Track periodic detection method based on seismic reflection data

Abstract

The invention discloses an orbit periodicity detection method based on seismic reflection data, which comprises the following steps: S1, acquiring seismic reflection data and borehole logging data, and converting the seismic reflection data from a travel time domain to a depth domain to generate a seismic data volume; s2, establishing a first seismic waveform prediction model containing astronomical orbit information based on the seismic reflection data, and removing the astronomical orbit information to generate a second seismic waveform prediction model; s3, respectively carrying out power spectrum analysis on the borehole logging data, the seismic data volume, the first seismic waveform prediction model and the second seismic waveform prediction model, and generating a power spectrum; s4, re-analyzing the whitening power spectrum to obtain different power spectrums; s5, comparing peak values in the four power spectrums with a known astronomical orbit signal peak value, and calculating a confidence result; and S6, using Monte Carlo analysis to verify the result, if the requirement is not met, adjusting the parameters of the first seismic waveform prediction model, and repeating the steps S2 to S6 until the requirement is met.

Description

Technical Field

[0001] The invention belongs to the technical field of geological and geophysical exploration, and in particular relates to a method for detecting orbital periodicity based on seismic reflection data. Background Art

[0002] Orbital cyclicity refers to periodic climate changes caused by changes in the Earth's orbital parameters (such as eccentricity, inclination, and precession). These periodic changes appear as sedimentary cycles in the geological record and are of great significance for the reconstruction of paleoclimate and paleoenvironment. Traditional orbital periodicity detection methods mainly rely on directly sampled data (such as drill core or borehole logging), but these methods are limited in coverage and depth. As a widely used geophysical exploration technology, seismic reflection data has the characteristics of wide coverage and high resolution, but its statistical ability to detect orbital periodicity is still unclear. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a method and system for detecting orbital periodicity based on seismic reflection data to solve the problems existing in the above prior art.

[0004] In a first aspect, to achieve the above-mentioned purpose, the present invention provides a method for detecting orbital periodicity based on seismic reflection data, comprising the following steps:

[0005] S1, obtain the seismic reflection data and borehole logging data set of the target area, perform denoising, filtering and normalization preprocessing, and convert the seismic reflection data from the travel time domain to the depth domain to generate a seismic data volume;

[0006] S2. Establishing a first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data, and removing the astronomical orbit information in the seismic waveform prediction model to update the model, so as to obtain a second seismic waveform prediction model;

[0007] S3, performing power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model, and the second seismic waveform prediction model to generate power spectra;

[0008] S4, performing spectrum whitening analysis on the power spectrum, and performing power spectrum analysis again to obtain four different power spectra;

[0009] S5. Combined with the confidence level, the peak values ​​in the four different power spectra are periodically compared with the peak values ​​of the known astronomical orbit signals to calculate the confidence results;

[0010] S6. Use Monte Carlo analysis to verify the result. If it does not meet the preset requirements, adjust the parameters of the first seismic waveform prediction model and repeat steps S2 to S6 until the preset requirements are met.

[0011] Optionally, in S1, the process of converting seismic reflection data from the travel time domain to the depth domain includes: based on the vertical seismic profile logging curve in the study area, fitting and generating a polynomial in the depth domain and the travel time domain applicable to the study area:

[0012] D=a*T+b

[0013] Among them, a and b are constants obtained by fitting, and the conversion from travel time domain to depth domain is achieved based on polynomials.

[0014] Optionally, in S2, in the process of establishing the first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data:

[0015] Seismic reflection data include: sedimentation background noise, variable sedimentation rate, astronomical orbit signal, density and seismic wavelet.

[0016] Optionally, in S3, in the process of performing power spectrum analysis on the borehole logging data set, the seismic data body, the first seismic waveform prediction model and the second seismic waveform prediction model to generate power spectra, Fourier transform is used to perform power spectrum analysis.

[0017] Optionally, in S4, the process of performing spectrum whitening analysis on the power spectrum includes:

[0018] Normalize the power spectrum and calculate the whitening factor:

[0019]

[0020] Where W(f) is the whitening factor, ∈ is a small constant used to avoid the denominator being zero, and P(f) is the power spectral density;

[0021] Multiply the whitening factor by the frequency domain signal to obtain the whitened frequency domain signal:

[0022] X white (f) = X(f)·W(f)

[0023] X white (f) is the frequency domain signal after whitening, and X(f) is the frequency domain signal.

