Method, device and electronic equipment for determining the location of groundwater migration channel

Abstract

The present invention provides a method, device, and electronic device for determining the location of a groundwater migration channel, relating to the technical field of geological exploration, including: obtaining seismic shot data of a to-be-detected area, converting it into frequency-domain seismic data, performing a Hankel transform on the data, and obtaining a corresponding Hankel matrix; performing eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix and corresponding left and right eigenvectors; calculating an eigenvalue dip vector based on the eigenvalue diagonal matrix to determine the eigenvalue lower limit of an interference signal; reconstructing a time-domain effective signal based on the eigenvalue lower limit, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector of the interference signal; and performing homeostasis imaging on the time-domain effective signal using an offset algorithm to obtain the location of the groundwater migration channel in the to-be-detected area. The method for determining the eigenvalue lower limit of the interference signal by this method can avoid the influence of extreme eigenvalues ​​on the separation of effective signals, thereby achieving high-precision imaging of the groundwater migration channel.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration, and in particular to a method, device and electronic equipment for determining the position of a groundwater migration channel. Background Art

[0002] Mining activities primarily damage the ecological environment through vegetation degradation, soil erosion, groundwater depletion, and frequent geological disasters. Vegetation restoration is particularly crucial in ecologically fragile mining areas, and vegetation growth is closely linked to groundwater accessibility. Traditional methods for selecting sites for vegetation restoration often rely on surface characteristics and limited water resource assessments, overlooking the critical influence of groundwater migration pathways and fracture systems on vegetation growth. This results in poor restoration results and an inability to maintain long-term ecological balance.

[0003] However, groundwater storage and migration pathways are often affected by complex geological structures, faults, and fissure networks. This limits the ability of traditional seismic exploration methods to identify groundwater pathways. In particular, methods based on linear signal separation have limited ability to resolve small-scale fractures and inhomogeneous geological structures. Furthermore, noise interference and multiple wave effects further reduce detection accuracy, making the identification of groundwater pathways unclear and difficult to meet the high-precision detection requirements for ecological restoration site selection. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device and electronic equipment for determining the position of a groundwater migration channel, so as to alleviate the technical problem of low imaging accuracy in existing methods for determining the position of a groundwater migration channel.

[0005] In a first aspect, the present invention provides a method for determining the position of a groundwater migration channel, comprising: obtaining seismic shot data of an area to be detected and converting the data into frequency domain seismic data; performing a Hankel transform on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data; performing eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, a left eigenvector and a right eigenvector corresponding to the eigenvalue diagonal matrix; wherein the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; calculating an eigenvalue dip vector based on the eigenvalue diagonal matrix to determine the eigenvalue lower limit of the interference signal based on the eigenvalue dip vector; reconstructing a time domain effective signal based on the eigenvalue lower limit, the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector of the interference signal; and performing homeostasis imaging on the time domain effective signal using an offset algorithm to obtain the position of the groundwater migration channel in the area to be detected.

[0006] Optionally, the eigenvalue inclination vector is calculated based on the eigenvalue diagonal matrix, including: calculating the difference between each group of adjacent eigenvalues ​​in the eigenvalue diagonal matrix to obtain multiple adjacent differences; calculating the arc tangent of each adjacent difference to obtain the corresponding eigenvalue inclination; and constructing the eigenvalue inclination vector based on all eigenvalue inclinations.

[0007] Optionally, determining the eigenvalue lower limit of the interference signal based on the eigenvalue dip vector includes: clustering the eigenvalue dip vector using a dip adaptive clustering algorithm to obtain a first type of dip set and a second type of dip set; wherein the minimum dip in the first type of dip set is greater than the maximum dip in the second type of dip set; determining the target adjacent eigenvalue corresponding to the minimum dip in the first type of dip set; and using the relatively smaller eigenvalue among the target adjacent eigenvalues ​​as the eigenvalue lower limit of the interference signal.

[0008] Optionally, based on the eigenvalue lower limit, eigenvalue diagonal matrix, left eigenvector and right eigenvector of the interference signal, the time domain effective signal is reconstructed, including: determining the target position of the eigenvalue lower limit of the interference signal in the eigenvalue diagonal matrix; based on the target position, intercepting the sub-eigenvalue matrix corresponding to the effective signal, the target left eigenvector and the target right eigenvector corresponding to the sub-eigenvalue matrix from the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector; weighting the sub-eigenvalue matrix corresponding to the effective signal to obtain an updated sub-eigenvalue matrix; reconstructing the effective signal wave field based on the updated sub-eigenvalue matrix, the target left eigenvector and the target right eigenvector; and performing an inverse Fourier transform on the effective signal wave field to obtain the time domain effective signal.

