Combining deep neural network and spatio-temporal clustering to automatically assess rockburst and seismic hazard – Case study from Marcel coal mine in Upper Silesian Basin, Poland

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Adam Lurka
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引用次数: 0

Abstract

Mine induced seismic events are a major safety concern in mining and require careful monitoring and management to reduce their effects. Therefore, an essential step in assessing seismic and rock burst hazards is the analysis of mine seismicity. Recently, deep neural networks have been used to automatically determine seismic wave arrival times, surpassing human performance and allowing their use in seismic data analysis such as seismic event location and seismic energy calculation. In order to properly automate the rockburst and seismic hazard assessment deep neural network phase picker and a spatio-temporal clustering method were utilized. Seismic and rockburst hazards were statistically quantified using two-way contingency tables for two categorical variables: seismic energy level of mine tremors and number of clusters. Correlations between several spatio-temporal clusters and a statistical association between two categorical variables: seismic energy level and cluster number indicate an increase of seismic hazard in the Marcel hard coal mine in Poland. A new automated tool has been elaborated to automatically identify high-stress areas in mines in the form of spatio-temporal clusters.

结合深度神经网络和时空聚类自动评估岩爆和地震危害--波兰上西里西亚盆地马塞尔煤矿案例研究
矿井诱发的地震事件是采矿业的一个主要安全问题,需要认真监测和管理,以减少其影响。因此,评估地震和岩爆危险的一个重要步骤就是分析矿井地震。最近,深度神经网络被用于自动确定地震波到达时间,其性能超过了人类,可用于地震数据分析,如地震事件定位和地震能量计算。为了实现岩爆和地震危险评估的自动化,我们使用了深度神经网络相位选择器和时空聚类方法。使用双向或然率表对地震和岩爆危险进行了统计量化,其中包括两个分类变量:矿井震颤的地震能量水平和集群数量。几个时空聚类之间的相关性以及两个分类变量(地震能量水平和聚类数量)之间的统计关联表明,波兰马塞尔硬煤矿的地震危害在增加。我们开发了一种新的自动工具,以时空聚类的形式自动识别矿井中的高应力区。
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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