A device for determining coal seam thickness based on channel wave and a regulation method thereof

Abstract

The present application belongs to the technical field of coal seam prediction, and discloses a device for determining coal seam thickness based on channel wave and a regulation method thereof. A sequence of seismic signals excited by diffraction of coal seams at different emission angles is obtained as an input set; a feature mapping point pair of the excited seismic signals is obtained through feature extraction and matching, and the mapping point pair is subjected to channel wave inversion processing; according to the channel wave inversion method, a candidate excited seismic signal feature point is selected as a coal seam thickness standard initial point, and is matched and mapped to the surrounding coal seam thickness detection adjacent area and filtered to obtain an accurate dense mapping point pair; a channel wave seismic signal identifier is calibrated, and internal and external parameters are obtained in combination with the mapping point pair; a coal seam thickness image point set is restored according to the channel wave seismic signal identifier parameters and the mapping point pair; and the channel wave inversion method is used for reconstruction to obtain an accurate dense coal seam thickness model. The present application has high data information accuracy and can be used for field operations.

Description

Technical Field

[0001] This invention belongs to the field of coal seam prediction technology, and particularly relates to a device and its control method for determining coal seam thickness based on channel waves. Background Technology

[0002] Water-rich goaf areas in coal mines can significantly impact mine safety. One mining area in a certain coal mine covers 7.09 square kilometers, with 80% consisting of coal seam goaf areas. The geological structure within this area is relatively clear, and most of the water accumulated in the goaf flows away through drainage tunnels, but the possibility of localized water accumulation cannot be ruled out. Another mining area covers 5.01 square kilometers, with 30% consisting of coal seam goaf areas. The geological structure within this area is unclear, and most of the water accumulated in the goaf flows away through drainage tunnels, but the possibility of localized water accumulation cannot be ruled out. The geological and hydrological conditions in these mining areas are complex, and the coal seams are severely threatened by water-rich (conductive) structures and old goaf water. Therefore, surface exploration is necessary to determine the water-bearing and water-conductive characteristics of the aquifers within the areas, providing a basis for future roadway and coal face planning.

[0003] Seismic transmitted waves excited in coal seams undergo multiple total reflections through the roof and floor of the coal seam, resulting in superposition and interference. Guided by the coal seam, these waves propagate only in two-dimensional space within and around the coal seam, forming channel waves. Channel waves are seismic transmitted channel waves that propagate only within the coal seam, also called coal seam waves or guided waves. In coal-bearing strata, the density of the coal seam is usually lower than that of the surrounding rocks above and below, leading to low propagation speeds of seismic transmitted channel waves within the coal seam. Based on the direction of particle vibration and its polarization characteristics, channel waves can be classified into two types: Love-type and Rayleigh-type. Love-type channel waves are formed by the interference of horizontally polarized shear waves (SH waves). The particles within the coal seam are parallel to the coal seam bedding plane and perpendicular to the wave propagation direction. The most important characteristic of channel waves is dispersion, meaning that vibrations of different frequencies propagate at different speeds, causing the channel wave train to disperse as it propagates. This also results in a significant difference between the phase velocity and the group velocity of the channel wave. Phase velocity refers to the propagation speed of a single-frequency harmonic vibration. Group velocity refers to the propagation speed of a wave packet composed of multiple harmonic vibrations with similar frequencies. The more intense the dispersion, the greater the difference between the phase velocity and the group velocity.

[0004] While channel wave exploration can be effective in assessing coal thickness, its application in predicting coal thickness still requires further exploration and research. Channel wave exploration utilizes the guiding effect of coal seams on seismic transmission channels to effectively explore various geological structures within the coal seam.

[0005] In mine seismic exploration, channel wave transmission is used to detect the distribution of anomalies within the working face (or roadway). Under stable coal seam thickness conditions, velocity distribution results are obtained by tomographic imaging using travel time information from each channel, thereby determining the location of the anomaly. When the coal seam thickness is unstable, and the detection target is the coal seam thickness distribution, the relevant technique involves multinomial fitting between the coal seam thickness revealed in the roadway and the velocity information obtained from tomographic imaging near the roadway to obtain the velocity-thickness correspondence observation results. Then, the velocity results from the tomographic imaging are converted into thickness distribution results. However, in the velocity-thickness correspondence observation results, the same velocity corresponds to multiple coal seam thicknesses, causing errors in the velocity-to-thickness distribution conversion results.

