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Advanced geological forecasting method and system based on perception while drilling

An advanced geological prediction and while-drilling technology, applied in neural learning methods, earthwork drilling, measurement, etc., can solve problems such as time-consuming and labor-intensive, still blank, poor geological identification, etc., to improve comprehensiveness and accuracy, Save manpower and time, improve the effect of forecast accuracy

Active Publication Date: 2021-12-10
SHANDONG UNIV
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AI Technical Summary

Problems solved by technology

When identifying and forecasting unfavorable geology, the change characteristics of tunnel surrounding rock parameters can reflect the location, scale, mechanical properties and rock mass integrity of unfavorable geology, etc. The geochemical characteristics of tunnel surrounding rock can reflect the type of unfavorable geology and the properties of fillings Therefore, the tunnel surrounding rock and unfavorable geological characteristics reflected by the above two parameters are complementary. Only by effectively combining and analyzing the above two parameters can the refinement and accurate identification of the tunnel surrounding rock conditions and unfavorable geology be achieved, so that Improve the comprehensiveness and accuracy of tunnel geological prediction results, but the above problems are not considered in the existing methods
[0006] The inventor found that, on the one hand, the existing technologies (CN112253049A, CN111238982A, CN110130883A, etc.) mainly focus on the testing of specific mechanical properties of a certain rock mass, such as compressive strength or wear resistance, etc. , it is impossible to continuously test and macroscopically respond to the mechanical properties of the rock mass in front of the tunnel face, and it is impossible to identify unfavorable geology; the technology for testing the geochemical characteristics of the rock mass while drilling is still blank
On the other hand, if the existing technology is to be used to analyze the physical and mechanical properties and geochemical characteristics of the surrounding rock during the drilling process, on-site sampling is required (it should be noted that for a tunnel of several kilometers, from the tunnel entrance to the tunnel shaft It takes several hours to conduct physical and mechanical tests and geochemical feature tests in the laboratory, which is also time-consuming and labor-intensive.

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[0038] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0040]It should be noted that the terminology used herein is only for describing specific embodiments, and is not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

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Abstract

The invention provides an advanced geological forecasting method and system based on perception while drilling, and relates to the technical field of advanced geological forecasting. The advanced geological forecasting method comprises the steps of: performing inversion based on drilling parameters to obtain tunnel surrounding rock physical and mechanical parameters; acquiring rock slag or rock powder based on a flushing fluid collected in the drilling process; acquiring geochemical characteristic parameters of rock slag or rock powder; and acquiring at least one of an unfavorable geological recognition result and a surrounding rock classification result by using a pre-trained deep learning model according to the obtained physical and mechanical parameters and geochemical characteristic parameters of the tunnel surrounding rock, so as to realize advanced geological forecast. According to the advanced geological forecasting method and system, advanced geological drilling is combined, geological features are comprehensively reflected from the two aspects of physical and mechanical property changes and geochemical characteristic parameter changes of tunnel surrounding rock, through collection and analysis of drilling parameters and flushing fluid in the advanced drilling process, big data and a deep learning algorithm are fused, and finally, advanced prediction of the geology in front of the tunnel face is realized.

Description

technical field [0001] The disclosure belongs to the technical field of advanced geological prediction, and in particular relates to an advanced geological prediction method and system based on perception-while-drilling. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Tunnel construction often encounters geological disasters such as water and mud inrush, landslides, and large deformations, causing heavy casualties, serious economic losses, and adverse social impacts. Unfavorable geology such as faults and karst is the biggest culprit and challenge for tunnel disasters. Due to the complex geological conditions underground and limited surface survey techniques, it is difficult to accurately grasp the unfavorable geological conditions along the tunnel in the early stage of construction. Advanced geological prediction is the most effective m...

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Application Information

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IPC IPC(8): G06F30/13G06F30/27G06N3/04G06N3/08E21B47/00E21F17/18G06F119/14
CPCG06F30/13G06F30/27G06N3/084E21B47/00E21F17/18G06F2119/14G06N3/045E21B2200/22E21D9/003E21B49/003E21B2200/20E21B49/005
Inventor 许振浩林鹏李术才谢辉辉石恒王朝阳刘福民邵瑞琦
Owner SHANDONG UNIV
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