Surface wave dispersion image generation method, medium, device and product

Abstract

The application discloses a surface wave dispersion image generation method, medium, equipment and product, relates to the surface wave exploration field, and the method comprises the steps of obtaining a time domain surface wave signal received by a seismic detector, and arranging the time domain surface wave signal according to spatial positions to form a trace gather; performing S transformation on the time domain surface wave signal of each seismic trace to obtain a complex time-frequency spectrum; based on the time-frequency spectrum, extracting a complex frequency spectrum corresponding to main energy of each seismic trace at each frequency point, and performing normalization processing to obtain a phase spectrum; selecting a plurality of trial phase velocities in a preset phase velocity scanning range, and for each frequency and trial phase velocity, coherently superimposing the phase spectrum corresponding to all seismic traces to construct an energy function in a frequency-phase velocity domain; and when the trial phase velocity is consistent with an actual surface wave phase velocity, obtaining a surface wave phase velocity dispersion energy image based on the energy function in the frequency-phase velocity domain. The application can improve the anti-interference ability and stability of surface wave dispersion imaging under complex geological or noise conditions.

Description

Technical Field

[0001] This invention relates to the field of surface wave exploration technology, and in particular to a method, medium, device and product for generating surface wave dispersion images. Background Technology

[0002] Surface wave exploration technology, as a common geophysical exploration method, has wide applications in engineering surveys, environmental geological investigations, geological hazard risk assessments, and mineral resource exploration. Compared with traditional drilling and destructive testing methods, surface wave exploration can utilize active or passive source data for detection, offering advantages such as convenient construction, minimal disturbance, and low cost. It can quickly obtain physical property information of geological media without damaging the strata, thus playing a crucial role in practical engineering construction and spatial planning, as well as in mineral resource prospecting prediction and target area selection.

[0003] The conventional technical process for surface wave exploration generally includes three steps: first, surface wave data acquisition; second, generation of dispersion energy maps and extraction of dispersion curves; and third, subsurface structure inversion and interpretation based on the dispersion curves. In this process, the dispersion energy map generated in the second step plays a crucial role; its quality directly affects the accuracy of dispersion curve extraction and further influences the reliability of shallow shear wave velocity structure inversion. Therefore, generating high-quality dispersion energy maps is an indispensable and important step in surface wave exploration.

[0004] Currently, common methods for generating Rayleigh wave dispersion energy maps mainly include: - Transformation - Transformation, phase-shifting methods, high-resolution linear Radon transform, frequency-Bessel transform, and various time-frequency analysis methods typically transform seismic records from the time-space domain to the frequency-velocity domain, forming an energy spectrum that can be used for dispersion curve extraction. However, in practical applications, these methods are affected by factors such as noise interference, data sampling intervals, and parameter sensitivity, leading to unstable image quality. For example, - Transformation significantly impacts the quality of dispersive energy spectrum images when bad channels or non-uniform sampling are present. While phase-shifting methods offer high computational efficiency, they still suffer from insufficient energy continuity and significant background energy interference under complex geological conditions or high-noise backgrounds. Different methods still have significant limitations in handling weak energy, recognizing higher-order patterns, suppressing background energy, improving resolution, and adapting to complex geological conditions. Therefore, existing dispersive energy map generation methods still struggle to simultaneously achieve accuracy, stability, and noise resistance in complex geological environments, and there is still room for improvement in the quality of dispersive energy maps. Summary of the Invention

[0005] The purpose of this invention is to address the weakness of existing surface wave dispersion imaging methods in terms of anti-interference capability and stability under complex geological or noisy conditions, and to propose a surface wave dispersion image generation method, comprising the following steps:

[0006] S1. Acquire the time-domain surface wave signals of active or passive seismic sources received by the seismic detector and arrange them according to their spatial location to form a gather;

[0007] S2. Perform an S-transform on the time-domain surface wave signal of each seismic trace in the trace set to obtain the complex-valued time-spectrum;

[0008] S3. Based on the complex-valued time-spectrum, extract the complex spectrum corresponding to the principal energy at each frequency point for each seismic trace, and normalize the complex spectrum to obtain the phase spectrum.

[0009] S4. Within the preset phase velocity scanning range, select a series of trial phase velocities. For each frequency and each trial phase velocity, coherently superimpose the corresponding phase spectra of all seismic traces to construct the energy function in the frequency-phase velocity domain.

[0010] S5. When the trial phase velocity is consistent with the actual surface wave phase velocity, the surface wave phase velocity dispersion energy image is obtained based on the energy function in the frequency-phase velocity domain.