[0009] Optionally, the migration algorithm includes: Kirchhoff migration algorithm.

[0010] In a second aspect, the present invention provides a device for determining the position of a groundwater migration channel, comprising: an acquisition and conversion module for acquiring seismic shot data of an area to be detected and converting it into frequency domain seismic data; a transformation module for performing Hankel transform on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data; a decomposition module for performing eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, a left eigenvector and a right eigenvector corresponding to the eigenvalue diagonal matrix; wherein the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; a calculation module and a determination module for calculating the eigenvalue dip vector based on the eigenvalue diagonal matrix to determine the eigenvalue lower limit of the interference signal based on the eigenvalue dip vector; a reconstruction module for reconstructing a time domain effective signal based on the eigenvalue lower limit, the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector of the interference signal; and an imaging module for performing homing imaging on the time domain effective signal using an offset algorithm to obtain the position of the groundwater migration channel in the area to be detected.

[0011] Optionally, the calculation module and determination are specifically used to: calculate the difference between each group of adjacent eigenvalues ​​in the eigenvalue diagonal matrix to obtain multiple adjacent differences; calculate the arc tangent of each adjacent difference to obtain the corresponding eigenvalue inclination; and construct an eigenvalue inclination vector based on all eigenvalue inclinations.

[0012] Optionally, the calculation and determination module is also used to: use the tilt adaptive clustering algorithm to cluster the eigenvalue tilt vector to obtain a first type of tilt set and a second type of tilt set; wherein the minimum tilt in the first type of tilt set is greater than the maximum tilt in the second type of tilt set; determine the target adjacent eigenvalue corresponding to the minimum tilt in the first type of tilt set; and use the relatively smaller eigenvalue among the target adjacent eigenvalues ​​as the lower limit of the eigenvalue of the interference signal.

[0013] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the aforementioned method for determining the location of the groundwater migration channel is implemented.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the aforementioned method for determining the location of a groundwater migration channel.

[0015] The method for determining the location of a groundwater migration channel provided by the present invention includes: obtaining seismic shot data of a to-be-detected area and converting it into frequency-domain seismic data; performing a Hankel transform on the frequency-domain seismic data to obtain a Hankel matrix corresponding to the frequency-domain seismic data; performing eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, a left eigenvector, and a right eigenvector corresponding to the eigenvalue diagonal matrix; and the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; calculating an eigenvalue dip vector based on the eigenvalue diagonal matrix to determine the eigenvalue lower limit of an interference signal based on the eigenvalue dip vector; reconstructing a time-domain effective signal based on the eigenvalue lower limit, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector of the interference signal; and performing homeostasis imaging on the time-domain effective signal using an offset algorithm to obtain the location of the groundwater migration channel in the to-be-detected area. The present invention determines the eigenvalue lower limit of the interference signal through the eigenvalue dip vector, which can effectively avoid the influence of extreme eigenvalues ​​on the separation of effective signals, improve the accuracy of the eigenvalue lower limit of the interference signal, thereby achieving high-precision signal separation and high-precision imaging of the groundwater migration channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flow chart of a method for determining the location of a groundwater migration channel provided by an embodiment of the present invention;

[0018] Figure 2 A flowchart of reconstructing a time-domain valid signal based on the eigenvalue lower limit, eigenvalue diagonal matrix, left eigenvector, and right eigenvector of an interference signal provided in an embodiment of the present invention;

[0019] Figure 3 A functional module diagram of a device for determining the position of a groundwater migration channel provided by an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0023] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0024] In order to solve the problem of accurate identification of groundwater migration channels, the embodiment of the present invention adopts optimized signal extraction technology to detect groundwater migration channels with high precision, thereby providing scientific and reasonable suggestions for site selection for vegetation restoration in ecologically fragile areas of mines. The embodiment of the present invention can overcome the limitations of traditional methods under complex geological conditions, effectively improve the detection accuracy and reliability, provide precise technical support for vegetation restoration in ecologically fragile areas of mines, and contribute to the long-term sustainable development of ecological restoration. The method provided by the embodiment of the present invention is described in detail below.

