Abstract
Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others. Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed. Findings. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment. They always need preprocessing in order to classify actual seismic events. Traditional detecting and classifying methods do not always yield precise results, which is especially disappointing when different events have a similar nature. The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one. This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events. Originality.Previously adopted methods are discussed in short, and a brief historical outline of Machine learning and deep learning development is presented. The recent advancement in discriminating MS events from other events is discussed in the context of these mechanisms, and finally conclusions and suggestions related to the relevant field are drawn. Practical implications. By means of machin learning and deep learning technology mine microseismic events can be identified accurately which allows to determine the source location so as to prevent rock burst. Keywords: rock burst, MS event, blasting event, noise event, machine learning, deep learning
Highlights
Introduction actualMS events from other various events is always a com-Microseismic (MS) monitoring is a useful short-term rock burst prediction tool that can forecast the occurrence of rock burst by extracting useful signals which propagate from the fracturing process of rock masses [1]
The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one
This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events
Summary
Applied methods are described and their low impact on classifying MS events is discussed. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment They always need preprocessing in order to classify actual seismic events. MS signals are different from the natural earthquake signal which has low magnitude and is highly influenced by various background noise sources characterised by an abrupt amplitude, comprised of human walking, vehicle sounds, electromagnetic interference and blasting vibrations, giving the appearance of MS events [5], [6]. In this paper we have presented an overview of the work done by various researchers applying machine learning and deep learning methods in mining to identify and classify MS events. Brief historical development of machine learning and deep learning methods are presented in the third section for a general understanding of the methods, the fourth section provides classification of the events performed by different scholars using machine learning and deep learning approaches (which is our prime concern), and the final section represents conclusion and suggestions in the related field
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