[0011] Furthermore, the formula for the S-transform is:

[0012]

[0013] in, The time-frequency spectrum is represented by x, where x represents the detector offset, t represents the time sampling point, and f represents the signal frequency. Represents a surface wave signal in the time domain. This represents the Gaussian window function. Indicates the time parameter.

[0014] Furthermore, S3 specifically refers to:

[0015] S31. Calculate the energy spectrum based on the time-frequency spectrum, using the following formula:

[0016]

[0017] in, The spectrum represents the energy spectrum, x represents the detector offset, t represents the time sampling point, and f represents the signal frequency. Represents the time-spectrum;

[0018] S32. For each seismic trace at each frequency point, search along the time axis of the energy spectrum for the time position with the maximum energy, represented as:

[0019]

[0020] in, This indicates the time position of the principal energy corresponding to the seismic trace with detector offset x and frequency f;

[0021] S33, in The corresponding complex spectrum is extracted at the point, and represented as:

[0022]

[0023] in, Represents the complex spectrum. Time-spectrum representation exist The value at;

[0024] S34. Normalize the complex spectrum to obtain the phase spectrum.

[0025] Furthermore, the phase spectrum is represented as:

[0026]

[0027]

[0028] in, Let x represent the phase spectrum, x represent the detector offset, and f represent the signal frequency. The amplitude spectrum represents the complex spectrum. Represents the complex spectrum. This represents the actual phase velocity.

[0029] Furthermore, the energy function in the frequency-phase velocity domain is expressed as:

[0030]

[0031] in, This represents the energy function in the frequency-phase velocity domain, where f represents frequency, c represents phase velocity, and N represents the number of seismic traces. This represents the phase spectrum of the nth seismic trace. Indicates complex conjugation. and These represent the detector offsets corresponding to the nth and mth seismic traces, respectively.

[0032] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described surface wave dispersion image generation method.

[0033] The present invention also proposes an electronic device, including a processor and a memory, wherein the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program including computer-readable instructions, and the processor is configured to invoke the computer-readable instructions to execute the above-described surface wave dispersion image generation method.

[0034] The present invention also proposes a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described surface wave dispersion image generation method.

[0035] The beneficial effects of the technical solution provided by this invention are:

[0036] This invention uses multi-channel surface wave records as input and extracts the complex spectrum information corresponding to the main surface wave energy in the time-frequency domain through S-transform. This effectively avoids interference caused by weak energy components when processing directly across the entire frequency band, improving the stability and reliability of spectrum extraction. Based on this, a coherence relationship model between the multi-channel complex spectra is constructed, and a frequency-velocity domain energy image is generated through phase velocity scanning. This achieves comprehensive utilization of the synergistic features of multi-channel frequency components, thereby significantly enhancing the focusing effect of dispersive energy during phase velocity scanning. This invention can improve the anti-interference capability and stability of surface wave dispersive imaging under complex geological or noisy conditions. Attached Figure Description

[0037] Figure 1 This is a flowchart of the surface wave dispersion image generation method according to an embodiment of the present invention;

[0038] Figure 2 This is a block diagram of an electronic device according to an exemplary embodiment of Embodiment 1 of the present invention;

[0039] Figure 3 These are earthquake records from the theoretical model in this embodiment of the invention;

[0040] Figure 4 This is an embodiment of the present invention. - The theoretical model seismic dispersion image obtained by transformation;

[0041] Figure 5 This is an embodiment of the present invention. - The theoretical model seismic dispersion image obtained by transformation;

[0042] Figure 6 This is a theoretical model seismic dispersion image obtained by the phase-shifting method in an embodiment of the present invention;

[0043] Figure 7 This is a theoretical model seismic dispersion image obtained by frequency-Hankle transform in an embodiment of the present invention;

[0044] Figure 8 This is a theoretical model seismic dispersion image obtained by the method of the present invention in this embodiment of the invention;

[0045] Figure 9 These are measured records of active source earthquakes in embodiments of the present invention;

[0046] Figure 10 This is an embodiment of the present invention. - Active source seismic dispersion image obtained by transformation;

[0047] Figure 11 This is an embodiment of the present invention. - Active source seismic dispersion image obtained by transformation;

[0048] Figure 12 This is an active source seismic dispersion image obtained by the phase-shifting method in an embodiment of the present invention;

[0049] Figure 13 This is an active source seismic dispersion image obtained by frequency-Hankle transform in an embodiment of the present invention;

[0050] Figure 14 This is an active source seismic dispersion image obtained by the method of the present invention in an embodiment of the present invention;

