Method, device and related equipment for processing multi-source mixed acquisition data in marine seismic exploration

Abstract

The present application discloses a method, device and related equipment for processing multi-source mixed acquisition data in marine seismic exploration. The method includes: obtaining a characteristic wavelet of air gun data based on the collected air gun seismic data; obtaining a characteristic wavelet of sparker data based on the collected sparker seismic data; integrating the characteristic wavelet of air gun data and the characteristic wavelet of sparker data according to the amplitude spectrum energy to obtain an integrated wavelet in the time domain; performing series inversion on the air gun data and the sparker data to obtain a reflection coefficient profile; and performing convolution operation using the integrated wavelet in the time domain and the reflection coefficient profile to obtain target integrated data. The present application realizes the integration of seismic data excited by an air gun source and seismic data excited by a sparker source, solves the problem of inconsistent wavelets of the two types of seismic data, and the integrated seismic data has both the frequency bands of seismic data excited by an air gun and seismic data excited by a sparker, thus obtaining broadband and high-resolution seismic data.

Description

Technical Field

[0001] The present application relates to the technical field of seismic data processing, and more specifically, to a method and device for processing multi-source mixed acquisition data in marine seismic exploration, and related equipment. Background Art

[0002] Achieving broadband and high resolution in data acquisition is the current pursuit goal of seismic exploration. To achieve broadband and high-resolution data acquisition, many geophysicists have done a lot of work in seismic wave excitation and reception. In terms of reception, techniques such as slant cable, up-down cable, "plow-type" towed cable, and "rake cable" have emerged. In terms of excitation, the technique of three-dimensional combination (gun array source) of air gun sources has emerged, but because it only uses air gun sources for combination, there is limited improvement in the expansion of the source frequency bandwidth after combination.

[0003] The main factor affecting seismic resolution is the seismic source wavelet. The higher the main frequency and the wider the frequency band of the source wavelet, the higher the resolution of seismic exploration. In marine seismic exploration, the high-frequency end of the frequency band range of the source wavelet excited by the commonly used air gun source is usually 100 Hz, and the higher one can be extended to about 200 Hz. Another source, the sparker source suitable for use in water areas, excites seismic waves with high frequencies. The low-frequency end can cover 100 Hz, and the high-frequency end can reach 1000 Hz or above. Therefore, by comprehensively utilizing the complementary characteristics of the two sources in the frequency band range and expanding the frequency band width of the seismic data collected by the towed cable, high-resolution data acquisition can be achieved.

[0004] For the seismic data excited by the sparker source, the frequency band of the data is mainly distributed at the high-frequency end, and the obtained seismic data has rich detailed features, but it is insufficient in representing large-scale frame structures and deep structures. For the seismic data excited by the air gun source, the frequency band of the data is mainly distributed at the low-frequency end, which is suitable for detecting large-scale and deeper geological structures, but lacks the detailed features that only high frequencies can endow. The relatively high frequency of the sparker source and the relatively low frequency of the air gun source make the two seismic data complementary in the frequency band range. However, due to different excitation methods, there are significant differences in amplitude, waveform, phase, frequency band width, etc. between the seismic data of the air gun source and the sparker source, that is, there is a serious inconsistency in the seismic wavelets of the two data.

[0005] How to fuse the seismic data obtained by mixed acquisition of air gun sources and sparker sources has become an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the present application provides a method and device for processing multi-source mixed acquisition data in marine seismic exploration, and related equipment, so as to realize the fusion processing of the seismic data obtained by mixed acquisition of air gun sources and sparker sources.

[0007] To achieve the above object, a first aspect of the present application provides a method for processing multi-source mixed acquisition data in marine seismic exploration, including:

[0008] Based on the acquired airgun seismic data, obtain the preprocessed airgun seismic data, the first velocity model, and the airgun data characteristic wavelet;

[0009] Based on the acquired sparker seismic data, obtain the sparker data time-domain imaging profile and the sparker data characteristic wavelet;

[0010] Integrate the airgun data characteristic wavelet and the sparker data characteristic wavelet to obtain the time-domain integrated wavelet;

[0011] Use the preprocessed airgun seismic data, the first velocity model, and the airgun data characteristic wavelet to perform impedance inversion to obtain an impedance profile;

[0012] Use the impedance profile, the sparker data time-domain imaging profile, and the sparker data characteristic wavelet to perform impedance inversion to obtain a reflection coefficient profile;

[0013] Use the time-domain integrated wavelet and the reflection coefficient profile to perform convolution operation to obtain the target integrated data.

[0014] Preferably, the process of obtaining the preprocessed airgun seismic data and the first velocity model based on the acquired airgun seismic data includes:

[0015] For the acquired airgun seismic data, remove the irregular interference and the regular interference composed of multiple waves to obtain the preprocessed airgun seismic data;

[0016] Perform dynamic correction stacking processing and velocity analysis on the preprocessed airgun seismic data to obtain the first velocity model.

[0017] Preferably, the process of obtaining the airgun data characteristic wavelet includes:

[0018] Use the preprocessed airgun seismic data and the first velocity model to perform prestack time migration (PSTM) processing on each shot gather to obtain the first PSTM result of each shot gather of the airgun seismic data;

[0019] Stack the first PSTM results of each shot gather of the airgun seismic data to obtain the airgun seismic data time-domain imaging profile;

[0020] Use the wavelet zero-phase method to convert the non-zero phase wavelet in the airgun seismic data time-domain imaging profile into a zero-phase wavelet to obtain the zero-phase airgun seismic data time-domain imaging profile;

[0021] Perform Fourier transform and smoothing processing on the data of each seismic trace in the time-domain imaging profile of the zero-phase air gun seismic data. When the reflection coefficient meets the condition of white noise, the amplitude spectrum of the seismic wavelet corresponding to each seismic trace in the air gun seismic data is obtained;

[0022] Take the mean value of the amplitude spectra of the seismic wavelets of each seismic trace in the air gun seismic data to obtain the amplitude spectrum of the characteristic wavelet of the air gun seismic data;

[0023] Perform inverse Fourier transform on the amplitude spectrum of the characteristic wavelet of the air gun seismic data to obtain the characteristic wavelet of the air gun data.

