Isometric wide-line reflection seismic surveying method, apparatus, device, medium and product

Abstract

The application discloses a heterogeneous wide-line reflection seismic exploration method, device, equipment, medium and product, and relates to the field of seismic exploration. The method comprises the following steps: acquiring data of a first receiving line and data of a second receiving line; picking up first arrival data according to the data of the first receiving line, and establishing a near-surface velocity model according to the first arrival data; taking the near-surface velocity model as a constraint model, and calculating a surface consistency static correction amount according to the data of the first receiving line and the data of the second receiving line; performing consistency correction on the data of the second receiving line according to the surface consistency static correction amount, so as to obtain corrected data; performing wavelet matching according to the data of the first receiving line and the corrected data, so as to obtain matched data; performing multi-scale fusion according to the corrected data and the matched data; and finally performing migration processing to obtain an imaging result. The application effectively solves the actual geological demand of deep and shallow exploration in metal mine exploration, so that the detection precision is improved.

Description

Technical Field

[0001] This application relates to the field of seismic exploration, and in particular to a heterogeneous broadband reflection seismic detection method, apparatus, equipment, medium, and product. Background Technology

[0002] Currently, the exploration depth for metallic minerals (such as copper, nickel, cobalt, and chromium) has extended from shallow (<500 meters) to deep (500-2000 meters or even 10 km tectonic setting). Hard rock areas typically have harsh surface conditions, large topographic relief, and high wave velocity and strong heterogeneity in the underground medium.

[0003] Current mainstream seismic exploration methods face the following challenges in hard rock areas: Conventional 2D seismic surveys suffer from low coverage and severe lateral reflection interference in complex geological structures, making it difficult to correct profile shifts. Traditional wide-line seismic surveys, while increasing coverage and azimuth, typically use a single type of detector (either all high-frequency or all low-frequency). Using only high-frequency detectors (e.g., >10Hz) results in good shallow resolution but rapid energy attenuation at depths, leading to weak signals at 10km. Using only low-frequency detectors (e.g., 5Hz nodes) provides strong penetration but insufficient resolution for shallow layers (0-500m), and the gradual first arrival wave leads to large first arrival picking errors, making it difficult to establish accurate near-surface velocity models. Static corrections are often high in hard rock areas, resulting in inaccurate velocity models and severely inadequate deep imaging capabilities. Simple hybrid use of wired and wireless methods exists: while some technologies use a hybrid approach, this is mostly for "blind spots" (using nodes where wired connections are insufficient) and does not consider frequency complementarity and data interaction.

[0004] For mineral resource exploration in hard rock complex structural areas, which aims to explore both shallow and deep areas, existing reflection seismic exploration technology has the following shortcomings: ① Single-frequency detector acquisition cannot simultaneously address the contradiction between "shallow fine velocity modeling" and "deep weak signal imaging" in complex surface and complex structural areas; ② When low-frequency nodes are used in hard rock areas, their deep exploration potential cannot be fully realized due to limitations in static correction accuracy; ③ Full 3D seismic exploration is extremely expensive, which is not conducive to large-scale deep geological exploration in metal mining areas.

[0005] Therefore, a seismic detection method that can take into account both shallow and deep areas is needed. Summary of the Invention

[0006] The purpose of this application is to provide a heterogeneous broad-line reflection seismic detection method, device, equipment, medium and product, which can effectively solve the actual geological needs of both deep and shallow exploration in metal mines, thereby improving the detection accuracy.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a heterogeneous broad-line reflection seismic detection method, including: Data is acquired from a first receiving line and data is acquired from a second receiving line; the first receiving line is a wired receiving line; the second receiving line is a wireless receiving line; the first receiving line and the second receiving line are parallel; the first receiving line acquires high-frequency data; the second receiving line acquires low-frequency data. The first arrival data is picked up based on the data from the first receiving line, and a near-surface velocity model is established based on the first arrival data; Based on the data from the first and second receiving lines, the near-surface velocity model is used as a constraint model to calculate the surface consistency static correction. Based on the surface consistency static correction amount, the data from the first receiving line and the data from the second receiving line are subjected to consistency correction to obtain the corrected data; Wavelet matching is performed based on the data from the first receiving line and the corrected data to obtain the matched data; Multi-scale fusion is performed based on the corrected data and the matched data to obtain multi-scale fused data. The multi-scale fused data is then subjected to offset processing to obtain the imaging results.

[0008] In one embodiment, picking up first-arrival data based on data from the first receiving line and establishing a near-surface velocity model based on the first-arrival data specifically includes: Based on the data from the first receiving line, the initial arrival data is picked up using a combination of automatic pickup and manual correction. Based on the first-arrival data, the first-arrival travel time tomography method is used, and an iterative inversion strategy combining Taylor expansion linearization and regularization constraints is employed to invert the near-surface velocity structure and obtain the near-surface velocity model; wherein the near-surface is at a depth of 0-500m.

[0009] In one embodiment, based on the data from the first receiving line and the data from the second receiving line, the near-surface velocity model is used as a constraint model to calculate the surface consistency static correction, specifically including: The static correction for the shot point is determined using a near-surface velocity model based on the actual surface elevation of any shot point, the seismic data processing reference surface, and the bedrock replacement velocity. The static correction amount of the high-frequency detector point is determined based on the ground elevation and coordinates of any high-frequency detector on the first receiving line. The total static correction of the first receiving line is determined based on the static correction of the shot point and the static correction of the high-frequency detector point. Based on the near-surface velocity model, spatial interpolation and velocity extraction are performed at the physical coordinates of the low-frequency node of the second receiving line to obtain the static correction amount of the low-frequency detector point. The total static correction of the second receiving line is determined based on the static correction of the shot point and the static correction of the low-frequency detector point. The surface consistency static correction is determined based on the total static correction of the first receiving line and the total static correction of the second receiving line.

[0010] In one embodiment, wavelet matching is performed based on the data from the first receiving line and the corrected data to obtain matched data, specifically including: Based on the data from the first receiving line and the corrected data, the autocorrelation function of high-frequency wired data and the autocorrelation function of low-frequency node data are calculated using a matching window. Based on the autocorrelation function of high-frequency wired data and the autocorrelation function of low-frequency node data, the smoothed global autocorrelation is extracted along the phase axis within a selected spatial window using multi-channel Gaussian weighted smoothing technology. The amplitude spectrum is determined using Fourier transform based on the smoothed global autocorrelation. The Wiener shaping filter is determined using the Wiener-Levinson recursive algorithm based on the amplitude spectrum. The corrected data is processed using the Wiener shaping filter to obtain matched data.

