Analysis of stress level during longwall mining of a coal seam with the use of seismic effect method

Wojtecki, Łukasz; Gołda, Iwona

doi:10.1088/1755-1315/261/1/012057

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…In hard coal mines in the Upper Silesian Coal Basin, PPV measurements in underground mine workings are more common, but they have not been used so far in the selected mine. The seismic energy of tremors E was calculated by a mine geophysics station using a numerical integrating method [33]. The energy of strong tremors, i.e., equal to or higher than 1 × 10 5 J, was verified with the calculations of the Upper Silesian Regional Seismological Network, which belong to the Central Mining Institute in Katowice, Poland [34].…”

Section: Characteristics Of the Input Datasetmentioning

confidence: 98%

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

et al. 2021

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Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.

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Section: Characteristics Of the Input Datasetmentioning

confidence: 98%

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

et al. 2021

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The influence of distant coal seam edges on seismic hazard during longwall mining

2020

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Underground coal seam mining has been carried out in the Upper Silesian Coal Basin, Poland, for many years and with a simultaneous increase in exploitation depth. Frequently, coal seams are not fully extracted due to numerous reasons which lead to their edges and remnants remaining in the rock mass. Even in the case of the full extraction of a coal seam, mining usually ends at the border of a protecting pillar to protect underground or surface objects, sometimes at the border of the mining area, or some distance from the old goaf or high throw fault. Extraction of subsequent coal seams in an analogous range results in a cluster of coal seam edges remaining. In the vicinity of the mentioned remainders, the disrupted stress distribution is expected. The infraction of the aforementioned equilibrium repeatedly results in the occurrence of strong mining tremors. The observations from the studied coal seam no. 408’s longwall panel indicated that mining works are able to disturb the present stress-strain equilibrium in the area of the edges of other coal seams, even if they are located at a greater vertical distance away. The seismological parameters and distributions have been applied for this purpose.

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Evaluation of Destress Blasting Effectiveness Using the Seismic Moment Tensor Inversion and Seismic Effect Methods

et al. 2022

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Analysis of stress level during longwall mining of a coal seam with the use of seismic effect method

Cited by 4 publications

References 7 publications

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

The influence of distant coal seam edges on seismic hazard during longwall mining

Evaluation of Destress Blasting Effectiveness Using the Seismic Moment Tensor Inversion and Seismic Effect Methods

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