[0024] Preferably, based on the collected sparker seismic data, the process of obtaining the time-domain imaging profile of the sparker data includes:

[0025] For the collected sparker seismic data, remove irregular interference and regular interference composed of multiple waves to obtain preprocessed sparker seismic data;

[0026] Perform dynamic correction stacking processing and velocity analysis on the preprocessed sparker seismic data to obtain the second velocity model;

[0027] Use the preprocessed sparker seismic data and the second velocity model to perform prestack time migration PSTM processing on each shot gather to obtain the second PSTM result of each shot gather of the sparker seismic data;

[0028] Stack the second PSTM results of each shot gather of the sparker seismic data to obtain the time-domain imaging profile of the sparker seismic data.

[0029] Preferably, the process of obtaining the characteristic wavelet of the sparker data includes:

[0030] Use the wavelet zero-phase method to convert the non-zero phase wavelet in the time-domain imaging profile of the sparker seismic data into a zero-phase wavelet to obtain the zero-phase time-domain imaging profile of the sparker seismic data;

[0031] Perform Fourier transform and smoothing processing on the data of each seismic trace in the zero-phase time-domain imaging profile of the sparker seismic data. When the reflection coefficient meets the condition of white noise, the amplitude spectrum of the seismic wavelet corresponding to each seismic trace in the sparker seismic data is obtained;

[0032] Take the mean value of the amplitude spectra of the seismic wavelets of each seismic trace in the sparker seismic data to obtain the amplitude spectrum of the characteristic wavelet of the sparker seismic data;

[0033] Perform inverse Fourier transform on the amplitude spectrum of the characteristic wavelet of the sparker seismic data to obtain the characteristic wavelet of the sparker data.

[0034] Preferably, the process of integrating the air gun data characteristic wavelet and the spark data characteristic wavelet to obtain the integrated wavelet in the time domain includes:

[0035] Obtaining a first energy value of the amplitude spectrum of the air gun data characteristic wavelet and obtaining a second energy value of the amplitude spectrum of the spark data characteristic wavelet;

[0036] Based on the first energy value and the second energy value, adjusting the amplitude spectra of the air gun data characteristic wavelet and the spark data characteristic wavelet to have the same energy value through amplitude translation, obtaining the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the spark data characteristic wavelet;

[0037] Adding the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the spark data characteristic wavelet to obtain the amplitude spectrum of the integrated wavelet;

[0038] Performing an inverse Fourier transform on the amplitude spectrum of the integrated wavelet to obtain the integrated wavelet in the time domain.

[0039] Preferably, the process of obtaining the first energy value of the amplitude spectrum of the air gun data characteristic wavelet includes:

[0040] Calculating the first energy value S of the amplitude spectrum of the air gun data characteristic wavelet using the following equation q :

[0041]

[0042] where F q (f) is the amplitude spectrum of the air gun data characteristic wavelet, f q1 is the low cut-off frequency of the air gun data characteristic wavelet, f q2 is the high cut-off frequency of the air gun data characteristic wavelet, is the coefficient of the Newton-Cotes quadrature formula.

[0043] Preferably, the process of obtaining the second energy value of the amplitude spectrum of the spark data characteristic wavelet includes:

[0044] Calculating the second energy value S of the amplitude spectrum of the spark data characteristic wavelet using the following equation d :

[0045]

[0046] where F d (f) is the amplitude spectrum of the spark data characteristic wavelet, f d1 is the low cut-off frequency of the spark data characteristic wavelet, f d2is the high cut-off frequency of the spark data characteristic wavelet, is the coefficient of the Newton-Cotes quadrature formula.

[0047] Preferably, based on the first energy value and the second energy value, the amplitude spectra of the air gun data characteristic wavelet and the spark data characteristic wavelet are adjusted to have the same energy value by amplitude translation, and the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the spark data characteristic wavelet are obtained, including:

[0048] The third energy value S' after translating the amplitude spectrum of the spark data characteristic wavelet by Δ is calculated using the following equation c : d :

[0049]

[0050] Δ is calculated by combining the following equation c :

[0051] S' d = S q

[0052] The adjusted amplitude spectrum F' of the spark data characteristic wavelet is calculated using the following equation d (f k ):

[0053] F' d (f k ) = F d (f k ) + Δ c

[0054] where the translation amount of the amplitude spectrum of the air gun data characteristic wavelet is 0, and the adjusted amplitude spectrum F' of the air gun data characteristic wavelet q (f k ) = F q (f k ).

[0055] The second aspect of the present application provides an apparatus for processing multi-source mixed acquisition data in marine seismic exploration, including:

[0056] An air gun seismic data processing unit, configured to obtain preprocessed air gun seismic data, a first velocity model, and an air gun data characteristic wavelet based on the acquired air gun seismic data;

[0057] A spark seismic data processing unit, configured to obtain a spark data time-domain imaging profile and a spark data characteristic wavelet based on the acquired spark seismic data;

[0058] A characteristic wavelet integration unit, configured to integrate the air gun data characteristic wavelet and the sparker data characteristic wavelet to obtain an integrated wavelet in the time domain;

[0059] A wave impedance profile acquisition unit, configured to perform wave impedance inversion on the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to obtain a wave impedance profile;

[0060] A reflection coefficient profile acquisition unit, configured to perform wave impedance inversion on the wave impedance profile, the sparker data time domain imaging profile, and the sparker data characteristic wavelet to obtain a reflection coefficient profile;

[0061] A target integrated data acquisition unit, configured to perform convolution operation on the integrated wavelet in the time domain and the reflection coefficient profile to obtain target integrated data.

[0062] A third aspect of the present application provides a marine seismic exploration multi-source mixed acquisition data processing device, including: a memory and a processor;

[0063] The memory is configured to store a program;

[0064] The processor is configured to execute the program to implement each step of the above-mentioned marine seismic exploration multi-source mixed acquisition data processing method.

[0065] A fourth aspect of the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, each step of the above-mentioned marine seismic exploration multi-source mixed acquisition data processing method is implemented.