[0011] In one embodiment, multi-scale fusion is performed based on the corrected data and the matched data to obtain multi-scale fused data, specifically including: The corrected data and the matched data are fused into a common-center surface element, and multi-scale fusion is performed using a time-varying weighting function to obtain multi-scale fused data; the time-varying weighting coefficients in the time-varying weighting function are determined using a time window region; the time window region is determined by the energy attenuation curve of the high-frequency signal in the target area.

[0012] In one embodiment, the time window region includes a shallow high-frequency dominance region, a mid-to-deep frequency band transition region, and a deep low-frequency dominance region.

[0013] Secondly, this application provides a heterogeneous broadband reflection seismic detection device, comprising: The earthquake source excitation system, the first receiving subsystem, the second receiving subsystem, and the data processing center; The source excitation system excites the source through multiple parallel shot lines; the first receiving subsystem includes a wired telemetry seismograph and a high-frequency detector; the second receiving subsystem includes a nodal seismograph and a low-frequency detector. The first receiving subsystem forms a first receiving line; the first receiving line is parallel to the gun line and is positioned between the two gun lines; the second receiving subsystem forms a second receiving line; the second receiving line is parallel to the gun line and is positioned between the two gun lines; both the first receiving subsystem and the second receiving subsystem are connected to the data processing center; The data processing center is used for: Data is acquired from a first receiving line and data is acquired from a second receiving line; the first receiving line is a wired receiving line; the second receiving line is a wireless receiving line; the first receiving line and the second receiving line are parallel; the first receiving line acquires high-frequency data; the second receiving line acquires low-frequency data. The first arrival data is picked up based on the data from the first receiving line, and a near-surface velocity model is established based on the first arrival data; Based on the data from the first and second receiving lines, the near-surface velocity model is used as a constraint model to calculate the surface consistency static correction. Based on the surface consistency static correction amount, the data from the first receiving line and the data from the second receiving line are subjected to consistency correction to obtain the corrected data; Wavelet matching is performed based on the data from the first receiving line and the corrected data to obtain the matched data; Multi-scale fusion is performed based on the corrected data and the matched data to obtain multi-scale fused data. The multi-scale fused data is then subjected to offset processing to obtain the imaging results.

[0014] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the heterogeneous wide-line reflection seismic detection method.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the heterogeneous wide-line reflection seismic detection method described above.

[0016] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the heterogeneous wide-line reflection seismic detection method.

[0017] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a heterogeneous wide-line reflection seismic detection method, apparatus, equipment, medium, and product. It acquires high-frequency data through a first receiving line and low-frequency data through a second receiving line. The first receiving line addresses the complex static correction problem in hard rock areas and the problem of fine imaging at shallow depths. The second receiving line addresses the signal shielding and attenuation problems at depths. Through multi-scale fusion of heterogeneous data, a high signal-to-noise ratio seismic profile is obtained, effectively addressing the practical geological needs of both deep and shallow depths in metal mineral exploration, thereby improving detection accuracy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a heterogeneous broad-line reflection seismic detection method. Figure 2 This is a schematic diagram of a heterogeneous broad-line reflection seismic detection method. Figure 3 Comparison of single-shot records before and after static correction; Figure 4 A comparison chart of the denoising effects of pre-stack multi-domain multi-method denoising; Figure 5 Comparison of the wavelet shaping effects before and after wired and nodal profiles; Figure 6 A comparison chart showing the overlay effect before and after heterogeneous wide-line data fusion; Figure 7 To construct a comparison diagram of the superimposed profile effects before and after guided filtering; Figure 8 This is a quantitative analysis diagram of the signal-to-noise ratio of heterogeneous wide-line superimposed profiles and single-line and single-node profiles. Figure 9 The final seismic reflection profile of the metal mining area at a depth of 10 km is shown. Figure 10 Schematic diagram of a heterogeneous broad-line reflection seismic detection device; Figure 11 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] In one exemplary embodiment, such as Figure 1 As shown, a heterogeneous broadband reflection seismic detection method is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In the embodiments of this application, the method includes the following steps.

[0023] Step 101: Acquire data from the first receiving line and the second receiving line; the first receiving line is a wired receiving line; the second receiving line is a wireless receiving line; the first receiving line and the second receiving line are parallel; the first receiving line is used to acquire high-frequency data; the second receiving line is used to acquire low-frequency data.

[0024] Step 102: Pick up the first arrival data based on the data from the first receiving line and establish a near-surface velocity model based on the first arrival data.

[0025] Step 103: Based on the data from the first receiving line and the data from the second receiving line, use the near-surface velocity model as a constraint model to calculate the surface consistency static correction.

[0026] Step 104: Perform consistency correction on the data of the first receiving line and the data of the second receiving line according to the surface consistency static correction amount to obtain the corrected data.

[0027] Step 105: Perform wavelet matching based on the data from the first receiving line and the corrected data to obtain the matched data.

[0028] Step 106: Perform multi-scale fusion based on the corrected data and the matched data to obtain multi-scale fused data.

[0029] Step 107: Perform offset processing on the multi-scale fused data to obtain the imaging result.

[0030] In an exemplary embodiment, first-arrival data is picked up based on the data from the first receiving line, and a near-surface velocity model is established based on the first-arrival data. Specifically, this includes: picking up first-arrival data using a combination of automatic picking and manual correction based on the data from the first receiving line; and using the first-arrival travel time tomography method, employing an iterative inversion strategy combining Taylor expansion linearization and regularization constraints to invert the near-surface velocity structure and obtain the near-surface velocity model; wherein the near-surface is at a depth of 0-500m.

[0031] In an exemplary embodiment, based on data from the first and second receiving lines, the near-surface velocity model is used as a constraint model to calculate the surface consistency static correction. Specifically, this includes: determining the shot point static correction using the near-surface velocity model based on the actual surface elevation of any shot point, the seismic data processing reference surface, and the bedrock replacement velocity; determining the high-frequency geophone point static correction based on the surface elevation and coordinates of any high-frequency geophone on the first receiving line; determining the total static correction of the first receiving line based on the shot point static correction and the high-frequency geophone point static correction; performing spatial interpolation and velocity extraction at the physical coordinates of the low-frequency nodes of the second receiving line using the near-surface velocity model to obtain the low-frequency geophone point static correction; determining the total static correction of the second receiving line based on the shot point static correction and the low-frequency geophone point static correction; and determining the surface consistency static correction based on the total static correction of the first and second receiving lines.

