Fine identification and description method and device for sand layer under unconformity surface and storage medium

Abstract

The invention belongs to the technical field of geophysical exploration, and particularly relates to a fine identification and description method and device for a sand layer under an unconformity surface and a storage medium. The method comprises the following steps: S1, determining seismic response frequency bands of sand bodies with different thicknesses according to seismic data; determining a dominant frequency band gather, and solving the time difference of each channel in the gather of each dominant frequency band; s2, carrying out time-division and frequency-division nonlinear correction on the CRP gather of the seismic signal of the predetermined target, and carrying out homodromous superposition on effective signals of each dominant frequency band; s3, calculating and establishing an integral frequency division processing model based on the event time difference of each trace in the trace gather; and S4, carrying out fine identification and depiction on the thin sand body under the unconformity surface by utilizing the integral frequency division processing model. According to the method, the selected speed is more accurate, and the CRP gather is pulled more flatly, so that the effective high-frequency information is superposed in the same phase, the loss of the high-frequency information is avoided, more high-frequency effective signals can be stored, and the finally obtained high-frequency information is more favorable for constructing detailed imaging.

Description

Technical Field

[0001] The present invention belongs to the technical field of geophysical exploration, and in particular relates to a method, a device and a storage medium for finely identifying and characterizing an unconformity surface. Background Art

[0002] Fine reservoir description refers to the fine geological characteristics research and remaining oil distribution description after the oil field is put into development, as the degree of exploitation deepens and the dynamic and static data increase, and the geological model for reservoir prediction is continuously improved. It can be subdivided into fine reservoir description in the early development stage, mid-development stage and late development stage. The precision of fine reservoir description in different periods varies due to different data possession levels.

[0003] Stratigraphic oil reservoirs are a major reservoir model in the Carboniferous System of the Tahe Oilfield. Although the data quality has been greatly improved through multiple rounds of processing of high-density seismic data collected in this area in recent years, the strong reflection of the unconformity surface shields the effective signals of some underlying truncated strata, which affects the accurate identification of the stratigraphic pinch-out points to a certain extent, and it is impossible to accurately identify the stratigraphic pinch-out characteristics and the characterization of thin sand bodies.

[0004] The prior art CN114966850A provides a sand body characterization method and device, including: obtaining the seismic frequency range corresponding to the target target layer, the seismic data corresponding to each target cube, the gamma curve corresponding to the target well, and the number of thin sand bodies, the number of medium sand bodies, and the number of thick sand bodies in the target well; filtering the gamma curve according to the seismic frequency range; performing time-frequency analysis on the gamma curve according to the seismic frequency range to obtain the main frequencies of the low-frequency band, the medium-frequency band, and the high-frequency band corresponding to the target target layer; performing frequency division processing on the seismic data corresponding to each target cube according to the main frequencies of the low-frequency band, the medium-frequency band, and the high-frequency band to obtain the low-frequency band, the medium-frequency band, and the high-frequency band frequency division data body corresponding to each target cube; determining the fusion ratio parameter according to the number of thin sand bodies, the number of medium sand bodies, and the number of thick sand bodies; and characterizing the target target layer according to the fusion ratio parameter and the low-frequency band, the medium-frequency band, and the high-frequency band frequency division data body corresponding to each target cube.

[0005] Aiming at the problems of thin sand bodies, rapid lateral changes, unconformity shielding, seismic imaging of thin sand layers, and difficulty in maintaining fidelity in thin sand reservoirs under the unconformity surface of the Carboniferous in Tahe, the present invention provides a method, device and storage medium for finely identifying and characterizing thin sand bodies under an unconformity surface from the perspective of a dominant frequency band. Summary of the invention

[0006] In order to solve the above-mentioned technical problems existing in the prior art, the present invention provides a method, device and storage medium for fine identification and characterization of unconformity surfaces, aiming to eliminate the shielding effect of unconformity surfaces on seismic signals as much as possible through amplitude-preserving and fidelity-preserving processing of seismic data, and study the integrated time-sharing and frequency-division processing and interpretation of target layers, so as to effectively enhance the interpretation ability for the identification of small faults, small fault blocks and thin layers; highlight the dominant frequency band of thin sand layers in the target layer interval, and further identify the distribution morphology of stratum pinch-out and thin sand bodies.