[0066] As can be seen from the above technical solutions, the present application first obtains preprocessed air gun seismic data, a first velocity model, and an air gun data characteristic wavelet based on the collected air gun seismic data. At the same time, based on the collected sparker seismic data, a sparker data time-domain imaging profile and a sparker data characteristic wavelet are obtained. Then, the air gun data characteristic wavelet and the sparker data characteristic wavelet are integrated to obtain a time-domain integrated wavelet, and when integrating, it is ensured that the air gun data characteristic wavelet and the sparker data characteristic wavelet have the same spectral energy. The fact that the air gun data characteristic wavelet and the sparker data characteristic wavelet have the same energy means that the two types of data have the same weight when integrating, and the relative amplitude of the high-frequency end components corresponding to the sparker source seismic data is relatively low (because its frequency band is wider), which is consistent with the actual characteristic that the high-frequency attenuation of seismic data is more serious while broadening the frequency band. Next, wave impedance inversion is performed using the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to obtain a wave impedance profile. Since the sparker source seismic data and the air gun source seismic data are preprocessed separately before integration to remove regular interference mainly composed of multiple waves and irregular interference mainly composed of random noise, the integrated profile can be regarded as only containing primary reflections at the interfaces, which is beneficial for the direct geological interpretation of the seismic profile. Wave impedance inversion is performed using the wave impedance profile, the sparker data time-domain imaging profile, and the sparker data characteristic wavelet to obtain a reflection coefficient profile. In the process of obtaining the reflection coefficient profile, serial inversion processing of the air gun source seismic data and the sparker source seismic data is adopted. Using the wave impedance model obtained by inverting the air gun source seismic data at the low-frequency end as the initial model, the sparker data time-domain imaging profile at the high-frequency end as the input data, and the sparker source characteristic wavelet as the input wavelet, wave impedance inversion is performed and the reflection coefficient profile is extracted therefrom, so that the extracted reflection coefficient profile also has the characteristics of wide frequency band and high resolution. Finally, convolution operation is performed using the time-domain integrated wavelet and the reflection coefficient profile to obtain the target integrated data. The present application realizes the integration of the seismic data excited by the air gun source and the seismic data excited by the sparker source, solves the problem of inconsistent wavelets of the two types of seismic data, and the integrated seismic data has both the frequency bands of the seismic data excited by the air gun and the seismic data excited by the sparker, obtaining wide-frequency and high-resolution seismic data, and using it can greatly improve the imaging accuracy and serve the fine exploration of resources such as oil and gas and the detection of deep geological structures. Description of the Drawings

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0068] Figure 1 Schematic diagram of the multi-source mixed acquisition data processing method for marine seismic exploration disclosed in the embodiments of the present application;

[0069] Figure 2 Schematic diagram of the multi-source mixed acquisition data processing device for marine seismic exploration disclosed in the embodiments of the present application;

[0070] Figure 3 Schematic diagram of the multi-source mixed acquisition data processing equipment for marine seismic exploration disclosed in the embodiments of the present application. Detailed implementation manners

[0071] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0072] As can be seen from the background art, due to different excitation methods, there are significant differences in amplitude, waveform, phase, frequency band width, etc. between the seismic data of the air gun source and the sparker source, that is, there is a serious inconsistency in the seismic wavelets of the two types of data. Therefore, when multi-source mixed acquisition is performed with an air gun and a sparker, the air gun and the sparker are often alternately excited at short time intervals, and the streamer respectively obtains the seismic data excited by the air gun source and the seismic data excited by the sparker source. That is, the obtained broadband seismic data is discrete, with the low-frequency part on the seismic profile excited by the air gun and the high-frequency part on the seismic profile excited by the sparker.

[0073] Therefore, it is necessary to integrate the two seismic profiles with different frequency band ranges, different amplitude amplitudes, and different waveform characteristics into the same seismic profile, so that this single seismic profile not only has the low-frequency characteristics of the seismic profile excited by the air gun but also has the high-frequency characteristics of the seismic profile excited by the sparker, while ensuring the consistency of the seismic wavelets from shallow to deep in the vertical direction and from near to far in the horizontal direction of the profile, so that the low-frequency obtained from the seismic data excited by the air gun and the high-frequency obtained from the seismic data excited by the sparker are reasonable in terms of energy contrast.

[0074] Next, the multi-source mixed acquisition data processing method for marine seismic exploration provided by the embodiments of the present application will be introduced. Please refer to Figure 1 , the multi-source mixed acquisition data processing method for marine seismic exploration provided by the embodiments of the present application may include the following steps:

[0075] Step S101, based on the collected air gun seismic data, obtain the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet.

[0076] Among them, the air gun seismic data can be preprocessed to remove irregular interference mainly composed of random noise and regular interference mainly composed of multiple waves, so as to obtain the preprocessed air gun seismic data; after analyzing the preprocessed air gun seismic data, a root mean square velocity model, that is, the first velocity model, can be obtained; the air gun data characteristic wavelet contains the seismic wavelet information of different seismic traces in the preprocessed air gun seismic data.

[0077] Step S102: Based on the collected spark seismic data, obtain the spark data time-domain imaging profile and the spark data characteristic wavelet.

[0078] Among them, the spark seismic data can be preprocessed to remove irregular interference mainly composed of random noise and regular interference mainly composed of multiple waves, so as to obtain the preprocessed spark seismic data; after analyzing the preprocessed spark seismic data, a root mean square velocity model, that is, the second velocity model, can be obtained; the spark data characteristic wavelet contains the seismic wavelet information of different seismic traces in the preprocessed spark seismic data.

[0079] Step S103: Integrate the air gun data characteristic wavelet and the spark data characteristic wavelet to obtain the time-domain integrated wavelet.

[0080] Exemplarily, the air gun data characteristic wavelet and the spark data characteristic wavelet can be integrated from aspects such as phase and amplitude, so that the processed air gun data characteristic wavelet and the spark data characteristic wavelet have the same energy value and phase value.

[0081] Step S104: Use the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to perform wave impedance inversion to obtain a wave impedance profile.

