A mine structure detection method based on multi-parameter imaging technology

Abstract

The application discloses a mine structure detection method based on a multi-parameter imaging technology, and belongs to the technical field of mine structure detection. The method comprises the following steps: S1, a laser scanning mode is used to scan and model the inner side of a mine tunnel, and an inner surface model of the mine is obtained; S2, the mine is divided into a plurality of groups, each group comprising sequentially arranged detection sections and prediction sections; S3, a collection device is arranged in the detection sections, and sound logging curves and density logging curves of the detection sections are obtained; S4, a geological model of the detection sections is constructed according to the information collected in step S3; S5, the prediction sections are sampled according to the geological models of adjacent detection sections, and the geological model is completed; and S6, a mine structure model is obtained according to the geological models of the detection sections and the prediction sections. According to the method, the inner surface model of the mine is established by the surface scanning mode, the inner surface model of the mine is filled correspondingly, and finally, the mine structure model is formed, and the parameter quantization of the mine model is realized.

Description

Technical Field

[0001] This invention relates to the technical field of mine structure detection, and in particular to a mine structure detection method based on multi-parameter imaging technology. Background Technology

[0002] With the development of modern industry, the demand for intelligent tunneling is extremely urgent. However, during mine tunneling, disasters such as coal and gas outbursts and water inrushes seriously threaten the safety of tunneling production and the personal safety of miners. Geological support technology is the foundation for ensuring the safety of intelligent coal production. It is the basic data source for geological prediction, disturbance perception, and risk assessment before, during, and after mine tunneling construction, and a prerequisite for the implementation of all key technologies in intelligent tunneling. Among these technologies, integrating detection instruments with tunneling machinery and conducting on-site detection is one of the most effective ways to ensure rapid mine tunneling. It enables parallel exploration and precise disaster management, fully utilizes the production efficiency of tunneling machinery, and is the most urgently needed support technology for the development of rapid and intelligent mine tunneling in the future. In existing mine detection, macroscopic data is usually used to obtain changes in the overall structure and then discover anomalies, without understanding the mine structure at a smaller level or having a specific quantitative method for mine detection. Summary of the Invention

[0003] The purpose of this invention is to provide a mine structure detection method based on multi-parameter imaging technology. This method establishes a mine inner surface model through scanning, serving as its internal framework. Subsequently, data from detection and prediction segments are used to fill the mine model according to the starting and ending coordinates, resulting in better filling effects. Variation standards are set for the detection and prediction segments to establish a correspondence between them. During calculation, the detection segments are used to acquire a three-dimensional model and corresponding calculation parameters, leading to good prediction results for the prediction segments. Setting prediction segments reduces the workload of detection, accelerates the detection process, and ultimately enables specific quantification of the mine, improving the understanding of mine performance indicators.

[0004] To achieve the above objectives, the present invention provides a mine structure detection method based on multi-parameter imaging technology, comprising the following steps:

[0005] S1: The inner side of the mine shaft is scanned and modeled using laser scanning to obtain the inner surface model of the mine shaft;

[0006] S2: Divide the mine into several groups, each group including a detection section and a prediction section set in sequence;

[0007] S3: Set up acquisition equipment in the detection section to obtain the acoustic logging curve and velocity logging curve of the detection section;

[0008] S4: Construct a geological model for the detection section using the information collected in step S3;

[0009] S5: Based on the geological models of adjacent detection sections, sample the predicted section to complete the geological model of the predicted section;

[0010] S6: Based on the geological models of the detection and prediction sections, the mine structure model is obtained.

[0011] Preferably, the process in step S2 is as follows:

[0012] A coordinate system is established for the inner surface model of the mine. First, the length of each group of the mine is adjusted according to the similarity of geological changes. The detection segment and prediction segment in each group are set sequentially and divided into detection segment and prediction segment on an average basis. The starting point coordinates and ending point coordinates of each detection segment and prediction segment are recorded. The similarity is set so that the geological conditions of the detection segment and prediction segment meet the similarity. Geological changes include formation stability, porosity, particle size and water content.

[0013] Preferably, the specific steps of step S4 are as follows:

[0014] The collected signal is denoted by the formula x. n , representing the information collected in the nth detection segment. For the detection segment, a rectangular grid geological model is constructed, and the grid density and grid size are set. The inner surface model of the mine is set in it. According to the detection segment and the prediction segment, the velocity model is established in the rectangular grid geological model with the help of acoustic logging curves and velocity logging curves. The detection segment combines the velocity model to perform wave inversion and corresponding error comparison analysis to obtain the geological model of the detection segment.

[0015] Preferably, in step S5, the predicted segment is sampled, and the geological model of the detection segment is used to perform calculations according to the inversion calculation method. Specifically, full waveform inversion calculation is adopted, and the calculation parameters are adjusted so that the full waveform inversion calculation can fit the three-dimensional model of the detection segment. Using the same parameters, the inversion calculation is performed based on the point data of the predicted segment to obtain the three-dimensional model of the predicted segment.

[0016] Preferably, the specific calculation process in step S6 is as follows:

[0017] The three-dimensional models of the detection section and the prediction section are filled into the mine inner surface model in step S1 according to the starting coordinates and ending coordinates in the coordinate system, forming a mine structure model with thickness.

[0018] Therefore, the mine structure detection method based on multi-parameter imaging technology described above, as used in this invention, has the following advantages:

[0019] In this invention, a mine inner surface model is established by scanning, serving as the internal skeleton. Subsequently, the mine model is filled with data from detection and prediction sections according to the starting and ending coordinates, resulting in better filling effects. Variation standards are set for the detection and prediction sections to establish a correspondence between them. During the calculation process, the detection sections are used to obtain a three-dimensional model and corresponding calculation parameters, thereby achieving good prediction results for the prediction sections. Setting prediction sections reduces the workload of detection and speeds up the exploration process.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 This is a flowchart of a mine structure detection method based on multi-parameter imaging technology according to the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Specific model specifications need to be selected and determined according to the actual specifications of the device, etc. The specific selection calculation method adopts existing technology in the art, and therefore will not be described in detail.

