Coal mine concealed geological structure detection method based on full-space gravity field

Abstract

The invention relates to the field of geologic structure detection, and discloses a coal mine hidden geologic structure detection method based on a full-space gravity field, which comprises the following steps of: performing array type measuring point arrangement in an underground limited space gravity field, acquiring gravitational acceleration of measuring points by adopting a gravimeter, identifying a source boundary of the gravity field based on boundary identification parameters, and determining a hidden geologic structure of a coal mine. The method comprises the following steps: carrying out multi-layer separation on a gravity potential field through a wavelet multi-scale decomposition method, carrying out source body embedding position prediction, carrying out three-dimensional inversion simulation on an irregular boundary and a combined inclined block model, determining basic parameters, establishing an inversion parameter library according to the basic parameters, and carrying out source body embedding position prediction; and testing the detection accuracy of the inversion method on the depth, the scale and the boundary of the model based on the inversion parameter library, obtaining known physical property information, performing gravity acceleration inversion of the measuring point through the inversion method, and obtaining and outputting a depth-residual gravity anomaly result map. The method is high in geological structure detection precision and is closer to a target body under the total space condition, and detection is not limited by underground conditions.

Description

Technical Field

[0001] The invention relates to the field of geological structure detection, and in particular to a method for detecting hidden geological structures in coal mines based on a full-space gravity field. Background Art

[0002] As the mining depth of underground coal mines increases, mine water inrush disasters have become a geological factor that seriously threatens the safe production of coal mines. It is imperative to increase the prevention and detection of mine water inrush disasters. Nationwide, the main water hazard problems in coal mines are caused by limestone water, structural water, and old empty water. The hidden geological structure of coal mines is one of the main water hazard channels. Accurate detection of geological structure development is an outstanding problem that needs to be solved urgently. However, traditional mine geophysical exploration methods have a series of problems: 1. The detection distance of geological radar is limited; 2. Slot wave detection is concentrated inside the working face; 3. The accuracy of radio wave perspective detection is not very high. Therefore, it is necessary to actively find new ideas and methods to solve the problem of coal mine geological structure detection more accurately and effectively, so as to effectively solve the impact of water hazards on safe production. Summary of the invention

[0003] The present invention aims to provide a method for detecting hidden geological structures in coal mines based on a full-space gravity field, so as to arrange points in an array underground and obtain gravity data of the measuring points, and to perform detection in a limited space underground based on the full-space gravity field detection method. The detection of geological structures is highly accurate, and the target body is closer under full-space conditions, and the detection is not limited by underground conditions.

[0004] In order to achieve the above object, the present invention adopts the following technical scheme:

[0005] A method for detecting hidden geological structures in coal mines based on a full-space gravity field, comprising:

[0006] S1, array measurement point arrangement in the limited gravity field underground;

[0007] S2, using a gravimeter to collect the gravity acceleration of the measuring point;

[0008] S3, identifying the gravity field source boundary based on the boundary identification parameter;

[0009] S4. Separate the gravity potential field into multiple layers by wavelet multi-scale decomposition method, and predict the source buried position based on the multi-layer separation results;

[0010] S5. Perform three-dimensional inversion simulation on irregular boundary and combined tilted block models, determine basic parameters, and establish an inversion parameter library based on the basic parameters;

[0011] S6. Testing the detection accuracy of the inversion method on the depth, scale and boundary of the model based on the inversion parameter library;

[0012] S7, obtaining known physical property information, performing gravity acceleration inversion of the measuring point by an inversion method, and obtaining a depth-residual gravity anomaly result map;

[0013] S8. Output depth-residual gravity anomaly results map.

