Position determination method and device for underground water migration channel and electronic equipment

Abstract

The invention provides a method and device for determining the position of an underground water migration channel and electronic equipment, and relates to the technical field of geological prospecting, and the method comprises the steps: obtaining seismic shot gather data of a to-be-detected region, converting the seismic shot gather data into frequency domain seismic data, and carrying out the Hankel transformation of the frequency domain seismic 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 angle vector based on the eigenvalue diagonal matrix to determine an eigenvalue lower limit of the interference signal; reconstructing a time domain effective signal based on the characteristic value lower limit, the characteristic value diagonal matrix, the left characteristic vector and the right characteristic vector of the interference signal; and performing homing imaging on the time domain effective signal by using an offset algorithm to obtain the position of the groundwater migration channel in the to-be-detected area. According to the method, the influence of extreme characteristic values on effective signal separation can be avoided by determining the lower limit of the characteristic values of the interference signals, and then high-precision imaging of the underground water migration channel is achieved.

Description

Technical Field

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

[0002] The damage caused by mining activities to the ecological environment is mainly reflected in vegetation degradation, soil erosion, groundwater depletion and frequent geological disasters. Especially in ecologically fragile areas of mines, vegetation restoration is the core link of ecological restoration, and vegetation growth is closely related to the accessibility of groundwater resources. Traditional vegetation restoration site selection methods usually rely on surface characteristics and limited water resource assessments, ignoring the key impact of groundwater migration channels and fracture systems on vegetation growth, resulting in poor restoration effects and failure to maintain long-term ecological balance.

[0003] However, the storage and migration channels of groundwater are often affected by complex geological structures, faults and fissure networks, which makes traditional seismic exploration methods limited in identifying groundwater channels, especially those based on linear signal separation technology, which have limited ability to distinguish small-scale fractures and inhomogeneous geological bodies. In addition, noise interference and multiple wave effects further reduce the detection accuracy, making the identification results of groundwater channels unclear and difficult to meet the needs of high-precision detection 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 existing in the existing method 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 a to-be-detected area 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 an 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 an interference signal based on the eigenvalue dip vector; reconstructing a time domain effective signal based on the eigenvalue lower limit of the interference signal, the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector; 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 to-be-detected area.

[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 a 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, reconstructing the effective signal in the time domain, 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 effective signal in the time domain.

[0009] Optionally, the offset algorithm includes: Kirchhoff offset 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, used to acquire seismic shot data of a to-be-detected area and convert it into frequency domain seismic data; a transformation module, used to perform Hankel transform on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data; a decomposition module, used to 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; a calculation module and a determination module, used to 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; a reconstruction module, used to reconstruct a time domain effective signal based on the eigenvalue lower limit of the interference signal, the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector; an imaging module, 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 to-be-detected area.

[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 a 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 including a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the aforementioned method for determining the location of a 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 position of the groundwater migration channel provided by the present invention includes: obtaining seismic shot data of the area to be detected and converting it into frequency domain seismic data; performing Hankel transformation on the frequency domain seismic data to obtain the Hankel matrix corresponding to the frequency domain seismic data; performing eigenvalue decomposition on the Hankel matrix to obtain the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector corresponding to the eigenvalue diagonal matrix; and the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; 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; reconstructing the time domain effective signal based on the eigenvalue lower limit of the interference signal, the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector; using the offset algorithm to perform homing imaging on the time domain effective signal to obtain the position of the groundwater migration channel in the area to be detected. The present invention can effectively avoid the influence of extreme eigenvalues ​​on the separation of effective signals by determining the eigenvalue lower limit of the interference signal through the eigenvalue dip vector, thereby improving the accuracy of the eigenvalue lower limit of the interference signal, thereby achieving high-precision signal separation, and then high-precision imaging of the groundwater migration channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying 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 for reconstructing a time domain valid signal based on an eigenvalue lower limit, an eigenvalue diagonal matrix, a left eigenvector and a 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] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 here 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 claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0024] In order to solve the problem of groundwater migration channel identification accuracy, the embodiment of the present invention adopts optimized signal extraction technology to detect groundwater migration channels with high accuracy, thereby providing scientific and reasonable suggestions for the site selection of 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 promote the long-term sustainable development of ecological restoration. The method provided by the embodiment of the present invention is described in detail below.