[0032] In an exemplary embodiment, wavelet matching is performed based on the data from the first received line and the corrected data to obtain matched data. Specifically, this includes: calculating the autocorrelation function of high-frequency wired data and the autocorrelation function of low-frequency node data using a matching window based on the data from the first received line and the corrected data; extracting the smoothed global autocorrelation along the in-phase axis within a selected spatial window using multi-channel Gaussian weighted smoothing technology based on the high-frequency wired data autocorrelation function and the low-frequency node data autocorrelation function; determining the amplitude spectrum using Fourier transform based on the smoothed global autocorrelation; determining the Wiener shaping filter using the Wiener-Levinson recursive algorithm based on the amplitude spectrum; and processing the corrected data using the Wiener shaping filter to obtain matched data.

[0033] In an exemplary embodiment, multi-scale fusion is performed based on the corrected data and the matched data to obtain multi-scale fused data. Specifically, this includes: fusing the corrected data and the matched data into a common-center element, and performing multi-scale fusion using a time-varying weighting function to obtain multi-scale fused data. The time-varying weighting coefficients in the time-varying weighting function are determined using a time window region. The time window region is determined by the energy attenuation curve of the high-frequency signal in the target area. The time window region includes a shallow high-frequency dominance region, a mid-to-deep frequency band transition region, and a deep low-frequency dominance region.

[0034] In another exemplary embodiment, this application provides a cost-effective, multi-scale heterogeneous wide-line seismic exploration method capable of simultaneously achieving high-precision shallow velocity modeling and deep, high-penetration imaging, meeting the needs for deep, surprising structural imaging in metal mines. Specific objectives include: utilizing a high-frequency wired system to address the complex static correction problem in hard rock areas and the problem of fine shallow imaging; utilizing a low-frequency nodal system to address deep signal shielding and attenuation problems; and obtaining high signal-to-noise ratio seismic profiles across a full depth range of 0-10 km through heterogeneous data fusion. Addressing the difficulties in seismic imaging in hard rock metal mine areas, this method effectively solves the practical geological requirement of "covering both shallow and deep" metal mine exploration, providing effective structural constraints for the precise location of ore-bearing rock masses in the second spatial dimension and imaging of deep magma channels, faults, and other ore-forming systems.

[0035] like Figure 2 As shown, the specific process of the heterogeneous broad-line reflection seismic detection method in practical applications includes the following steps.

[0036] Step S1: Deployment of heterogeneous observation system.

[0037] Survey line design: Two parallel receiving survey lines are laid out.

[0038] Line spacing setting: The line spacing is set according to the Fresnel zone radius at the target layer depth. For hard rock areas, a line spacing of 20m-40m is generally recommended. This distance ensures both the discretization of underground reflection points to suppress lateral interference and the rationality of the common center point surface element division during processing.

[0039] Channel spacing setting: High-density acquisition is adopted, and the channel spacing is preferably 5m-10m. In this embodiment, a channel spacing of 5m is used to avoid spatial aliasing of shallow high-frequency signals.

[0040] Differentiation in detector placement: Wired terminals: The detectors are buried in pits to reduce high-frequency wind noise; and they are used in combination (such as triangular or linear combinations) to suppress secondary surface interference to the greatest extent.

[0041] Node end: Inserted vertically into the ground to ensure tight coupling.

[0042] Step S2: Wide-line excitation and synchronous acquisition.

[0043] The "multi-gun, multi-line" pattern is adopted. For example, three gun lines are deployed, with a spacing of 20m between gun lines and a spacing of 20m between gun points.

[0044] During firing, the wired system can monitor seismic records in real time, ensuring the validity of each shot's data. Simultaneously, the real-time monitoring records of the wired system can be used to constrain and quality control the validity of "blind" data collected by wireless nodes. Data from the wired receivers is stored in SEG-D format at the workstation housing the field acquisition software. Data from the wireless nodes is stored internally within each node. After construction is completed, the nodes are brought back indoors for data download and shot gather processing to obtain the original field shot record data. Data is stored electronically at the field workstation. Data acquired via the wired receivers is collected using the Sercel-428 seismic instrument; data from the wireless nodes is directly stored within each node and then uniformly brought back indoors for data download and shot gather processing.

[0045] Step S3: High-precision near-surface modeling.

[0046] The first arrival wave is picked up from the data of the first receiving line (wired 35Hz detector) using a combination of automatic pickup and manual correction. Because the 35Hz detector is sensitive to high-frequency signals and employs combined reception, its first arrival wave is very crisp, resulting in higher pickup accuracy. The data from the first receiving line is the original single-shot seismic record.

[0047] Automatic picking combined with manual correction is specifically implemented through the first arrival picking module in a seismic data processing platform (such as the Omega system), following the specific algorithm and interaction process: ① Apply a bandpass filter (such as filtering out low-frequency surface waves and extremely high-frequency wind noise) to the first earthquake dataset (high-frequency wired data) and perform automatic gain control by windowing to highlight the initial energy of the first arrival wave.

[0048] ② Energy ratio calculation for time windows (STA / LTA algorithm application): The system slides along each seismic record to calculate the energy ratio of the short-time average (STA) window to the long-time average (LTA) window. Since the energy changes abruptly when the first arrival wave arrives in high-frequency data, the first extreme point or the point of maximum slope of the energy ratio curve is marked by the algorithm as the initial picking time.

[0049] ③ Spatial Consistency Constraint (Cross-correlation Correction): To address single-trace pick-up jumps caused by local scattering in hard rock areas, the module extracts waveforms from adjacent seismic traces (e.g., 5-10 traces before and after) near the initial pick-up time and performs cross-correlation calculations. Utilizing the similarity of the first-arrival waveforms of adjacent traces, abnormal pick-up points with cycle skipping are eliminated, and the pick-up time is automatically "captured" to a specific phase of the first-arrival wave (e.g., the first peak, trough, or zero-crossing point), forming a continuous automatic pick-up curve.

