A signal energy spectrum imaging method and device, electronic equipment and medium

Abstract

Embodiments of the present application provide a signal energy spectrum imaging method and device, electronic equipment and medium, which solve the problems of low frequency velocity spectrum precision and non-concentrated energy focusing in the prior art. The signal energy spectrum imaging method comprises: performing observation system loading processing on collected seismic single shot data, and generating trace head information including offset distance; performing surface wave signal extraction processing on the loaded seismic single shot data, and extracting surface wave signals; performing split-offset multi-time window surface wave signal selection processing on the extracted surface wave signals along the signal apparent velocity direction; performing signal enhancement processing on the surface wave signals in the selected target time window, and determining the time-frequency transform spectrum of each trace surface wave signal after the signal enhancement processing in the target time window; determining the frequency-phase velocity spectrum according to the time-frequency transform spectrum of each trace surface wave signal after the signal enhancement processing in the target time window, and fusing the frequency-phase velocity spectrum of each frequency band to obtain a surface wave signal energy spectrum imaging result.

Description

Technical Field

[0001] This application relates to the field of seismic data processing in petroleum exploration, and in particular to a signal energy spectrum imaging method, device, electronic equipment, and medium. Background Technology

[0002] With the continuous deepening of oil and gas exploration, the western region has become the main battleground for oil and gas exploration. However, seismic data acquisition in oil exploration in the western region faces complex problems such as large surface undulations, severe development of low-velocity zones, complex noise, complex stratigraphic structures, and large variations in longitudinal and transverse seismic wave velocities. Therefore, the oil seismic data acquired in the western region often suffers from severe noise and prominent static correction issues. Oil seismic data acquired and processed using single P-wave acquisition provides relatively limited information for subsequent fracture prediction, while converted wave data is more sensitive to information such as fractures. Therefore, multi-wave exploration technology has become an effective means of high-precision interpretation techniques such as fracture prediction.

[0003] However, the converted wave data in the currently acquired multi-wave data cannot be statically corrected based on near-surface velocity alone. Most of them use the near-surface correction of P-waves for empirical processing, which has low accuracy. It is necessary to establish an effective method to model the near-surface velocity of converted waves, so as to obtain the correction of the converted waves themselves and improve the accuracy of static correction.

[0004] In seismology, body waves are seismic waves directly generated by the vibration of the earthquake source and propagating within the Earth's interior. Surface waves are secondary waves derived from body waves at the Earth's surface, primarily propagating there. They have the highest energy and a wave speed of approximately 3.8 km / s, lower than body waves, and are often the last to be recorded. In seismic data from oil and gas exploration in western China, surface wave signals are often discarded as noise and not utilized. However, surface wave signals precisely reflect the propagation of shear waves in near-surface strata with low descent rates. Utilizing surface wave signals to establish near-surface velocity models facilitates the actual implementation of static correction processing for converted waves.

[0005] One of the key technologies for establishing a near-surface velocity model of surface wave signals is how to achieve high-precision imaging of the energy spectrum of surface wave signals. The existing surface wave signal energy spectrum imaging schemes have low frequency and velocity spectrum accuracy and poor energy focusing, which is not conducive to the subsequent automatic acquisition of frequency and velocity curves and the inversion processing of near-surface velocity of surface waves. Summary of the Invention

[0006] This invention provides a signal energy spectrum imaging method, device, electronic device, and medium, which solves the problems of low frequency velocity spectrum accuracy and poor energy focusing in the prior art, making it difficult to automatically pick up frequency velocity curves.

[0007] To solve the above-mentioned technical problems, this specification is implemented as follows:

[0008] Firstly, a signal energy spectrum imaging method is provided, including:

[0009] The acquired seismic single-shot data is loaded and processed by the observation system. The resulting track head information, including offset, is generated for the seismic single-shot data after loading and processing.

[0010] Surface wave signals are extracted from the processed seismic single-shot data.

[0011] The extracted surface wave signal is subjected to multi-time window surface wave signal selection processing with offset distance along the signal apparent velocity direction;

[0012] The surface wave signal within the selected target time window is subjected to signal enhancement processing, and the time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window is determined.

[0013] Based on the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window, the frequency-phase velocity spectrum is determined by frequency band, and the frequency-phase velocity spectra of each frequency band are fused to obtain the surface wave signal energy spectrum imaging result.

[0014] Optionally, the step of performing surface wave signal extraction processing on the loaded seismic single-shot data to extract the surface wave signal specifically includes:

[0015] Frequency division scanning and amplitude spectrum analysis were performed on the single-shot seismic data to obtain the frequency band range of the surface wave signal;

[0016] The single-shot seismic data is filtered using a bandpass filter, and the filtered single-shot seismic data includes surface wave signals within a set frequency band.

[0017] Based on the inter-channel offset and on-channel time information of the surface wave signal within the set frequency band, the apparent velocity range of the surface wave signal is determined.

[0018] The intermediate velocity values ​​within the apparent velocity range are extracted, and the filtered seismic single-shot data are corrected and leveled to make the surface wave signals in the positive and negative offset directions approximately horizontal.

[0019] Surface wave signals within each time window are extracted from the corrected and flattened seismic single-shot data using a moving time window.

[0020] The intermediate velocity value is used to perform inverse correction and flattening on the surface wave signals extracted in each time window to recover the surface wave signals that conform to the original distribution.

[0021] Optionally, the step of extracting surface wave signals within each time window from the corrected and flattened seismic single-shot data using a moving time window specifically includes:

[0022] For the corrected and leveled seismic single-shot data, the data signals are extracted sequentially using the inter-track time windows that move according to a set step size;

[0023] The root mean square amplitude (RMS) of the data signal within the current time window is determined. This RMS amplitude is compared to a set signal threshold. If it exceeds the threshold, the data signal within the current time window is determined to be a surface wave signal; otherwise, it is determined to be another type of seismic signal. This process continues until it is determined whether all data signals within all time windows are surface wave signals.

[0024] Based on the judgment results, the surface wave signal within each time window is extracted.

[0025] Optionally, the step of performing multi-time-window surface wave signal selection processing on the extracted surface wave signal along the signal apparent velocity direction includes:

[0026] Multiple discontinuous first rectangular time windows with different offset distances are selected in the positive and negative offset directions of the surface wave signal;

[0027] Based on the apparent velocity range of the surface wave signal, the slope along the apparent velocity direction is determined using the inter-channel apparent velocity, inter-channel offset, and on-channel time information of the surface wave signal. Using the slope, inter-channel offset, and on-channel time information, a second rectangular time window composed of the minimum and maximum apparent velocities is selected along the apparent velocity direction.

[0028] The intersection of the multiple discontinuous first rectangular time windows and the second rectangular time windows is taken as the selected target time window to perform signal enhancement processing on the surface wave signal within the target time window.