[0082] Step S105: Use the wave impedance profile, the spark data time-domain imaging profile, and the spark data characteristic wavelet to perform wave impedance inversion to obtain a reflection coefficient profile.

[0083] Step S106: Use the time-domain integrated wavelet and the reflection coefficient profile to perform convolution operation to obtain the target integrated data.

[0084] First, based on the collected air gun seismic data, the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet are obtained. At the same time, based on the collected sparker seismic data, the sparker data time-domain imaging profile and the sparker data characteristic wavelet are obtained. Then, the air gun data characteristic wavelet and the sparker data characteristic wavelet are integrated to obtain the time-domain integrated wavelet. When integrating, it is ensured that the air gun data characteristic wavelet and the sparker data characteristic wavelet have the same spectral energy. The fact that the air gun data characteristic wavelet and the sparker data characteristic wavelet have the same energy means that the two types of data have the same weight when integrated, and the relative amplitude of the high-frequency component corresponding to the sparker source seismic data is relatively low (because its frequency band is wider), which is consistent with the actual characteristic of severe high-frequency attenuation of seismic data while broadening the frequency band. Next, wave impedance inversion is performed using the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to obtain a wave impedance profile. Since the sparker source seismic data and the air gun source seismic data are preprocessed separately before integration to remove regular interference mainly composed of multiple waves and irregular interference mainly composed of random noise, the integrated profile can be regarded as only containing primary reflections at the interface, which is beneficial for the direct geological interpretation of the seismic profile. Wave impedance inversion is performed using the wave impedance profile, the sparker data time-domain imaging profile, and the sparker data characteristic wavelet to obtain a reflection coefficient profile. In the process of obtaining the reflection coefficient profile, a series inversion process of the air gun source seismic data and the sparker source seismic data is adopted. The wave impedance model obtained by inverting the air gun source seismic data at the low-frequency end is used as the initial model, the sparker data time-domain imaging profile at the high-frequency end is used as the input data, and the sparker source characteristic wavelet is used as the input wavelet for wave impedance inversion and the reflection coefficient profile is extracted therefrom, so that the extracted reflection coefficient profile also has the characteristics of wide frequency band and high resolution. Finally, convolution operation is performed using the time-domain integrated wavelet and the reflection coefficient profile to obtain the target integrated data. This application realizes the integration of the seismic data excited by the air gun source and the seismic data excited by the sparker source, solves the problem of inconsistent wavelets of the two types of seismic data, and the integrated seismic data has the frequency bands of both the seismic data excited by the air gun and the seismic data excited by the sparker, obtaining wide-frequency and high-resolution seismic data. Using it can greatly improve the imaging accuracy and serve the fine exploration of oil and gas and other resources and the detection of deep geological structures.

[0085] In some embodiments of this application, the process of obtaining the preprocessed air gun seismic data and the first velocity model based on the collected air gun seismic data in step S101 may include:

[0086] S1. For the collected air gun seismic data, irregular interference and regular interference composed of multiple waves are removed to obtain the preprocessed air gun seismic data.

[0087] S2. Perform dynamic correction stacking processing and velocity analysis on the preprocessed air gun seismic data to obtain a first velocity model.

[0088] Specifically, by performing dynamic correction stacking processing and velocity analysis on the preprocessed air gun seismic data, a root mean square velocity model can be obtained, and this root mean square velocity model is the first velocity model.

[0089] In some embodiments of the present application, the process of obtaining the characteristic wavelet of the air gun data in step S101 may include:

[0090] S1. Use the preprocessed air gun seismic data and the first velocity model to perform PSTM (Pre-Stack Time Migration) processing on each shot gather to obtain the first PSTM result of each shot gather of the air gun seismic data.

[0091] S2. Stack the first PSTM results of each shot gather of the air gun seismic data to obtain an imaging profile of the air gun seismic data in the time domain.

[0092] S3. Use the wavelet zero-phase method to convert the non-zero phase wavelet in the imaging profile of the air gun seismic data in the time domain into a zero-phase wavelet to obtain an imaging profile of the air gun seismic data in the time domain after zero-phase conversion.

[0093] S4. Perform Fourier transform and smoothing processing on the data of each seismic trace in the imaging profile of the air gun seismic data in the time domain after zero-phase conversion. Under the condition that the reflection coefficient satisfies white noise, the amplitude spectrum of the seismic wavelet corresponding to each seismic trace in the air gun seismic data can be obtained.

[0094] S5. Take the mean value of the amplitude spectra of the seismic wavelets of each seismic trace in the air gun seismic data to obtain the amplitude spectrum of the characteristic wavelet of the air gun seismic data.

[0095] S6. Perform inverse Fourier transform on the amplitude spectrum of the characteristic wavelet of the air gun seismic data to obtain the characteristic wavelet of the air gun data.

[0096] It should be noted that if the wavelet changes greatly along the time direction in the air gun seismic data, the imaging profile of the air gun seismic data in the time domain after zero-phase conversion can be geologically interpreted first to obtain different seismic reflection wave groups, and then the wavelet is extracted for each wave group respectively, thus constituting the characteristic wavelet of the air gun data.

[0097] In some embodiments of the present application, the process of obtaining the imaging profile of the sparker seismic data in the time domain based on the collected sparker seismic data in step S102 may include:

[0098] S1. For the collected sparker seismic data, remove the irregular interference and the regular interference composed of multiple waves to obtain the preprocessed sparker seismic data.

[0099] S2. Perform dynamic correction stacking processing and velocity analysis on the preprocessed sparker seismic data to obtain the second velocity model.

[0100] S3. Use the preprocessed sparker seismic data and the second velocity model to perform PSTM processing on each shot gather to obtain the second PSTM result of each shot gather of the sparker seismic data.

[0101] S4. Stack the second PSTM results of each shot gather of the sparker seismic data to obtain the time-domain imaging profile of the sparker seismic data.

[0102] In some embodiments of the present application, the process of obtaining the sparker data characteristic wavelet in step S102 may include:

[0103] S1. Use the wavelet zero-phase method to convert the non-zero-phase wavelet in the time-domain imaging profile of the sparker seismic data into a zero-phase wavelet to obtain the zero-phase time-domain imaging profile of the sparker seismic data.