[0006] Based on the above analysis, the problems and defects of the existing technology are as follows: In the confirmation of coal seam thickness, the existing technology is easily affected by the coal seam formation in the exploration area, resulting in some deviation in the accuracy of the obtained data information, and it cannot provide an accurate theoretical basis for safe construction. Summary of the Invention

[0007] To overcome the problems existing in related technologies, the present invention discloses an apparatus and control method for determining coal seam thickness based on channel waves.

[0008] The technical solution is as follows: a method for determining coal seam thickness based on channel waves, comprising the following steps:

[0009] S1, use a channel wave seismic signal identifier to obtain the seismic signal sequence excited by the coal seam diffraction at different emission angles as the input set, obtain the feature mapping point pairs of the excited seismic signals through feature extraction and matching, and perform channel wave inversion processing on the mapping point pairs;

[0010] S2. Based on the channel wave inversion method, select candidate excitation seismic signal feature points as the standard initial points for coal seam thickness, perform matching mapping and filtering on the adjacent areas of coal seam thickness detection, and obtain accurate dense mapping point pairs; calibrate the channel wave seismic signal identifier, and obtain the internal and external parameters by combining the mapping point pairs; reconstruct the coal seam thickness image point set based on the parameters of the channel wave seismic signal identifier and the mapping point pairs.

[0011] S3 uses the channel wave inversion method for reconstruction. The initial coal seam thickness image is generated by selecting the standard initial point of coal seam thickness, and the dense coal seam thickness image is obtained by channel wave inversion in the adjacent area of ​​the grid coal seam thickness detection. The erroneous coal seam thickness image is filtered according to the constraint conditions to obtain the accurate dense coal seam thickness model.

[0012] In step S1, the channel wave seismic signal identifier performs multi-view diffraction on the target to obtain the excited seismic signal sequence and use it as the input set.

[0013] In step S2, obtaining accurate dense mapping point pairs includes:

[0014] Feature point extraction was performed using the DOG algorithm. The excited seismic signal sequence was screened based on the reference excited seismic signal, and excited seismic signals with an angle less than 60 degrees between their principal optical axis and the reference excited seismic signal were selected as candidate excited seismic signals. For each feature point m in the reference map, the corresponding candidate matching point m′ was found in the candidate excited seismic signals according to the epipolar constraint. Using the slot wave inversion method, the zero-mean normalized cross-correlation coefficient T was selected as the objective function to calculate the T values ​​of the mapped point pairs and sort them by size.

[0015]

[0016] In the formula, T(m,m′) represents the zero-mean normalized cross-correlation value of the mapping points, m represents the feature point, m′ represents the candidate matching point, i represents the number of excited seismic signal pulses, and W represents the number of excited seismic signal pulses. This represents the average excitation frequency of the seismic signal pulse centered at feature point m. Let W represent the average pulse frequency of the seismic signal pulses excited around the candidate matching point m′, A represent the frequency of the excited seismic signal pulses, and A′ represent the frequency of the candidate excited seismic signal pulses.

[0017] Furthermore, the channel wave inversion method includes: during the channel wave inversion process, selecting feature points greater than the threshold μ1 as the standard initial point for coal seam thickness, performing channel wave inversion in the adjacent area of ​​coal seam thickness detection, and selecting feature points greater than the threshold μ2 as reserve matching points, where μ1>μ2;

[0018] For all matching points of the reference-excited seismic signal, a one-to-many match is established at the center of the candidate-excited seismic signal with a fixed pulse size; for points of the reference-excited seismic signal, a mixed match is established for all points within the pulse.

[0019] Furthermore, the step of establishing a hybrid matching of all points within a pulse for the reference-excited seismic signal includes: mapping point u in feature point m and candidate mapping point u′ in candidate matching point m′ are a pair of matching points, and slot wave inversion is performed;

[0020] If the pulse size is set to N×N, then the pulse mapping points in the feature point m of the reference figure and the pulse points in the candidate matching point m′ are matched one by one. Under the premise of satisfying the mapping gradient constraint and confidence constraint, the correlation coefficient T of the groove wave inversion mapping point pair is calculated. The groove wave inversion points with a value greater than the threshold μ3 are selected as the initial points of the coal seam thickness standard for secondary groove wave inversion. The groove wave inversion points with a value greater than the threshold μ4 are selected as reserve matching points, where μ3>μ4.