[0025] Example 1

[0026] Figure 1 A flow chart of a method for determining the location of a groundwater migration channel provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method specifically includes the following steps:

[0027] Step S102: obtaining seismic shot gather data of the area to be detected and converting it into frequency domain seismic data.

[0028] To determine groundwater migration pathways in the area to be surveyed, it is first necessary to use seismic survey methods to obtain seismic shot data for the area to be surveyed, for subsequent data processing and analysis. Because seismic shot data contain different frequency components, which are often associated with different geological structures, converting the time-domain signal to the frequency domain can more clearly separate the individual frequency components within the signal. Therefore, after obtaining the seismic shot data for the area to be surveyed, embodiments of the present invention perform a Fourier transform on the seismic shot data to convert it into frequency-domain seismic data.

[0029] Step S104: Perform Hankel transformation on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data.

[0030] It is known that the Hankel matrix is ​​a special type of matrix, and its construction method can well capture the autocorrelation of the signal. In seismic data, the interference signal is often strong in energy, linearly distributed, has strong autocorrelation, and presents a low-rank characteristic as a whole. Therefore, by processing the Hankel matrix, the interference signal can be effectively separated, and then the effective signal can be extracted, providing a basis for high-precision imaging of groundwater migration channels. Therefore, after obtaining the frequency domain seismic data, it is further subjected to a Hankel transform to obtain the Hankel matrix corresponding to the frequency domain seismic data. In the embodiment of the present invention, the interference signal is a reflected wave, and the effective signal is a scattered wave.

[0031] Step S106 , performing eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, and left eigenvectors and right eigenvectors corresponding to the eigenvalue diagonal matrix.

[0032] After obtaining the Hankel matrix, it is necessary to perform eigenvalue decomposition on the Hankel matrix, that is, to extract the eigenvalues ​​of the Hankel matrix. The formula for eigenvalue decomposition is: S = UΩV T , where S represents the Hankel matrix corresponding to the frequency domain seismic data, Ω represents the eigenvalue diagonal matrix, U represents the left eigenvector corresponding to the eigenvalue diagonal matrix, and V represents the right eigenvector corresponding to the eigenvalue diagonal matrix. The eigenvalue diagonal matrix is ​​composed of the eigenvalue σ i (1≤i≤n), where the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal, and n represents the number of eigenvalues, whose value is determined by the size of the seismic shot gather data. Specifically, if the dimension of the left eigenvector U is m×m; the dimension of the eigenvalue diagonal matrix Ω is m×l; and the dimension of the right eigenvector V is l×l; then n=min(m,l).

[0033] The eigenvalue diagonal matrix primarily reflects the energy and correlation of various signals in seismic data. The present invention considers reflected waves to be interference signals, which have high energy and good autocorrelation. Therefore, the corresponding eigenvalues ​​are large and are positioned at the front of the eigenvalue diagonal matrix. On the contrary, valid signals are positioned at the back of the eigenvalue diagonal matrix. Based on the above analysis, the eigenvalue decomposition formula can be further expressed as: Among them, Ω r Represents the sub-eigenvalue matrix corresponding to the interference signal (decreasing along the diagonal), Ω d Represents the sub-eigenvalue matrix corresponding to the effective signal (decreasing along the diagonal), U r Represents Ω r The corresponding left eigenvector, U d Represents Ω d The corresponding left eigenvector, V r Represents Ω r The corresponding right eigenvector, V d Represents Ω d The corresponding right eigenvector. But Ω r and Ω d That is, the lower limit of the characteristic value of the interference signal is an unknown parameter in the current step. As long as the value of this parameter is determined, the effective signal can be separated.

[0034] Step S108 : calculating the eigenvalue dip vector based on the eigenvalue diagonal matrix, so as to determine the eigenvalue lower limit of the interference signal based on the eigenvalue dip vector.

[0035] If the lower limit of the interference signal's eigenvalue is determined directly based on the distribution characteristics of the interference signal and valid signal's eigenvalues, it is impossible to avoid the impact of extreme values ​​on the accuracy of the lower limit of the interference signal's eigenvalue. For example, if the eigenvalues ​​are: 200, 50, 30, 3, 2.4, 2.1, 1.8, 1.5, 1, 0.7. If similarity is calculated directly on the eigenvalues, the presence of the extreme value 200 is likely to classify 200 into one category, and the remaining eigenvalues ​​into another category, resulting in inaccurate wavefield separation (i.e., separation of interference signals from valid signals).