[0051] Figure 15 These are the actual measurement records of the passive source virtual gather in the embodiments of this invention;

[0052] Figure 16 This is an embodiment of the present invention. - The dispersion image of the passive source virtual gather obtained by transformation;

[0053] Figure 17 This is an embodiment of the present invention. - The dispersion image of the passive source virtual gather obtained by transformation;

[0054] Figure 18 This is a passive source virtual gather dispersion image obtained by the phase-shifting method in this embodiment of the invention;

[0055] Figure 19 This is the passive source virtual gather dispersion image obtained by frequency-Hankle transform in this embodiment of the invention;

[0056] Figure 20 This is the passive source virtual gather dispersion image obtained by the method of the present invention in this embodiment of the invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0058] The flowchart of the surface wave dispersion image generation method of this invention is as follows: Figure 1 Specifically, it includes the following steps:

[0059] S1. Acquire time-domain surface wave signals from multiple active or passive seismic sources received by seismic detectors. ,in The detector offset. These are the time sampling points.

[0060] Specifically, linear or two-dimensional arrays are deployed to receive active or passive seismic source signals, obtaining raw time series. Necessary preprocessing is performed on each seismic trace, including mean removal, trend removal, bandpass filtering (to preserve effective surface wave frequencies), and anomalous amplitude removal. Based on the spatial coordinates of the seismic detectors, the preprocessed time series are arranged sequentially to form common shot gathers or virtual shot gathers. It is ensured that each trace within the gather has clearly defined offset information.

[0061] Offset distance is a core parameter in geophysical exploration, measuring the distance between the excitation point and the center of the geophone array. This parameter directly affects the design of the distance between the seismic source and the geophones, with a typical offset distance range of 200-300 meters. The received record of a single seismic geophone is called a seismic trace. An active seismic source generates seismic waves artificially, while a passive seismic source utilizes naturally occurring seismic waves for detection. Multiple seismic traces are collected according to their spatial location to form a trace set.

[0062] Surface waves are a type of seismic wave that primarily propagate along the Earth's surface or layered interfaces, with energy concentrated in the shallow subsurface. Surface waves typically have large amplitudes, low frequencies, slow propagation speeds, and slow attenuation. They appear later in the seismic record, have long durations, and are often a major factor causing ground damage. They are not only the most prominent part of seismograms but also a crucial source of information for revealing shallow subsurface structures, assessing earthquake hazards, and studying source physics.

[0063] On a seismic waveform diagram (vertical axis represents amplitude, horizontal axis represents time), surface wave signals typically have the following characteristics: late arrival time: arriving immediately after the first arrival of the P-wave (longitudinal wave) and S-wave (transverse wave); maximum amplitude: often the strongest part of the entire seismic record, easily identifiable; long period and low frequency: manifested as slow, undulating "wave packets", with an oscillation period significantly longer than the volume wavelength; long duration: due to its slow decay and frequent appearance in a dispersed form (different frequencies have different velocities), it can be stretched into a very long tailwave sequence.

[0064] By utilizing the dispersion characteristics of surface waves, the velocity structure of underground shear waves can be inverted through multichannel recording to detect geological structures, soil and rock mechanical properties, and foundation conditions. This is the commonly used "surface wave exploration method".

[0065] S2. Perform an S-transform on the time-domain surface wave signal of each seismic trace in the trace set to obtain the complex-valued time-spectrum. The formula is as follows:

[0066]

[0067] in, The time-frequency spectrum is represented by x, where x represents the detector offset, t represents the time sampling point, and f represents the signal frequency. Represents a surface wave signal in the time domain. This represents the Gaussian window function. The S-transform represents the time parameter. It can simultaneously obtain information in both the time and frequency domains and has good time-frequency localization characteristics.

[0068] S3. Based on the complex-valued time-spectrum, extract the complex spectrum corresponding to the principal energy at each frequency point for each seismic trace, and normalize the complex spectrum to obtain the phase spectrum.

[0069] Specifically:

[0070] S31. Calculate the energy spectrum based on the time-frequency spectrum, using the following formula:

[0071]

[0072] in, The spectrum represents the energy spectrum, x represents the detector offset, t represents the time sampling point, and f represents the signal frequency. Represents the time-spectrum;

[0073] S32. For each seismic trace at each frequency point, search along the time axis of the energy spectrum for the time position with the maximum energy, represented as:

[0074]

[0075] in, This indicates the time position of the principal energy corresponding to the seismic trace with detector offset x and frequency f;

[0076] S33, in The corresponding complex spectrum is extracted at the principal energy location and represented as follows:

[0077]

[0078] in, This represents a complex spectrum, which implicitly contains phase information corresponding to the frequency components. Time-spectrum representation exist The value at the point is obtained; the complex spectrum corresponding to the main energy of the surface wave is extracted in the time-frequency domain by S-transform, which effectively avoids the interference caused by weak energy components when processing directly in the whole frequency band, and improves the stability and reliability of spectrum extraction.

