Tunnel advanced geology forecasting method and system and storage medium

Abstract

The invention discloses a tunnel advanced geological forecast method and system and a storage medium, and relates to the technical field of geological survey of tunnel engineering. The method comprises the following specific steps: applying an artificial seismic source, and collecting seismic wave signals; generating a forecast image according to the seismic wave signal; constructing an analysis model based on a BP neural network; and inputting the forecast image into the analysis model for processing so as to obtain a forecast result. According to the method, the theoretical advantage of automatically mining sample features by using deep learning is used for reference, the unfavorable geology position and range information can be quickly and efficiently obtained, a reference basis is provided for site construction, the problem that result analysis depends on expert experience is solved, the unfavorable geology identification efficiency is improved, and the risk of missed judgment and misjudged judgment is reduced.

Description

technical field [0001] The invention relates to the technical field of geological survey of tunnel engineering, and more specifically relates to a tunnel advanced geological prediction method, system and storage medium. Background technique [0002] The scale of the current tunnel project is constantly expanding, and the complexity of the corresponding geological conditions and the difficulty of construction are also increasing. Although a lot of geological survey work has been invested in the survey and design stage of the tunnel, due to the complexity of geological and topographic conditions, the current level of survey technology, and the constraints of time and funds, it is generally difficult for the geological data in the survey stage to reach the accuracy of the construction stage. requirements. [0003] During the tunnel construction process, the geological body in front of the tunnel face is complex and changeable. The methods commonly used in the prior art include...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to the complex on-site environment, the imaging results often contain a variety of interference information

For example, in the seismic wave method detection, various noise disturbances are reflected in the imaging results, making it difficult to identify the reflection interface of the real stratum, and it is impossible to accurately predict the geological conditions of the tunnel.

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