[0021] Furthermore, candidate mapping point u′ and mapping point u are a pair of excited seismic signal mapping points, and candidate matching point m′ and feature point m are another adjacent pair of excited seismic signal mapping points. The mapping gradient constraint formula is:

[0022] ||(uu)-(mm)|| ∞ ≤λ

[0023] In the formula, λ is the threshold for mapping the gradient;

[0024] Confidence constraints are used to obtain accurate dense mapping point pairs. The formula for the confidence constraint is:

[0025] x(m) = max{|A(m+δ)-A(m)|}

[0026] δ∈{(1,0),(-1,0),(0,1),(0,-1)}

[0027] In the formula, x(m) represents the confidence constraint value, A represents the excited seismic signal pulse, m represents the feature point, A(m) represents the excited seismic signal pulse centered at m, and δ represents the constraint factor value.

[0028] In step S2, the coal seam thickness image point set is reconstructed based on the parameters of the channel wave seismic signal identifier and the mapping point pair, including:

[0029] S201, the process of calibrating the trough wave seismic signal identification instrument: the process of calculating the internal parameters of the trough wave seismic signal identification instrument based on the imaging principle of the trough wave seismic signal identification instrument.

[0030] S202, based on the feature points and matching of the excited seismic signal sequence, select two input excited seismic signals as references, and calculate the fundamental matrix Y of the reference excited seismic signal point pair;

[0031] Wherein, the fundamental matrix Y satisfies the equation m′Ym=0, the initial value L′ and the current value L of the intrinsic parameter matrix of the reference excited seismic signal pair are estimated, m′ and m are a pair of matching points, the essential matrix of the excited seismic signal point pair is calculated and the rotation and translation components are extracted;

[0032] S203. Given the intrinsic and extrinsic parameters of the channel wave seismic signal identifier and the feature mapping point pairs, use triangulation to obtain the set of coal seam thickness image points corresponding to the feature points.

[0033] In step S3, the erroneous coal seam thickness image is filtered according to the constraint conditions to obtain an accurate dense coal seam thickness model, including:

[0034] S301 uses a dense reconstruction algorithm based on coal seam thickness images, takes the coal seam thickness image point set obtained by channel wave inversion as the candidate coal seam thickness standard initial point, generates an initial coal seam thickness image, and reconstructs the coal seam thickness image point set cloud using the channel wave inversion method based on coal seam thickness images.

[0035] S302, Perform channel wave inversion in the adjacent area of ​​the grid coal seam thickness detection. The channel wave inversion condition is that there is no coal seam thickness image that is similar to the initial coal seam thickness image or has a large average correlation coefficient in the adjacent area of ​​the coal seam thickness detection.

[0036] S303, for the coal seam thickness image retrieved by channel wave inversion, uses geometric consistency and excitation seismic signal gray-scale consistency constraints for screening to generate a dense coal seam thickness model.

[0037] In step S301, the coal seam thickness image is a coal seam thickness image centered on a set of coal seam thickness image points, with the vector pointing from that point to the origin of the reference-excited seismic signal channel wave seismic signal identifier as the normal vector. The coal seam thickness image is selected as the initial coal seam thickness image for channel wave inversion, with the normal vector of the coal seam thickness image and the angle between the light rays, as well as the correlation coefficient between the coal seam thickness image and the projection of the reference-excited seismic signal and the candidate-excited seismic signal as constraints.

[0038] Another object of the present invention is to provide an apparatus for determining coal seam thickness based on channel waves, and to implement the aforementioned control method for determining coal seam thickness based on channel waves. The apparatus includes:

[0039] The channel wave seismic signal identifier is used to acquire the seismic signal sequence excited by diffraction of coal seams at different emission angles as the input set; feature mapping point pairs of the excited seismic signals are obtained through feature extraction and matching, and channel wave inversion processing is performed on the mapping point pairs;

[0040] The coal seam thickness image point set acquisition module is used to select candidate excited seismic signal feature points as the standard initial points for coal seam thickness according to the channel wave inversion method, match and map them to the surrounding coal seam thickness detection adjacent areas and filter them to obtain accurate dense mapping point pairs; calibrate the channel wave seismic signal identifier and obtain the intrinsic and extrinsic parameters by combining the mapping point pairs; and reconstruct the coal seam thickness image point set based on the channel wave seismic signal identifier parameters and the mapping point pairs.