[0050] ④ Linear Moveout (LMO) Translation Display: In the interactive display interface, an approximate near-surface replacement velocity (e.g., 3000 m / s) is applied to the original shot gather data for linear moveout correction. This operation "flattens" the original hyperbolic or polygonal distribution of the first arrival wave.

[0051] ⑤ Outlier Identification: In the LMO domain, a correct first arrival pick point should appear as an approximately horizontal straight line. If obvious protrusions, depressions, or discrete points appear, the operator can visually identify them as "false picks"; the software interface's polygon selection tool can be used to batch delete abnormal pick points interfered with by noise.

[0052] ⑥ After the correction is completed, the system cancels the LMO correction, restores the pickup time to the absolute time domain, and outputs a high-precision, cycle-free absolute first arrival time database for subsequent tomographic velocity inversion.

[0053] Using this high-quality first-arrival data, the near-surface velocity structure (0-500m depth) was inverted using the first-arrival travel time tomography method, and a high-precision near-surface velocity model was established. .

[0054] The propagation of seismic waves in the subsurface medium follows a function equation. For any... Root rays, their travel time It is the slowness (the reciprocal of velocity) along the ray path. The line integral.

[0055] .

[0056] in, underground space coordinates The slowness model at that location, For the infinitesimal element of the ray path, underground space coordinates The velocity model at the location. The underground space of the target work area is discretized into... A uniform or non-uniform grid. The discretized travel time equations can be expressed as a system of linear equations: .

[0057] Written in matrix form: .

[0058] in, This is the observed high-frequency first-arrival time column vector; Let be the column vector of grid slowness to be determined; The ray path length matrix (i.e., the Jacobian sensitivity matrix) has the following elements. Indicates the first The ray passed through the first The length of each grid. For the first The travel time of the ray, The first ray to pass through The slowness of each grid.

[0059] Due to the ray path Also relies on the unknown slowness model This is a typical nonlinear inversion problem. This application, based on the LATTE open-source framework, employs an iterative inversion strategy combining Taylor expansion linearization with regularization constraints, specifically including the following sub-steps: Step S3-1: Initial model construction including undulating terrain.

[0060] High-precision DEM (Digital Elevation Model) data from hard rock areas collected in the field were imported into the system and used as the boundary conditions for undulating surfaces in tomographic imaging. An initial slowness model was established that follows the terrain undulations. This ensures that the near-surface grid perfectly matches the actual rugged mountain terrain.

[0061] Step S3-2: Forward modeling based on solving the functional equation.

[0062] In the current model ( Under the condition of (number of iterations), the system uses the Fast Marching Method (FMM) or the finite difference method to solve the equation and calculate the theoretical travel time. In this process, the Jacobian matrix is ​​calculated using reverse time ray tracing. .

[0063] Step S3-3: Construction of travel time residuals and objective function.

[0064] Calculate the high-precision first arrival time of the actual pickup. Out of date with theory The residual vector between To address the issues of strong scattering in hard rock regions and the tendency for inversion to fall into local minima, this application constructs an objective function with Tikhonov regularization (smoothing constraints) within the LATTE framework. .

[0065] .

[0066] in, This represents the update amount for the slow-speed model. For data fitting terms (L2 norm). It is a second-order Laplace smoothing operator matrix in space, used to constrain drastic changes in velocity between adjacent grids and ensure the rationality of the geological model; This is the regularization damping weight coefficient, used to balance data fit and model smoothness.

[0067] Step S3-4: Solving using the conjugate gradient method and updating the model.

[0068] By differentiating the objective function using the LSQR algorithm or the preconditional conjugate gradient method and setting it to zero, the optimal model update amount can be obtained. Then update the slow-speed model: .

[0069] This represents the current model in the (k+1)th iteration.

[0070] Step S3-5: Iterative convergence determination.

[0071] Repeat steps S3-2 to S3-4. Since the input in this application is extremely high-precision initial arrival data obtained from a 35Hz high-frequency detector, the travel time residual... The initial error is extremely small, which greatly improves the convergence speed of the objective function. When the overall root mean square error (RMS Error) is less than a set threshold or the maximum number of iterations is reached, iteration stops. The final model output at this point is the high-precision near-surface velocity model required in step S4. .

[0072] Step S4: Constrain static correction.

[0073] The second receiving line (node ​​5Hz detector) often has a relatively "bulky" first-arrival waveform due to its low main frequency and blurred starting point. Furthermore, it is limited by the low signal-to-noise ratio of single-point reception, resulting in large errors when directly picking up the first-arrival waveform. Therefore, the waveform established in step S3 is used instead. model The static correction for surface consistency is calculated as a constraint model. This static correction is then applied to the second receiver line (node ​​data). The specific calculation process is as follows: Step S4-1: Define the unified reference plane and replacement speed.

[0074] For hard rock areas with dramatic undulations, the system first establishes a unified seismic data processing reference surface (DatumElevation, denoted as...). ) and bedrock replacement velocity (denoted as ). The selected elevation is usually a flat surface lower than the lowest elevation in the work area. according to The average velocity of unweathered hard rock in the middle and deep layers is determined (for example, by selecting a constant between 3500 m / s and 4500 m / s).

[0075] Step S4-2: Calculate the static correction amount for surface consistency at the shot point.

[0076] For any _th_ in a broad-line observation system The actual surface elevation of each artillery point is recorded as follows: The plane coordinates are ( The system in the velocity model In the middle, a one-dimensional vertical velocity micro-profile is extracted downwards along this coordinate point. Static correction amount at the shot point. It consists of two parts: "actual travel time of the stripped weathered layer" and "travel time of the filling and replacement layer," and its mathematical formula is as follows: .

[0077] The integral term represents the actual travel time of the seismic wave as it propagates vertically from the actual shot point on the ground surface to the reference surface. To fill the replacement layer timeout, To determine the actual travel time of the weathered layer, This is a discretized velocity model extracted along the survey line in the three-dimensional velocity model.

[0078] Step S4-3: Calculate the static correction of the ground consistency detector point for the first receiving line (high frequency wired).

[0079] Similarly, for the first receiving line... A high-frequency detector, with a ground elevation of [missing information]. The coordinates are ( Its receiver static correction amount The calculation formula is: .

[0080] This is a discretized velocity model extracted along the first receiving line. For the high-frequency wired system... Cannon, No. The total static correction value is the data. .