[0104] S2. Perform Fourier transform and smoothing processing on the data of each seismic trace in the zero-phase time-domain imaging profile of the sparker seismic data. When the reflection coefficient satisfies white noise conditions, the amplitude spectrum of the seismic wavelet corresponding to each seismic trace in the sparker seismic data can be obtained.

[0105] S3. Take the mean value of the amplitude spectra of the seismic wavelets of each seismic trace in the sparker seismic data to obtain the amplitude spectrum of the characteristic wavelet of the sparker seismic data.

[0106] S4. Perform inverse Fourier transform on the amplitude spectrum of the characteristic wavelet of the sparker seismic data to obtain the sparker data characteristic wavelet.

[0107] In some embodiments of the present application, the process of integrating the air gun data characteristic wavelet and the sparker data characteristic wavelet in step S103 to obtain the time-domain integrated wavelet may include:

[0108] S1. Obtain the first energy value of the amplitude spectrum of the air gun data characteristic wavelet and obtain the second energy value of the amplitude spectrum of the sparker data characteristic wavelet.

[0109] S2. Based on the first energy value and the second energy value, adjust the amplitude spectra of the air gun data characteristic wavelet and the sparker data characteristic wavelet to have the same energy value through amplitude translation to obtain the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the sparker data characteristic wavelet.

[0110] Exemplarily, the amplitude spectrum energy (the area enclosed by the square of the amplitude in the frequency domain) of the air gun data characteristic wavelet and the spark data characteristic wavelet can be calculated respectively, and the amplitude value of the weak amplitude spectrum energy characteristic wavelet can be adjusted to have the same amplitude spectrum energy value as that of the strong amplitude spectrum energy characteristic wavelet by the method of translating and adjusting the amplitude value.

[0111] S3. Add the amplitude spectrum of the adjusted air gun data characteristic wavelet and the amplitude spectrum of the adjusted spark data characteristic wavelet to obtain the amplitude spectrum of the integrated wavelet.

[0112] S4. Perform an inverse Fourier transform on the amplitude spectrum of the integrated wavelet to obtain the integrated wavelet in the time domain.

[0113] In some embodiments of the present application, the process of S1 for obtaining the first energy value of the amplitude spectrum of the air gun data characteristic wavelet may include:

[0114] The first energy value S of the amplitude spectrum of the air gun data characteristic wavelet is calculated by using the following equation q :

[0115]

[0116] The above process is to calculate the area enclosed by the amplitude energy curve of the amplitude spectrum of the air gun data characteristic wavelet by using the Newton-Cotes approximate quadrature formula. Among them, F q (f) is the amplitude spectrum of the air gun data characteristic wavelet, and f q1 is the low cut-off frequency of the air gun data characteristic wavelet, and f q2 is the high cut-off frequency of the air gun data characteristic wavelet, is the coefficient of the Newton-Cotes quadrature formula and can be obtained by looking up a table.

[0117] In some embodiments of the present application, the process of S1 for obtaining the second energy value of the amplitude spectrum of the spark data characteristic wavelet may include:

[0118] The second energy value S of the amplitude spectrum of the spark data characteristic wavelet is calculated by using the following equation d :

[0119]

[0120] The above process is to calculate the area enclosed by the amplitude energy curve of the amplitude spectrum of the spark data characteristic wavelet by using the Newton-Cotes approximate quadrature formula. Among them, F d (f) is the amplitude spectrum of the spark data characteristic wavelet, and f d1 is the low cut-off frequency of the spark data characteristic wavelet, and f d2 is the high cut-off frequency of the spark data characteristic wavelet, is the coefficient of the Newton-Cotes quadrature formula and can be obtained by looking up a table.

[0121] For the airgun data characteristic wavelet and the spark data characteristic wavelet, since the energy value of the amplitude spectrum of the airgun data characteristic wavelet is usually higher, the amplitude spectrum of the airgun data characteristic wavelet can be kept unchanged, and by keeping the curve shape of the amplitude spectrum of the spark data characteristic wavelet and translating the amplitude spectrum of the spark data characteristic wavelet upward until they have the same energy value.

[0122] Based on this, in some embodiments of the present application, the above S2 adjusts the amplitude spectra of the airgun data characteristic wavelet and the spark data characteristic wavelet to have the same energy value through amplitude translation based on the first energy value and the second energy value, and obtains the adjusted amplitude spectrum of the airgun data characteristic wavelet and the adjusted amplitude spectrum of the spark data characteristic wavelet, which may include:

[0123] S21, calculating the translation Δ of the amplitude spectrum of the spark data characteristic wavelet using the following equation c to obtain the third energy value S′ after translation d :

[0124]

[0125] S22, solving the following equation to calculate Δ c :

[0126] S′ d = S q

[0127] S23, calculating the adjusted amplitude spectrum F′ of the spark data characteristic wavelet using the following equation d (f k ):

[0128] F′ d (f k ) = F d (f k ) + Δ c

[0129] where the translation amount of the amplitude spectrum of the airgun data characteristic wavelet is 0, and the adjusted amplitude spectrum F′ of the airgun data characteristic wavelet q (f k ) = F q (f k ).

[0130] At this time, the amplitude spectrum F c (f k ) of the integrated wavelet in the above S3 can be expressed as:

[0131] F c (f k ) = F′ d (f k ) + F q (f k )

[0132] The marine seismic exploration multi-source mixed acquisition data processing device provided by the embodiments of the present application will be described below. The marine seismic exploration multi-source mixed acquisition data processing device described below can be correspondingly referred to the marine seismic exploration multi-source mixed acquisition data processing method described above.