[0024] Optionally, the process of performing spectral whitening analysis on the power spectrum further includes:

[0025] Perform an inverse Fourier transform on the whitened frequency domain signal and convert it back to the time domain signal:

[0026]

[0027] Optionally, in S6, the preset requirement is that the confidence level is greater than or equal to 90%.

[0028] In a second aspect, the present invention further provides a system for detecting orbital periodicity based on seismic reflection data, which is used to implement a method for detecting orbital periodicity based on seismic reflection data. The system comprises:

[0029] The data acquisition module is used to acquire the seismic reflection data and borehole logging data set of the target area, and perform denoising, filtering and normalization preprocessing on them, and convert the seismic reflection data from the travel time domain to the depth domain to generate a seismic data volume;

[0030] A model building module, used to establish a first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data, and generate a second seismic waveform prediction model by removing the astronomical orbit information from the first seismic waveform prediction model;

[0031] A power spectrum analysis module is used to perform power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model and the second seismic waveform prediction model respectively to generate corresponding power spectra;

[0032] A spectrum whitening module is used to perform spectrum whitening analysis on the generated power spectrum, and perform power spectrum analysis again to obtain a whitened power spectrum;

[0033] A comparison analysis module is used to periodically compare the peak value in the whitened power spectrum with the peak value of the known astronomical orbit signal in combination with the confidence level to calculate the confidence result;

[0034] The Monte Carlo analysis module is used to perform Monte Carlo analysis verification on the calculation results. If the confidence results do not meet the preset requirements, the parameters of the first seismic waveform prediction model are adjusted and the system operation is repeated until the preset requirements are met.

[0035] In a third aspect, the present invention further provides a computer terminal device, comprising:

[0036] one or more processors;

[0037] A memory, coupled to the processor, for storing one or more programs;

[0038] When the one or more programs are executed by the one or more processors, the one or more processors implement steps of a method for detecting orbit periodicity based on seismic reflection data.

[0039] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a method for detecting orbital periodicity based on seismic reflection data are implemented.

[0040] Compared with the prior art, the present invention has the following advantages and technical effects:

[0041] The present invention provides an orbit periodicity detection method based on seismic reflection data, comprising: S1, acquiring seismic reflection data and borehole logging data sets in a target area, performing denoising, filtering and normalization preprocessing, and converting the seismic reflection data from a travel time domain to a depth domain to generate a seismic data volume; S2, establishing a first seismic waveform prediction model containing astronomical orbit information based on the seismic reflection data, and removing the astronomical orbit information in the seismic waveform prediction model to update the model, so as to obtain a second seismic waveform prediction model; S3, performing power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model and the second seismic waveform prediction model to generate a power spectrum; S4, performing spectrum whitening analysis on the power spectrum, and performing power spectrum analysis again to obtain four different power spectra; S5, combining confidence levels, periodically comparing peak values ​​in the four different power spectra with known astronomical orbit signal peak values, and calculating confidence results; S6, using Monte Carlo analysis to verify the results, if the preset requirements are not met, adjusting the parameters of the first seismic waveform prediction model, and repeating steps S2 to S6 until the preset requirements are met. The present invention provides a method for detecting orbital periodicity using seismic reflection data, which realizes reliable detection of orbital periodicity in seismic reflection data by combining periodic expression of seismic reflection data, spectral background estimation, model construction and synthetic modeling, Monte Carlo set analysis, and eccentricity modulation and constraint analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0045] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0046] The present invention provides a method for detecting orbital periodicity using seismic reflection data, which realizes reliable detection of orbital periodicity in seismic reflection data by combining periodic expression of seismic reflection data, spectral background estimation, model construction and synthetic modeling, Monte Carlo set analysis, and eccentricity modulation and constraint analysis.