[0036] In order to avoid the influence of extreme numerical eigenvalues ​​on wavefield separation, after obtaining the eigenvalue diagonal matrix, the embodiment of the present invention defines the inclination angle by using the slope change between adjacent eigenvalues, and reduces the dimensionality of the two-dimensional eigenvalue diagonal matrix to a one-dimensional eigenvalue inclination angle vector. i ) are coordinate points marked in a rectangular coordinate system and fitted into a curve, then a line of the "L-type" type can be obtained. According to the distribution characteristics of the eigenvalues, it can be seen that the inclination angle between adjacent eigenvalues ​​of the interference signal is close to 90 degrees, while the inclination angle between adjacent eigenvalues ​​of the effective signal is close to 0 degrees. Therefore, if the eigenvalue inclination vector is divided into two categories, the interference signal eigenvalue and the effective signal eigenvalue can be accurately distinguished. The embodiment of the present invention maps the eigenvalue diagonal matrix to the eigenvalue inclination vector. Its main information has not changed, but the calculation difficulty is reduced, and the influence of extreme values ​​on the final result can be reduced. This improvement not only improves the calculation accuracy of the lower limit of the eigenvalue of the interference signal, but also significantly enhances the robustness to abnormal eigenvalues, thereby ensuring the stability and reliability of the overall calculation results.

[0037] Step S110 : reconstructing a time domain valid signal based on the eigenvalue lower limit, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector of the interference signal.

[0038] After determining the lower limit of the characteristic value of the interference signal, combined with the content of the above step S106, it can be seen that U can be clearly separated from U, Ω, V. d ,Ω d , V d , according to U d ,Ω d , V d An effective signal wave field in the frequency domain can be reconstructed. In the embodiment of the present invention, the effective signal wave field is also the wave field response of the groundwater migration channel. The effective signal wave field can be transformed from the frequency domain to the time domain to obtain the effective signal in the time domain.

[0039] Step S112: Using an offset algorithm to perform homing imaging on the effective time domain signal, the location of the groundwater migration channel in the area to be detected is obtained.

[0040] The embodiment of the present invention does not specifically limit the offset algorithm, and the user can choose according to actual needs. Optionally, the offset algorithm includes: Kirchhoff offset algorithm. After obtaining the location of the groundwater migration channel in the area to be detected, the relationship between the groundwater flow path and the accessibility of vegetation roots can be monitored and evaluated based on the distribution characteristics of the groundwater migration channel. Combined with the surface ecological conditions, it can assist in the scientific planning of vegetation restoration site selection and determine the optimal regional location suitable for vegetation restoration. The embodiment of the present invention can provide an important scientific basis for the comprehensive ecological restoration of ecologically fragile areas in mines, and solve the problem of inaccurate site selection during mine ecological restoration.

[0041] The method for determining the location of a groundwater migration channel provided by an embodiment of the present invention includes: obtaining seismic shot gather data of a to-be-detected area and converting it into frequency domain seismic data; performing a Hankel transform on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data; performing eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, a left eigenvector, and a right eigenvector corresponding to the eigenvalue diagonal matrix; wherein the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; calculating an eigenvalue dip vector based on the eigenvalue diagonal matrix to determine the eigenvalue lower limit of an interference signal based on the eigenvalue dip vector; reconstructing a time domain effective signal based on the eigenvalue lower limit, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector of the interference signal; and performing homeostasis imaging on the time domain effective signal using an offset algorithm to obtain the location of the groundwater migration channel in the to-be-detected area. The present invention determines the eigenvalue lower limit of the interference signal through the eigenvalue dip vector, which can effectively avoid the influence of extreme eigenvalues ​​on the separation of effective signals, improve the accuracy of the eigenvalue lower limit of the interference signal, thereby achieving high-precision signal separation and high-precision imaging of the groundwater migration channel.

[0042] In an optional implementation, in the above step S108, calculating the eigenvalue dip vector based on the eigenvalue diagonal matrix specifically includes the following steps:

[0043] S201, calculating the difference between each group of adjacent eigenvalues ​​in the eigenvalue diagonal matrix to obtain a plurality of adjacent differences.

[0044] S202, calculating the arc tangent of each adjacent difference to obtain the corresponding eigenvalue inclination.

[0045] S203: Construct an eigenvalue inclination vector based on all eigenvalue inclinations.