[0041] The coal seam thickness model acquisition module is used to reconstruct the coal seam using the channel wave inversion method. It selects the standard initial point of coal seam thickness to generate an initial coal seam thickness image, and performs channel wave inversion in the adjacent area of ​​the grid coal seam thickness detection to obtain a dense coal seam thickness image. Based on the constraint conditions, it filters out the erroneous coal seam thickness images to obtain an accurate dense coal seam thickness model.

[0042] Combining all the above technical solutions, the advantages and positive effects of the present invention are as follows: the device and method for determining coal seam thickness based on channel waves provided by the present invention are easy to operate, are not affected by the coal seam formation in the exploration area, obtain high accuracy of data information, and can be used in field operations.

[0043] Compared with traditional reconstruction methods based on two-dimensional excitation seismic signals, the method provided by this invention can quickly obtain a high-precision dense point cloud model, thus accelerating the generation speed of coal seam thickness models. It employs a channel wave inversion method, using characteristic points of candidate excitation seismic signals as standard initial points for coal seam thickness, and using the zero-mean normalized cross-correlation coefficient as the matching standard for channel wave inversion. Under the premise of satisfying mapping gradient constraints and confidence constraints, channel wave inversion points are selected as standard initial points for coal seam thickness for secondary channel wave inversion, increasing the number of standard initial points for coal seam thickness and improving the density and accuracy of the matching. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0045] Figure 1 This is a flowchart of the method for determining coal seam thickness based on channel waves provided in an embodiment of the present invention;

[0046] Figure 2 This is a flowchart of the process for recovering a dense coal seam thickness image point set provided in an embodiment of the present invention;

[0047] Figure 3 This is a flowchart of the reconstruction of a dense coal seam thickness model provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the device for determining coal seam thickness based on channel waves provided in an embodiment of the present invention;

[0049] In the figure: 1. Channel wave seismic signal identification instrument; 2. Coal seam thickness image point set acquisition module; 3. Coal seam thickness model acquisition module. Detailed Implementation

[0050] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Example 1, as Figure 1As shown, the method for controlling coal seam thickness based on channel wave analysis provided in this embodiment of the invention includes the following steps:

[0052] S1, use a channel wave seismic signal identifier to obtain the seismic signal sequence excited by the coal seam diffraction at different emission angles as the input set; obtain the feature mapping point pairs of the excited seismic signals through feature extraction and matching, and perform channel wave inversion processing on the mapping point pairs;

[0053] S2. Based on the channel wave inversion method, select the candidate excited seismic signal feature points as the standard initial points of coal seam thickness, perform matching mapping and filtering on the adjacent areas of the surrounding coal seam thickness detection, and obtain accurate dense mapping point pairs.

[0054] The channel wave seismic signal identifier 1 is calibrated, and the intrinsic and extrinsic parameters are obtained by combining the mapping point pairs. The coal seam thickness image point set is recovered based on the parameters of the channel wave seismic signal identifier 1 and the mapping point pairs.

[0055] S3 uses the channel wave inversion method for reconstruction. The initial coal seam thickness image is generated by selecting the standard initial point of coal seam thickness, and the dense coal seam thickness image is obtained by channel wave inversion in the adjacent area of ​​the grid coal seam thickness detection. The erroneous coal seam thickness image is filtered according to the constraint conditions to obtain the accurate dense coal seam thickness model.

[0056] In step S1 of this embodiment of the invention, the channel wave seismic signal identification instrument 1 performs multi-view diffraction on the target to obtain the excited seismic signal sequence and use it as the input set.

[0057] In step S2 of this embodiment of the invention, obtaining accurate dense mapping point pairs includes:

[0058] The DOG algorithm was used to extract feature points. The seismic signal sequence was screened based on the reference excitation seismic signal. Seismic signals with an angle of less than 60 degrees between the principal optical axis and the reference excitation seismic signal were selected as candidate excitation seismic signals. For each feature point m in the reference map, the corresponding candidate matching point m′ was found in the candidate excitation seismic signals according to the epipolar constraint. Then, the slot wave inversion method was used, and the zero-mean normalized cross-correlation coefficient T was selected as the objective function to calculate the T value of the mapping point pair and sort them by size.