[0029] Optionally, the signal enhancement processing of the surface wave signal within the selected target time window specifically includes:

[0030] Perform Fourier transform on the surface wave signal within the selected target time window;

[0031] The frequency domain signal obtained from the Fourier transform is enhanced using the following formula:

[0032] A'(ω,k).e -iφ′(ω,k) =A a (ω,k).e -iφ(ω,k)

[0033] Where A(ω,k) represents the amplitude spectrum, φ(ω,k) represents the phase spectrum, ω represents the frequency, k represents the wave number, a=rω+b is a frequency-dependent linear enhancement function, and r and b are constants greater than zero;

[0034] An inverse Fourier transform is performed on the frequency domain signal after signal enhancement processing to obtain the surface wave signal after signal enhancement processing within the target time window.

[0035] Optionally, determining the time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window specifically includes:

[0036] For the surface wave signal after signal enhancement processing within the target time window, starting from the single-channel signal with the minimum negative offset and ending with the single-channel signal with the maximum positive offset, the spatiotemporal domain signal to time-frequency domain signal transformation processing is performed on each surface wave signal using the following formula, to obtain the time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window:

[0037]

[0038] Where H(β,ω) represents the time-frequency domain signal, h(t) represents the time-space domain signal, ω represents the frequency, β represents the position of the window function on the time axis, t represents time, and p, q, and l are constants greater than zero.

[0039] Optionally, the step of determining the frequency-phase velocity spectrum by dividing the frequency bands based on the time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window, and fusing the frequency-phase velocity spectra of each frequency band to obtain the surface wave signal energy spectrum imaging result, specifically includes:

[0040] The low-frequency band range where the surface wave signal is stronger and the high-frequency band range where the surface wave signal is weaker are determined by frequency division scanning.

[0041] For surface wave signals in the low-frequency band, based on the time-frequency transform spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the low-frequency band, the phase velocity value corresponding to each frequency value in the low-frequency band is determined sequentially with a set frequency step size; based on each frequency value in the low-frequency band and its corresponding phase velocity value, the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band is determined.

[0042] For surface wave signals in the high-frequency band, based on the time-frequency transform spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the high-frequency band, the phase velocity value corresponding to each frequency value in the high-frequency band is determined sequentially with a set frequency step size; based on each frequency value in the high-frequency band and its corresponding phase velocity value, the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band is determined.

[0043] The frequency-phase velocity spectrum of the surface wave signal in the low-frequency band and the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band are fused, and the frequency-phase velocity spectrum in the set frequency band obtained by fusion is used as the signal energy spectrum imaging result.

[0044] Optionally, the method for determining the phase velocity value corresponding to each frequency value specifically includes:

[0045] For each frequency value, the time corresponding to the maximum energy value at the current frequency in the time-frequency transform spectrum of two different surface wave signals is obtained, and the time difference between each two surface wave signals is determined.

[0046] The phase velocity between all channels within the target time window at the current frequency is determined by using the ratio of the inter-channel distance to the time difference between each pair of surface wave signals.

[0047] The average phase velocity between all channels within the target time window at the current frequency is taken as the phase velocity value at the current frequency.

[0048] Optionally, there is an overlapping frequency band between the low-frequency band range and the high-frequency band range; and

[0049] The fusion of the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band and the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band specifically includes:

[0050] Based on the frequency-phase velocity spectrum in the low-frequency band and the frequency-phase velocity spectrum in the high-frequency band, the average value of two energy values ​​at the same frequency in the overlapping frequency band is taken, while the energy values ​​at each frequency in the non-overlapping frequency band remain unchanged, and the frequency-phase velocity spectrum in the set frequency band is obtained by fusion.

[0051] Secondly, a signal energy spectrum imaging device is provided, comprising:

[0052] The loading module is used to load the acquired seismic single-shot data into the observation system. After loading, the track head information generated for the seismic single-shot data includes the offset distance.

[0053] The extraction module is used to perform surface wave signal extraction processing on the seismic single-shot data after it has been loaded and processed by the loading module, and to extract the surface wave signal.

[0054] The selection module is used to perform multi-time-window surface wave signal selection processing with offset distance along the signal apparent velocity direction on the surface wave signal extracted by the extraction module.

[0055] The signal enhancement module is used to enhance the surface wave signal within the selected target time window.

[0056] The time-frequency transformation module is used to determine the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window;

[0057] The imaging module is used to determine the frequency-phase velocity spectrum by dividing the frequency bands based on the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window, and to fuse the frequency-phase velocity spectra of each frequency band to obtain the surface wave signal energy spectrum imaging result.

[0058] Optionally, the extraction module specifically includes:

[0059] The scanning analysis submodule is used to perform frequency division scanning and amplitude spectrum analysis on the single-shot seismic data to obtain the frequency band range of the surface wave signal;

[0060] The bandpass filter submodule is used to filter the seismic single-shot data using a bandpass filter. The filtered seismic single-shot data includes surface wave signals within a set frequency band.

[0061] The apparent velocity determination submodule is used to determine the apparent velocity range of the surface wave signal based on the inter-channel offset and on-channel time information of the surface wave signal within the set frequency band.

[0062] The correction submodule is used to extract intermediate velocity values ​​within the apparent velocity range and perform correction and flattening processing on the filtered seismic single-shot data so that the surface wave signals in the positive and negative offset directions are in an approximately horizontal state.

[0063] The extraction submodule is used to extract surface wave signals within each time window from the corrected and flattened seismic single-shot data using a moving time window.

[0064] The inverse correction submodule is used to perform inverse correction and flattening processing on the extracted surface wave signals within each time window using the intermediate velocity value, so as to restore the surface wave signals that conform to the original distribution.

[0065] Optionally, the selection module specifically includes:

[0066] The first time window selection submodule is used to select multiple discontinuous first rectangular time windows with different offset distances in the positive and negative offset directions of the surface wave signal.

[0067] The second time window selection submodule is used to determine the slope along the signal's visual velocity direction based on the visual velocity range of the surface wave signal, using the inter-channel visual velocity, inter-channel offset, and on-channel time information of the surface wave signal, and to select a second rectangular time window composed of the minimum visual velocity and the maximum visual velocity along the signal's visual velocity direction using the slope, inter-channel offset, and on-channel time information.

[0068] The target time window selection submodule is used to select the intersection of the time windows between the plurality of discontinuous first rectangular time windows and the second rectangular time windows as the selected target time window, so as to perform signal enhancement processing on the surface wave signal within the target time window.

