Microseismic Monitoring System Establishment and its Application to Xinzhangzi Coal Mine

Duan, Dong; Tang, Chun An; Feng, Xiao Jing

doi:10.4028/www.scientific.net/amr.396-398.99

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…In the microseismic monitoring process, except for the microseismic events generated by reservoir fracturing, the microseismic events generated by the activation of primary fractures may also be recorded [8][9][10]. The microseismic monitoring technique also plays a particularly important role in coal mines [11][12][13][14][15], in which uses the signals emitted in the coal or rock fracture process to proceed the mechanical stability analysis of coal or rock material and the associated rock structure. This technique is a real-time, dynamic, and continuous geophysics method [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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

Shang,

Li,

Zhang

et al. 2023

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

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

Shang,

Li,

Zhang

et al. 2023

Applied Sciences

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Energy efficiency of timber construction joint with wooden screw

et al. 2019

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The topicality of studied subject is based on inappropriate usage of different materials in wooden structures, since using metal joint in wooden construction leads to reduction of construction working life cycle. In this regard, a study of several options for wooden screw carving was conducted in order to determine the most appropriate one (screw). There are several different types of carving, such as: trapezoidal, triangular, rectangular, etc. The most appropriate ones are trapezoidal and triangular-section threads for wooden screws. The effectiveness of a wooden screw thread was determined from the conditions (capacity) of shear fracture and bearing stress. After analyzing the results of calculations of the specific capacity for shear fracture and bearing stress, it can be concluded that the value of specific bearing capacity for shear fracture is less than the value of bearing stress by 1.3%. Therefore, a further comparison of the trapezoidal thread and the triangular cross-section will proceed from the spalling conditions. It has been found that the most effective thread is a triangular cross-section. Based on the results of calculations, a mathematical model has been established that describes the dependence of the carrying capacity on the screw diameter.

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Microseismic Monitoring System Establishment and its Application to Xinzhangzi Coal Mine

Cited by 2 publications

References 4 publications

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

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

Energy efficiency of timber construction joint with wooden screw

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