[0081] Step S4-4: Calculate the cross-system constrained static correction for the second receiving line (low-frequency node).

[0082] The second receiving line (low-frequency node system) of this application suffers from a low first-arrival waveform frequency (5Hz main frequency), making direct first-arrival picking prone to cycle skipping and unable to independently retrieve accurate surface velocities. Therefore, this application employs a cross-spatial mapping constraint strategy: utilizing the continuous velocity field obtained by inverting the high-frequency first-arrival from the first receiving line. The physical coordinates of the low-frequency node of the second receiving line directly Spatial interpolation and velocity extraction are performed at point 1. For point 2... Low-frequency nodes (elevation) ), its receiver static correction amount The formula for calculating mandatory constraints is: .

[0083] For the discretized velocity model extracted along the second receiving line, for the low-frequency node system... Cannon, No. The total static correction value is the data. .

[0084] Step S4-5: Application of static correction values.

[0085] The calculated total static correction amount and They are used as time translation operators and applied to each sampling point of the corresponding seismic trace. : .

[0086] The corrected seismic shot collection. For the seismic shot collection before correction, These are the correction values ​​corresponding to different receiving lines.

[0087] This consistency correction successfully eliminates the severe time shift effects of undulating terrain and low-velocity zones on the data from the two receiver lines, unifying all data to the reference plane. This lays a very high time reference accuracy for subsequent wavelet matching and seamless fusion and superposition of heterogeneous data.

[0088] Step S5: Heterogeneous data preprocessing and wavelet matching.

[0089] Denoising: Denoising was performed on both sets of data in multiple domains (FK domain, Radon domain).

[0090] Wavelet Matching: Because the instrument response curves of Sercel 428 (35Hz) and SmartSolo (5Hz) are different, there are differences in phase and amplitude spectra.

[0091] Step S5-1: Dynamic optimization of spatiotemporal matching windows for homogeneous geology.

[0092] A time window is selected as the matching window when the signal-to-noise ratio of the shallow to medium layer (e.g., 1s-2s) is good, the physical location of the two survey lines is closest, and the overlap of the surface elements of the common underground center point is the highest.

[0093] Step S5-2: Amplitude spectrum estimation based on autocorrelation.

[0094] For the first in the matching window High-frequency wired data and corresponding low-frequency node data Calculate their autocorrelation functions respectively. and : .

[0095] .

[0096] in The time delay is usually taken as the wavelet length, such as 100ms. This is the lower time limit for the matching window. This is the upper limit of the matching window time.

[0097] Step S5-3: Spatial multichannel smoothing and fidelity autocorrelation extraction.

[0098] Because the signal-to-noise ratio of single-channel data in hard rock regions is extremely low, the wavelet extracted from single-channel autocorrelation is highly unstable. This application employs a multi-channel spatial Gaussian weighted smoothing technique within a selected spatial window (including...). (The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the full context.) .

[0099] .

[0100] in The weighting factor is a spatial Gaussian factor, with a larger weight the closer to the center point, in order to suppress the interference of random scattering noise in the hard rock region on the autocorrelation calculation. The global autocorrelation of high-frequency wired data after smoothing. The global autocorrelation is the smoothed low-frequency node data. This represents the number of seismic traces within the spatial window.

[0101] Step S5-4: Construction of statistical wavelet and design of matched filter.

[0102] Smoothed global autocorrelation and A Fourier transform is performed to obtain the energy spectrum, and then the square root is taken to obtain the amplitude spectrum. Using the Wiener-Levinson recursive algorithm, the representative statistical wavelets of the first receiving line are reconstructed. Representative statistical wavelet of the second receiving line Subsequently, the Wiener-shaped filter is solved in the frequency-wavenumber domain or the time domain. High-frequency wire wavelet For the desired output, use low-frequency nodal wavelet Given the input, solve the regularized equation: .

[0103] in For input wavelet With the expected wavelet The cross-correlation. Finally, the solved filter... The unified action applied to the low-frequency node dataset eliminates the amplitude and phase spectrum distortions caused by the electromechanical performance differences between the 5Hz and 35Hz detectors, resulting in completely consistent dynamic characteristics between the two sets of data in the overlapping frequency band, thus laying the physical foundation for subsequent multi-scale fusion.

[0104] Step S6: Multi-scale frequency band weighted fusion.

[0105] The two sets of corrected and matched data are multiplied by the seismic data of the first and second receiving lines respectively using a time-varying weighting function, and the data of the two receiving lines are superimposed on a common center point element.

[0106] Design a time-varying weighting function (fusion formula in step S6-3), assign higher superposition weights to high-frequency wired data and lower weights to low-frequency node data in shallow time windows; the weights change linearly in the transition zone; assign dominant weights to low-frequency node data in deep time windows, retain only the low-frequency components of wired data, perform horizontal superposition, and generate multi-scale superposition profiles.

[0107] Step S6-1: Dynamic delineation of time-domain fusion partitions.

[0108] High-frequency components of seismic waves attenuate extremely rapidly in hard rock, while low-frequency components have insufficient resolution in shallow layers. Based on the energy attenuation curve of the high-frequency signal in the target area, this application divides the full-depth seismic record into three time window regions, where the full-depth seismic record is the record along the longitudinal direction of the first and second receiver lines: Shallow high-frequency dominant region: .For example This region is primarily characterized by high-resolution, fine-structure information provided by the first receiving line.

[0109] Mid-to-deep frequency band transition region: .For example This region is the energy transition zone between high and low frequency signals.

[0110] Deep low-frequency dominant region: The region is dominated by deep-penetrating low-frequency effective signals provided by the second receiving line.

[0111] Step S6-2: Construction of the time-varying weight function.

[0112] To avoid creating "artificial pseudo-phase axes" or causing the Gibbs effect at the fusion boundary, this application abandons the direct splicing method of hard cutting and instead designs a set of smooth transition time-varying weighting coefficients based on half-cosine taper. and : For the time-varying weighting coefficients of the first receiving line (high-frequency wired data) Defined as.

[0113] .

[0114] Time-varying weighting coefficients for the second receiving line (low-frequency node data) Defined as.

[0115] .

[0116] The above formula guarantees that at any time... The sum of the two sets of weighted coefficients is always equal to 1, that is This ensures the conservation of total energy in the seismic data before and after fusion, without compromising the true lithological reflection amplitude characteristics of the strata.