[0069] Optionally, the signal enhancement module specifically includes:

[0070] The Fourier transform submodule is used to perform Fourier transform on the surface wave signal within the selected target time window;

[0071] The signal enhancement submodule is used to enhance the frequency domain signal obtained by Fourier transform using the following formula:

[0072] A'(ω,k).e -iφ′(ω,k) =A a (ω,k).e -iφ(ω,k)

[0073] Where A(ω,k) represents the amplitude spectrum, φ(ω,k) represents the phase spectrum, ω represents the frequency, k represents the wave number, a=rω+b is a frequency-dependent linear enhancement function, and r and b are constants greater than zero;

[0074] The inverse Fourier transform submodule is used to perform an inverse Fourier transform on the frequency domain signal after signal enhancement processing to obtain the surface wave signal after signal enhancement processing within the target time window.

[0075] Optionally, the time-frequency transformation module is specifically used to perform a time-frequency transformation on each surface wave signal after signal enhancement processing within the target time window, starting from the single-channel signal with the minimum negative offset and ending with the single-channel signal with the maximum positive offset, using the following formula to obtain the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window:

[0076]

[0077] Where H(β,ω) represents the time-frequency domain signal, h(t) represents the time-space domain signal, ω represents the frequency, β represents the position of the window function on the time axis, t represents time, and p, q, and l are constants greater than zero.

[0078] Optionally, the imaging module specifically includes:

[0079] The frequency band division submodule is used to determine the low-frequency band range where the surface wave signal is stronger and the high-frequency band range where the surface wave signal is weaker based on the frequency division scanning.

[0080] The low-frequency band processing submodule is used to determine the phase velocity value corresponding to each frequency value in the low-frequency band range based on the time-frequency transformation spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the low-frequency band range and sequentially at a set frequency step size; and to determine the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band range based on each frequency value and its corresponding phase velocity value in the low-frequency band range.

[0081] The high-frequency band processing submodule is used to determine the phase velocity value corresponding to each frequency value in the high-frequency band range based on the time-frequency transformation spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the high-frequency band range and sequentially at a set frequency step size; and to determine the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band range based on each frequency value and its corresponding phase velocity value in the high-frequency band range.

[0082] The fusion submodule is used to fuse the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band and the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band, and use the fused frequency-phase velocity spectrum in the set frequency band as the energy spectrum imaging result of the surface wave signal.

[0083] Thirdly, an electronic device is provided, including a memory and a processor electrically connected to the memory, the memory storing a computer program executable by the processor, the computer program, when executed by the processor, implementing the steps of the method described in the first aspect.

[0084] Fourthly, a computer-readable storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0085] The above technical solution has the following beneficial effects:

[0086] In this embodiment of the invention, a pure surface wave signal without effective reflection signals is extracted in the spatiotemporal domain. A multi-time window with offsets along the apparent velocity direction is used to select the surface wave signal for time-frequency analysis. After signal enhancement processing, the energy intensity of the surface wave signal is increased. Then, a high-precision time-frequency transform is used to divide the frequency bands and determine the frequency-phase velocity spectrum. Through frequency domain energy spectrum fusion and velocity scanning, a high-precision surface wave signal energy spectrum imaging result is obtained. The surface wave signal energy spectrum obtained using this embodiment of the invention features interference suppression, good energy focusing, and high accuracy, facilitating subsequent high-precision frequency-velocity curve picking and surface wave near-surface velocity inversion processing. This also facilitates the subsequent calculation of more accurate correction values ​​and improves static correction accuracy. Attached Figure Description

[0087] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0088] Figure 1 This is a flowchart of the signal energy spectrum imaging method in an embodiment of the present invention;

[0089] Figure 2 This is a schematic diagram of the signal energy spectrum imaging device in an embodiment of the present invention;

[0090] Figure 3 This is a specific structural example diagram of the extraction module in the signal energy spectrum imaging device in an embodiment of the present invention;

[0091] Figure 4 This is a specific structural example diagram of a selected module in the signal energy spectrum imaging device in an embodiment of the present invention;

[0092] Figure 5 This is a specific structural example diagram of the signal enhancement module in the signal energy spectrum imaging device in this embodiment of the invention;

[0093] Figure 6 This is a specific structural example diagram of the imaging module in the signal energy spectrum imaging device in this embodiment of the invention;

[0094] Figure 7(a) is a schematic diagram of frequency-phase velocity spectrum imaging obtained using existing technology;

[0095] Figure 7(b) is a schematic diagram of frequency-phase velocity spectrum imaging obtained using an embodiment of the present invention;

[0096] Figure 8 This is a schematic diagram of the structure of the electronic device in an embodiment of the present invention. Detailed Implementation

[0097] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, 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. The drawing numbers in this application are only used to distinguish the various steps in the solution and are not used to limit the execution order of the various steps. The specific execution order is as described in the specification.

[0098] To address the problems existing in the prior art, embodiments of the present invention provide a signal energy spectrum imaging method. Figure 1 This is a schematic flowchart of the signal energy spectrum imaging method according to an embodiment of the present invention.

[0099] like Figure 1 As shown, the signal energy spectrum imaging method provided in this embodiment of the invention includes the following steps:

[0100] S101. The collected seismic single-shot data is loaded and processed by the observation system. The track head information generated after loading and processing includes the offset distance for the seismic single-shot data.

[0101] S102. Perform surface wave signal extraction processing on the loaded seismic single-shot data to extract the surface wave signal;

[0102] S103. Perform multi-time-window surface wave signal selection processing along the signal apparent velocity direction for the extracted surface wave signal with offset distance.

[0103] S104. Perform signal enhancement processing on the surface wave signal within the selected target time window, and determine the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window.

[0104] S105. Based on the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window, determine the frequency-phase velocity spectrum by frequency band, and fuse the frequency-phase velocity spectra of each frequency band to obtain the surface wave signal energy spectrum imaging result.

[0105] In the specific implementation of S101, a seismic data acquisition system is typically used to collect seismic single-shot data. Seismic data acquisition is the first and most important step in oil and gas seismic exploration engineering, and an indispensable piece of equipment is the seismic data acquisition system. Conventionally, the device that senses seismic signals is called a seismic detector, and the device that collects and records seismic signals is called a seismic exploration instrument. Seismic detectors and seismic exploration instruments always work together to achieve complete seismic data acquisition; that is, functionally, detectors and exploration instruments are an inseparable whole. From a system perspective, and to meet development needs, the seismic signal sensing and acquisition devices, mainly including seismic detectors and seismic exploration instruments, are collectively referred to as a seismic data acquisition system. Seismic single-shot data consists of vibrations generated at a predetermined location and received at multiple locations. Each receiving location corresponds to a seismic trace, and the seismic data for each trace is the seismic amplitude sampled at predetermined sampling intervals.