[0023] Example

[0024] like Figure 1 As shown, this invention provides a mine structure detection method based on multi-parameter imaging technology, comprising the following steps:

[0025] S1: Laser scanning is used to scan and model the inner side of the mine shaft, obtaining an inner surface model. This model is then used to measure the inner surface of the mine shaft, achieving...

[0026] S2: The mine is divided into several groups, each group including sequentially set detection and prediction sections. By segmenting, the size of the calculation target is reduced, thereby accelerating the calculation speed.

[0027] S3: Set up acquisition equipment in the detection section to obtain the acoustic logging curve and velocity logging curve of the detection section;

[0028] S4: Construct a geological model for the detection section using the information collected in step S3. The collected signal formula is denoted as x. n, representing the information collected in the nth detection segment. For each detection segment, a rectangular grid geological model is constructed, with grid density and grid size set. The inner surface model of the mine is set within this model. Based on the detection and prediction segments, a velocity model is established in the rectangular grid geological model using acoustic logging curves and velocity logging curves. Wave inversion is performed on the detection segment in conjunction with the velocity model, and corresponding error comparison analysis is conducted to obtain the geological model of the detection segment. By using logging curves and wave inversion in the grid geological model, a high degree of restoration of the geological model of the detection segment is achieved, improving the calculation accuracy.

[0029] S5: Sample the predicted segment and perform calculations according to the geological model of the detection segment using the inversion calculation method. Specifically, full waveform inversion calculation is adopted, and the calculation parameters are adjusted so that the full waveform inversion calculation can fit the three-dimensional model of the detection segment. Using the same parameters, inversion calculation is performed based on the point data of the predicted segment to obtain the three-dimensional model of the predicted segment. With the help of the detection segment and the predicted segment divided by the previous geological changes, the detection segment is precisely calculated, while the calculation requirements for the predicted segment are reduced, and the mine model can be constructed more quickly, thus speeding up the work efficiency.

[0030] S6: Based on the geological models of the detection and prediction sections, the mine structure model is obtained. The specific process is as follows:

[0031] The three-dimensional models of the detection section and the prediction section are filled into the mine inner surface model in step S1 according to the starting coordinates and ending coordinates in the coordinate system, forming a mine structure model with thickness. The mine inner surface model is used as the positioning basis to realize the filling of the geological models of the detection section and the prediction section, and finally the mine structure model is obtained.

[0032] Therefore, this invention employs a mine structure detection method based on multi-parameter imaging technology. A mine inner surface model is established through scanning, serving as the internal framework. Subsequently, data from detection and prediction segments are used to fill the mine model according to the starting and ending coordinates, resulting in better filling performance. Variation standards are set for the detection and prediction segments to establish a correspondence between them. During calculation, the detection segments are used to acquire a three-dimensional model and corresponding calculation parameters, leading to good prediction results for the prediction segments. Setting prediction segments reduces the workload of detection and accelerates the detection process.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for mine structure detection based on multi-parameter imaging technology, characterized in that: Includes the following steps: S1: The inner side of the mine shaft is scanned and modeled using laser scanning to obtain the inner surface model of the mine shaft; S2: Divide the mine into several groups, each group including a detection section and a prediction section set in sequence; The specific process in S2 is as follows: A coordinate system is established for the inner surface model of the mine. First, the length of each group of the mine is adjusted according to the similarity of geological changes. The detection segment and prediction segment in each group are set in sequence and divided into detection segment and prediction segment on an average basis. The starting point coordinates and ending point coordinates of each detection segment and prediction segment are recorded. The similarity is set so that the geological conditions of the detection segment and prediction segment meet the similarity. Geological changes include formation stability, porosity, particle size and water content. S3: Set up acquisition equipment in the detection section to obtain the acoustic logging curve and velocity logging curve of the detection section; S4: Construct a geological model for the detection section using the information collected in step S3; The specific steps of step S4 are as follows: The collected signal formula is recorded as The information collected by the nth detection section is represented by n, and for the detection section, a rectangular grid geological model is constructed, the grid density and grid size are set, and the mine inner surface model is set therein, Based on the detection section and the prediction section, a velocity model is established in the rectangular grid geological model using acoustic logging curves and velocity logging curves. Wave inversion is performed on the detection section in conjunction with the velocity model, and corresponding error comparison analysis is conducted to obtain the geological model of the detection section. S5: Based on the geological models of adjacent detection sections, sample the predicted section to complete the geological model of the predicted section; S6: Based on the geological models of the detection and prediction sections, the mine structure model is obtained.

2. The method as claimed in claim 1, wherein the method is based on multi-parameter imaging technique. In step S5, the predicted segment is sampled, and the geological model of the detection segment is used to perform calculations according to the inversion calculation method. Specifically, full waveform inversion calculation is adopted, and the calculation parameters are adjusted so that the full waveform inversion calculation can fit the three-dimensional model of the detection segment. Using the same parameters, the inversion calculation is performed based on the point data of the predicted segment to obtain the three-dimensional model of the predicted segment.

3. The method as claimed in claim 1, wherein the method is based on multi-parameter imaging technique. In step S6, the specific calculation process is as follows: The three-dimensional models of the detection section and the prediction section are filled into the mine inner surface model in step S1 according to the starting coordinates and ending coordinates in the coordinate system, forming a mine structure model with thickness.

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