[0046] Based on the description above, we can know that a set of adjacent eigenvalues: σ i and σ i+1 The corresponding eigenvalue inclination angle formula can be expressed as: θ i =arctan(σ i+1 -σ i), 0≤θ i ≤90, the eigenvalue inclination can accurately characterize the local geometric characteristics of the eigenvalue change. By calculating the eigenvalue inclinations corresponding to all adjacent eigenvalues, the eigenvalue inclination vector (θ1, θ2, ..., θ n-1 ).

[0047] In an optional implementation, in step S108, determining the lower limit of the eigenvalue of the interference signal based on the eigenvalue dip vector specifically includes the following steps:

[0048] S301 , clustering the eigenvalue inclination angle vectors using an inclination adaptive clustering algorithm to obtain a first type of inclination angle set and a second type of inclination angle set; wherein the minimum inclination angle in the first type of inclination angle set is greater than the maximum inclination angle in the second type of inclination angle set.

[0049] S302: Determine a target adjacent eigenvalue corresponding to the minimum inclination angle in the first type of inclination angle set.

[0050] S303: Taking a relatively smaller eigenvalue among the target adjacent eigenvalues ​​as the lower limit of the eigenvalue of the interference signal.

[0051] Specifically, the process of clustering the eigenvalue inclination vector using the inclination adaptive clustering algorithm is as follows:

[0052] (1)(θ1,θ2,…,θ n-1 ) is considered as one class.

[0053] (2) Calculate the similarity between each two classes and redefine the two most similar classes into a new class; the similarity calculation formula is: Among them, d ij represents θ i and θ j The similarity between Represents a redefined class, with the mean of all elements in the class as the position of the new class.

[0054] (3) Recalculate the similarities of all updated classes and iterate multiple times until only two classes remain, that is, obtain the first class inclination set and the second class inclination set.

[0055] If the minimum tilt angle in the first type of tilt angle set is greater than the maximum tilt angle in the second type of tilt angle set, it means that the first type of tilt angle set is the tilt angle set corresponding to the eigenvalue of the interference signal, and the second type of tilt angle set is the tilt angle set corresponding to the eigenvalue of the valid signal. Therefore, the target adjacent eigenvalue corresponding to the minimum tilt angle in the first type of tilt angle set is the tilt angle corresponding to the lower limit of the eigenvalue of the interference signal and its previous eigenvalue. For example, if the minimum tilt angle in the first type of tilt angle set is θ k , and θ is known k=arctan(σ k+1 -σ k ), that is, θ k The corresponding target adjacent eigenvalue is: k+1 and σ k , and it is known that σ k+1 >σ k , therefore, the lower limit of the characteristic value of the interference signal is σ k Therefore, after dividing the eigenvalue dip vector into two categories, the eigenvalue lower limit of the interference signal can be further determined.

[0056] In an optional embodiment, as Figure 2 As shown, the above step S110 reconstructs the time domain valid signal based on the eigenvalue lower limit, eigenvalue diagonal matrix, left eigenvector and right eigenvector of the interference signal, and specifically includes the following steps:

[0057] Step S1101: determine the target position of the lower limit of the eigenvalue of the interference signal in the eigenvalue diagonal matrix.

[0058] Step S1102 , based on the target position, extract the sub-eigenvalue matrix corresponding to the valid signal, the target left eigenvector and the target right eigenvector corresponding to the sub-eigenvalue matrix from the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector.

[0059] Step S1103: weighting the sub-eigenvalue matrix corresponding to the valid signal to obtain an updated sub-eigenvalue matrix.

[0060] Step S1104 : reconstructing the effective signal wavefield based on the updated sub-eigenvalue matrix, the target left eigenvector, and the target right eigenvector.

[0061] Step S1105 , performing inverse Fourier transform on the effective signal wave field to obtain a time domain effective signal.

[0062] Based on the above content, we can know that if the lower limit of the characteristic value of the interference signal is σ k , then we can determine that its target position in the eigenvalue diagonal matrix is ​​the kth eigenvalue. Based on this, we can further split the eigenvalue diagonal matrix Ω into And its corresponding left eigenvector U is divided into [U r U d ], and its corresponding right eigenvector V T Split into That is, the sub-eigenvalue matrix Ω corresponding to the effective signal is intercepted d , the target left eigenvector U corresponding to the sub-eigenvalue matrix d and the target right eigenvector V d .