[0007] To achieve the above object, the technical solution of the present invention is as follows:

[0008] A method for finely identifying and characterizing unconformity surfaces, comprising:

[0009] S1. Determine the seismic response frequency bands of sand bodies of different thicknesses according to seismic data; determine the dominant frequency band track gathers for the seismic response signals of sand bodies of different thicknesses in the seismic standard track, and obtain the time difference of each track in the track gathers of each dominant frequency band;

[0010] S2: Perform time-frequency division nonlinear correction on the CRP gathers of the seismic signals of the predetermined target, and perform co-directional stacking of the effective signals in each dominant frequency band to improve the stacking imaging accuracy;

[0011] S3: Establish an overall frequency division processing model based on the time difference of each event axis in the channel gather;

[0012] S4. Use the overall frequency division processing model to finely identify and characterize the thin sand bodies under the unconformity surface.

[0013] Furthermore, determining the seismic response frequency bands of sand bodies of different thicknesses according to the seismic data in step S1 specifically includes: acquiring the seismic data of the study area, and then analyzing and evaluating the frequency, signal-to-noise ratio, and energy of the seismic data to determine the frequency band range of the target layer segment in the study area.

[0014] Furthermore, the target sand body is determined through drilling data, and the seismic profile position of the target sand body is obtained after fine calibration. The data spectrum analysis is performed on the target layer segment, and the frequency band range and main frequency size are determined through the decibel spectrum, thereby obtaining the reflection frequency range of the target layer segment.

[0015] Furthermore, the seismic standard traces in step S1 include CRP trace gathers, CMP trace gathers, and CDP trace gathers.

[0016] Furthermore, in step S1, the dominant frequency band track set for the seismic response signals of sand bodies of different thicknesses in the seismic standard track is determined, specifically including: taking the thin sand body on the well after fine calibration as a single target layer, counting the thickness range of the thin sand body, and after spectrum analysis of the single target layer, if the energy of the reflected wave is concentrated in a certain frequency band, and the energy of the frequency band exceeds 60% of the total energy, then the frequency band is determined as the dominant frequency band.

[0017] Furthermore, in step S1, the time difference of each channel in each dominant frequency band channel gather is obtained, which is specifically calculated by the following formula:

[0018] C(t-τ)=max[f(t)·x(t-τ)]

[0019]

[0020] Among them, τ represents the time difference of the movement of a certain track in the gather, t represents time, C(t-τ) represents the maximum cross-correlation value of a certain track in the gather when the time shift is τ, x(t-τ) represents the seismic track of a certain track in the gather when the time shift is τ, and N represents the total number of seismic tracks in the gather.

[0021] Furthermore, the nonlinear correction in step S2 is specifically as follows: time-shifting each channel in the channel gather according to the time difference of each channel in the channel gather obtained in step S1, performing downward correction when the time shift is positive, and performing upward correction when the time shift is negative.

[0022] Furthermore, in step S2, the effective signals of each dominant frequency band are co-directionally superimposed, and then the event axis time difference and velocity are obtained for the superimposed imaging, and the rationality of steps S1 and S2 is analyzed from the following three aspects:

[0023] Whether the signal-to-noise ratio of the gather is improved;

[0024] Whether the interference of diffraction waves on velocity analysis has been eliminated;

[0025] Is the velocity field position obtained after the reflection wave is returned to its original position more accurate?

[0026] If the results of the three judgment conditions are all yes, then the parameter selections in steps S1 and S2 are reasonable.

[0027] Furthermore, the specific steps of obtaining the event time difference include: obtaining a model channel in the CRP gather, cross-correlating the model channel with each channel in the gather, and obtaining the corresponding moving time difference when the maximum cross-correlation value is obtained, which is the event time difference.