[0079] S34. Normalize the complex spectrum to obtain the phase spectrum, which is expressed as:

[0080]

[0081]

[0082] in, Let x represent the phase spectrum, x represent the detector offset, and f represent the signal frequency. The amplitude spectrum represents the complex spectrum. Represents the complex spectrum. This represents the actual phase velocity.

[0083] This step is equivalent to retaining only the phase information and discarding the amplitude information, making subsequent superposition insensitive to amplitude changes and enhancing the stability of the method.

[0084] S4. Select a series of trial phase velocities within the preset phase velocity scanning range. Phase velocity refers to the propagation speed of a wave at its constant phase point in space. For each frequency and each trial phase velocity, coherently superimpose the corresponding phase spectra of all seismic traces to construct an energy function in the frequency-phase velocity domain.

[0085] The energy function in the frequency-phase velocity domain is expressed as:

[0086]

[0087] in, This represents the energy function in the frequency-phase velocity domain, where f represents frequency, c represents phase velocity, and N represents the number of seismic traces. This represents the phase spectrum of the nth seismic trace. Indicates complex conjugation. and These represent the detector offsets corresponding to the nth and mth seismic traces, respectively.

[0088] By constructing a coherent superposition model between multi-channel normalized phase spectra, the comprehensive utilization of the synergistic characteristics of multi-channel frequency components is realized, thereby significantly enhancing the focusing effect of dispersive energy during phase velocity scanning.

[0089] S5, When testing phase velocity When the phase velocity matches the actual surface wave phase velocity, the multi-channel complex spectrum will produce a coherent superposition effect after phase shift compensation, thus forming a significant energy concentration at the corresponding frequency-phase velocity position, which is the surface wave phase velocity dispersion energy image. In the generated frequency-phase velocity domain energy image, the ridge line of the high-energy band represents the surface wave phase velocity dispersion curve.

[0090] The energy ridge can be picked up automatically or manually to obtain clear dispersion data points for underground structure inversion.

[0091] In one exemplary embodiment, a computer-readable storage medium is included, which stores a computer program that, when executed by a processor, implements the surface wave dispersion image generation method described above.

[0092] Please see Figure 4 In one exemplary embodiment, the device further includes an electronic device including at least one processor, at least one memory, and at least one communication bus.

[0093] The memory stores a computer program, which includes computer-readable instructions. The processor calls the computer-readable instructions stored in the memory through the communication bus to execute the aforementioned surface wave dispersion image generation method.

[0094] In one exemplary embodiment, a computer program product is proposed, including a computer program / instructions that, when executed by a processor, implement the steps of the surface wave dispersion image generation method described above.

[0095] To verify the effectiveness of the method of this invention, experimental tests were conducted using surface wave data from three seismic records, including a simple layered theoretical model dataset, an active source measured dataset, and a passive source measured dataset. The passive source data was processed into a virtual gather record.

[0096] The dispersion images obtained by the method of this invention and the comparative method are compared. The comparative method includes traditional dispersion generation methods and several improved methods, which can represent dispersion energy extraction methods under different technical routes, specifically including: - Transformation - Transformation, phase-shifting method, and improved frequency Bessel transform (frequency-Hankler transform). The present invention's method (SMPS) is compared with the above methods under the same data conditions to verify its advantages in dispersive energy resolution, focusing, continuity, and noise resistance.

[0097] Evaluation criteria include: resolution and focusing degree of dispersion energy, continuity of dispersion energy peak, background energy suppression effect, and usable frequency band range; theoretical models can be further evaluated by their fit to theoretical dispersion curves.

[0098] Figures 3-8 It compares the earthquake records from the theoretical model with the dispersion images obtained from various models, among which... Figure 3 It is a theoretical model earthquake record. Figures 4-8 They are - Transformation - The seismic dispersion image of the theoretical model obtained by transformation, phase shift method, frequency-Hankle transform, and the method of this invention. From Figures 3-8 It can be seen that the method of this invention has better energy resolution and continuity compared with traditional dispersive image generation methods, and effectively suppresses background energy and energy tails, thereby reducing the possibility of pattern misidentification; moreover, the dispersive image of this invention also has a good fit with the theoretical dispersive curve. Therefore, this method significantly improves the accuracy and reliability of dispersive images.