[0014] The principles and advantages of this solution are: in actual application, the present invention uses the full-space gravity field to detect the development of hidden geological structures in coal mines, which has the advantages of high efficiency, accuracy, non-destructiveness and ultra-sensitivity. Since the seismic wave method of detecting structures requires blasting or hammering excitation and has high requirements for underground conditions, the geological radar detection distance is too short, and electromagnetic methods are seriously interfered. Gravity detection overcomes the shortcomings of conventional methods such as low accuracy, unclear abnormal response, and strong interference from tunnels. In addition, multi-parameter acquisition, high-precision field source boundary identification, multi-layer separation of potential fields, and three-dimensional sparse inversion processing significantly improve the detection accuracy of hidden geological structures under complex conditions in coal mines, providing geological guarantees for the prevention and control of water hazards in deep coal mining.

[0015] Preferably, as an improvement, the array-type measuring point arrangement includes arranging measuring points in a diamond shape on two longitudinal grooves and the cut-eye bottom plate, longitudinal layer and top plate of the working surface.

[0016] Technical effect: It is easy to control the top plate, bottom plate and two side panels.

[0017] Preferably, as an improvement, the boundary identification parameter is the arccosine value of the ratio of the total horizontal derivative to the analytical signal amplitude, and the calculation model of the boundary identification parameter θ is:

[0018]

[0019] Where THDR(x,y,z) is the total horizontal derivative and ASM is the amplitude of the analytical signal.

[0020] Technical effect: The deep abnormal amplitude is enhanced through the ratio of the derivative, which plays a role in enhancing the edge and improving the resolution, and can make the identified boundary position clearer.

[0021] Preferably, as an improvement, the calculation model of the total horizontal derivative is:

[0022]

[0023] Where f is the gravity anomaly data of a certain measuring point of the field source, Vzx and Vzy are the first-order derivatives of the potential field along the x and y directions.

[0024] Technical effect: It is convenient to determine the boundary position of the gravity anomaly field source through the maximum value based on the plane grid to achieve the boundary detection effect.

[0025] Preferably, as an improvement, the calculation model of the analytical signal amplitude is:

[0026]

[0027] Where VDR represents the vertical first-order derivative.

[0028] Technical effect: It is convenient to analyze the signal amplitude model to determine the boundary position of the field source based on the maximum position of the vertical gravity anomaly.

[0029] Preferably, as an improvement, the inversion parameter library includes grid size, density, boundary constraint parameters and noise data.

[0030] Technical effect: It can be used as basic parameters to support inversion.

[0031] Preferably, as an improvement, the inversion method is an Lp norm three-dimensional sparse gravity inversion method.

[0032] Technical effect: It can improve boundary constraint effect and resolution, and effectively reduce the interference of false structures caused by negative density. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of a method for detecting hidden geological structures in coal mines based on the full-space gravity field;

[0034] Figure 2 This is a schematic diagram of the layout of the top and bottom plates of the tunnel and the measurement points along the layers;

[0035] Figure 3 This is a schematic diagram of the arrangement of two drifts and cut-eye measuring points in the mining face;

[0036] Figure 4 It is a schematic diagram of the irregular boundary model;

[0037] Figure 5 It is a schematic diagram of the combined tilted block model;

[0038] Figure 6 This is the depth-residual gravity anomaly results map. DETAILED DESCRIPTION

[0039] The following is further described in detail through specific implementation methods:

[0040] The embodiment is basically as shown in the attached Figure 1 As shown:

[0041] A method for detecting hidden geological structures in coal mines based on a full-space gravity field, comprising:

[0042] S1, array measurement point arrangement in the limited gravity field underground; specifically, Figure 2As shown in the figure, measuring points are arranged in a diamond shape on the two drifts and the cut-eye bottom plate, the bed and the top plate of the working face. The distance between every two measuring points is 5m, and the measuring points are numbered, such as "1-1#, 1-2#, 1-3#, 1-4#, 2-1#, 2-2#, 2-3#, 2-4#..."; Figure 2 The arrangement of measuring points in the longitudinal section of the tunnel is shown in the figure. Four measuring points form a measuring point array, which is distributed in a diamond shape. They are named 1-1#, 1-2#, 1-3#, and 1-4# in the order of roof, layer, bottom plate, and layer. Figure 3 The figure shows the schematic diagram of the arrangement of measuring points in two drifts and a cutting eye of the mining face. The distance between the two measuring point arrays is 5m. The point numbers in the figure represent the measuring point arrays. The measuring point arrays are arranged in the drift, cutting eye and drift below the working face.