[0133] Please refer to Figure 2 , the marine seismic exploration multi-source mixed acquisition data processing device provided by the embodiments of the present application may include:

[0134] An air gun seismic data processing unit 21, configured to obtain preprocessed air gun seismic data, a first velocity model, and an air gun data characteristic wavelet based on the collected air gun seismic data;

[0135] An electric spark seismic data processing unit 22, configured to obtain an electric spark data time-domain imaging profile and an electric spark data characteristic wavelet based on the collected electric spark seismic data;

[0136] A characteristic wavelet integration unit 23, configured to integrate the air gun data characteristic wavelet and the electric spark data characteristic wavelet to obtain a time-domain integrated wavelet;

[0137] A wave impedance profile acquisition unit 24, configured to perform wave impedance inversion by using the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to obtain a wave impedance profile;

[0138] A reflection coefficient profile acquisition unit 25, configured to perform wave impedance inversion by using the wave impedance profile, the electric spark data time-domain imaging profile, and the electric spark data characteristic wavelet to obtain a reflection coefficient profile;

[0139] A target integrated data acquisition unit 26, configured to perform convolution operation by using the time-domain integrated wavelet and the reflection coefficient profile to obtain target integrated data.

[0140] In some embodiments of the present application, the process of the air gun seismic data processing unit 21 obtaining the preprocessed air gun seismic data and the first velocity model based on the collected air gun seismic data may include:

[0141] For the collected air gun seismic data, irregular interference and regular interference composed of multiple waves are removed to obtain preprocessed air gun seismic data;

[0142] Performing dynamic correction stacking processing and velocity analysis on the preprocessed air gun seismic data to obtain a first velocity model.

[0143] In some embodiments of the present application, the process by which the air gun seismic data processing unit 21 obtains the characteristic wavelet of the air gun data may include:

[0144] Using the preprocessed air gun seismic data and the first velocity model, performing prestack time migration (PSTM) processing on each shot gather to obtain the first PSTM result of each shot gather;

[0145] Stacking the first PSTM results of each shot gather to obtain the air gun seismic data time-domain imaging profile;

[0146] Using the wavelet zero-phase method to convert the non-zero phase wavelet in the air gun seismic data time-domain imaging profile into a zero-phase wavelet, obtaining the zero-phase air gun seismic data time-domain imaging profile;

[0147] Performing Fourier transform and smoothing processing on the data of each seismic trace in the zero-phase air gun seismic data time-domain imaging profile. Under the condition that the reflection coefficient satisfies white noise, the amplitude spectrum of the seismic wavelet corresponding to each seismic trace in the air gun seismic data is obtained;

[0148] Taking the average value of the amplitude spectra of the seismic wavelets of each seismic trace in the air gun seismic data to obtain the amplitude spectrum of the characteristic wavelet of the air gun seismic data;

[0149] Performing inverse Fourier transform on the amplitude spectrum of the characteristic wavelet of the air gun seismic data to obtain the characteristic wavelet of the air gun data.

[0150] In some embodiments of the present application, the process by which the sparker seismic data processing unit 22 obtains the sparker data time-domain imaging profile based on the collected sparker seismic data may include:

[0151] For the collected sparker seismic data, removing irregular interference and regular interference composed of multiple waves to obtain the preprocessed sparker seismic data;

[0152] Performing dynamic correction stacking processing and velocity analysis on the preprocessed sparker seismic data to obtain a second velocity model;

[0153] Using the preprocessed sparker seismic data and the second velocity model, performing prestack time migration (PSTM) processing on each shot gather to obtain the second PSTM result of each shot gather;

[0154] Stacking the second PSTM results of each shot gather to obtain the sparker seismic data time-domain imaging profile.

[0155] In some embodiments of the present application, the process by which the spark seismic data processing unit 22 obtains the spark seismic data characteristic wavelet may include:

[0156] Using the wavelet zero-phase method to convert the non-zero phase wavelet in the time-domain imaging profile of the spark seismic data into a zero-phase wavelet, obtaining the time-domain imaging profile of the spark seismic data after zero-phase conversion;

[0157] Performing Fourier transform and smoothing processing on the data of each seismic trace in the time-domain imaging profile of the zero-phase converted spark seismic data, and obtaining the amplitude spectrum of the seismic wavelet corresponding to each seismic trace in the spark seismic data when the reflection coefficient satisfies white noise conditions;

[0158] Taking the average of the amplitude spectra of the seismic wavelets of each seismic trace in the spark seismic data to obtain the amplitude spectrum of the characteristic wavelet of the spark seismic data;

[0159] Performing inverse Fourier transform on the amplitude spectrum of the characteristic wavelet of the spark seismic data to obtain the spark seismic data characteristic wavelet.

[0160] In some embodiments of the present application, the process by which the characteristic wavelet integration unit 23 integrates the air gun data characteristic wavelet and the spark seismic data characteristic wavelet to obtain the integrated wavelet in the time domain may include:

[0161] Obtaining a first energy value of the amplitude spectrum of the air gun data characteristic wavelet, and obtaining a second energy value of the amplitude spectrum of the spark seismic data characteristic wavelet;

[0162] Based on the first energy value and the second energy value, adjusting the amplitude spectra of the air gun data characteristic wavelet and the spark seismic data characteristic wavelet to have the same energy value through amplitude translation, obtaining the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the spark seismic data characteristic wavelet;

[0163] Adding the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the spark seismic data characteristic wavelet to obtain the amplitude spectrum of the integrated wavelet;

[0164] Performing inverse Fourier transform on the amplitude spectrum of the integrated wavelet to obtain the integrated wavelet in the time domain.

[0165] In some embodiments of the present application, the process by which the characteristic wavelet integration unit 23 obtains the first energy value of the amplitude spectrum of the air gun data characteristic wavelet may include:

[0166] Calculating the first energy value S of the amplitude spectrum of the air gun data characteristic wavelet using the following equation q :

[0167]

[0168] Among them, F q (f) is the amplitude spectrum of the air gun data characteristic wavelet, and f q1 is the low cut-off frequency of the air gun data characteristic wavelet, and f q2 is the high cut-off frequency of the air gun data characteristic wavelet, is the coefficient of the Newton-Cotes quadrature formula.

