Automatic identification of rock formation interface based on borehole imaging

Liu, Cancan; Zheng, Xigui; Yang, Lei; Li, Peng; Shahani, Niaz Muhammad; Wang, Cong; Guo, Xiaowei

doi:10.1080/15567036.2021.1903121

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Figure 14 shows the process. The geological exploration-while-drilling technology of roof rock strata developed by researchers [34][35][36] is used to identify the type interface and fractures [37,38] of roadway-roof rock strata and obtain the weak interlayer height and the development of fractures and other information. Roof support parameters are designed according to field-measured information to make bolt support as effective as possible.…”

Section: Maximum Arch Fall and Bolt Support-while-drilling (Bwd)mentioning

confidence: 99%

The Roof-Fall Mechanism and Support-While-Drilling Technology of the Rectangular Roadway with Layered Roofs and Weak Interlayers

et al. 2023

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Studies have confirmed the poor stability of layered roofs with weak interlayers, and it is necessary to study the roof-caving mechanism of such roadways. A model of a weak interlayer was established to study the influence of the layer position and horizontal stress of layered roofs with weak interlayers on the stability of roadway roofs. FLAC3D numerical simulation software was used to study the damage characteristics of weak interlayers and different horizontal stresses on roadway roofs. Based on Proctor’s theory, a maximum caving arch model was proposed to obtain the maximum caving arch height and span range. The pressure measurement coefficient and the vertical displacement change of the roof strata conformed to corresponding linear functions by fitting the pressure measurement coefficient and the maximum vertical displacement. A bolt-while-drilling support method (BWD) was proposed based on team measurement-while-drilling (MWD) to accurately determine the location of weak rock layers and the development range of plastic zones. A maximum caving arch model was proposed to obtain the range of influence of the maximum caving arch and the range of the maximum slope collapse angle. Furthermore, a method of anchor rod support-while-drilling was proposed and tested on-site in Jingu Coal Industry, Guxian County, Shanxi Province, China. According to the site conditions, short anchor cables were used to pass through the weak interlayer of roofs, with a good support effect. The results provide a new method for layered roof support containing weak interlayers.

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Section: Maximum Arch Fall and Bolt Support-while-drilling (Bwd)mentioning

confidence: 99%

The Roof-Fall Mechanism and Support-While-Drilling Technology of the Rectangular Roadway with Layered Roofs and Weak Interlayers

et al. 2023

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Fracture Identification Under Unstable Drilling Conditions Based on Proposed Multi-parameter Voting Method

et al. 2023

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Study on automatic lithology identification based on convolutional neural network and deep transfer learning

Li,

Dong,

Zhang

et al. 2024

Discov Appl Sci

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Automatic and fast rock classification identification is an important part of geotechnical intelligent survey system. Image based supervised deep learning analysis, especially for convolutional neural networks (CNN), has potential in optimizing lithologic classification and interpretation using borehole core images. However, the accuracy and efficiency of lithology identification models are low at present. In this work, a systematic and enormous rock data framework based on the geological rock classification system is firstly established to provide rock learning datasets. The dataset is composed of approximately 150,000 images of rock samples, which covers igneous rocks, sedimentary rocks, and metamorphic rocks. Secondly, based on CNN-deep transfer learning algorithm, an end-to-end, image-to-label rock lithology identification is established. Finally, the generalization of the proposed model and the field drilling core verification test show that the constructed intelligent rock recognition model has an ability to identify rocks quickly and accurately, and the recognition accuracy of 12 kinds of common engineering rocks is more than 95%. The proposed rock intelligent classification model provides a convenient and fast tool for field geologists and scientific researchers.

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Automatic identification of rock formation interface based on borehole imaging

Cited by 4 publications

References 21 publications

The Roof-Fall Mechanism and Support-While-Drilling Technology of the Rectangular Roadway with Layered Roofs and Weak Interlayers

The Roof-Fall Mechanism and Support-While-Drilling Technology of the Rectangular Roadway with Layered Roofs and Weak Interlayers

Fracture Identification Under Unstable Drilling Conditions Based on Proposed Multi-parameter Voting Method

Study on automatic lithology identification based on convolutional neural network and deep transfer learning

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