is a time-consuming, tedious task that is easy to bring into subjective opinions. For these reasons, automatic classification of microseismic records is urgently needed. Throughout the years, many automatic classification methods have been proposed to address the abovementioned problems in seismic and microseismic fields. Scarpetta et al. 12 established a specialized neural discrimination method for low magnitude seismic events, quarry blasts, underwater explosions, and thunder sources at Mt. Vesuvius Volcano, Italy. Langer 13 , Esposito 14 and Curilem 15 used the machine learning to classify seismic records at the Soufriere Hills volcano (Montserrat), Stromboli island (southern Italy) and the Villarrica volcano (Chile), respectively. Malovichko 16 utilized a set of seismic characteristics and the multivariate maximum-likelihood Gaussian classifier, to quantify a probability that a particular event belongs to a population of blas...

Machine Learning in Earthquake Seismology

“…Neural networks have proven useful for the important tasks of discriminating seismic events based on their source depths (Mousavi et al 2016a), epicentral distance (e.g., Kuyuk & Ohno 2018, Mousavi et al 2019b, and source type (e.g., Nakano et al 2019, Peng et al 2020, Köhler et al 2022. Mousavi et al (2019b) presented an unsupervised deeplearning approach that used a self-supervised convolutional autoencoder for feature learning and dimensionality reduction to discriminate local from teleseismic waveforms.…”

“…Nakano et al (2019) trained a CNN to discriminate tectonic tremor from local earthquakes based on their time-frequency representation. Peng et al (2020) used a capsule neural network to classify microearthquake records in underground mines into five classes: microearthquakes, blasts, ore extraction, mechanical noise, and electromagnetic interference. Köhler 2022) combined the empirical matched field method and a CNN to differentiate iceberg calving events from earthquakes and to classify them based on their locations.…”

Intelligent Microseismic Monitoring of Mineral Equipment Manufacturing Industry Based on Deep Learning in the Artificial Intelligence Environment

“…Thereby, the actual SV is determined by AS. When the SV is smaller than the strength of the SR and corollary equipment of the face, the recovery ratio can be improved (Peng et al, 2020;Othman et al, 2021). Otherwise, it ought to be reduced.…”

A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing

“…Our mining-induced microseismic data came from the Huangtupo Copper and Zinc Mine, located in Hami City, China. For the specific conditions of this mine refer to the existing literature [39][40][41]. A mining-induced microseismic monitoring system was installed to monitor the safety of goaves in the mine.…”