[0169] In some embodiments of the present application, the process of the characteristic wavelet integration unit 23 obtaining the second energy value of the amplitude spectrum of the spark data characteristic wavelet may include:

[0170] Calculating the second energy value S of the amplitude spectrum of the spark data characteristic wavelet by using the following equation d :

[0171]

[0172] Among them, F d (f) is the amplitude spectrum of the spark data characteristic wavelet, and f d1 is the low cut-off frequency of the spark data characteristic wavelet, and f d2 is the high cut-off frequency of the spark data characteristic wavelet, is the coefficient of the Newton-Cotes quadrature formula.

[0173] In some embodiments of the present application, based on the first energy value and the second energy value, the characteristic wavelet integration unit 23 adjusts the amplitude spectra of the air gun data characteristic wavelet and the spark data characteristic wavelet to have the same energy value through amplitude translation, and obtains the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the spark data characteristic wavelet, which may include:

[0174] Calculating the translation Δ of the amplitude spectrum of the spark data characteristic wavelet by using the following equation c to obtain the third energy value S' d :

[0175]

[0176] Combining the following equation to calculate Δ c :

[0177] S' d = S q

[0178] Calculating the adjusted amplitude spectrum F' of the spark data characteristic wavelet by using the following equation d (f k ):

[0179] F′ d (f k )=F d (f k )+Δ c

[0180] Among them, the translation amount of the amplitude spectrum of the air gun data characteristic wavelet is 0, and the amplitude spectrum F′ of the adjusted air gun data characteristic wavelet q (f k )=F q (f k ).

[0181] The multi-source mixed acquisition data processing device for marine seismic exploration provided by the embodiments of the present application can be applied to multi-source mixed acquisition data processing equipment for marine seismic exploration, such as a computer, etc. Optionally, Figure 3 shows the hardware structure block diagram of the multi-source mixed acquisition data processing equipment for marine seismic exploration. Referring to Figure 3 , the hardware structure of the multi-source mixed acquisition data processing equipment for marine seismic exploration may include: at least one processor 31, at least one communication interface 32, at least one memory 33, and at least one communication bus 34.

[0182] In the embodiments of the present application, the number of the processor 31, the communication interface 32, the memory 33, and the communication bus 34 is at least one, and the processor 31, the communication interface 32, and the memory 33 complete mutual communication through the communication bus 34;

[0183] The processor 31 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application, etc.;

[0184] The memory 33 may include a high-speed RAM memory, and may also include a non-volatile memory, etc., such as at least one disk memory;

[0185] Among them, the memory 33 stores a program, and the processor 31 can call the program stored in the memory 33, and the program is used for:

[0186] Based on the collected air gun seismic data, obtain the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet;

[0187] Based on the collected sparker seismic data, obtain the sparker data time-domain imaging profile and the sparker data characteristic wavelet;

[0188] Integrate the air gun data characteristic wavelet and the sparker data characteristic wavelet to obtain an integrated wavelet in the time domain;

[0189] Use the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to perform wave impedance inversion to obtain a wave impedance profile;

[0190] Use the wave impedance profile, the sparker data time domain imaging profile, and the sparker data characteristic wavelet to perform wave impedance inversion to obtain a reflection coefficient profile;

[0191] Use the integrated wavelet in the time domain and the reflection coefficient profile to perform convolution operation to obtain the target integrated data.

[0192] Optionally, the refinement function and the extension function of the program can be referred to the above description.

[0193] The embodiment of the present application also provides a storage medium, which can store a program suitable for execution by a processor, and the program is used for:

[0194] Based on the collected air gun seismic data, obtain the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet;

[0195] Based on the collected sparker seismic data, obtain the sparker data time domain imaging profile and the sparker data characteristic wavelet;

[0196] Integrate the air gun data characteristic wavelet and the sparker data characteristic wavelet to obtain an integrated wavelet in the time domain;

[0197] Use the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to perform wave impedance inversion to obtain a wave impedance profile;

[0198] Use the wave impedance profile, the sparker data time domain imaging profile, and the sparker data characteristic wavelet to perform wave impedance inversion to obtain a reflection coefficient profile;

[0199] Use the integrated wavelet in the time domain and the reflection coefficient profile to perform convolution operation to obtain the target integrated data.

[0200] Optionally, the refinement function and the extension function of the program can be referred to the above description.

[0201] In summary:

[0202] This application first obtains preprocessed air gun seismic data, a first velocity model, and an air gun data characteristic wavelet based on the collected air gun seismic data. At the same time, based on the collected sparker seismic data, a sparker data time-domain imaging profile and a sparker data characteristic wavelet are obtained. Then, the air gun data characteristic wavelet and the sparker data characteristic wavelet are integrated to obtain an integrated wavelet in the time domain, ensuring that the air gun data characteristic wavelet and the sparker data characteristic wavelet have the same spectral energy during integration. The fact that the air gun data characteristic wavelet and the sparker data characteristic wavelet have the same energy means that the two types of data have the same weight during integration, and the relative amplitude of the high-frequency component corresponding to the sparker source seismic data is relatively low (because its frequency band is wider), which is consistent with the actual characteristic of severe high-frequency attenuation of seismic data while broadening the frequency band. Next, wave impedance inversion is performed using the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to obtain a wave impedance profile. Since the sparker source seismic data and the air gun source seismic data are preprocessed separately before integration to remove regular interference mainly composed of multiple waves and irregular interference mainly composed of random noise, the integrated profile can be regarded as only containing primary reflections at the interface, which is conducive to the direct geological interpretation of the seismic profile. Wave impedance inversion is performed using the wave impedance profile, the sparker data time-domain imaging profile, and the sparker data characteristic wavelet to obtain a reflection coefficient profile. During the process of obtaining the reflection coefficient profile, a series inversion process of the air gun source seismic data and the sparker source seismic data is adopted. The wave impedance model obtained by inverting the air gun source seismic data at the low-frequency end is used as the initial model, the sparker data time-domain imaging profile at the high-frequency end is used as the input data, and the sparker source characteristic wavelet is used as the input wavelet for wave impedance inversion and the reflection coefficient profile is extracted therefrom, so that the extracted reflection coefficient profile also has the characteristics of wide frequency band and high resolution. Finally, convolution operation is performed using the integrated wavelet in the time domain and the reflection coefficient profile to obtain the target integrated data. This application realizes the integration of the seismic data excited by the air gun source and the seismic data excited by the sparker source, solves the problem of inconsistent wavelets of the two types of seismic data, and the integrated seismic data has both the frequency bands of the seismic data excited by the air gun and the seismic data excited by the sparker, obtaining wide-frequency and high-resolution seismic data. Using it can greatly improve the imaging accuracy and serve the fine exploration of oil and gas and other resources and the detection of deep geological structures.