[0063] When the sub-eigenvalue matrix corresponding to the effective signal is cut from the eigenvalue diagonal matrix, the eigenvalue corresponding to the interference signal will be discarded. However, due to the eigenvalue coupling effect, the discarded eigenvalues ​​must contain part of the effective signal energy. Therefore, in order to compensate for the damage to the effective signal during the eigenvalue cutting process, the sub-eigenvalue matrix Ω corresponding to the effective signal is obtained. d Afterwards, the embodiment of the present invention assigns reasonable weights to the eigenvalues ​​corresponding to the effective signals based on the distribution of the effective signals in the eigenvalue matrix in a weighted manner to obtain a high-amplitude-preserving effective signal wave field, thereby protecting the effective signal energy as much as possible during the separation process, and thus effectively protecting the dynamic characteristics of the signal.

[0064] The sub-eigenvalue matrix Ω corresponding to the known effective signal d The eigenvalues ​​in decrease along the diagonal, σ k+1 to σ n All belong to the eigenvalues ​​corresponding to the effective signal, and σ k+1 The largest, so this value contains the most effective signal information, σ n The smallest, it contains the least effective signal information. Therefore, in order to effectively protect the effective signal energy during the separation process, σ should be k+1 The largest increase is n The increase in the value of σ is the smallest. That is, the overall increase in the eigenvalue corresponding to the effective signal should be from σ k+1 to σ n Monotonically decreasing.

[0065] In view of this, the embodiment of the present invention uses the formula The sub-eigenvalue matrix Ω corresponding to the effective signal d Perform weighted processing to obtain the updated sub-eigenvalue matrix, where γ represents the inversion operator, σ n represents the nth eigenvalue in the eigenvalue diagonal matrix Ω, that is, the smallest eigenvalue; k represents a preset weight factor, which ranges from 2 to 7, for example. Represents the updated sub-eigenvalue matrix. n is the last eigenvalue in the characteristic matrix, is a constant, and Ω d It decreases monotonically along the diagonal, so Monotonically increasing; and γ is a reversal operator, so Monotonically decreasing, that is, the sub-eigenvalue matrix Ω corresponding to the effective signal d The weight coefficients are all greater than 1 and monotonically decreasing.

[0066] In the embodiment of the present invention, the effective signal wave field S d The reconstructed expression is: After obtaining the effective signal wave field, performing inverse Fourier transform on it can obtain the effective signal in the time domain.

[0067] In summary, the method for determining the location of groundwater migration pathways provided by the embodiments of the present invention can accurately identify groundwater migration pathways, ensuring that vegetation roots have sufficient access to water resources, and significantly improving the success rate of vegetation restoration in ecologically fragile areas. This method is widely applicable to ecological restoration projects in abandoned mining areas and ecologically fragile areas. It offers the advantages of high efficiency, high accuracy, and wide applicability, effectively promoting the sustainable development of the ecological environment in mining areas.

[0068] Example 2

[0069] An embodiment of the present invention also provides a device for determining the position of a groundwater migration channel, which is mainly used to execute the method for determining the position of a groundwater migration channel provided in the above-mentioned embodiment 1. The following is a detailed introduction to the device for determining the position of a groundwater migration channel provided in the embodiment of the present invention.

[0070] Figure 3 A functional module diagram of a device for determining the position of a groundwater migration channel provided by an embodiment of the present invention, such as Figure 3 As shown, the device mainly includes: an acquisition and conversion module 11, a transformation module 12, a decomposition module 13, a calculation and determination module 14, a reconstruction module 15, and an imaging module 16, wherein:

[0071] The acquisition and conversion module 11 is used to acquire seismic shot gather data of the area to be detected and convert it into frequency domain seismic data.

[0072] The transformation module 12 is used to perform Hankel transformation on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data.

[0073] The decomposition module 13 is used to perform eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, and left eigenvectors and right eigenvectors corresponding to the eigenvalue diagonal matrix; wherein the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal.

[0074] The calculation and determination module 14 is configured to calculate an eigenvalue dip vector based on the eigenvalue diagonal matrix, so as to determine an eigenvalue lower limit of the interference signal based on the eigenvalue dip vector.

[0075] The reconstruction module 15 is configured to reconstruct a time-domain valid signal based on the eigenvalue lower limit, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector of the interference signal.

[0076] The imaging module 16 is used to perform homing imaging on the effective time domain signal using an offset algorithm to obtain the location of the groundwater migration channel in the area to be detected.