[0106] After the acquired seismic single-shot data is loaded by the observation system, trace head information is generated. This information typically includes offset and coverage count. Offset refers to the distance from the excitation point to the center of the nearest geophone array, often decomposed into two components: vertical offset (distance perpendicular to the array line) and longitudinal offset (distance from the projection of the excitation point onto the array line to the center of the first geophone array). Cover count is the number of times the same point on the subsurface interface is repeatedly observed.

[0107] In the specific implementation of S102, a preferred procedure includes the following steps:

[0108] S1021. Perform frequency division scanning and amplitude spectrum analysis on the processed seismic single-shot data to obtain the frequency range of the surface wave signal;

[0109] It should be noted that by performing frequency division scanning and amplitude spectrum analysis on seismic single-shot data, the approximate frequency range of the surface wave signal can be obtained.

[0110] S1022. The single-shot seismic data is filtered using a bandpass filter. The filtered single-shot seismic data includes surface wave signals within a set frequency band.

[0111] A band-pass filter is a device that allows waves of a specific frequency band to pass through while blocking other frequency bands. By using a band-pass filter to filter seismic single-shot data, and by setting the frequency band range of the waves that are allowed to pass through, surface wave signals within a narrow frequency band can be obtained.

[0112] S1023. Determine the apparent velocity range of the surface wave signal based on the inter-channel offset and on-channel time information of the surface wave signal within the set frequency band.

[0113] The approximate apparent velocity range of the surface wave signal can be calculated by taking the inter-track offset and on-track time values ​​of the surface wave signal.

[0114] S1024. Extract the intermediate velocity value within the apparent velocity range, and perform correction and leveling processing on the filtered seismic single-shot data so that the surface wave signal in the positive and negative offset directions is in an approximately horizontal state.

[0115] It should be noted that the filtered seismic single-shot data includes surface wave signals within a specified frequency band.

[0116] S1025. Surface wave signals within each time window are extracted from the corrected and flattened seismic single-shot data using a moving time window.

[0117] In the specific implementation of S1025, preferably, for the corrected and leveled seismic single-shot data, data signals are extracted sequentially using inter-trace time windows that move according to a set step size; the root mean square amplitude value of the data signal in the current time window is determined, and the root mean square amplitude value is compared with a set signal threshold value. If it is greater than the set signal threshold value, the data signal in the current time window is determined to be a surface wave signal; otherwise, the data signal in the current time window is determined to be another seismic signal, until it is determined whether the data signals in all time windows are surface wave signals; and based on the determination results, the surface wave signals in each inter-trace time window are extracted.

[0118] As can be seen in S1025, the corrected seismic single-shot data is extracted from different inter-track time windows. In specific implementation, the inter-track moving step size can be set to one-third of the time window length. Of course, other reasonable inter-track moving step sizes can also be set. The specific value is not limited. The root mean square amplitude value of the data signal in the time window is used to determine whether the data signal in the time window is a surface wave signal. The time window is continuously moved until it is determined whether the data signal in all time windows is a surface wave signal. In this way, pure surface wave signals without effective reflection signals can be extracted.

[0119] S1026. Using the intermediate velocity values ​​within the apparent velocity range, perform reverse correction and flattening processing on the extracted surface wave signals within each time window to recover the surface wave signals that conform to the original distribution.

[0120] In the specific implementation of S103, since the acquired seismic single-shot data only underwent observation system loading processing without other noise suppression processing, the extracted surface wave signals may contain anomalous noise, which will affect the accuracy of subsequent time-frequency analysis. Therefore, multi-time-window surface wave signal selection processing based on offset is required. A preferred specific process includes the following steps:

[0121] S1031. Select multiple discontinuous rectangular time windows with different offset distances in the positive and negative offset directions of the surface wave signal; for easy distinction, the multiple discontinuous rectangular time windows with different offset distances selected in this step are called the first rectangular time window.

[0122] S1032. Based on the apparent velocity range of the surface wave signal determined in S1023, the slope along the apparent velocity direction of the signal is determined using the inter-channel apparent velocity, inter-channel offset, and on-channel time information of the surface wave signal. Using this slope, inter-channel offset, and on-channel time information, a rectangular time window composed of the minimum and maximum apparent velocities is selected along the apparent velocity direction of the signal. For ease of distinction, the rectangular time window composed of the minimum and maximum apparent velocities selected in this step is called the second rectangular time window.

[0123] S1033. The intersection of the time windows between the multiple discontinuous first rectangular time windows with different offsets selected in S1031 and the second rectangular time window selected in S1032 is taken as the selected target time window, which is the time window required for signal enhancement processing in the next step S104.

[0124] In the specific implementation of S104, a preferred procedure for signal enhancement processing of the surface wave signal within the selected target time window includes the following steps:

[0125] S1041. Perform Fourier transform on the surface wave signal within the target time window in S103;

[0126] S1042. The frequency domain signal obtained by Fourier transform is subjected to coherent signal enhancement processing using formula (1) to facilitate stronger signal energy focusing on the subsequent time-frequency transform spectrum and easier identification:

[0127] A'(ω,k).e -iφ′(ω,k) =A a (ω,k).e -iυ(ω,k) (1)

[0128] Where A(ω,k) represents the amplitude spectrum, φ(ω,k) represents the phase spectrum, ω represents the frequency, k represents the wave number, a=rω+b is a frequency-dependent linear enhancement function, and r and b are constants greater than zero;

[0129] S1043. Perform an inverse Fourier transform on the frequency domain signal after signal enhancement processing to obtain the surface wave signal after signal enhancement processing within the target time window. The obtained surface wave signal has stronger coherence.

[0130] In the specific implementation of S104, a preferred process for determining the time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window includes the following steps:

[0131] S1044. For the surface wave signal after signal enhancement processing within the target time window in S1043, starting from the single-channel signal with the minimum negative offset and ending with the single-channel signal with the maximum positive offset, the spatiotemporal domain signal to time-frequency domain signal transformation processing is carried out on each surface wave signal using formula (2) to obtain the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window:

[0132]

[0133] Where H(β,ω) represents the time-frequency domain signal, h(t) represents the time-space domain signal, ω represents the frequency, β represents the position of the window function on the time axis, t represents time, and p, q, and l are constants greater than zero.

[0134] In the specific implementation of S105, a preferred procedure includes the following steps:

[0135] S1051. Determine the low-frequency band range where the surface wave signal is stronger and the high-frequency band range where the surface wave signal is weaker based on the frequency division scanning.

[0136] According to the frequency division scanning in S1021, the frequency band range where the surface wave signal is strong (e.g., 1-12Hz) and the frequency band range where the surface wave signal is weak but still exists (e.g., 12-18Hz) can be determined. For ease of distinction, the frequency band range where the surface wave signal is strong (1-12Hz) can be called the low frequency band range, and the frequency band range where the surface wave signal is weak (12-18Hz) can be called the high frequency band range. In order to improve the energy of the imaging spectrum of the surface wave signal at the high frequency end (12-18Hz), frequency band spectral imaging can be performed.