[0059]

[0060] In the formula, T(m,m′) represents the zero-mean normalized cross-correlation value of the mapping points, m represents the feature point, m′ represents the candidate matching point, i represents the number of excited seismic signal pulses, and W represents the number of excited seismic signal pulses. This represents the average excitation frequency of the seismic signal pulse centered at feature point m. Let W represent the average pulse frequency of the seismic signal pulses excited around the candidate matching point m′, A represent the frequency of the excited seismic signal pulses, and A′ represent the frequency of the candidate excited seismic signal pulses. The larger the zero-mean normalized cross-correlation coefficient T, the greater the similarity of the feature mapping point pairs, and the better the matching point obtained.

[0061] The groove wave inversion method includes: during the groove wave inversion process, selecting feature points greater than the threshold μ1 as the standard initial point of coal seam thickness, performing groove wave inversion in the adjacent area of ​​coal seam thickness detection, and selecting feature points greater than the threshold μ2 as reserve matching points, where μ1>μ2;

[0062] For all matching points of the reference-excited seismic signal, a one-to-many match is established at the center of the candidate-excited seismic signal with a fixed pulse size; for points of the reference-excited seismic signal, a mixed match is established for all points within the pulse.

[0063] For example, the point-based hybrid matching of all points within the pulse for the reference-excited seismic signal includes: mapping point u in feature point m and candidate mapping point u′ in candidate matching point m′ are a pair of matching points, and slot wave inversion is performed;

[0064] If the pulse size is set to N×N, N=3, then the pulse mapping points in the feature point m of the reference figure and the pulse points in the candidate matching point m′ are matched one by one. Under the premise of satisfying the mapping gradient constraint and confidence constraint, the correlation coefficient T of the groove wave inversion mapping point pair is calculated. The groove wave inversion points with a value greater than the threshold μ3 are selected as the standard initial points of coal seam thickness for secondary groove wave inversion. The groove wave inversion points with a value greater than the threshold μ4 are selected as reserve matching points, where μ3>μ4.

[0065] For example, candidate mapping point u′ and mapping point u are a pair of excited seismic signal mapping points, and candidate matching point m′ and feature point m are another adjacent pair of excited seismic signal mapping points. The mapping gradient constraint formula is:

[0066] ||(uu)-(m′-m)|| ∞ ≤λ

[0067] In the formula, λ is the threshold for mapping the gradient;

[0068] Confidence constraints are used to obtain accurate dense mapping point pairs. The formula for the confidence constraint is:

[0069] x(m) = max{|A(m+δ)-A(m)|}

[0070] δ∈{(1,0),(-1,0),(0,1),(0,-1)}

[0071] In the formula, x(m) represents the confidence constraint value, A represents the excited seismic signal pulse, m represents the feature point, A(m) represents the excited seismic signal pulse centered at m, and δ represents the constraint factor value.

[0072] In step S2 of this embodiment of the invention, as follows Figure 2 As shown, the coal seam thickness image point set reconstructed based on the parameters of the channel wave seismic signal identifier 1 and the mapping point pairs includes:

[0073] S201, the calibration process of the channel wave seismic signal identifier 1 is the process of calculating the internal parameters of the channel wave seismic signal identifier 1 based on the imaging principle of the channel wave seismic signal identifier 1.

[0074] S202. Based on the feature points and matching of the excited seismic signal sequence, select two input excited seismic signals as references, calculate the fundamental matrix Y of the reference excited seismic signal point pair, where the fundamental matrix Y satisfies the equation m′Ym=0, estimate the initial value L′ and the current value L of the intrinsic parameter matrix of the reference excited seismic signal pair, m′ and m are a pair of matching points, calculate the essential matrix of the excited seismic signal point pair and extract the rotation and translation components;

[0075] S203. Given the intrinsic and extrinsic parameters of the channel wave seismic signal identifier 1 and the feature mapping point pairs, use triangulation to obtain the coal seam thickness image point set corresponding to the feature points.

[0076] In step S3 of this embodiment of the invention, as follows Figure 3 As shown, the accurate dense coal seam thickness model is obtained by filtering out errors in the coal seam thickness image based on constraints, including:

[0077] S301 uses a dense reconstruction algorithm based on coal seam thickness images, takes the coal seam thickness image point set obtained by channel wave inversion as the candidate coal seam thickness standard initial point, generates an initial coal seam thickness image, and reconstructs the coal seam thickness image point set cloud using the channel wave inversion method based on coal seam thickness images.