[0025] Embodiment 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, such as Figure 1 As shown, the method specifically comprises 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] In order to determine the underground water migration channel in the area to be detected, it is first necessary to use the seismic survey method to obtain the seismic shot data of the area to be detected for subsequent data processing and analysis. Since the seismic shot data contains different frequency components, and these frequency components are often related to different geological structures, the various frequency components in the signal can be more clearly separated by converting the time domain signal to the frequency domain. Therefore, after obtaining the seismic shot data of the area to be detected, the embodiment of the present invention performs Fourier transform on the seismic shot data to convert it into frequency domain seismic data.

[0029] Step S104, performing 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 capture the autocorrelation of the signal very well. In seismic data, the interference signal is often of high energy, linear distribution, strong autocorrelation, and low rank characteristics 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 a left eigenvector and a right eigenvector 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, 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, n represents the number of eigenvalues, and its 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; the dimension of the right eigenvector V is l×l; then n=min(m,l).

[0033] The eigenvalue diagonal matrix mainly reflects the energy and correlation of various signals in the seismic data. The embodiment of the present invention considers that the reflected wave is an interference signal, which has a large energy and a good autocorrelation. Therefore, the corresponding eigenvalue is large and is located at the front in the eigenvalue diagonal matrix. On the contrary, the effective signal is located at the back in 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 With 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 eigenvalue of the interference signal is determined directly based on the distribution characteristics of the eigenvalues ​​of the interference signal and the effective signal, it is impossible to avoid the influence of the extreme value on the accuracy of the lower limit of the eigenvalue of the interference signal. For example, if the eigenvalues ​​are: 200, 50, 30, 3, 2.4, 2.1, 1.8, 1.5, 1, 0.7. If the eigenvalues ​​are directly calculated for similarity, the existence of the extreme value 200 is likely to classify 200 into one category, and the remaining eigenvalues ​​into one category, which will lead to inaccurate wavefield separation (that is, separation of interference signals and effective signals).

[0036] In order to avoid the influence of extreme numerical eigenvalues ​​on wave field separation, after obtaining the eigenvalue diagonal matrix, the embodiment of the present invention defines the dip angle by using the slope change between adjacent eigenvalues, and reduces the two-dimensional eigenvalue diagonal matrix to a one-dimensional eigenvalue dip angle vector. i ) are used as coordinate points to mark in a rectangular coordinate system and fit into a curve, then a line of the "L-type" type can be obtained. According to the distribution characteristics of the eigenvalues, 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 eigenvalues ​​of the interference signal and the eigenvalues ​​of the effective signal can be accurately distinguished. The embodiment of the present invention maps the eigenvalue diagonal matrix to the eigenvalue inclination vector, and 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 in 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 The 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 to obtain the location of the groundwater migration channel in the area to be detected.

[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 according to 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 best 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 mining areas and solve the problem of inaccurate site selection during the ecological restoration of mining.

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

[0042] In an optional implementation, in the above step S108, calculating the eigenvalue inclination 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 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 the above 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 characteristic value inclination angle vectors using an inclination angle 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, determining 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 eigenvalue lower limit of the interference signal.

[0051] Specifically, the process of clustering the eigenvalue inclination vector by 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 every two classes and redefine the two most similar classes as a new class; the similarity calculation formula is: Among them, d ij Represents θ i With θ j The similarity between Represents a redefined class, and the mean of all elements in the class is taken 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 we know that θ 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 eigenvalue of the interference signal is σ k Therefore, after the eigenvalue dip vector is divided into two categories, the eigenvalue lower limit of the interference signal can be further determined.

[0056] In an optional embodiment, if Figure 2 As shown, the above step S110 reconstructs the time domain effective 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, determining a target position of the eigenvalue lower limit of the interference signal in the eigenvalue diagonal matrix.

[0058] Step S1102, based on the target position, respectively extract 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.

[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 wave field based on the updated sub-eigenvalue matrix, the target left eigenvector and the target right eigenvector.

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

[0062] Based on the above content, it can be seen 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 split 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 eigenvalue 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, thereby 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 n All of them belong to the eigenvalues ​​corresponding to the effective signal, and σ k+1 The maximum value contains the most effective signal information. 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 That is, the overall increase of the eigenvalue corresponding to the effective signal should be from σ k+1 to 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 is monotonically increasing; and γ is a reverse 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, for the effective signal wave field S d The reconstructed expression is: After obtaining the effective signal wave field, an inverse Fourier transform is performed on it to obtain the effective signal in the time domain.