[0137] S1052. For surface wave signals in the low-frequency band, based on the time-frequency transformation spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the low-frequency band, determine the phase velocity value corresponding to each frequency value in the low-frequency band sequentially with a set frequency step size; determine the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band based on each frequency value in the low-frequency band and its corresponding phase velocity value.

[0138] The method for determining the phase velocity value corresponding to each frequency value in S1052 may specifically include the following steps:

[0139] S521. For each frequency value, obtain the time corresponding to the maximum energy value at the current frequency in the time-frequency transformation spectrum of two different surface wave signals, and determine the time difference between each two surface wave signals.

[0140] S522. Determine the phase velocity of all channels within the target time window at the current frequency by using the ratio of the inter-channel distance to the time difference between each pair of surface wave signals.

[0141] S523. Take the average phase velocity of all channels within the target time window at the current frequency as the phase velocity value at the current frequency.

[0142] An example illustrating the spectral imaging process in the low-frequency band:

[0143] Step (a): For surface wave signals in the low frequency band (1-12Hz), starting from the minimum frequency value (1Hz), calculate the time corresponding to the maximum energy value at that frequency in the time-frequency transformation spectrum of two different surface wave signals, thereby obtaining the time difference between the two surface wave signals.

[0144] Step (b): Using the ratio of the inter-channel distance to the time difference of the two surface wave signals in step (a), obtain the phase velocity between the two current channels at the current frequency;

[0145] Step (c): Using steps (a) and (b), calculate the phase velocity between all channels within the target time window at the current frequency. Sum and average the calculated phase velocities between all channels to obtain the phase velocity value at that frequency.

[0146] Step (d): Based on steps (a) to (c), start calculating the phase velocity value of the next frequency value (1.5Hz) with a set frequency step size (e.g., 0.5Hz) until the phase velocity values ​​corresponding to all frequency values ​​in the low frequency band (1-12Hz) are calculated. Finally, based on the calculated frequency values ​​and phase velocity values, obtain the frequency-phase velocity spectrum of the surface wave signal in the low frequency band (1-12Hz).

[0147] S1053. For surface wave signals in the high-frequency band, based on the time-frequency transformation spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the high-frequency band, the phase velocity value corresponding to each frequency value in the high-frequency band is determined sequentially with a set frequency step size; based on each frequency value in the high-frequency band and its corresponding phase velocity value, the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band is determined.

[0148] The method for determining the phase velocity value corresponding to each frequency value in S1053 can also adopt the specific steps of S521 to S523 above. That is to say, the method for determining the phase velocity value corresponding to each frequency value is applicable in both the low frequency band and the high frequency band.

[0149] An example illustrating the spectral imaging process in the high-frequency band:

[0150] In practice, to avoid frequency misalignment, the high-frequency band range can be set to 10-18Hz, overlapping the low-frequency band range by 2Hz. The surface wave signal within this high-frequency band range is processed using the same steps (a)-(d) described above. It can be understood that for the surface wave signal within the high-frequency band range (10-18Hz), it should start from the minimum frequency value (10Hz) and be calculated sequentially with a set frequency step size (e.g., 0.5Hz). Finally, based on the calculated frequency value and phase velocity value, the frequency-phase velocity spectrum of the surface wave signal within the high-frequency band range (10-18Hz) is obtained.

[0151] It should be noted that the high frequency band range can also be directly set to 12-18Hz, meaning that there may be no overlap between the low frequency band range and the high frequency band range.

[0152] S1054. The frequency-phase velocity spectrum of the surface wave signal in the low frequency band and the frequency-phase velocity spectrum of the surface wave signal in the high frequency band are fused, and the frequency-phase velocity spectrum obtained by fusion in the set frequency band is used as the energy spectrum imaging result of the surface wave signal.

[0153] If there is an overlapping frequency band between the low-frequency band and the high-frequency band, for example, for the frequency-phase velocity spectrum of the low-frequency band (1-12Hz) and the frequency-phase velocity spectrum of the high-frequency band (10-18Hz), the two energy values ​​at the same frequency in the overlapping frequency band (10-12Hz) can be averaged, while the energy values ​​at each frequency in the non-overlapping frequency band (1-10Hz, 12-18Hz) remain unchanged. The resulting frequency-phase velocity spectrum (1-18Hz) within the set frequency band is then fused to obtain the final surface wave signal energy spectrum imaging result.

[0154] If there is no overlapping frequency band between the low-frequency band and the high-frequency band, the frequency-phase velocity spectrum of the low-frequency band (1-12Hz) and the frequency-phase velocity spectrum of the high-frequency band (12-18Hz) are fused directly according to the processing method corresponding to the non-overlapping frequency band.

[0155] The signal energy spectrum imaging method provided in this invention extracts pure surface wave signals without effective reflection signals in the spatiotemporal domain, selects surface wave signals for time-frequency analysis using multi-time windows with offsets along the signal apparent velocity direction, improves the energy intensity of the surface wave signals through signal enhancement processing, determines the frequency-phase velocity spectrum by dividing the frequency band using high-precision time-frequency transformation, and obtains high-precision surface wave signal energy spectrum imaging results through frequency domain energy spectrum fusion and velocity scanning.

[0156] Based on the same technical concept, embodiments of the present invention provide a signal energy spectrum imaging device, such as... Figure 2 As shown, it includes:

[0157] The loading module 201 is used to perform observation system loading processing on the acquired seismic single-shot data. After loading processing, the track head information generated for the seismic single-shot data includes the offset distance.

[0158] Extraction module 202 is used to perform surface wave signal extraction processing on the seismic single-shot data after loading and processing by loading module 201, and extract the surface wave signal;

[0159] The selection module 203 is used to perform multi-time window surface wave signal selection processing along the signal apparent velocity direction on the surface wave signal extracted by the extraction module 202;

[0160] Signal enhancement module 204 is used to perform signal enhancement processing on the surface wave signal within the selected target time window;

[0161] The time-frequency transformation module 205 is used to determine the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window;

[0162] The imaging module 206 is used to determine the frequency-phase velocity spectrum by dividing the frequency bands based on the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window, and to fuse the frequency-phase velocity spectra of each frequency band to obtain the surface wave signal energy spectrum imaging result.

[0163] A preferred possible structure for the extraction module 202 is as follows: Figure 3 As shown, it can specifically include:

[0164] The scanning analysis submodule 2021 is used to perform frequency division scanning and amplitude spectrum analysis on the seismic single-shot data to obtain the frequency band range of the surface wave signal;

[0165] The bandpass filter submodule 2022 is used to filter the seismic single-shot data using a bandpass filter. The filtered seismic single-shot data includes surface wave signals within a set frequency band.