[0078] It can be understood that the coal seam thickness image is a coal seam thickness image centered on a set of coal seam thickness image points, with the vector pointing from that point to the origin of the reference-excited seismic signal channel wave seismic signal identifier 1 as the normal vector. Using the normal vector of the coal seam thickness image and the angle between the light rays, as well as the correlation coefficient between the projections of the coal seam thickness image onto the reference-excited seismic signal and the candidate-excited seismic signal, as constraints, coal seam thickness images that meet the conditions are selected as the initial coal seam thickness images for channel wave inversion.

[0079] S302, perform channel wave inversion within the adjacent region of the grid coal seam thickness detection. The channel wave inversion condition is that there is no coal seam thickness image similar to the initial coal seam thickness image or with a large average correlation coefficient within the adjacent region of the coal seam thickness detection.

[0080] It can be understood that the center of the channel wave inversion coal seam thickness image is the intersection of the ray from the center of the grid in the adjacent area of ​​the coal seam thickness detection and the plane where the initial coal seam thickness image is located. Channel wave inversion is performed on each candidate excited seismic signal according to the process described above. When there are a sufficient number of candidate excited seismic signals that meet the channel wave inversion conditions, the channel wave inversion is successful.

[0081] S303, for the coal seam thickness image retrieved by channel wave inversion, uses geometric consistency and excitation seismic signal gray-scale consistency constraints for screening to generate a dense coal seam thickness model.

[0082] When using dense reconstruction based on coal seam thickness images, obtaining dense point clouds requires dividing the excited seismic signal sequence into grids. The grid size is selectable; the smaller the grid, the denser the obtained point cloud.

[0083] Example 2, as Figure 4 As shown, the device for determining coal seam thickness based on channel waves provided in this embodiment of the invention includes:

[0084] The channel wave seismic signal identifier 1 is used to acquire the seismic signal sequence excited by the diffraction of coal seams at different emission angles as the input set; through feature extraction and matching, feature mapping point pairs of the excited seismic signals are obtained, and channel wave inversion processing is performed on the mapping point pairs;

[0085] The coal seam thickness image point set acquisition module 2 is used to select candidate excited seismic signal feature points as the standard initial points of coal seam thickness according to the channel wave inversion method, match and map them to the surrounding coal seam thickness detection adjacent areas and filter them to obtain accurate dense mapping point pairs; calibrate the channel wave seismic signal identifier 1, and obtain the internal and external parameters by combining the mapping point pairs; and recover the coal seam thickness image point set according to the parameters of the channel wave seismic signal identifier 1 and the mapping point pairs.

[0086] The coal seam thickness model acquisition module 3 is used to reconstruct the coal seam thickness using the channel wave inversion method. It selects the standard initial point of coal seam thickness to generate an initial coal seam thickness image, and performs channel wave inversion in the adjacent area of ​​the grid coal seam thickness detection to obtain a dense coal seam thickness image. Based on the constraint conditions, it filters out the erroneous coal seam thickness images to obtain an accurate dense coal seam thickness model.

[0087] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0088] The information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0089] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments.

[0090] Based on the technical solutions described in the above embodiments of the present invention, the following application examples can be further proposed.

[0091] According to embodiments of this application, the present invention also provides a computer device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above-described method embodiments.

[0092] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps described in the various method embodiments above.

[0093] This invention also provides an information data processing terminal, which, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments. The information data processing terminal is not limited to mobile phones, computers, or switches.

[0094] This invention also provides a server that, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments.

[0095] This invention also provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0097] Experiments show that the method provided in this invention, compared with traditional reconstruction methods based on two-dimensional excitation seismic signals, can quickly obtain a high-precision dense point cloud model, thus accelerating the generation speed of coal seam thickness models. The method employs a channel wave inversion approach, using candidate excitation seismic signal feature points as standard initial points for coal seam thickness, and zero-mean normalized cross-correlation coefficients as the matching standard for channel wave inversion. Under the premise of satisfying mapping gradient constraints and confidence constraints, channel wave inversion points are selected as standard initial points for coal seam thickness for secondary channel wave inversion, increasing the number of standard initial points for coal seam thickness and improving the density and accuracy of the matching.