[0047] Embodiment 1

[0048] like Figure 1 As shown, this embodiment provides a method for detecting orbit periodicity based on seismic reflection data, including:

[0049] S1, obtain the seismic reflection data and borehole logging data set of the target area, perform denoising, filtering and normalization preprocessing, and convert the seismic reflection data from the travel time domain to the depth domain to generate a seismic data volume;

[0050] S2. Establishing a first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data, and removing the astronomical orbit information in the seismic waveform prediction model to update the model, so as to obtain a second seismic waveform prediction model;

[0051] S3, performing power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model, and the second seismic waveform prediction model to generate power spectra;

[0052] S4, performing spectrum whitening analysis on the power spectrum, and performing power spectrum analysis again to obtain four different power spectra;

[0053] S5. Combined with the confidence level, the peak values ​​in the four different power spectra are periodically compared with the peak values ​​of the known astronomical orbit signals to calculate the confidence results;

[0054] S6. Use Monte Carlo analysis to verify the result. If it does not meet the preset requirements, adjust the parameters of the first seismic waveform prediction model and repeat steps S2 to S6 until the preset requirements are met.

[0055] As an implementation method of this embodiment, in S1, the process of converting seismic reflection data from the travel time domain to the depth domain includes: based on the vertical seismic profile logging curve in the study area, fitting and generating a polynomial in the depth domain and travel time domain applicable to the study area:

[0056] D=a*T+b

[0057] Among them, a and b are constants obtained by fitting, and the conversion from travel time domain to depth domain is achieved based on polynomials.

[0058] Specifically, the data preparation process includes:

[0059] 1) Obtain the seismic reflection data volume A and borehole logging data set B of the target area, and obtain the seismic reflection data and borehole logging data of the target area. The seismic reflection data includes seismic records and related parameters, and the borehole logging data includes information such as acoustic impedance, velocity and density.

[0060] 2) Seismic reflection data and borehole logging data are preprocessed separately, including denoising, filtering and normalization, to improve the quality and consistency of the data.

[0061] 3) Convert seismic data A from the travel time domain (TWT) to the depth domain or age domain to generate seismic data volume C. The specific conversion process is as follows: There are many ways to convert seismic data A in the TWT domain into C in the depth domain. For example, the vertical seismic profile logging curve (VSP) in the study area can be used to fit and generate a polynomial between the depth D and the travel time T applicable to the study area:

[0062] D=a*T+b

[0063] Wherein, a and b are constants obtained by fitting.

[0064] The above formula can be used to realize the conversion of seismic volumes in two different domains.

[0065] 3) In addition, the velocity volume generated by the seismic processing process can also be used to convert the two seismic volumes using the petrel software.

[0066] As an implementation method of this embodiment, in S2, in the process of establishing a first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data:

[0067] Seismic reflection data include: sedimentation background noise, variable sedimentation rate, astronomical orbit signal, density and seismic wavelet.

[0068] Specifically, the initial model setting process includes:

[0069] 1) Using OpendTect or Tesserial software, establish a seismic waveform prediction model D for the seismic reflection data volume A to simulate the actual geological and geophysical parameters that can affect the identification of stratigraphic cycles, including variable sedimentary background noise, variable sedimentation rate, astronomical orbital signals, density, and seismic wavelets.

[0070] The specific process of using A to generate D is as follows: A represents the data body obtained by artificial seismic acquisition of actual sedimentary strata, which contains information such as stratum thickness, special geological bodies (such as river channels), sedimentary pressure, temperature, sedimentary noise, sedimentary rate, astronomical orbital signal, rock density, seismic wavelet, etc. The present invention only discusses the influence of sedimentary background noise, variable sedimentary rate, astronomical orbital signal, density, and seismic wavelet on the results. Through the artificial analysis of A seismic data in the early stage, the specific values ​​of sedimentary background noise, variable sedimentary rate, astronomical orbital signal, density, and seismic wavelet can be obtained. Using the above values, an initial geological model F with stratum thickness and density is established, and the establishment of the geological model can be generated by the above software. After the geological model is generated, the software is also used to simulate artificial earthquakes, and a seismic waveform prediction model D can be generated.

[0071] 2) Among them, the variable deposition background noise that needs to be input into the software is AR(1) noise, and the red noise parameter φ is variable.

[0072] 3) The addition of astronomical orbital signals. The orbital signals are constructed based on the eccentricity, inclination angle, etc. of the astronomical solution of Laskar et al. (2004). The research of Laskar et al. (2004) showed that the sampling can be increased once every thousand years (that is, the period of the stratigraphic cycle is one thousand years). The parameter changes can be made in the above software.

[0073] 4) The sound velocity and density model is generated based on the B data. The specific generation is based on the Gardner formula:

[0074] ρ=a*v p b

[0075] Among them, ρ is density, Vp is sound velocity, both come from logging data B, a and b are constants, which can be obtained by fitting the above two data.