[0117] Step S6-3: Multi-scale dynamic fusion under frequency band constraints.

[0118] Let the high-frequency wired record after matching correction be... Low-frequency nodes are recorded as The final multi-scale fusion output record The formula for calculating it is:

[0119] .

[0120] Step S7: Targeted imaging and interpretation.

[0121] S6 output multi-scale fused data It provides three crucial prior conditions for high-precision imaging that conventional single-system acquisition cannot provide: kinematic consistency directly determines the convergence accuracy of S7 diffraction waves; the dynamic broadband characteristics break through the imaging resolution limit of S7; and the continuous structural tensor field ensures the global effectiveness of S7 in constructing guided filters.

[0122] The conventional migration method is combined with multi-scale fused data output from S6. Migration processing is performed, and a larger migration aperture is used for steep structures in hard rock areas.

[0123] The offset profile is then subjected to construction-guided filtering to further enhance the continuity of the profile structure. The fused data from step S6 is then processed at a deeper level. The high signal-to-noise ratio low-frequency effective wave of the node system was successfully introduced, enabling It possesses continuous phase characteristics across the entire depth range. This provides a globally stable and continuous construction guidance tensor field for the construction guidance filtering algorithm in this step, ensuring that the filtering operator can effectively enhance the actual steep-dipping channel of the metal mine without compromising the boundary preservation of the fault.

[0124] The following examples from the field will provide further explanation.

[0125] Overview of the pilot zone.

[0126] The experimental area is located in a copper-nickel mining area in Xinjiang. The surface is characterized by typical Gobi desert and eroded residual mountain landforms, with exposed bedrock and dramatic changes in surface elevation. The exploration objective is to determine the deep (shallower than 2 km) structural features of known underground copper-nickel deposits, as well as the deep (5 km-10 km) magma channels and ore-controlling fault structures.

[0127] Implementation parameters.

[0128] Data collection must be carried out strictly in accordance with the methods described in this application: High-frequency line: Sercel-428XL instrument, 35Hz detector, 3 series 2 parallel combination, channel spacing 5m, total number of channels 1000.

[0129] Low-frequency line: SmartSolo-IGU-16HR 5Hz node, single-point embedding, channel spacing 5m, parallel spacing with high-frequency line 20m, total number of channels 1000.

[0130] Excitation: 3 firing lines, 2 controllable seismic sources of 28 tons each, with two vertically superimposed vibrations; firing distance 20m.

[0131] Observation system: A heterogeneous wide-line observation system consisting of "3 shots and 2 lines" with a full coverage of up to 750 times and a maximum coverage of up to 1,500 times.

[0132] Comparison of data processing results.

[0133] Weathering layer velocity distribution characteristics obtained through tomographic static correction analysis: The lateral velocity of the surface varies greatly; the surface velocity in the depression is less than 1000 m / s, and the low-velocity zone is thicker; the velocity in the exposed rock area at the top of the mountain is greater than 5000 m / s, and the bedrock is directly exposed; below the weathered layer, the bedrock velocity is generally greater than 5000 m / s.

[0134] like Figure 3 As shown, after applying static correction, the original single-shot record was significantly improved. The first arrival became smoother and more continuous, and the previously indistinct phase axis of the reflected wave became clear and distinct. The time difference caused by surface undulations and velocity variations in shallow, low-velocity layers was effectively eliminated, laying a solid foundation for subsequent denoising, signal enhancement, and velocity analysis.

[0135] like Figure 4 As shown, after multi-domain and multi-method pre-stack denoising, the messy and high-energy interference waves in the original record are effectively suppressed, the phase axis of the effective reflected wave is clearly revealed, the wave field energy becomes more balanced, and the signal-to-noise ratio and resolution of the data are significantly improved.

[0136] In this processing, wavelet shaping technology was used to eliminate the influence of waveform inconsistencies between the two detectors. The specific steps are as follows: A typical superposition segment with a high signal-to-noise ratio was selected from the original data; the seismic wavelets of the two detector data were estimated separately; a shaping filter operator was calculated; this operator was applied to convert the wavelet shape (including amplitude and phase spectra) of the data recorded by one detector to match the wavelet shape of the other detector; the matched data was output. A comparison of the cross-correlation functions before and after wavelet shaping shows that: before shaping, the main lobe of the cross-correlation function of the two detector data was wide and the side lobes were obvious, indicating low similarity. After shaping, the main lobe of the cross-correlation function became extremely sharp, indicating that the waveform similarity had been greatly improved, meeting the requirements for in-phase superposition, such as... Figure 5 As shown.

[0137] Without wavelet shaping, the fused and superimposed profile suffers from low signal-to-noise ratio (SNR) due to the mutual cancellation of effective signals caused by waveform inconsistencies. However, the fused and superimposed profile after wavelet shaping exhibits enhanced effective waves due to the convergence of frequency and phase, while random noise is suppressed, significantly improving the SNR and imaging quality of the full data superimposed profile. Wavelet shaping effectively resolves the frequency and phase differences between the two detectors, achieving high-quality data fusion and significantly improving the SNR and imaging quality of the superimposed profile. Figure 6 As shown.

[0138] Structure-guided filtering primarily achieves the following objectives: ① Effectively suppressing random noise. Random noise is prevalent in seismic data, affecting the continuity and interpretability of phase axes. Structure-guided filtering significantly improves the signal-to-noise ratio by predicting and weighted median filtering along local structural trends, making the effective signal more prominent. ② Preserving structural details. Unlike traditional smoothing or mean filtering, this method can identify and retain discontinuous structures such as faults and fractures during the denoising process, preventing structural information from being blurred or erased, and ensuring the accuracy of geological interpretation. ③ Enhancing the continuity of phase axes. For tilted, curved, or intersecting phase axes, structure-guided filtering can enhance them along their local dip direction, improving the continuity and lateral consistency of phase axes, facilitating stratigraphic tracing and structural analysis. ④ Providing high-quality input for fault detection. After filtering, the data noise is reduced and the structure is clearer, making it more suitable for high-precision fault detection and attribute extraction, improving the reliability and resolution of automatic fault identification algorithms. As an advanced seismic data enhancement method, structure-guided filtering demonstrates significant advantages in improving data quality, protecting geological structures, and supporting detailed interpretation. In this process, we applied it to actual post-stack data, which effectively improved the profile quality and provided a reliable data foundation for subsequent fault detection and structural interpretation.