[0098] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for controlling coal seam thickness based on channel wave, characterized in that, The method includes the following steps: S1, use the channel wave seismic signal identifier (1) to obtain the seismic signal sequence excited by the coal seam diffraction at different emission angles as the input set, and obtain the feature mapping point pair of the excited seismic signal through feature extraction and matching, and perform channel wave inversion processing on the mapping point pair; S2, according to the channel wave inversion method, select the candidate excited seismic signal feature points as the standard initial points of coal seam thickness, match and map to the adjacent areas of coal seam thickness detection and filter to obtain accurate dense mapping point pairs; calibrate the channel wave seismic signal identifier (1) and obtain the internal and external parameters by combining the mapping point pairs; recover the coal seam thickness image point set according to the parameters of the channel wave seismic signal identifier (1) and the mapping point pairs. Obtaining accurate dense mapping point pairs includes: Feature point extraction was performed using the DOG algorithm. Based on the reference-excited seismic signal, the sequence of excited seismic signals was screened, selecting those with an angle less than 60 degrees between their principal optical axis and the reference-excited seismic signal as candidate excited seismic signals. For each feature point m in the reference map, a corresponding candidate matching point was found among the candidate excited seismic signals according to epipolar constraints. Using the channel wave inversion method, the zero-mean normalized cross-correlation coefficient T is selected as the objective function. The T values ​​of the mapping point pairs are calculated and sorted by size. ； In the formula, This represents the cross-correlation values ​​of the mapping points with respect to zero-mean normalization. Representing feature points, Indicates candidate matching points, This indicates the number of seismic signal pulses that were triggered. This represents the triggered seismic signal pulse. Represented by feature points The average excitation frequency of the seismic signal pulse centered on the center. Indicates candidate matching points seismic signal pulses excited by the center Average pulse frequency, This indicates the frequency of the excited seismic signal pulse. Indicates the frequency of the candidate excitation seismic signal pulse; S3 uses the channel wave inversion method for reconstruction. The initial coal seam thickness image is generated by selecting the standard initial point of coal seam thickness, and the dense coal seam thickness image is obtained by channel wave inversion in the adjacent area of ​​the grid coal seam thickness detection. The erroneous coal seam thickness image is filtered according to the constraint conditions to obtain the accurate dense coal seam thickness model.

2. The method for controlling coal seam thickness based on channel wave determination according to claim 1, characterized in that, In step S1, the channel wave seismic signal identifier (1) performs multi-view diffraction on the target to obtain the excited seismic signal sequence and use it as the input set.

3. The method for controlling coal seam thickness based on channel wave determination according to claim 1, characterized in that, The groove wave inversion method includes: during the groove wave inversion process, selecting feature points greater than the threshold μ1 as the standard initial point of coal seam thickness, performing groove wave inversion in the adjacent area of ​​coal seam thickness detection, and selecting feature points greater than the threshold μ2 as reserve matching points, where μ1>μ2; For all matching points of the reference-excited seismic signal, a one-to-many match is established at the center of the candidate-excited seismic signal with a fixed pulse size; for points of the reference-excited seismic signal, a mixed match is established for all points within the pulse.

4. The method for controlling coal seam thickness based on channel wave determination according to claim 3, characterized in that, The process of establishing a mixed matching of all points within a pulse for the reference-excited seismic signal includes: the mapped point u in the feature point m and the candidate matching points. Candidate mapping points in For a pair of matching points, perform groove wave inversion; Given a pulse size of N×N, the intra-pulse mapping points and candidate matching points in feature point m of the reference image are... One-to-one matching of the pulse points is performed. Under the premise of satisfying the mapping gradient constraint and confidence constraint, the correlation coefficient T of the groove wave inversion mapping point pair is calculated. Groove wave inversion points with a value greater than the threshold μ3 are selected as the standard initial points of coal seam thickness for secondary groove wave inversion. Groove wave inversion points with a value greater than the threshold μ4 are selected as reserve matching points, where μ3>μ4.

5. The method for controlling coal seam thickness based on channel wave determination according to claim 4, characterized in that, Candidate mapping points The mapping point u is a pair of mapping points of the excited seismic signal, and a candidate matching point. The feature point m is another adjacent pair of seismic signal mapping points, and the mapping gradient constraint formula is: ； In the formula, It is the threshold for mapping the gradient; Confidence constraints are used to obtain accurate dense mapping point pairs. The formula for the confidence constraint is: ； ； In the formula, This represents the confidence constraint value. This represents the triggered seismic signal pulse. Representing feature points, This represents a seismic signal pulse excited around the center m. This represents the constraint factor value.