[0099] Figures 9-14 It is a comparison of the measured records of active source earthquakes and the dispersion images obtained from various models, among which, Figure 9 These are measured records of active-source earthquakes. Figures 10-14 They are - Transformation - Active source seismic dispersion images obtained by transformation, phase shift method, frequency-Hankle transform, and the method of this invention. From Figures 9-14 It can be seen that the method of the present invention not only performs well in terms of dispersion energy resolution and continuity, but also shows superior noise resistance to the unavoidable noise in the measured data. In addition, since the energy of the data is relatively weak after 65Hz, the dispersion image of the traditional method is difficult to generate an effective energy trend after 65Hz, but the method of the present invention can still generate a clear and effective energy trend for the weak surface wave energy in this frequency band.

[0100] Figures 15-20 It is a comparison of the measured records of the passive source virtual gather and the dispersion images obtained from various models, among which, Figure 15 This is a test record of the passive source virtual collection. Figures 16-20 They are - Transformation - The passive source virtual gather dispersion image obtained by transformation, phase shift method, frequency-Hankle transform, and the method of this invention. From Figures 15-20It can be seen that the effective frequency band range is relatively short for this passive source noise data. However, in addition to having a very good effect in terms of dispersion energy resolution and continuity, the method of the present invention also has a wider frequency band range of usable energy trends. Furthermore, the excellent performance of this method in this passive source noise data also demonstrates its strong noise resistance capability.

[0101] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating surface wave dispersion images, characterized in that, Includes the following steps: S1. Acquire the time-domain surface wave signals of active or passive seismic sources received by the seismic detector and arrange them according to their spatial location to form a gather; S2. Perform an S-transform on the time-domain surface wave signal of each seismic trace in the trace set to obtain the complex-valued time-spectrum; S3. Based on the complex-valued time-spectrum, extract the complex spectrum corresponding to the principal energy at each frequency point for each seismic trace, and normalize the complex spectrum to obtain the phase spectrum. S4. Select multiple trial phase velocities within the preset phase velocity scanning range. For each frequency and each trial phase velocity, coherently superimpose the corresponding phase spectra of all seismic traces to construct the energy function in the frequency-phase velocity domain. S5. When the probe phase velocity is consistent with the actual surface wave phase velocity, the surface wave phase velocity dispersion energy image is obtained based on the energy function in the frequency-phase velocity domain.

2. The method for generating a surface wave dispersion image according to claim 1, characterized in that, The formula for the S-transform is: in, The time-frequency spectrum is represented by x, where x represents the detector offset, t represents the time sampling point, and f represents the signal frequency. Represents a surface wave signal in the time domain. This represents the Gaussian window function. Indicates the time parameter.

3. The method for generating a surface wave dispersion image according to claim 1, characterized in that, S3 specifically refers to: S31. Calculate the energy spectrum based on the time-frequency spectrum, using the following formula: in, The spectrum represents the energy spectrum, x represents the detector offset, t represents the time sampling point, and f represents the signal frequency. Represents the time-spectrum; S32. For each seismic trace at each frequency point, search along the time axis of the energy spectrum for the time position with the maximum energy, represented as: in, This indicates the time position of the principal energy corresponding to the seismic trace with detector offset x and frequency f; S33, in The corresponding complex spectrum is extracted at the point, and represented as: in, Represents the complex spectrum. Time-spectrum representation exist The value at; S34. Normalize the complex spectrum to obtain the phase spectrum.

4. The method for generating a surface wave dispersion image according to claim 1, characterized in that, The phase spectrum is represented as: in, Let x represent the phase spectrum, x represent the detector offset, and f represent the signal frequency. The amplitude spectrum represents the complex spectrum. Represents the complex spectrum. This represents the actual phase velocity.

5. The method for generating a surface wave dispersion image according to claim 1, characterized in that, The energy function in the frequency-phase velocity domain is expressed as: in, This represents the energy function in the frequency-phase velocity domain, where f represents frequency, c represents phase velocity, and N is the number of seismic traces. This represents the phase spectrum of the nth seismic trace. Indicates complex conjugation. and These represent the detector offsets corresponding to the nth and mth seismic traces, respectively.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the method as described in any one of claims 1-5.

7. An electronic device, characterized in that, The device includes a processor and a memory, the processor being interconnected with the memory, wherein the memory is used to store a computer program, the computer program including computer-readable instructions, and the processor is configured to invoke the computer-readable instructions to perform the method as described in any one of claims 1-5.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-5.

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