[0139] contrast Figure 7 The effect of the filter before and after the structure is constructed shows that the high-frequency noise in the shallow red box area is significantly reduced after filtering, and the continuity of the in-phase axis is increased.

[0140] A quantitative analysis of the signal-to-noise ratio was performed on the seismic profiles obtained by the multi-scale heterogeneous broad-line seismic detection method proposed in this application, such as... Figure 8 As shown in the comparison results of time-frequency domain signal-to-noise ratio, the fused profile improved by 24.1% compared to the single wired profile and by 12.8% compared to the single-node profile; the coherence signal-to-noise ratio comparison results showed that the fused profile improved by 36.2% compared to the single wired profile and by 19.7% compared to the single-node profile.

[0141] like Figure 9 As shown, in the final 10km depth migration profile, the boundary between the shallow (0-2km) ore-bearing rock mass and the surrounding rock is clearly visible, the deep occurrence of the ore-bearing rock mass is clear, and the deep extension range can be quantitatively interpreted; the continuity of the in-phase reflection axis in the upper crust in the deep (7-8km) part is high, and it is clear and easy to interpret; the deep magma channels and fault structures of the metal ore are obvious, which can effectively reveal the magmatic mineralization system in this area.

[0142] Based on the same inventive concept, this application also provides a heterogeneous broad-line reflection seismic detection device for implementing the heterogeneous broad-line reflection seismic detection method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the heterogeneous broad-line reflection seismic detection device provided below can be found in the limitations of the heterogeneous broad-line reflection seismic detection method described above, and will not be repeated here.

[0143] In one exemplary embodiment, such as Figure 10 As shown, a heterogeneous wide-line reflection seismic detection device is provided, comprising: The earthquake source excitation system, the first receiving subsystem, the second receiving subsystem, and the data processing center; The source excitation system excites the source through multiple parallel shot lines; the first receiving subsystem includes a wired telemetry seismograph and a high-frequency detector; the second receiving subsystem includes a nodal seismograph and a low-frequency detector. The first receiving subsystem forms a first receiving line; the first receiving line is parallel to the gun line and is positioned between the two gun lines; the second receiving subsystem forms a second receiving line; the second receiving line is parallel to the gun line and is positioned between the two gun lines; both the first receiving subsystem and the second receiving subsystem are connected to the data processing center; The data processing center is used for: Data is acquired from a first receiving line and data is acquired from a second receiving line; the first receiving line is a wired receiving line; the second receiving line is a wireless receiving line; the first receiving line and the second receiving line are parallel; the first receiving line is used to acquire high-frequency data; the second receiving line is used to acquire low-frequency data. The first arrival data is picked up based on the data from the first receiving line, and a near-surface velocity model is established based on the first arrival data; Based on the data from the first and second receiving lines, the near-surface velocity model is used as a constraint model to calculate the surface consistency static correction. Based on the surface consistency static correction amount, the data from the first receiving line and the data from the second receiving line are subjected to consistency correction to obtain the corrected data; Wavelet matching is performed based on the data from the first receiving line and the corrected data to obtain the matched data; Multi-scale fusion is performed based on the corrected data and the matched data to obtain multi-scale fused data. The multi-scale fused data is then subjected to offset processing to obtain the imaging results.

[0144] In another exemplary embodiment, such as Figure 10As shown, the specific structure of the heterogeneous wide-line reflection seismic detection device of this application in practical applications includes: Earthquake source excitation system: A large-tonnage seismic source vehicle combination or well-fired firing is employed, with multiple (e.g., 3) parallel firing lines designed to provide wide azimuth illumination. The seismic source communicates with the seismic instrument vehicle via a high-power vehicle-mounted radio, and the instrument vehicle's acquisition software controls the activation of the seismic source firing system. The instrument vehicle serves as a data acquisition vehicle, including a seismic source control and node GPS synchronization unit, as well as a Sercel wired receiver acquisition control and monitoring unit. The firing lines are... Figure 10 SL1-SL3 are shown in the diagram. The distance between excitation points is 20m. The distance between adjacent excitation lines is 20m.

[0145] The first receiving subsystem (high-frequency reference line) includes equipment: a wired telemetry seismograph (such as the Sercel 428-XL series) and sensors: a high natural frequency analog detector string (main frequency...). 30Hz (preferably 35Hz). Deployment method: Combined reception. A multi-string, multi-parallel (e.g., 3 strings, 2 parallel) area combination is used to suppress high-frequency random interference and ensure clear first arrival waves. The first receiving subsystem provides a high-precision "time scale" and shallow structural information. It connects to the seismic instrument vehicle via a Sercel-428xl acquisition chain crossover cable, and data acquisition and transmission are controlled by the acquisition software. The first receiving line in the first receiving subsystem is... Figure 10 In RL1, the distance between the detector points is 5m.

[0146] The second receiving subsystem (low-frequency deep probe) includes equipment: independent storage nodal seismographs (such as SmartSolo) and sensors: low natural frequency detectors (primary frequency...). 5Hz). Deployment method: Single-point reception at the detector point. The second receiving subsystem receives deep, weak reflection signals. It is connected to the seismic instrument vehicle via GPS equipment, and the GPS time at the source excitation is recorded by the acquisition software for use in subsequent node data acquisition. The first receiving line in the second receiving subsystem is... Figure 10 In RL2, the distance between the detector points is 5m. The distance between the two receiving lines is 20m.

[0147] Data processing center (storage medium): Stores specialized data processing algorithms used to perform preprocessing of field measured data and fusion, multi-domain denoising, overlay, and offset of multi-scale heterogeneous data.

[0148] To address the technical challenges of drastic lateral variations in near-surface velocities in metal mines and complex mountainous hard rock areas, leading to difficulties in static correction and low signal-to-noise ratios due to strong deep scattering, this application provides a system comprising a wired receiving line using a combination of high-frequency detectors and a wireless receiving line using independent low-frequency nodes. The methodology includes: establishing a high-precision near-surface velocity model using clear first-arrival information from the high-frequency wired data; performing constrained static correction on the low-frequency node data; eliminating instrument response differences between the two acquisition systems through multi-domain denoising and wavelet matching; and finally, employing time-varying frequency band weighting technology to fuse high-frequency shallow information with low-frequency deep information for targeted migration imaging. This application achieves high-precision imaging across the entire depth range (0-10 km), effectively solving the detection bottleneck of "inaccurate shallow imaging and inaccessible deep imaging" in hard rock areas.

