Collapsing method for detailed recognition of seismogenic structures activated during underground mining.

Leśniak, Andrzej; Śledź, Elżbieta; Mirek, Katarzyna

doi:10.1002/essoar.10502435.1

2020

DOI: 10.1002/essoar.10502435.1

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Collapsing method for detailed recognition of seismogenic structures activated during underground mining.

Andrzej Leśniak¹,

Elżbieta Śledź²,

Katarzyna Mirek³

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2021

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Cited by 2 publications

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Induced Seismicity and Detailed Fracture Mapping as Tools for Evaluating HDR Reservoir Volume

Węglińska

Leśniak

2021

Energies

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The main goal of this paper was to estimate the heat exchange rock mass volume of a hot dry rock (HDR) geothermal reservoir based on microseismicity location. There are two types of recorded microseismicity: induced by flowing fluid (wet microseismicity) and induced by stress mechanisms (dry microseismicity). In this paper, an attempt was made to extract events associated with the injected fluid flow. The authors rejected dry microseismic events with no hydraulic connection with the stimulated fracture network so as to avoid overestimating the reservoir volume. The proposed algorithm, which includes the collapsing method, automatic cluster detection, and spatiotemporal cluster evolution from the injection well, was applied to the microseismic dataset recorded during stimulation of the Soultz-sous-Forets HDR field in September 1993. The stimulated reservoir volume obtained from wet seismicity using convex hulls is approximately five times smaller than the volume obtained from the primary cloud of located events.

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Induced Seismicity and Detailed Fracture Mapping as Tools for Evaluating HDR Reservoir Volume

Węglińska

Leśniak

2021

Energies

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Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Zhao

Guo

et al. 2021

IJGI

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Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

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Collapsing method for detailed recognition of seismogenic structures activated during underground mining.

Cited by 2 publications

References 0 publications

Induced Seismicity and Detailed Fracture Mapping as Tools for Evaluating HDR Reservoir Volume

Induced Seismicity and Detailed Fracture Mapping as Tools for Evaluating HDR Reservoir Volume

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

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