[0077] The present invention provides a device for determining the location of a groundwater migration channel, which is used to obtain seismic shot gather data from a target area and convert it into frequency domain seismic data; perform a Hankel transform on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data; perform eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, a left eigenvector, and a right eigenvector corresponding to the eigenvalue diagonal matrix; wherein the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; calculate an eigenvalue dip vector based on the eigenvalue diagonal matrix to determine the eigenvalue lower limit of an interference signal based on the eigenvalue dip vector; reconstruct a time domain effective signal based on the eigenvalue lower limit, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector of the interference signal; and perform a homeostasis imaging on the time domain effective signal using an offset algorithm to determine the location of the groundwater migration channel in the target area. The device determines the eigenvalue lower limit of the interference signal using the eigenvalue dip vector, which effectively avoids the influence of extreme eigenvalues ​​on effective signal separation, improves the accuracy of the eigenvalue lower limit of the interference signal, and thus achieves high-precision signal separation, thereby achieving high-precision imaging of the groundwater migration channel.

[0078] Optionally, the calculation and determination module 14 is specifically configured to:

[0079] The difference between each set of adjacent eigenvalues ​​in the eigenvalue diagonal matrix is ​​calculated to obtain multiple adjacent differences.

[0080] Calculate the arctangent of each adjacent difference to obtain the corresponding eigenvalue inclination.

[0081] Construct an eigenvalue dip vector based on all eigenvalue dips.

[0082] Optionally, the calculation and determination module 14 is further configured to:

[0083] The eigenvalue inclination angle vectors are clustered using an inclination adaptive clustering algorithm to obtain a first type of inclination angle set and a second type of inclination angle set; wherein the minimum inclination angle in the first type of inclination angle set is greater than the maximum inclination angle in the second type of inclination angle set.

[0084] Determine the target adjacent eigenvalue corresponding to the minimum inclination angle in the first type of inclination angle set.

[0085] The relatively smaller eigenvalue among the target adjacent eigenvalues ​​is taken as the lower limit of the eigenvalue of the interference signal.

[0086] Optionally, the reconstruction module 15 is specifically configured to:

[0087] Determine the target position of the lower limit of the eigenvalue of the interference signal in the eigenvalue diagonal matrix.

[0088] Based on the target position, the sub-eigenvalue matrix corresponding to the effective signal, the target left eigenvector and the target right eigenvector corresponding to the sub-eigenvalue matrix are respectively intercepted from the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector.

[0089] The sub-eigenvalue matrix corresponding to the valid signal is weighted to obtain an updated sub-eigenvalue matrix.

[0090] The effective signal wavefield is reconstructed based on the updated sub-eigenvalue matrix, the target left eigenvector, and the target right eigenvector.

[0091] Perform inverse Fourier transform on the effective signal wave field to obtain the effective signal in the time domain.

[0092] Optionally, the migration algorithm includes: Kirchhoff migration algorithm.

[0093] Example 3

[0094] See also Figure 4 An embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0095] The memory 61 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 63 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0096] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0097] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0098] The processor 60 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method may be performed by hardware integrated logic circuits or software instructions within the processor 60. The processor 60 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention may be directly executed by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software modules may be located in storage media well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory 61 , and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0099] The embodiments of the present invention provide a computer program product for a method, device, and electronic device for determining the location of a groundwater migration channel, including a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method in the previous method embodiment. For specific implementation, please refer to the method embodiment and will not be repeated here.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0101] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0102] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0103] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" and the like indicate positions or locations based on the positions shown in the accompanying drawings, or the positions or locations in which the inventive product is typically placed when in use. These terms are intended solely to facilitate the description of the present invention and to simplify the description, and are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third," etc., are used solely to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0104] Furthermore, terms such as "horizontal," "vertical," and "overhanging" do not necessarily imply that a component must be absolutely horizontal or overhanging, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but rather that it can be slightly tilted.

[0105] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the location of a groundwater migration channel, characterized in that: include: Obtain seismic shot gather data of the area to be detected and convert it into frequency domain seismic data; Performing a Hankel transform on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data; Performing eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, a left eigenvector and a right eigenvector corresponding to the eigenvalue diagonal matrix; wherein the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; Calculating an eigenvalue dip vector based on the eigenvalue diagonal matrix to determine an eigenvalue lower limit of the interference signal based on the eigenvalue dip vector; Reconstructing a time-domain valid signal based on the eigenvalue lower limit of the interference signal, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector; The time domain effective signal is imaged using an offset algorithm to obtain the location of the groundwater migration channel in the area to be detected.