[0149] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 11 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores heterogeneous broadband reflection seismic detection data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a heterogeneous broadband reflection seismic detection method.

[0150] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 11 The embodiments show more or fewer components, combinations of certain components, or different component arrangements. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the above-described method embodiments.

[0151] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.

[0152] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.

[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0154] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0155] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0156] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A heterogeneous broad-line reflection seismic detection method, characterized in that, include: Acquire data from the first receiving line and the second receiving line; The first receiving line is a wired receiving line; The second receiving line is a wireless receiving line; The first receiving line and the second receiving line are parallel; the first receiving line is used to acquire high-frequency data; the second receiving line is used to acquire low-frequency data. The first arrival data is picked up based on the data from the first receiving line, and a near-surface velocity model is established based on the first arrival data; Based on the data from the first and second receiving lines, the near-surface velocity model is used as a constraint model to calculate the surface consistency static correction. Based on the surface consistency static correction amount, the data from the first receiving line and the data from the second receiving line are subjected to consistency correction to obtain the corrected data; Wavelet matching is performed based on the data from the first receiving line and the corrected data to obtain the matched data; Multi-scale fusion is performed based on the corrected data and the matched data to obtain multi-scale fused data. The multi-scale fused data is then subjected to offset processing to obtain the imaging results.

2. The heterogeneous broad-line reflection seismic detection method according to claim 1, characterized in that, First arrival data is picked up based on the data from the first receiving line, and a near-surface velocity model is established based on the first arrival data, specifically including: Based on the data from the first receiving line, the initial arrival data is picked up using a combination of automatic pickup and manual correction. Based on the first-arrival data, the first-arrival travel time tomography method is used, and an iterative inversion strategy combining Taylor expansion linearization and regularization constraints is employed to invert the near-surface velocity structure and obtain the near-surface velocity model; wherein the near-surface is at a depth of 0-500m.

3. The heterogeneous broad-line reflection seismic detection method according to claim 1, characterized in that, Based on the data from the first and second receiving lines, and using the near-surface velocity model as a constraint model, the surface consistency static correction is calculated, specifically including: The static correction for the shot point is determined using a near-surface velocity model based on the actual surface elevation of any shot point, the seismic data processing reference surface, and the bedrock replacement velocity. The static correction amount of the high-frequency detector point is determined based on the ground elevation and coordinates of any high-frequency detector on the first receiving line. The total static correction of the first receiving line is determined based on the static correction of the shot point and the static correction of the high-frequency detector point. Based on the near-surface velocity model, spatial interpolation and velocity extraction are performed at the physical coordinates of the low-frequency node of the second receiving line to obtain the static correction amount of the low-frequency detector point. The total static correction of the second receiving line is determined based on the static correction of the shot point and the static correction of the low-frequency detector point. The surface consistency static correction is determined based on the total static correction of the first receiving line and the total static correction of the second receiving line.

4. The heterogeneous broad-line reflection seismic detection method according to claim 1, characterized in that, Wavelet matching is performed based on the data from the first receiving line and the corrected data to obtain the matched data, specifically including: Based on the data from the first receiving line and the corrected data, the autocorrelation function of high-frequency wired data and the autocorrelation function of low-frequency node data are calculated using a matching window. Based on the autocorrelation function of high-frequency wired data and the autocorrelation function of low-frequency node data, the smoothed global autocorrelation is extracted along the phase axis within a selected spatial window using multi-channel Gaussian weighted smoothing technology. The amplitude spectrum is determined using Fourier transform based on the smoothed global autocorrelation. The Wiener shaping filter is determined using the Wiener-Levinson recursive algorithm based on the amplitude spectrum. The corrected data is processed using the Wiener shaping filter to obtain matched data.

5. The heterogeneous broad-line reflection seismic detection method according to claim 1, characterized in that, Multi-scale fusion is performed based on the corrected data and the matched data to obtain multi-scale fused data, specifically including: The corrected data and the matched data are fused into a common-center surface element, and multi-scale fusion is performed using a time-varying weighting function to obtain multi-scale fused data; the time-varying weighting coefficients in the time-varying weighting function are determined using a time window region; the time window region is determined by the energy attenuation curve of the high-frequency signal in the target area.

6. The heterogeneous broad-line reflection seismic detection method according to claim 5, characterized in that, The time window region includes a shallow high-frequency dominant region, a mid-to-deep frequency band transition region, and a deep low-frequency dominant region.

7. A heterogeneous broad-line reflection seismic detection device, characterized in that, include: The earthquake source excitation system, the first receiving subsystem, the second receiving subsystem, and the data processing center; The seismic source excitation system excites the seismic source through multiple parallel shot lines; The first receiving subsystem includes a wired telemetry seismograph and a high-frequency detector; The second receiving subsystem includes a nodal seismograph and a low-frequency detector; The first receiving subsystem forms a first receiving line; the first receiving line is parallel to the gun line and is positioned between the two gun lines; the second receiving subsystem forms a second receiving line; the second receiving line is parallel to the gun line and is positioned between the two gun lines; both the first receiving subsystem and the second receiving subsystem are connected to the data processing center; The data processing center is used for: Data is acquired from a first receiving line and data is acquired from a second receiving line; the first receiving line is a wired receiving line; the second receiving line is a wireless receiving line; the first receiving line and the second receiving line are parallel; the first receiving line is used to acquire high-frequency data; the second receiving line is used to acquire low-frequency data. The first arrival data is picked up based on the data from the first receiving line, and a near-surface velocity model is established based on the first arrival data; Based on the data from the first and second receiving lines, the near-surface velocity model is used as a constraint model to calculate the surface consistency static correction. Based on the surface consistency static correction amount, the data from the first receiving line and the data from the second receiving line are subjected to consistency correction to obtain the corrected data; Wavelet matching is performed based on the data from the first receiving line and the corrected data to obtain the matched data; Multi-scale fusion is performed based on the corrected data and the matched data to obtain multi-scale fused data. The multi-scale fused data is then subjected to offset processing to obtain the imaging results.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the heterogeneous broad-line reflection seismic detection method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the heterogeneous broad-line reflection seismic detection method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the heterogeneous broad-line reflection seismic detection method as described in any one of claims 1-6.

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