[0028] The present invention also provides a method and device for finely identifying and characterizing unconformity surfaces, which adopts the above-mentioned finely identifying and characterizing unconformity surfaces method, comprising:

[0029] A data acquisition module, used to acquire seismic data of a target layer segment;

[0030] The dominant frequency band determination module is used to determine the dominant frequency band gathers for the seismic response signals of sand bodies of different thicknesses in the seismic standard traces, and to obtain the gather time difference of each dominant frequency band;

[0031] The correction module is used to perform time-frequency division nonlinear correction on the CRP gathers of the seismic signals of the predetermined target, and to perform co-directional superposition of the effective signals in each dominant frequency band;

[0032] A model building module, used to build an overall frequency division processing model;

[0033] The fine identification and characterization model is used to finely identify and characterize the thin sand bodies under the unconformity surface based on the overall frequency division processing model.

[0034] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned method for finely identifying and characterizing unconformity surfaces is implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The method for fine identification and characterization of unconformity surface provided by the present invention first obtains the dominant frequency band gathers, and obtains the gather time difference of each dominant frequency band, uses the gather time difference to correct the gathers, and then performs co-directional superposition of effective signals of each dominant frequency band, establishes an overall frequency division processing model, and uses the overall frequency division processing model for fine identification and characterization. The present invention selects a more accurate speed and pulls the CRP gathers flatter, so that effective high-frequency information is superimposed in phase, the loss of high-frequency information is avoided, more high-frequency effective signals can be preserved, and the high-frequency information finally obtained is more conducive to the imaging of structural details. The advantage of this model is that it truly obtains a true high-resolution, high-fidelity, and high-signal-to-noise ratio profile by dividing the processing parameters obtained in different frequency bands, such as dynamic correction, velocity, and residual static correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The figure is a flow chart of the method of the present invention.

[0038] Figure 2 Schematic diagram of geological targets in an embodiment of the present invention.

[0039] Figure 3 It is a schematic diagram of the dominant frequency band of the target layer section of the geological target in an embodiment of the present invention.

[0040] Figure 4 It is a schematic diagram of the dominant frequency band of the target layer section of the geological target in an embodiment of the present invention after time division and frequency division processing.

[0041] Figure 5 It is a schematic diagram of the process of establishing the overall frequency division processing model according to an embodiment of the present invention.

[0042] Figure 6 Schematic diagram of an overall frequency division processing model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the protection scope of the present invention.

[0044] It should be noted that, unless otherwise specifically stated, the relative arrangements of components and steps, and numerical expressions set forth in these embodiments should not be construed as limiting the scope of the present invention.

[0045] The following description of the exemplary embodiments is merely illustrative and is not intended to limit the present invention and its application or use in any sense. Techniques, methods and devices known to ordinary technicians in the relevant field may not be discussed in detail here, but where applicable, these techniques, methods and devices should be considered as part of this specification.

[0046] Embodiment 1

[0047] The present invention provides a method for fine identification and characterization of unconformity surface. Aiming at the problems of thin sand bodies, rapid lateral changes, unconformity shielding, and difficulty in seismic imaging and fidelity preservation of thin sand layers in thin reservoirs under the unconformity surface of the Carboniferous System in Tahe, the present invention mainly adopts medium and high frequency frequency division target processing technology to solve the problems of high frequency imaging and fidelity preservation of thin sand bodies. Since the seismic signal is shielded by the unconformity surface and the interference of multiple waves between layers, it is difficult to image and preserve the amplitude of the thin sand layer under the unconformity surface. In addition, the thickness of different sand bodies in the target layer is different, and the dominant frequency bands required for their imaging are different. At the same time, the effective signal and interference wave development in different frequency bands are different. It is necessary to carry out frequency division target processing for different dominant frequency bands of the target layer.