[0076] 5) The seismic wavelet is a statistical seismic wavelet generated based on A.

[0077] As an implementation method in this embodiment, in S2, a first seismic waveform prediction model containing astronomical orbit information is established based on seismic reflection data, and the astronomical orbit information update model in the seismic waveform prediction model is removed to obtain a second seismic waveform prediction model;

[0078] Specifically, the process of generating a model includes:

[0079] The earthquake waveform prediction model D is generated by using the earthquake simulation algorithm in OpendTect or Tesseral software. This model contains astronomical orbit information and is the first earthquake waveform prediction model. According to the same setting parameters, the astronomical orbit signal is removed to generate the earthquake wave prediction model E, which is the second earthquake waveform prediction model. The generation method is the same as the first step of the initial model setting, and the process is completed in the above software.

[0080] As an implementation method in this embodiment, in S3, in the process of performing power spectrum analysis on the borehole logging data set, the seismic data body, the first seismic waveform prediction model and the second seismic waveform prediction model to generate power spectra, Fourier transform is used to perform power spectrum analysis.

[0081] Specifically, the process of power spectrum analysis includes:

[0082] Through Fourier transform, power spectrum analysis is performed on the seismic data body C, the seismic waveform prediction model D (including astronomical orbit information), the seismic waveform prediction model E (removing astronomical orbit information), and the logging data B in the work area (such as the wave impedance signal generated by the acoustic wave time difference and density) to generate a power spectrum; among them, the logging data B in the work area is the borehole logging data set B.

[0083] The specific analysis process is as follows:

[0084] (1) Performing Fourier transform (FFT) on the preprocessed time domain signals (i.e., C, D, E, B mentioned above) to convert the time domain signals into frequency domain signals.

[0085] Fourier transform formula:

[0086]

[0087] Among them, x(t) is the time domain signal, X(f) is the frequency domain signal, and f is the frequency.

[0088] (2) Calculate the power spectrum

[0089] Taking the square of the modulus of the Fourier transform result, we get the power spectral density (PSD) of the signal:

[0090] P(f)=|X(f)| 2

[0091] The power spectrum represents the energy distribution of a signal at different frequencies.

[0092] As an implementation manner in this embodiment, in S4, the process of performing spectrum whitening analysis on the power spectrum includes:

[0093] Perform spectrum whitening analysis on the above power spectrum to remove the influence of sedimentation noise.

[0094] Spectral whitening process:

[0095] (1) Calculate the whitening factor:

[0096] Normalize the power spectrum and calculate the whitening factor:

[0097]

[0098] Where W(f) is the whitening factor, ∈ is a small constant used to avoid the denominator being zero, and P(f) is the power spectral density;

[0099] Apply a whitening factor:

[0100] Multiply the whitening factor by the frequency domain signal to obtain the whitened frequency domain signal:

[0101] X white (f) = X(f)·W(f)

[0102] X white (f) is the frequency domain signal after whitening, X(f) is the frequency domain signal, and P(f) is generated in the previous step.

[0103] (2) Inverse Fourier transform:

[0104] As an implementation manner in this embodiment, the process of performing spectral whitening analysis on the power spectrum further includes:

[0105] Perform an inverse Fourier transform on the whitened frequency domain signal and convert it back to the time domain signal:

[0106]

[0107] As an implementation in this embodiment, in S6, the preset requirement is that the confidence level is greater than or equal to 90%.

[0108] As an additional implementation manner in this embodiment, in S4, spectrum whitening analysis is performed on the power spectrum, and power spectrum analysis is performed again to obtain four different power spectra;

[0109] Specifically, frequency domain signal analysis is performed: power spectrum analysis is performed again on the whitened signal.

[0110] As an additional implementation in this embodiment, in S5, the peak values ​​in the four different power spectra are periodically compared with the peak values ​​of the known astronomical orbit signal in combination with the confidence level to calculate the confidence result;

[0111] Specifically, a cyclic comparison is performed:

[0112] The peaks in the four different power spectra generated are periodically compared with the peaks of the known astronomical orbit signals;

[0113] Set the confidence level (coincidence rate), and the spectral peaks exceeding the required confidence level can be regarded as statistically significant peaks. The coincidence rate is generally set to 90%, and the coincidence rate between the peak of the simulation result and the peak of the known astronomical orbit signal reaches 90%, that is, it can be considered to have reached the confidence level.