2. The method for determining the location of a groundwater migration channel according to claim 1, characterized in that: Calculating an eigenvalue dip vector based on the eigenvalue diagonal matrix includes: Calculating the difference between each group of adjacent eigenvalues ​​in the eigenvalue diagonal matrix to obtain a plurality of adjacent differences; Calculating the arc tangent of each adjacent difference to obtain the corresponding eigenvalue inclination; The eigenvalue inclination vector is constructed based on all eigenvalue inclinations.

3. The method for determining the location of a groundwater migration channel according to claim 1, wherein: Determining the eigenvalue lower limit of the interference signal based on the eigenvalue dip vector includes: Clustering the eigenvalue inclination angle vectors using an inclination adaptive clustering algorithm to obtain a first type of inclination angle set and a second type of inclination angle set; wherein the minimum inclination angle in the first type of inclination angle set is greater than the maximum inclination angle in the second type of inclination angle set; Determine a target adjacent eigenvalue corresponding to a minimum inclination angle in the first type of inclination angle set; A relatively smaller eigenvalue among the target adjacent eigenvalues ​​is used as the lower limit of the eigenvalue of the interference signal.

4. The method for determining the location of a groundwater migration channel according to claim 1, wherein: Reconstructing a time-domain valid signal based on the eigenvalue lower limit of the interference signal, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector includes: Determine a target position of a lower limit of an eigenvalue of the interference signal in the eigenvalue diagonal matrix; Based on the target position, respectively intercepting a sub-eigenvalue matrix corresponding to a valid signal, a target left eigenvector, and a target right eigenvector corresponding to the sub-eigenvalue matrix from the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector; Weighting the sub-eigenvalue matrix corresponding to the valid signal to obtain an updated sub-eigenvalue matrix; reconstructing a valid signal wavefield based on the updated sub-eigenvalue matrix, the target left eigenvector, and the target right eigenvector; Perform inverse Fourier transform on the effective signal wave field to obtain the time domain effective signal.

5. The method for determining the location of a groundwater migration channel according to claim 4, characterized in that: The migration algorithm includes: Kirchhoff migration algorithm.

6. A device for determining the position of a groundwater migration channel, characterized in that: include: An acquisition and conversion module is used to acquire seismic shot gather data of the area to be detected and convert it into frequency domain seismic data; a transform module, configured to perform a Hankel transform on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data; a decomposition module, configured to perform eigenvalue decomposition on the Hankel matrix to obtain an eigenvalue diagonal matrix, and left eigenvectors and right eigenvectors corresponding to the eigenvalue diagonal matrix; wherein the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; A calculation module and a determination module, configured to calculate an eigenvalue dip vector based on the eigenvalue diagonal matrix, so as to determine an eigenvalue lower limit of the interference signal based on the eigenvalue dip vector; A reconstruction module, configured to reconstruct a time-domain valid signal based on the eigenvalue lower limit of the interference signal, the eigenvalue diagonal matrix, the left eigenvector, and the right eigenvector; The imaging module is used to perform homing imaging on the time domain effective signal using an offset algorithm to obtain the position of the groundwater migration channel in the area to be detected.

7. The device for determining the position of a groundwater migration channel according to claim 6, characterized in that: The calculation and determination module is specifically used for: Calculating the difference between each group of adjacent eigenvalues ​​in the eigenvalue diagonal matrix to obtain a plurality of adjacent differences; Calculating the arc tangent of each adjacent difference to obtain the corresponding eigenvalue inclination; The eigenvalue inclination vector is constructed based on all eigenvalue inclinations.

8. The device for determining the position of a groundwater migration channel according to claim 6, characterized in that: The calculation and determination module is further configured to: Clustering the eigenvalue inclination angle vectors using an inclination adaptive clustering algorithm to obtain a first type of inclination angle set and a second type of inclination angle set; wherein the minimum inclination angle in the first type of inclination angle set is greater than the maximum inclination angle in the second type of inclination angle set; Determine a target adjacent eigenvalue corresponding to a minimum inclination angle in the first type of inclination angle set; A relatively smaller eigenvalue among the target adjacent eigenvalues ​​is used as the lower limit of the eigenvalue of the interference signal.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method for determining the location of the groundwater migration channel according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for determining the position of the groundwater migration channel according to any one of claims 1 to 5 is implemented.

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