[0067] In summary, the method for determining the location of groundwater migration channels provided by the embodiments of the present invention can accurately identify groundwater migration channels, ensure that vegetation roots can obtain sufficient water resources, and significantly improve the success rate of vegetation restoration in ecologically fragile areas. This method can be widely used in ecological restoration projects in abandoned mining areas and ecologically fragile areas, and has the advantages of high efficiency, high accuracy, and wide applicability, and can effectively promote the sustainable development of the ecological environment in mining areas.

[0068] Embodiment 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 the 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 the 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 used to calculate 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.

[0075] The reconstruction module 15 is used to reconstruct the effective signal in the time domain 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 device for determining the position of the groundwater migration channel provided by the embodiment of the present invention is used to obtain the seismic shot data of the area to be detected and convert it into frequency domain seismic data; perform Hankel transformation on the frequency domain seismic data to obtain the Hankel matrix corresponding to the frequency domain seismic data; perform eigenvalue decomposition on the Hankel matrix to obtain the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector corresponding to the eigenvalue diagonal matrix; and the eigenvalues ​​in the eigenvalue diagonal matrix decrease along the diagonal; calculate the eigenvalue inclination vector based on the eigenvalue diagonal matrix to determine the eigenvalue lower limit of the interference signal based on the eigenvalue inclination vector; reconstruct the time domain effective signal based on the eigenvalue lower limit of the interference signal, the eigenvalue diagonal matrix, the left eigenvector and the right eigenvector; use the offset algorithm to perform homing imaging on the time domain effective signal to obtain the position of the groundwater migration channel in the area to be detected. The device can effectively avoid the influence of extreme eigenvalues ​​on the separation of effective signals by determining the eigenvalue lower limit of the interference signal through the eigenvalue inclination vector, and improve the accuracy of the eigenvalue lower limit of the interference signal, so as to achieve high-precision signal separation, and then high-precision imaging of the groundwater migration channel.

[0078] Optionally, the calculation and determination module 14 is specifically used for:

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

[0080] Calculate the arc tangent 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 used for:

[0083] The characteristic value inclination angle vector is clustered by using an inclination angle 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 used for:

[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] Based on the updated sub-eigenvalue matrix, the target left eigenvector and the target right eigenvector, the effective signal wave field is reconstructed.

[0091] The effective signal wave field is subjected to inverse Fourier transform to obtain the effective signal in the time domain.

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

[0093] Embodiment 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, the processor 60, the communication interface 63 and the memory 61 are connected through 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 a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[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 only 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. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can 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 gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. 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] A computer program product of a method, device and electronic device for determining the location of a groundwater migration channel provided in an embodiment of the present invention includes 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. The specific implementation can be found in the method embodiment, which 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 a computer device (which can be a personal computer, a server, or a network device, etc.) to perform 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, etc. Various media that can store program codes.

[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, further definition and explanation thereof is not required 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", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0104] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0105] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 Hankel transformation 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 an 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 by 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 the 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; Calculate 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, characterized in that: Determining the eigenvalue lower limit of the interference signal based on the eigenvalue dip vector includes: The characteristic value inclination angle vector is clustered by using an inclination angle 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 the minimum inclination angle in the first type of inclination angle set; A relatively smaller eigenvalue among the target adjacent eigenvalues ​​is used as the eigenvalue lower limit of the interference signal.

4. The method for determining the location of a groundwater migration channel according to claim 1, characterized in that: 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, including: Determine a target position of the eigenvalue lower limit 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 wave field based on the updated sub-eigenvalue matrix, the target left eigenvector, and the target right eigenvector; The effective signal wave field is subjected to inverse Fourier transform 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 offset algorithm includes: Kirchhoff offset 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 transformation module, used for performing Hankel transformation on the frequency domain seismic data to obtain a Hankel matrix corresponding to the frequency domain seismic data; A decomposition module, used 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 line; 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 an interference signal based on the eigenvalue dip vector; A reconstruction module, used 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 location 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; Calculate 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 also used for: The characteristic value inclination angle vector is clustered by using an inclination angle 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 the minimum inclination angle in the first type of inclination angle set; A relatively smaller eigenvalue among the target adjacent eigenvalues ​​is used as the eigenvalue lower limit 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 described in 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, which, when executed by a processor, implement the method for determining the location of a groundwater migration channel according to any one of claims 1 to 5.

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