[0114] As an additional implementation in this embodiment, in S6, Monte Carlo analysis is used to verify the result. If it does not meet the preset requirements, the parameters of the first seismic waveform prediction model are adjusted, and steps S2 to S6 are repeated until the preset requirements are met.

[0115] Specifically: if the comparison results are consistent and the confidence level reaches 90%, it means that the orbital periodicity meets the requirements of practical applications; if they are inconsistent; consider modifying the various parameters in the first seismic waveform prediction model setting, and repeat steps S2 to S6 until the actual application requirements are met.

[0116] Based on this, an orbit periodicity detection method based on seismic reflection data is provided in an embodiment of the present invention, including: S1, obtaining seismic reflection data and borehole logging data sets in a target area, performing denoising, filtering and normalization preprocessing, and converting the seismic reflection data from the travel time domain to the depth domain to generate a seismic data volume; S2, establishing a first seismic waveform prediction model containing astronomical orbit information based on the seismic reflection data, and removing the astronomical orbit information in the seismic waveform prediction model to update the model, and obtaining a second seismic waveform prediction model; S3, performing power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model, and the second seismic waveform prediction model to generate a power spectrum; S4, performing spectral whitening analysis on the power spectrum, and performing power spectrum analysis again to obtain 4 different power spectra; S5, combining the confidence level, periodically comparing the peaks in the 4 different power spectra with the known astronomical orbit signal peaks, and calculating the confidence result; S6, using Monte Carlo analysis to verify the result, if it does not meet the preset requirements, adjusting the parameters of the first seismic waveform prediction model, and repeating steps S2 to S6 until the preset requirements are met. The present invention provides a method for detecting orbital periodicity using seismic reflection data, which realizes reliable detection of orbital periodicity in seismic reflection data by combining periodic expression of seismic reflection data, spectral background estimation, model construction and synthetic modeling, Monte Carlo set analysis, and eccentricity modulation and constraint analysis.

[0117] Embodiment 2

[0118] In this embodiment, a computer terminal device is provided, including:

[0119] one or more processors;

[0120] A memory, coupled to the processor, for storing one or more programs;

[0121] When the one or more programs are executed by the one or more processors, the one or more processors implement the methods in the above embodiments.

[0122] In this embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the method in the above embodiment is implemented.

[0123] In this embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the method in the above embodiment.

[0124] The above program can be run in the processor, or it can also be stored in the memory (or computer-readable medium), which includes permanent and non-permanent, removable and non-removable media. Information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0125] These computer programs can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more blocks can be implemented by different modules corresponding to different steps.

[0126] This embodiment provides such a device or system. The system is called a track periodicity detection system based on seismic reflection data, and includes:

[0127] The data acquisition module is used to acquire the seismic reflection data and borehole logging data set of the target area, and perform denoising, filtering and normalization preprocessing on them, and convert the seismic reflection data from the travel time domain to the depth domain to generate a seismic data volume;

[0128] A model building module, used to establish a first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data, and generate a second seismic waveform prediction model by removing the astronomical orbit information from the first seismic waveform prediction model;

[0129] A power spectrum analysis module is used to perform power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model and the second seismic waveform prediction model respectively to generate corresponding power spectra;

[0130] A spectrum whitening module is used to perform spectrum whitening analysis on the generated power spectrum, and perform power spectrum analysis again to obtain a whitened power spectrum;

[0131] A comparison analysis module is used to periodically compare the peak value in the whitened power spectrum with the peak value of the known astronomical orbit signal in combination with the confidence level to calculate the confidence result;

[0132] The Monte Carlo analysis module is used to perform Monte Carlo analysis verification on the calculation results. If the confidence results do not meet the preset requirements, the parameters of the first seismic waveform prediction model are adjusted and the system operation is repeated until the preset requirements are met.

[0133] The system or device is used to implement the functions of the method in the above-mentioned embodiment. Each module in the system or device corresponds to each step in the method, which has been explained in the method and will not be repeated here.

