Volume 42 Issue 6
Nov.  2023
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Article Contents
Cao Yangbing, Wu Yang, Zhang Peng, Jiang Zhihao, Zhang Siyi, Huang Zhenping. Prediction model and variation law of P-wave velocity of single fracture granite in an underground water-sealed storage cavern[J]. Bulletin of Geological Science and Technology, 2023, 42(6): 12-20. doi: 10.19509/j.cnki.dzkq.tb20220279
Citation: Cao Yangbing, Wu Yang, Zhang Peng, Jiang Zhihao, Zhang Siyi, Huang Zhenping. Prediction model and variation law of P-wave velocity of single fracture granite in an underground water-sealed storage cavern[J]. Bulletin of Geological Science and Technology, 2023, 42(6): 12-20. doi: 10.19509/j.cnki.dzkq.tb20220279

Prediction model and variation law of P-wave velocity of single fracture granite in an underground water-sealed storage cavern

doi: 10.19509/j.cnki.dzkq.tb20220279
  • Received Date: 17 Jun 2022
  • Accepted Date: 27 Jul 2022
  • Rev Recd Date: 23 Jul 2022
  • Objective

    Revealing the variation behavior of P-wave velocity in fractured rock masses is of great significance for the quality grading and stability evaluation of rock masses for engineering purposes.

    Methods

    The nonfilling single fracture granite of an under ground water sealed storage cavern was taken as the research object. Based on borehole television images, hydraulic fracturing geostress tests, and ultrasonic full waveform logging, the geometric characteristics, stress state, and P-wave velocity of 384 groups of single fracture granites were obtained. An evolutionary neural network model for the prediction of the P-wave velocity of granite with a single fracture was constructed, and the variation behavior of the key indexes affecting the P-wave velocity of granite with a single fracture was analyzed.

    Results

    The study shows that the P-wave velocity of a single fracture granite in the water-sealed storage cavern is distributed around 4 300-5 330 m/s, and 82.3% of the P-wave velocity varies within 4 700-5 200 m/s. It is reasonable and feasible to select the fracture normal stress, average aperture, and dip angle as prediction indexes of the P-wave velocity of granite with a single fracture. The field test data sets are divided into training samples and test samples. The P-wave velocity prediction model of granite with a single fracture, based on the evolutionary neural network model, is constructed. The neural network weight and threshold are optimized by the genetic algorithm.The maximum test error of the prediction model is only 2.9%, and the test error of 85% of the samples is less than 1.5%.The prediction model thus yields high accuracy.

    Conclusion

    The variation feature of the P-wave velocity revealed that the P-wave velocity of granite with a single fracture increases with increasing normal stress on the fracture. However, the increase in the P-wave velocity decreases gradually when the normal stress increases to 5 MPa. The P-wave velocity decreases with an increasing fracture aperture. The P-wave velocity increases with an increasing dip angle. However, no difference occurred considering that the fracture dip angle is less than 40°.

     

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