IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3758-d819706.html
   My bibliography  Save this article

Automatic Recognition of Faults in Mining Areas Based on Convolutional Neural Network

Author

Listed:
  • Guangui Zou

    (State Key Laboratory of Coal Resources and Safety Mining, China University of Mining and Technology, Beijing 100083, China
    College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Hui Liu

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Ke Ren

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Bowen Deng

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Jingwen Xue

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

Abstract

Tectonic interpretation is critical to a coal mine’s safe production, and fault interpretation is an essential component of seismic tectonic interpretation. With the increasing necessity for accuracy in fault interpretation in coal mines, it is increasingly challenging to achieve greater accuracy only through traditional fault interpretation. The convolutional neural network (CNN) is a machine learning method established in recent years and it has been widely applied in coal mine fault interpretation because of its powerful feature-learning and classification capabilities. To improve the accuracy and efficiency of fault interpretation in coal mines, an automatic seismic fault identification method based on the convolutional neural network has been developed. Taking a mining area in eastern Yunnan province as an example, the CNN model realized automatic identification of faults with eight seismic attributes as feature inputs, and the model-training parameters were optimized and compared. Ten faults in the area were selected to analyze the prediction effect, and a comparative experiment was done with model structure parameters and training sets. The experimental results indicate that the training parameters have a significant influence on the training time and testing accuracy of the model, while structural parameters and training sets affect the actual prediction effect of the model. By comparison, the fault results predicted by the convolutional neural network are in good agreement with the manual interpretation, and the accuracy of the model is more than 85%, which proves that this method has certain feasibility and provides a new way to shorten the fault interpretation period and improve the interpretation accuracy.

Suggested Citation

  • Guangui Zou & Hui Liu & Ke Ren & Bowen Deng & Jingwen Xue, 2022. "Automatic Recognition of Faults in Mining Areas Based on Convolutional Neural Network," Energies, MDPI, vol. 15(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3758-:d:819706
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3758/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/10/3758/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ganesh Bahadur Singh & Rajneesh Rani & Nonita Sharma & Deepti Kakkar, 2021. "Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 12(4), pages 1-22, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sergey Zhironkin & Elena Dotsenko, 2023. "Review of Transition from Mining 4.0 to 5.0 in Fossil Energy Sources Production," Energies, MDPI, vol. 16(15), pages 1-35, August.
    2. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiapeng Cui & Feng Tan, 2023. "Rice Plaque Detection and Identification Based on an Improved Convolutional Neural Network," Agriculture, MDPI, vol. 13(1), pages 1-15, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3758-:d:819706. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.