[0048] like Figure 1 Shown is a flow chart of the method of the present invention, comprising:

[0049] S1. Obtain seismic data in the study area, then analyze and evaluate the frequency, signal-to-noise ratio, and energy of the seismic data to determine the frequency band range of the target layer in the study area. Figure 2 The geological targets of this embodiment are shown as follows: Figure 2 As shown in the figure, although the multiple sets of thin sand bodies beneath the T50 unconformity have certain reflection energy, they cannot meet the requirements of tracking and characterization. From the analysis of drilling data, there are two sets of thin sand bodies under the T50 unconformity in this well, but the original seismic data cannot effectively reflect the reflection characteristics of the two sets of thin sand bodies in seismic.

[0050] The target sand body is determined through drilling data, and the seismic profile position of the target sand body is obtained after fine calibration. The data spectrum analysis is performed on the target layer segment, and the frequency band range and main frequency size are determined by using 23db through the decibel spectrum to obtain the reflection frequency range of the target layer segment.

[0051] Then, the frequency band range of the target layer is tested, a seismic standard track is established, and the dominant frequency band of the seismic standard track of the target layer is determined, specifically including: taking the thin sand body on the well after fine calibration as a single target layer, counting the thickness range of the thin sand body, and after the spectrum analysis of the single target layer, if the energy of the reflected wave is concentrated in a certain frequency band, and the energy of the frequency band exceeds 60% of the total energy, then the frequency band is determined as the dominant frequency band. The seismic standard track includes CRP track gathers, CMP track gathers, CDP track gathers, etc., and the target layer can also be a single sand body.

[0052] like Figure 3 As shown in the figure, the original seismic data is shown. Through the spectrum analysis of the target layer segment, the frequency bandwidth is 6-70Hz and the main frequency is 35Hz. It can be seen that the dominant frequency band of the main target layer is concentrated in this range, and time-division and frequency-division target processing is required for this frequency band.

[0053] Obtain the gather time difference of each dominant frequency band, which is calculated by the following formula:

[0054] C(t-τ)=max[f(t)·x(t-τ)]

[0055]

[0056] Among them, τ represents the time difference of the movement of a certain track in the gather, t represents time, C(t-τ) represents the maximum cross-correlation value of a certain track in the gather when the time shift is τ, x(t-τ) represents the seismic track of a certain track in the gather when the time shift is τ, and N represents the total number of seismic tracks in the gather.

[0057] S2: Perform time-frequency division nonlinear correction on the CRP gathers of the predetermined target seismic signals, and perform co-directional superposition of the effective signals in each dominant frequency band;

[0058] According to the dominant frequency band of the seismic standard trace of the target layer, the corresponding dominant frequency band gathers are extracted from the CRP gathers, and time-division and frequency-division nonlinear corrections are performed on each dominant frequency band gather, and effective signals are stacked in the same direction on the dominant frequency band gathers after time-division and frequency-division nonlinear corrections. Through this step, the time difference of the event axis after stacking imaging, the velocity within the dominant frequency band and the time window range of the dominant frequency band are obtained, which ultimately further enhances the seismic reflection signal of the thin sand layer.

[0059] Due to the complexity of underground propagation of seismic waves, the difficulty in locating the propagation velocity of seismic waves (anisotropy) and the limitations of the migration method, the reflection event axes of the CRP gathers obtained by pre-stack time migration and pre-stack depth migration at the same reflection point are difficult to flatten, and the existing seismic processing technology has not been able to solve the above problems well. The present invention mainly selects CRP gathers or CDP gathers as standard seismic traces, and the selection of the dominant frequency band is mainly through fine calibration of the thin sand body drilled as a single target layer, and the thickness range of the thin sand body in the study area is statistically analyzed. After the spectrum analysis of the target layer, the reflection frequency range of the target layer is determined to obtain its dominant frequency band, and then the gather correction sample points are extracted for control, and the correction seismic standard trace is established. Under the guidance of the seismic standard trace, the event time difference of the frequency band is obtained, and the gather is corrected by time and frequency division, so that the stacked gathers corresponding to the sand body of the target layer are more concentrated.

