基于振动信号的岩石单轴抗压强度钻进预测实验研究

doi:10.13722/j.cnki.jrme.2023.0960

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Abstract In order to study the response relationship between the vibration signal with drilling and the geomechanical parameters of the rock mass，and to perceive and predict the uniaxial compressive strength of the rock accurately and quickly，a research on the prediction of uniaxial compressive strength of the rock based on the vibration signal with drilling was carried out. Based on indoor drilling experiments of four types of raw rock(coal) specimens，namely granite，limestone，sandstone and coal，the GA-BP neural network model was constructed by combining Fourier transform and vibration signal noise reduction methods，and the prediction performance of the model before and after the noise reduction，as well as the models with different noise reduction methods，were compared and analyzed. The results show that there is a responsive relationship between the vibration signal with drilling and the uniaxial compressive strength of rock，and the uniaxial compressive strength of rock can be predicted by using the vibration signal while drilling. The GA-BP neural network prediction model using Adobe Audition software to denoise the vibration signal has a determination coefficient R2 of 0.838，a root mean square error of 7.063 MPa，and an average absolute error of 5.347 MPa. The results are better than the original prediction model and the general noise reduction method prediction model. Compared with the original prediction model，the prediction accuracy of the optimal noise reduction model is improved by 6.3 %，the root mean square error is reduced by 1.954 MPa，and the average absolute error is reduced by 1.621 MPa. There are some differences in the prediction effect of different lithology in the same prediction model. The GA-BP neural network prediction model of noise reduction signal has excellent prediction ability for uniaxial compressive strength. The method can provide a basis for the measurement of rock mass geomechanical parameters while drilling.

Key words： rock mechanics vibration signals with the drill uniaxial compressive strength Fourier transform signal noise reduction artificial neural networks

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	Articles by authors
	HAO Jian1
	LIU Heqing1
	LIU Jiankang1
	LYU Jiaqing1
	ZHENG Yining2
	LIU Jianrong3

Cite this article:

HAO Jian1,LIU Heqing1,LIU Jiankang1, et al. Experimental study of rock uniaxial compressive strength prediction with drilling based on vibration signals[J]. , 2024, 43(6): 1406-1424.

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http://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2023.0960 OR http://rockmech.whrsm.ac.cn/EN/Y2024/V43/I6/1406

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