[0043] S2. Use a high-precision and high-sensitivity gravimeter to collect the gravity acceleration of the measuring points (such as 1-1#, 1-2#, 1-3#, 1-4#), where the gravity acceleration includes absolute gravity acceleration and relative gravity acceleration. Then move the gravimeter to the next measuring point array, measure the gravity acceleration data in the order of each measuring point, and complete the data collection of all measuring points.

[0044] S3. Identify the gravity field source boundary based on the boundary identification parameter, wherein the boundary identification parameter is the arccosine value of the ratio of the total horizontal derivative to the analytical signal amplitude, and the calculation model of the boundary identification parameter θ is:

[0045]

[0046] Where THDR(x,y,z) is the total horizontal derivative and ASM is the amplitude of the analytical signal.

[0047] All the measuring point data are divided into four measuring lines and processed separately. The composition of the four measuring lines is as follows: the measuring points with the same number in each measuring point array are one measuring line, that is, 1-1#, 2-1#, 3-1#..., 1-2#, 2-2#, 3-2#..., 1-3#, 2-3#, 3-3#..., 1-4#, 2-4#, 3-4#...; the measuring points in each measuring point array form corresponding measuring lines, and each measuring line uses the arc cosine value of the ratio of the total horizontal derivative to the analytical signal amplitude as the parameter for accurate identification of the gravity field source boundary.

[0048] Based on the plane grid, the calculation model of the total horizontal derivative is:

[0049]

[0050] Where f is the gravity anomaly data of a certain measuring point of the field source, which is detected by the gravimeter, and Vzx and Vzy are the first-order derivatives of the potential field along the x and y directions.

[0051] Based on the vertical direction, the calculation model of the analytical signal amplitude is:

[0052]

[0053] Where VDR represents the vertical first-order derivative.

[0054] The boundary position of the abnormal gravity field source can be determined by finding the maximum value of the boundary identification parameter θ.

[0055] S4. The gravity potential field contains comprehensive information on material density at different depths and scales. The wavelet multiscale decomposition method is used to separate the gravity potential field into multiple layers, and the absolute gravity acceleration and relative gravity acceleration that can more intuitively express the information of each depth and scale are obtained. The multi-layer separation obtains the depth and scale information, that is, the three-dimensional position information is obtained. Based on the multi-layer separation results, the buried position of the abnormal source can be predicted. Wavelet transform is a time-frequency analysis method for signals. The wavelet multiscale decomposition method decomposes the gravity anomaly into low-frequency components and high-frequency components. The low-order details of the wavelet transform do not change with the change of the total order. Through wavelet multiscale decomposition, the gravity field changes are decomposed into multiple layers related to information sources such as depth, scale and density. It is also possible to separate regional anomalies and local anomalies in the gravity field data, and obtain the results of gravity anomaly identification, separation, signal denoising, inversion imaging, etc.

[0056] S5, for irregular boundary and combined tilted block model, three-dimensional inversion simulation is carried out to determine basic parameters, and an inversion parameter library is established according to the basic parameters. Since the regular model is not representative, the irregular boundary and combined tilted block model are adopted in this embodiment, and the inversion effect is good and fits the actual situation. Specifically, an irregular boundary and combined tilted block model are established to verify the inversion method adopted by the gravity acceleration data. In this embodiment, the inversion method is the Lp norm three-dimensional sparse gravity inversion method. The inversion parameter library is determined by inversion, and the data of the inversion parameter library include grid subdivision size, density, boundary constraint parameters and noise settings.