[0060] After determining the dominant frequency band range of the target layer segment, in order to achieve the maximum co-directional superposition of effective signals from the low frequency band to the high frequency band, it is necessary to perform time-division and frequency-division nonlinear correction on the CRP channel gather. Specifically, by performing targeted time-division and frequency-division selection on the dominant frequency band range of the target layer segment obtained in step S1, the time-division and frequency-division processing results are superimposed. Specifically, through the time difference of each channel in the channel gather obtained in step S1, each channel in the channel gather is time-shifted, and a downward correction is performed when the time shift is positive, and an upward correction is performed when the time shift is negative.

[0061] Then, the phase axis time difference and velocity of the optimized stack imaging of the thin sand layer are obtained, and the pre-stack time migration velocity spectrum analysis is corrected. The following aspects are analyzed:

[0062] (1) Whether the signal-to-noise ratio of the gather is improved;

[0063] (2) Whether the interference of diffraction waves on velocity analysis has been eliminated;

[0064] (3) Whether the velocity field position obtained after the reflection wave is returned to its original position is more accurate.

[0065] After analyzing these three aspects, if there is improvement in all of them, it proves that the previous steps and parameter selections are reasonable.

[0066] In addition, the specific steps of obtaining the event time difference include: obtaining the model channel in the CRP data set, using the model channel to perform cross-correlation with each channel in the data set, and the corresponding moving time difference when the maximum cross-correlation value is obtained is the event time difference.

[0067] The event time difference calculated in step S2 needs to use a realistic mathematical model of the seismic record, the purpose of which is mainly to eliminate the original noise existing in the dominant frequency band and to increase the reflection energy of the previous thin layer of sand body.

[0068] A realistic mathematical model of earthquake records can be expressed as follows:

[0069] S(t,x)=[R(t)·T1(t,x)·M(t,x)]·SL(t)·T2(t,x)·R1(t,x)·W(t)+N(t)

[0070] Where S(t,x) is the seismic trace at offset x; R(t) is the reflection coefficient sequence; T1(t,r) is the time-varying transmission and multiple wave effect; M(t,x) is the normal movement correction related to the offset; SL(t) is the diffusion loss and spherical diffusion effect; T2(t,r) is the time-varying absorption effect or inelastic attenuation effect; R1(t,x) is the shallow reverberation and recording system effect related to the offset; W(t) is the source wavelet; N(t) is various noises.

[0071] The purpose of time-frequency division seismic data processing is to eliminate the influence of the above factors in seismic records and obtain effective seismic reflections. The factors that have a greater impact on the attenuation of subwavelength energy are the spherical diffusion effect SL(t) and the time-varying absorption effect T2(t,r). For SL(t), there is a compensation formula:

[0072] β=20Lgt

[0073] It can be seen that β is time-varying and its unit is dB. For T2(t,r), there is a wavelet amplitude attenuation formula:

[0074] A(t)=A0×10-8.69πft2Q≈A0×10-1.3ftQ

[0075] It can be seen that the amplitude A(t) is related to time t and frequency f. Different frequencies and different times have corresponding amplitude attenuation. At the same time, the seismic wavelet W(t) and reflection coefficient R(t) are both time-varying.

[0076] Figure 4 The figure shows the schematic diagram of the dominant frequency band of the target layer of the geological target after time division and frequency division processing. Figure 4 As shown in the figure, after time-sharing and frequency-division target processing, the bandwidth of seismic data is increased to 6-85Hz, the main frequency is increased to 45Hz, the overall reflection energy of the target layer is improved, the wave group characteristics are clear, and by comparing with the drilling data after calibration, the thin sand body underlying the unconformity surface can be effectively tracked and characterized.