6. The method for controlling coal seam thickness based on channel wave determination according to claim 1, characterized in that, In step S2, the coal seam thickness image point set is recovered based on the parameters of the channel wave seismic signal identifier (1) and the mapping point pair, including: S201, the process of calibrating the trough wave seismic signal identifier (1) is based on the imaging principle of the trough wave seismic signal identifier (1) and the process of calculating the internal parameters of the trough wave seismic signal identifier (1). S202, based on the feature points and matching of the excited seismic signal sequence, select two input excited seismic signals as references, and calculate the fundamental matrix Y of the reference excited seismic signal point pair; Wherein, the fundamental matrix Y satisfies the equation The initial values ​​of the intrinsic parameter matrix of the reference excitation seismic signal pair are estimated. and the current value L of the intrinsic parameter matrix, m is a pair of matching points. Calculate the essential matrix of the excited seismic signal point pair and extract the rotation and translation components. S203, Given the internal and external parameters of the channel wave seismic signal identifier (1) and the feature mapping point pairs, use triangulation to find the coal seam thickness image point set corresponding to the feature points.

7. The method for controlling coal seam thickness based on channel wave determination according to claim 1, characterized in that, In step S3, the erroneous coal seam thickness image is filtered according to the constraint conditions to obtain an accurate dense coal seam thickness model, including: S301 uses a dense reconstruction algorithm based on coal seam thickness images, takes the coal seam thickness image point set obtained by channel wave inversion as the candidate coal seam thickness standard initial point set, generates an initial coal seam thickness image, and reconstructs the coal seam thickness image point set cloud using the channel wave inversion method based on coal seam thickness images. S302, Perform channel wave inversion in the adjacent area of ​​the grid coal seam thickness detection. The channel wave inversion condition is that there is no coal seam thickness image that is similar to the initial coal seam thickness image or has a large average correlation coefficient in the adjacent area of ​​the coal seam thickness detection. S303, for the coal seam thickness image retrieved by channel wave inversion, uses geometric consistency and excitation seismic signal gray-scale consistency constraints for screening to generate a dense coal seam thickness model.

8. The method for controlling coal seam thickness based on channel wave determination according to claim 7, characterized in that, In step S301, the coal seam thickness image is a coal seam thickness image with the coal seam thickness image point set as the center and the vector pointing from the center point of the point set to the origin of the reference excited seismic signal channel wave seismic signal identifier (1) as the normal vector; the coal seam thickness image is selected as the initial coal seam thickness image for channel wave inversion with the normal vector of the coal seam thickness image and the angle between the light rays, as well as the correlation coefficient between the coal seam thickness image and the projection of the reference excited seismic signal and the candidate excited seismic signal as the constraint conditions.

9. A device for determining coal seam thickness based on channel waves, characterized in that, The apparatus for implementing the method for determining coal seam thickness based on channel waves according to any one of claims 1-8 includes: The channel wave seismic signal identifier (1) is used to obtain the seismic signal sequence excited by the diffraction of coal seam at different emission angles as the input set; the feature mapping point pairs of the excited seismic signals are obtained by feature extraction and matching, and the channel wave inversion processing is performed on the mapping point pairs; The coal seam thickness image point set acquisition module (2) is used to select candidate excited seismic signal feature points as the standard initial points of coal seam thickness according to the channel wave inversion method, match and map to the adjacent areas of coal seam thickness detection and filter to obtain accurate dense mapping point pairs; calibrate the channel wave seismic signal identifier (1) and obtain internal and external parameters by combining the mapping point pairs; and recover the coal seam thickness image point set according to the parameters of the channel wave seismic signal identifier (1) and the mapping point pairs. The coal seam thickness model acquisition module (3) is used to reconstruct the coal seam using the channel wave inversion method, select the standard initial point of coal seam thickness to generate the initial coal seam thickness image, and obtain the dense coal seam thickness image by channel wave inversion in the adjacent area of ​​the grid coal seam thickness detection; filter the erroneous coal seam thickness image according to the constraint conditions to obtain the accurate dense coal seam thickness model.

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