[0166] The apparent velocity determination submodule 2023 is used to determine the apparent velocity range of the surface wave signal based on the inter-channel offset and on-channel time information of the surface wave signal within the set frequency band.

[0167] The correction submodule 2024 is used to extract the intermediate velocity value within the apparent velocity range and perform correction and flattening processing on the filtered seismic single-shot data so that the surface wave signal in the positive and negative offset directions is in an approximately horizontal state.

[0168] Extraction submodule 2025 is used to extract surface wave signals within each time window from the corrected and flattened seismic single-shot data using moving time windows;

[0169] The inverse correction submodule 2026 is used to perform inverse correction and flattening processing on the surface wave signals extracted in each time window using the intermediate velocity value, so as to restore the surface wave signals that conform to the original distribution.

[0170] Preferably, the extraction submodule 2025 is specifically used to extract data signals sequentially from the corrected and leveled seismic single-shot data using inter-trace time windows that move according to a set step size; determine the root mean square amplitude value of the data signal in the current time window, compare the root mean square amplitude value with a set signal threshold value, if it is greater than the signal threshold value, then determine that the data signal in the current time window is a surface wave signal; otherwise, determine that the data signal in the current time window is another seismic signal, until it is determined whether the data signals in all time windows are surface wave signals; and based on the determination results, extract the surface wave signals in each inter-trace time window.

[0171] Preferably, one possible structure of module 203 is selected, such as Figure 4 As shown, it can specifically include:

[0172] The first time window selection submodule 2031 is used to select multiple discontinuous first rectangular time windows with different offset distances in the positive and negative offset directions of the surface wave signal.

[0173] The second time window selection submodule 2032 is used to determine the slope along the signal's visual velocity direction based on the visual velocity range of the surface wave signal, using the inter-channel visual velocity, inter-channel offset, and on-channel time information of the surface wave signal, and to select a second rectangular time window composed of the minimum visual velocity and the maximum visual velocity along the signal's visual velocity direction using the slope, inter-channel offset, and on-channel time information.

[0174] The target time window selection submodule 2033 is used to select the intersection of the time windows between the plurality of discontinuous first rectangular time windows and the second rectangular time windows as the selected target time window, so as to perform signal enhancement processing on the surface wave signal within the target time window.

[0175] A preferred possible structure for the signal enhancement module 204 is as follows: Figure 5 As shown, it can specifically include:

[0176] Fourier transform submodule 2041 is used to perform Fourier transform on the surface wave signal within the selected target time window;

[0177] Signal enhancement submodule 2042 is used to enhance the frequency domain signal obtained by Fourier transform using the following formula:

[0178] A'(ω,k).e -iφ′(ω,k) =A a (ω,k).e -iφ(ω,k)

[0179] Where A(ω,k) represents the amplitude spectrum, φ(ω,k) represents the phase spectrum, ω represents the frequency, k represents the wave number, a=rω+b is a frequency-dependent linear enhancement function, and r and b are constants greater than zero;

[0180] The inverse Fourier transform submodule 2043 is used to perform an inverse Fourier transform on the frequency domain signal after signal enhancement processing to obtain the surface wave signal after signal enhancement processing within the target time window.

[0181] Preferably, the time-frequency transformation module 205 is specifically used to perform a time-frequency transformation on each surface wave signal after signal enhancement processing within the target time window, starting from the single-channel signal with the minimum negative offset and ending with the single-channel signal with the maximum positive offset, using the following formula to obtain the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window:

[0182]

[0183] Where H(β,ω) represents the time-frequency domain signal, h(t) represents the time-space domain signal, ω represents the frequency, β represents the position of the window function on the time axis, t represents time, and p, q, and l are constants greater than zero.

[0184] A preferred possible structure for the imaging module 206 is as follows: Figure 6 As shown, it can specifically include:

[0185] The frequency band division submodule 2061 is used to determine the low-frequency band range where the surface wave signal is stronger and the high-frequency band range where the surface wave signal is weaker based on the frequency division scanning.

[0186] The low-frequency band processing submodule 2062 is used to determine the phase velocity value corresponding to each frequency value in the low-frequency band range based on the time-frequency transformation spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the low-frequency band range and sequentially at a set frequency step size; and to determine the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band range based on each frequency value and its corresponding phase velocity value in the low-frequency band range.

[0187] The high-frequency band processing submodule 2063 is used to determine the phase velocity value corresponding to each frequency value in the high-frequency band range based on the time-frequency transformation spectrum of each surface wave signal in the target time window, starting from the minimum frequency value in the high-frequency band range and with a set frequency step size; and to determine the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band range based on each frequency value and its corresponding phase velocity value in the high-frequency band range.

[0188] The fusion submodule 2064 is used to fuse the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band and the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band, and use the fused frequency-phase velocity spectrum in the set frequency band as the energy spectrum imaging result of the surface wave signal.

[0189] Both the low-frequency processing submodule 2062 and the high-frequency processing submodule 2063 can determine the phase velocity value corresponding to each frequency value using the following methods:

[0190] For each frequency value, the time corresponding to the maximum energy value at the current frequency in the time-frequency transform spectrum of two different surface wave signals is obtained to determine the time difference between each pair of surface wave signals; the ratio of the inter-channel distance to the time difference between each pair of surface wave signals is used to determine the phase velocity between all channels within the target time window at the current frequency; the average phase velocity between all channels within the target time window at the current frequency is taken as the phase velocity value at the current frequency.

[0191] If there is an overlapping frequency band between the low-frequency band and the high-frequency band, the fusion submodule 2064 is specifically used to, based on the frequency-phase velocity spectrum in the low-frequency band and the frequency-phase velocity spectrum in the high-frequency band, average the two energy values ​​at the same frequency in the overlapping frequency band, keep the energy values ​​at each frequency in the non-overlapping frequency band unchanged, and fuse them to obtain the frequency-phase velocity spectrum in the set frequency band.

[0192] Obviously, the signal energy spectrum imaging device of this embodiment can be used as... Figure 1 The execution body of the method shown can therefore be implemented in... Figure 1 The steps and corresponding functions are shown below. Since the principle is the same, they will not be described in detail here.

[0193] The signal energy spectrum imaging method and apparatus provided in this invention obtains surface wave signal energy spectra with the characteristics of interference suppression, good energy focusing, and high accuracy. This facilitates subsequent high-precision frequency velocity curve picking and surface wave near-surface velocity inversion processing, and makes it easier to obtain more accurate correction values ​​and improve static correction accuracy.

