Computing method for reflecting overall splitting damage process of argillaceous siltstone

Abstract

The invention discloses a computing method for reflecting the overall splitting damage process of argillaceous siltstone. The computing method comprises the following steps: step S1, carrying out wavelet packet decomposition on acoustic emission signals emitted in the splitting evolution process of argillaceous siltstone so as to obtain an energy spectrum coefficient central frequency band F1 of a wavelet pocket; Step S2, computing the main frequency F2 of the acoustic emission signals; step S3, finally determining the optimal frequency band, namely the characteristic frequency band F according to the energy spectrum coefficient central frequency band F1 of the wavelet packet and the main frequency F2 of the acoustic emission signals, wherein F=F1UF2; and acquiring dynamic characteristics at different phases in rock failure and the cross-scale fracture process of rock according to the optimal frequency band. By adopting the technical scheme provided by the invention, fractional frequency study can be carried out on acoustic emission waveform in the splitting process of argillaceous siltstone, the acoustic emission information at different frequency bands and the sensitiveness of the fracture problem can be considered fully, and the acquired data are rich and high in reliability.

Description

Technical field [0001] The invention relates to the technical field of rock splitting detection, and in particular to a calculation method that uses acoustic emission characteristic frequency bands to reflect the whole process of splitting failure of argillaceous siltstone. Background technique [0002] The essential cause of rock failure is the initiation and expansion of internal micro-cracks, as well as the inter-connection between the cracks, and finally the process of forming a macro-fracture surface. Due to the opacity of rock materials and the three-dimensional problem of fracture, it is very difficult to monitor the evolution process of rock damage and fracture. However, rock fracture has an evolution process from microscopic development to macroscopic scale. Choose an optimal frequency band (characteristic frequency band) that accurately, without interference, and can reflect this evolution process in real time, which is useful for studying the macroscopic fracture proce...

AI Technical Summary

Problems solved by technology

[0006] Second, the currently selected acoustic emission sensor is a broadband sensor, the effective monitoring frequency band is too wide, and many noise signals are also collected, which has a great impact on the monitoring results, and even misjudgments may occur

[0007] Third, the response speed of the sensor is inversely proportional to the frequency band, and the wide-band sensor will inevitably lead to a slow response speed, but the cracking and failure of the argillaceous siltstone takes a short time

Clearly, the slower response is not sufficient for monitoring

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