[0077] S3: Establish an overall frequency division processing model based on the event axis time difference and speed;

[0078] Based on the event time difference of the stack imaging obtained in step S2, the velocity in the dominant frequency band and the time window range of the dominant frequency band, combined with the reflection signal, energy, phase relationship, noise characteristics and other parameters of different sand layers, the fine velocity spectrum of pre-stack time migration of different sand layers is corrected and the dominant main frequency band is determined, and it is iterated repeatedly to finally establish the velocity and correction model of the overall frequency division migration processing. The establishment of the frequency division model mainly uses wavelet frequency division technology to improve the signal-to-noise ratio and resolution of the data. Specifically, Figure 5 As shown in the figure, through the analysis results of the dominant frequency bands of the seismic standard traces, the corresponding dominant frequency band gathers in the CRP gathers are extracted for time-sharing and frequency-sharing nonlinear correction, and the dominant frequency bands after the time-sharing and frequency-sharing nonlinear correction are stacked in the same direction with effective signals, so as to optimize the time difference, velocity, dominant frequency band value and time window range of the stacking imaging of the thin sand layer, and then correct the fine velocity spectrum of the pre-stack time migration of different sand layer segments and determine the dominant main frequency band, and iterate repeatedly, and finally establish the velocity and correction model of the overall frequency-sharing migration processing. After the frequency-sharing processing, the original thin sand layer has weak reflection, and the energy is significantly enhanced.

[0079] Figure 6 FIG. 4 shows the frequency division processing model of this embodiment. Figure 5 As shown, the processed frequency-division model has an obvious relationship between strong and weak seismic waves, clear layers, and easier identifiable lateral changes; the composite wave is separated, the energy of weak signals is significantly enhanced, and the high-frequency signal is effectively flattened.

[0080] S4. Use the overall frequency division processing model to finely identify and characterize the thin sand bodies under the unconformity surface.

[0081] The present invention selects a more accurate speed and a flatter CRP gather, so that effective high-frequency information is superimposed in phase, avoiding the loss of high-frequency information, and can save more high-frequency effective signals. The high-frequency information finally obtained is more conducive to the imaging of structural details. The advantage of this model is that it can obtain a true high-resolution, high-fidelity, and high signal-to-noise ratio profile by dividing the processing parameters obtained in different frequency bands, such as dynamic correction, velocity, and residual static correction.

[0082] The method for fine identification and characterization of unconformity surfaces provided by the present invention can mainly achieve the following three effects:

[0083] After correction, the faults of CRP gather stacking results are clearer, which is conducive to the analysis and detection of small faults and cracks;

[0084] After correction, the noise of CRP gather stacking profile is further suppressed, the signal-to-noise ratio is improved, and the weak thin layer imaging is enhanced;

[0085] The corrected CRP gathers can better meet the data requirements of AVO analysis and prestack inversion.

[0086] Embodiment 2

[0087] The present invention further provides a method and device for finely identifying and characterizing unconformity surfaces, which adopts the method for finely identifying and characterizing unconformity surfaces provided in the first embodiment, and comprises:

[0088] A data acquisition module, used to acquire seismic data of a target layer segment;

[0089] The dominant frequency band determination module is used to determine the dominant frequency band gathers for the seismic response signals of sand bodies of different thicknesses in the seismic standard traces, and to obtain the gather time difference of each dominant frequency band;

[0090] The correction module is used to perform time-frequency division nonlinear correction on the CRP gathers of the seismic signals of the predetermined target, and to perform co-directional superposition of the effective signals in each dominant frequency band;

[0091] A model building module, used to build an overall frequency division processing model;

[0092] The fine identification and characterization model is used to finely identify and characterize the thin sand bodies under the unconformity surface based on the overall frequency division processing model.

[0093] Embodiment 3

[0094] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for finely identifying and characterizing unconformity surfaces provided in the first embodiment is implemented.

[0095] The above specific implementation methods are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to examples, a person skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for fine identification and characterization of sand layers under unconformity surfaces. It is characterized in that include: S1. Determine the seismic response frequency bands of sand bodies of different thicknesses according to seismic data; Determine the dominant frequency band gathers for the seismic response signals of sand bodies of different thicknesses in the seismic standard channels, and obtain the time difference of each channel in the gathers of each dominant frequency band; S2: Perform time-frequency division nonlinear correction on the CRP gathers of the seismic signals of the predetermined target, and perform co-directional stacking of the effective signals in each dominant frequency band to improve the stacking imaging accuracy; S3: Establish an overall frequency division processing model based on the time difference of each event axis in the channel gather; S4. Use the overall frequency division processing model to finely identify and characterize the thin sand bodies under the unconformity surface.