[0194] like Figure 7(a) , 7(b) The figures shown are schematic diagrams of surface wave signal frequency-phase velocity spectrum imaging obtained using existing technologies and embodiments of the present invention. By comparison, it can be seen that the embodiments of the present invention can obtain frequency-phase velocity spectrum imaging schematic diagrams with higher accuracy, more focused energy, and better noise suppression, which facilitates the automatic acquisition of frequency velocity curves and the establishment of near-surface inversion models, and also lays a good foundation for obtaining high-precision static correction values.

[0195] Figure 8 This is a schematic diagram of the structure of an electronic device according to one embodiment of this specification. Please refer to it. Figure 8 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0196] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0197] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0198] The processor executes the program stored in the memory. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it. When the computer program is executed by the processor, it implements the various processes of any of the above-described signal energy spectrum imaging method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0199] The signal energy spectrum imaging method disclosed in the above embodiments can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0200] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of any of the above-described signal energy spectrum imaging method embodiments and achieves the same technical effect. To avoid repetition, further details are omitted here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0201] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0202] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0203] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A signal energy spectrum imaging method, characterized in that, include: The acquired seismic single-shot data is loaded and processed by the observation system. The resulting track head information, including offset, is generated for the seismic single-shot data after loading and processing. Surface wave signals are extracted from the processed seismic single-shot data. The extracted surface wave signal is subjected to multi-time window surface wave signal selection processing with offset distance along the signal apparent velocity direction; The surface wave signal within the selected target time window is subjected to signal enhancement processing, and the time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window is determined. Based on the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window, the frequency-phase velocity spectrum is determined by frequency band, and the frequency-phase velocity spectra of each frequency band are fused to obtain the surface wave signal energy spectrum imaging result.

2. The signal energy spectrum imaging method as described in claim 1, characterized in that, The process of extracting surface wave signals from the processed seismic single-shot data specifically includes: Frequency division scanning and amplitude spectrum analysis were performed on the single-shot seismic data to obtain the frequency band range of the surface wave signal; The single-shot seismic data is filtered using a bandpass filter, and the filtered single-shot seismic data includes surface wave signals within a set frequency band. Based on the inter-channel offset and on-channel time information of the surface wave signal within the set frequency band, the apparent velocity range of the surface wave signal is determined. The intermediate velocity values ​​within the apparent velocity range are extracted, and the filtered seismic single-shot data are corrected and leveled to make the surface wave signals in the positive and negative offset directions approximately horizontal. Surface wave signals within each time window are extracted from the corrected and flattened seismic single-shot data using a moving time window. The intermediate velocity value is used to perform inverse correction and flattening on the surface wave signals extracted in each time window to recover the surface wave signals that conform to the original distribution.

3. The signal energy spectrum imaging method as described in claim 2, characterized in that, The extraction of surface wave signals within each time window from the corrected and flattened seismic single-shot data using a moving time window specifically includes: For the corrected and leveled seismic single-shot data, the data signals are extracted sequentially using the inter-track time window that moves according to the set step size; The root mean square amplitude (RMS) of the data signal within the current time window is determined. This RMS amplitude is compared to a set signal threshold. If it exceeds the threshold, the data signal within the current time window is determined to be a surface wave signal; otherwise, it is determined to be another type of seismic signal. This process continues until it is determined whether all data signals within all time windows are surface wave signals. Based on the judgment results, the surface wave signal within each time window is extracted.

4. The signal energy spectrum imaging method as described in claim 2, characterized in that, The process of selecting surface wave signals from the extracted signals by multiple time windows along the apparent velocity direction, specifically including: Multiple discontinuous first rectangular time windows with different offset distances are selected in the positive and negative offset directions of the surface wave signal; Based on the apparent velocity range of the surface wave signal, the slope along the apparent velocity direction is determined using the inter-channel apparent velocity, inter-channel offset, and on-channel time information of the surface wave signal. Using the slope, inter-channel offset, and on-channel time information, a second rectangular time window composed of the minimum and maximum apparent velocities is selected along the apparent velocity direction. The intersection of the multiple discontinuous first rectangular time windows and the second rectangular time windows is taken as the selected target time window to perform signal enhancement processing on the surface wave signal within the target time window.

5. The signal energy spectrum imaging method as described in claim 1, characterized in that, The signal enhancement processing for the surface wave signal within the selected target time window specifically includes: Perform Fourier transform on the surface wave signal within the selected target time window; The frequency domain signal obtained from the Fourier transform is enhanced using the following formula: A′(ω,k).e -iφ′(ω，k) =A a (ω, k).e -iφ(ω，k) Where A(ω, k) represents the amplitude spectrum, φ(ω, k) represents the phase spectrum, ω represents the frequency, k represents the wave number, a = rω + b is a frequency-dependent linear enhancement function, and r and b are constants greater than zero; An inverse Fourier transform is performed on the frequency domain signal after signal enhancement processing to obtain the surface wave signal after signal enhancement processing within the target time window.

6. The signal energy spectrum imaging method as described in claim 1, characterized in that, The time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window specifically includes: For the surface wave signal after signal enhancement processing within the target time window, starting from the single-channel signal with the minimum negative offset and ending with the single-channel signal with the maximum positive offset, the spatiotemporal domain signal to time-frequency domain signal transformation processing is performed on each surface wave signal using the following formula, to obtain the time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window: Where H(β, ω) represents the time-frequency domain signal, h(t) represents the time-space domain signal, ω represents the frequency, β represents the position of the window function on the time axis, t represents time, and p, q, and l are constants greater than zero.

7. The signal energy spectrum imaging method as described in claim 2, characterized in that, The process involves determining the frequency-phase velocity spectrum by dividing the frequency bands based on the time-frequency transform spectrum of each surface wave signal after signal enhancement processing within the target time window, and fusing the frequency-phase velocity spectra of each band to obtain the surface wave signal energy spectrum imaging result. Specifically, this includes: The low-frequency band range where the surface wave signal is stronger and the high-frequency band range where the surface wave signal is weaker are determined by frequency division scanning. For surface wave signals in the low-frequency band, based on the time-frequency transform spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the low-frequency band, the phase velocity value corresponding to each frequency value in the low-frequency band is determined sequentially with a set frequency step size; based on each frequency value in the low-frequency band and its corresponding phase velocity value, the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band is determined. For surface wave signals in the high-frequency band, based on the time-frequency transform spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the high-frequency band, the phase velocity value corresponding to each frequency value in the high-frequency band is determined sequentially with a set frequency step size; based on each frequency value in the high-frequency band and its corresponding phase velocity value, the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band is determined. The frequency-phase velocity spectrum of the surface wave signal in the low-frequency band and the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band are fused, and the frequency-phase velocity spectrum in the set frequency band obtained by fusion is used as the energy spectrum imaging result of the surface wave signal.