[0057] like Figure 4 As shown in the figure, the abnormal boundary in the irregular boundary model is an irregular boundary with a density of 1 g·cm －3 The grid of the downhole measurement array data collection is 5×5, and the data interval along the working face and perpendicular to the working face is 5m, which contains Gaussian noise with a standard deviation of 2% of the absolute value of the data plus 0.01mGal. For the irregular boundary model, the Lp norm three-dimensional sparse inversion method is used for processing. Compared with other inversion methods, it has better constraint effect on the boundary and improved resolution.

[0058] like Figure 5 As shown in the figure, the densities of the two abnormal blocks in the model are 0.8 g·cm －3 and 1g·cm－3 The grid of the downhole measurement point array data acquisition is 5×5, and the data interval along the working face and perpendicular to the working face is 5m, which contains Gaussian noise with a standard deviation of 2% of the absolute value of the data plus 0.05mGal. The Lp norm three-dimensional sparse inversion method is used for this model, which can effectively reduce the interference of false negative density structures compared with other inversion methods.

[0059] S6. Test the detection accuracy of the inversion method on the depth, scale and boundary of the model based on the inversion parameter library; when the accuracy does not meet the requirements, re-simulate and determine the inversion parameter library data to provide accurate inversion parameters for actual data processing.

[0060] S7, combining the collected gravity acceleration information of the measuring points with the known physical property information, and performing Lp norm three-dimensional sparse gravity inversion, processing the acceleration information to obtain high-resolution depth information, forming Figure 6 The depth-residual gravity anomaly results map shown;

[0061] S8. Analyze the inversion accuracy and boundary accuracy of each survey line and output the detection results; combine geological data with actual conditions, compare and analyze the results obtained with the actual conditions, and verify the inversion accuracy and boundary accuracy of the structure.

[0062] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for detecting hidden geological structures in coal mines based on full-space gravity field, characterized in that: include: S1, array measurement point arrangement in the limited gravity field underground; S2, using a gravimeter to collect the gravity acceleration of the measuring point; S3, identifying the gravity field source boundary based on the boundary identification parameter; S4, using wavelet multi-scale decomposition method to separate the gravity potential field into multiple layers, and predict the source body burial position based on the multi-layer separation results; S5. Perform three-dimensional inversion simulation on irregular boundary and combined tilted block models, determine basic parameters, and establish an inversion parameter library based on the basic parameters; S6. Testing the detection accuracy of the inversion method on the depth, scale and boundary of the model based on the inversion parameter library; S7, obtaining known physical property information, performing gravity acceleration inversion of the measuring point by an inversion method, and obtaining a depth-residual gravity anomaly result map; S8. Output depth-residual gravity anomaly results map.

2. The method for detecting hidden geological structures in coal mines based on the full-space gravity field according to claim 1 is characterized in that: The array type measuring point arrangement includes arranging measuring points in a diamond shape on two drifts and the cut-eye bottom plate, the layer and the top plate of the working face.

3. The method for detecting hidden geological structures in coal mines based on the full-space gravity field according to claim 1 is characterized in that: The boundary identification parameter is the arccosine value of the ratio of the total horizontal derivative to the analytical signal amplitude. The calculation model of the boundary identification parameter θ is: Where THDR(x,y,z) is the total horizontal derivative and ASM is the amplitude of the analytical signal.

4. The method for detecting hidden geological structures in coal mines based on the full-space gravity field according to claim 3 is characterized in that: The calculation model of the total horizontal derivative is: Where f is the gravity anomaly data of a certain measuring point of the field source, Vzx and Vzy are the first-order derivatives of the potential field along the x and y directions.

5. The method for detecting hidden geological structures in coal mines based on the full-space gravity field according to claim 4 is characterized in that: The calculation model of the analytical signal amplitude is: Where VDR represents the vertical first-order derivative.

6. The method for detecting hidden geological structures in coal mines based on the full-space gravity field according to claim 1, characterized in that: The inversion parameter library includes grid size, density, boundary constraint parameters and noise data.

7. The method for detecting hidden geological structures in coal mines based on the full-space gravity field according to claim 1, characterized in that: The inversion method is an Lp norm three-dimensional sparse gravity inversion method.

Images