[0134] Through the above-mentioned implementation mode, the problem of orbital periodicity detection based on seismic reflection data in the related technology is solved, thereby ensuring that reliable detection of orbital periodicity in seismic reflection data can be achieved by combining the periodic expression of seismic reflection data, spectral background estimation, model construction and synthetic modeling, Monte Carlo set analysis, and eccentricity modulation and constraint analysis.

[0135] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for detecting orbital periodicity based on seismic reflection data, characterized in that: The following steps are involved: S1, obtain the seismic reflection data and borehole logging data set of the target area, perform denoising, filtering and normalization preprocessing, and convert the seismic reflection data from the travel time domain to the depth domain to generate a seismic data volume; S2. Establishing a first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data, and removing the astronomical orbit information in the seismic waveform prediction model to update the model, thereby obtaining a second seismic waveform prediction model; S3, performing power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model, and the second seismic waveform prediction model to generate power spectra; S4, performing spectrum whitening analysis on the power spectrum, and performing power spectrum analysis again to obtain four different power spectra; S5. Combined with the confidence level, the peak values ​​in the four different power spectra are periodically compared with the peak values ​​of the known astronomical orbit signals to calculate the confidence results; S6. Use Monte Carlo analysis to verify the result. If it does not meet the preset requirements, adjust the parameters of the first seismic waveform prediction model and repeat steps S2 to S6 until the preset requirements are met.

2. The method according to claim 1, characterized in that In S1, the process of converting seismic reflection data from the travel time domain to the depth domain includes: based on the vertical seismic profile logging curve in the study area, fitting and generating polynomials in the depth domain and travel time domain applicable to the study area: D=a*T+b Among them, a and b are constants obtained by fitting, and the conversion from travel time domain to depth domain is achieved based on polynomials.

3. The method according to claim 1, characterized in that In S2, in the process of establishing the first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data: Seismic reflection data include: sedimentation background noise, variable sedimentation rate, astronomical orbit signal, density and seismic wavelet.

4. The method according to claim 1, characterized in that: In S3, in the process of performing power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model and the second seismic waveform prediction model to generate power spectra, Fourier transform is used to perform power spectrum analysis.

5. The method according to claim 1, characterized in that In S4, the process of performing spectrum whitening analysis on the power spectrum includes: Normalize the power spectrum and calculate the whitening factor: Where W(f) is the whitening factor, ∈ is a small constant used to avoid the denominator being zero, and P(f) is the power spectral density; Multiply the whitening factor by the frequency domain signal to obtain the whitened frequency domain signal: X white (f)=X(f)·W(f) X white (f) is the frequency domain signal after whitening, and X(f) is the frequency domain signal.

6. The method according to claim 5, characterized in that In S4, the process of performing spectrum whitening analysis on the power spectrum further includes: Perform an inverse Fourier transform on the whitened frequency domain signal and convert it back to the time domain signal:

7. The method according to claim 1, characterized in that In S6, the preset requirement is that the confidence level is greater than or equal to 90%.

8. A system for detecting orbital periodicity based on seismic reflection data, characterized in that: The system comprises: The data acquisition module is used to acquire the seismic reflection data and borehole logging data set of the target area, and perform denoising, filtering and normalization preprocessing on them, and convert the seismic reflection data from the travel time domain to the depth domain to generate a seismic data volume; A model building module, used to establish a first seismic waveform prediction model containing astronomical orbit information based on seismic reflection data, and generate a second seismic waveform prediction model by removing the astronomical orbit information from the first seismic waveform prediction model; A power spectrum analysis module is used to perform power spectrum analysis on the borehole logging data set, the seismic data volume, the first seismic waveform prediction model and the second seismic waveform prediction model respectively to generate corresponding power spectra; A spectrum whitening module is used to perform spectrum whitening analysis on the generated power spectrum, and perform power spectrum analysis again to obtain a whitened power spectrum; A comparison analysis module is used to periodically compare the peak value in the whitened power spectrum with the peak value of the known astronomical orbit signal in combination with the confidence level to calculate the confidence result; The Monte Carlo analysis module is used to perform Monte Carlo analysis verification on the calculation results. If the confidence results do not meet the preset requirements, the parameters of the first seismic waveform prediction model are adjusted and the system operation is repeated until the preset requirements are met.

9. A computer terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the various steps of the orbit periodicity detection method based on seismic reflection data as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the orbit periodicity detection method based on seismic reflection data as described in any one of claims 1 to 7 is implemented.

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