8. The signal energy spectrum imaging method as described in claim 7, characterized in that, The method for determining the phase velocity value corresponding to each frequency value specifically includes: For each frequency value, the time corresponding to the maximum energy value at the current frequency in the time-frequency transform spectrum of two different surface wave signals is obtained, and the time difference between each two surface wave signals is determined. The phase velocity between all channels within the target time window at the current frequency is determined by using the ratio of the inter-channel distance to the time difference between each pair of surface wave signals. The average phase velocity between all channels within the target time window at the current frequency is taken as the phase velocity value at the current frequency.

9. The signal energy spectrum imaging method as described in claim 7, characterized in that, There is an overlapping frequency band between the low-frequency band and the high-frequency band; and The fusion of the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band and the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band specifically includes: Based on the frequency-phase velocity spectrum in the low-frequency band and the frequency-phase velocity spectrum in the high-frequency band, the average value of two energy values ​​at the same frequency in the overlapping frequency band is taken, while the energy values ​​at each frequency in the non-overlapping frequency band remain unchanged, and the frequency-phase velocity spectrum in the set frequency band is obtained by fusion.

10. A signal energy spectrum imaging device, characterized in that, include: The loading module is used to load the acquired seismic single-shot data into the observation system. After loading, the track head information generated for the seismic single-shot data includes the offset. The extraction module is used to perform surface wave signal extraction processing on the seismic single-shot data after it has been loaded and processed by the loading module, and to extract the surface wave signal. The selection module is used to perform multi-time-window surface wave signal selection processing with offset distance along the signal apparent velocity direction on the surface wave signal extracted by the extraction module. The signal enhancement module is used to enhance the surface wave signal within the selected target time window. The time-frequency transformation module is used to determine the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window; The imaging module is used to determine the frequency-phase velocity spectrum by dividing the frequency bands based on the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window, and to fuse the frequency-phase velocity spectra of each frequency band to obtain the surface wave signal energy spectrum imaging result.

11. The signal energy spectrum imaging device as described in claim 10, characterized in that, The extraction module specifically includes: The scanning analysis submodule is used to perform frequency division scanning and amplitude spectrum analysis on the single-shot seismic data to obtain the frequency band range of the surface wave signal; The bandpass filter submodule is used to filter the seismic single-shot data using a bandpass filter. The filtered seismic single-shot data includes surface wave signals within a set frequency band. The apparent velocity determination submodule is used to determine the apparent velocity range of the surface wave signal based on the inter-channel offset and on-channel time information of the surface wave signal within the set frequency band. The correction submodule is used to extract intermediate velocity values ​​within the apparent velocity range and perform correction and flattening processing on the filtered seismic single-shot data so that the surface wave signals in the positive and negative offset directions are in an approximately horizontal state. The extraction submodule is used to extract surface wave signals within each time window from the corrected and flattened seismic single-shot data using a moving time window. The inverse correction submodule is used to perform inverse correction and flattening processing on the surface wave signals extracted in each time window using the intermediate velocity value, so as to restore the surface wave signals that conform to the original distribution.

12. The signal energy spectrum imaging device as described in claim 11, characterized in that, The selection module specifically includes: The first time window selection submodule is used to select multiple discontinuous first rectangular time windows with different offset distances in the positive and negative offset directions of the surface wave signal. The second time window selection submodule is used to determine the slope along the signal's visual velocity direction based on the visual velocity range of the surface wave signal, using the inter-channel visual velocity, inter-channel offset, and on-channel time information of the surface wave signal, and to select a second rectangular time window composed of the minimum visual velocity and the maximum visual velocity along the signal's visual velocity direction using the slope, inter-channel offset, and on-channel time information. The target time window selection submodule is used to select the intersection of the time windows between the plurality of discontinuous first rectangular time windows and the second rectangular time windows as the selected target time window, so as to perform signal enhancement processing on the surface wave signal within the target time window.

13. The signal energy spectrum imaging device as described in claim 10, characterized in that, The signal enhancement module specifically includes: The Fourier transform submodule is used to perform Fourier transform on the surface wave signal within the selected target time window; The signal enhancement submodule is used to enhance the frequency domain signal obtained by Fourier transform using the following formula: A′(ω,k).e iφ′(ω，k) =A a (ω, k).e -iφ(ω，k) Where A(ω, k) represents the amplitude spectrum, φ(ω, k) represents the phase spectrum, ω represents the frequency, k represents the wave number, a = rω + b is a frequency-dependent linear enhancement function, and r and b are constants greater than zero; The inverse Fourier transform submodule is used to perform an inverse Fourier transform on the frequency domain signal after signal enhancement processing to obtain the surface wave signal after signal enhancement processing within the target time window.

14. The signal energy spectrum imaging device as described in claim 10, characterized in that, The time-frequency transformation module is specifically used to perform a time-frequency transformation on each surface wave signal after signal enhancement processing within the target time window, starting from the single-channel signal with the minimum negative offset and ending with the single-channel signal with the maximum positive offset, using the following formula to obtain the time-frequency transformation spectrum of each surface wave signal after signal enhancement processing within the target time window: Where H(β, ω) represents the time-frequency domain signal, h(t) represents the time-space domain signal, ω represents the frequency, β represents the position of the window function on the time axis, t represents time, and p, q, and l are constants greater than zero.

15. The signal energy spectrum imaging device as described in claim 11, characterized in that, The imaging module specifically includes: The frequency band division submodule is used to determine the low-frequency band range where the surface wave signal is stronger and the high-frequency band range where the surface wave signal is weaker based on the frequency division scanning. The low-frequency band processing submodule is used to determine the phase velocity value corresponding to each frequency value in the low-frequency band range based on the time-frequency transformation spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the low-frequency band range and sequentially at a set frequency step size; and to determine the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band range based on each frequency value and its corresponding phase velocity value in the low-frequency band range. The high-frequency band processing submodule is used to determine the phase velocity value corresponding to each frequency value in the high-frequency band range based on the time-frequency transformation spectrum of each surface wave signal within the target time window, starting from the minimum frequency value in the high-frequency band range and sequentially at a set frequency step size; and to determine the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band range based on each frequency value and its corresponding phase velocity value in the high-frequency band range. The fusion submodule is used to fuse the frequency-phase velocity spectrum of the surface wave signal in the low-frequency band and the frequency-phase velocity spectrum of the surface wave signal in the high-frequency band, and use the fused frequency-phase velocity spectrum in the set frequency band as the energy spectrum imaging result of the surface wave signal.

16. An electronic device, characterized in that, include: A memory and a processor electrically connected to the memory, the memory storing a computer program executable by the processor, the computer program, when executed by the processor, implementing the steps of the method as described in any one of claims 1 to 9.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 9.

Images