2. The method according to claim 1, It is characterized in that Determining the seismic response frequency bands of sand bodies of different thicknesses according to seismic data in step S1 specifically includes: acquiring seismic data of the study area, then analyzing and evaluating the frequency, signal-to-noise ratio, and energy of the seismic data to determine the frequency band range of the target layer section in the study area.

3. The method according to claim 2, It is characterized in that The target sand body is determined through drilling data, and the seismic profile position of the target sand body is obtained after fine calibration. The data spectrum analysis is performed on the target layer segment, and the frequency band range and main frequency size are determined through the decibel spectrum, thereby obtaining the reflection frequency range of the target layer segment.

4. The method according to claim 1, It is characterized in that The seismic standard traces in step S1 include CRP trace set, CMP trace set and CDP trace set.

5. The method according to claim 1, It is characterized in that In step S1, the dominant frequency band track set for the seismic response signals of sand bodies of different thicknesses in the seismic standard track is determined, specifically comprising: taking the thin sand body on the well after fine calibration as a single target layer, counting the thickness range of the thin sand body, and after spectrum analysis of the single target layer, if the energy of the reflected wave is concentrated in a certain frequency band, and the energy of the frequency band exceeds 60% of the total energy, then the frequency band is determined as the dominant frequency band.

6. The method according to claim 1, It is characterized in that In step S1, the time difference of each channel in each dominant frequency band channel gather is obtained, which is specifically calculated by the following formula: C(t-τ)=max[f(t)·x(t-τ)] Among them, τ represents the time difference of the movement of a certain track in the gather, t represents time, C(t-τ) represents the maximum cross-correlation value of a certain track in the gather when the time shift is τ, x(t-τ) represents the seismic track of a certain track in the gather when the time shift is τ, and N represents the total number of seismic tracks in the gather.

7. The method according to claim 1, It is characterized in that The nonlinear correction in step S2 is specifically: time-shifting each channel in the channel gather according to the time difference of each channel in the channel gather obtained in step S1, performing downward correction when the time shift is positive, and performing upward correction when the time shift is negative.

8. The method according to claim 1, It is characterized in that In step S2, the effective signals of each dominant frequency band are co-directionally superimposed, and then the event axis time difference and velocity are obtained for the superimposed imaging, and the rationality of steps S1 and S2 is analyzed from the following three aspects: Whether the signal-to-noise ratio of the gather is improved; Whether the interference of diffraction waves on velocity analysis has been eliminated; Is the velocity field position obtained after the reflection wave is returned to its original position more accurate? If the results of the three judgment conditions are all yes, then the parameter selections in steps S1 and S2 are reasonable.

9. The method according to claim 8, It is characterized in that The specific steps of obtaining the event time difference include: obtaining the model channel in the CRP gather, using the model channel to perform cross-correlation with each channel in the gather, and the corresponding moving time difference when the maximum cross-correlation value is obtained is the event time difference.

10. A method and apparatus for fine identification and characterization of unconformity surfaces, using the method for fine identification and characterization of unconformity surfaces according to any one of claims 1 to 9, It is characterized in that include: A data acquisition module, used to acquire seismic data of a target layer segment; The dominant frequency band determination module is used to determine the dominant frequency band gathers for the seismic response signals of sand bodies of different thicknesses in the seismic standard traces, and to obtain the gather time difference of each dominant frequency band; The correction module is used to perform time-frequency division nonlinear correction on the CRP gathers of the seismic signals of the predetermined target, and to perform co-directional superposition of the effective signals in each dominant frequency band; A model building module, used to build an overall frequency division processing model; The fine identification and characterization model is used to finely identify and characterize the thin sand bodies under the unconformity surface based on the overall frequency division processing model.

11. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the method for finely identifying and characterizing unconformity surfaces according to any one of claims 1 to 9 is implemented.

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