[0203] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0204] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0205] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing multi-source mixed acquisition data in marine seismic exploration, characterized in that, Including: Based on the collected air gun seismic data, obtain the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet; Based on the collected sparker seismic data, obtain the sparker data time-domain imaging profile and the sparker data characteristic wavelet; Integrate the air gun data characteristic wavelet and the sparker data characteristic wavelet to obtain the time-domain integrated wavelet; Use the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to perform wave impedance inversion to obtain a wave impedance profile; Use the wave impedance profile, the sparker data time-domain imaging profile, and the sparker data characteristic wavelet to perform wave impedance inversion to obtain a reflection coefficient profile; Perform convolution operation using the time-domain integrated wavelet and the reflection coefficient profile to obtain the target integrated data; The process of integrating the air gun data characteristic wavelet and the sparker data characteristic wavelet to obtain the time-domain integrated wavelet includes: Obtain the first energy value of the amplitude spectrum of the air gun data characteristic wavelet and obtain the second energy value of the amplitude spectrum of the sparker data characteristic wavelet; Based on the first energy value and the second energy value, adjust the amplitude spectra of the air gun data characteristic wavelet and the sparker data characteristic wavelet to have the same energy value through amplitude translation, obtaining the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the sparker data characteristic wavelet; Add the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the sparker data characteristic wavelet to obtain the amplitude spectrum of the integrated wavelet; Perform inverse Fourier transform on the amplitude spectrum of the integrated wavelet to obtain the time-domain integrated wavelet.

2. The method according to claim 1, characterized in that, The process of obtaining the preprocessed air gun seismic data and the first velocity model based on the collected air gun seismic data includes: For the collected air gun seismic data, remove irregular interference and regular interference composed of multiple waves to obtain the preprocessed air gun seismic data; Perform dynamic correction stacking processing and velocity analysis on the preprocessed air gun seismic data to obtain the first velocity model.

3. The method according to claim 1, wherein The process of obtaining the air gun data characteristic wavelet includes: Use the preprocessed air gun seismic data and the first velocity model to perform prestack time migration (PSTM) processing on each shot gather to obtain the first PSTM result of each shot gather of the air gun seismic data; Stack the first PSTM results of each shot gather of the air gun seismic data to obtain the air gun seismic data time-domain imaging profile; Use the wavelet zero-phase method to convert the non-zero phase wavelet in the air gun seismic data time-domain imaging profile into a zero-phase wavelet to obtain the zero-phase air gun seismic data time-domain imaging profile; Perform Fourier transform and smoothing processing on the data of each seismic trace in the zero-phase air gun seismic data time-domain imaging profile, and when the reflection coefficient satisfies the condition of white noise, obtain the amplitude spectrum of the seismic wavelet corresponding to each seismic trace in the air gun seismic data; Take the average of the amplitude spectra of the seismic wavelets of each seismic trace in the air gun seismic data to obtain the amplitude spectrum of the characteristic wavelet of the air gun seismic data; Perform an inverse Fourier transform on the amplitude spectrum of the characteristic wavelet of the air gun seismic data to obtain the characteristic wavelet of the air gun data.

4. The method according to claim 1, wherein The process of obtaining the time-domain imaging profile of the sparker data based on the collected sparker seismic data includes: For the collected sparker seismic data, remove irregular interference and regular interference composed of multiple waves to obtain the preprocessed sparker seismic data; Perform dynamic correction stacking processing and velocity analysis on the preprocessed sparker seismic data to obtain a second velocity model; Use the preprocessed sparker seismic data and the second velocity model to perform prestack time migration (PSTM) processing on each shot gather to obtain the second PSTM result of each shot gather of the sparker seismic data; Stack the second PSTM results of each shot gather of the sparker seismic data to obtain the time-domain imaging profile of the sparker seismic data.

5. The method according to claim 1, wherein The process of obtaining the characteristic wavelet of the sparker data includes: Use the wavelet zero-phase method to convert the non-zero phase wavelet in the time-domain imaging profile of the sparker seismic data into a zero-phase wavelet to obtain the zero-phase time-domain imaging profile of the sparker seismic data; Perform Fourier transform and smoothing processing on the data of each seismic trace in the zero-phase time-domain imaging profile of the sparker seismic data. Under the condition that the reflection coefficient satisfies white noise, the amplitude spectrum of the seismic wavelet corresponding to each seismic trace in the sparker seismic data can be obtained; Take the average value of the amplitude spectra of the seismic wavelets of each seismic trace in the sparker seismic data to obtain the amplitude spectrum of the characteristic wavelet of the sparker data; Perform an inverse Fourier transform on the amplitude spectrum of the characteristic wavelet of the sparker data to obtain the characteristic wavelet of the sparker data.

6. The method according to claim 1, wherein The process of obtaining the first energy value of the amplitude spectrum of the air gun data characteristic wavelet includes: The first energy value S of the amplitude spectrum of the air gun data characteristic wavelet is calculated using the following equation q : Among them, F q (f) is the amplitude spectrum of the air gun data characteristic wavelet, and f q1 is the low cut-off frequency of the air gun data characteristic wavelet, and f q2 is the high cut-off frequency of the air gun data characteristic wavelet, is the coefficient of the Newton-Cotes quadrature formula.

7. The method according to claim 1, wherein The process of obtaining the second energy value of the amplitude spectrum of the sparker data characteristic wavelet includes: The second energy value S of the amplitude spectrum of the electro-spark data characteristic wavelet is calculated using the following equation d :[[]]END]] Among them, F d (f) is the amplitude spectrum of the spark data characteristic wavelet, f d1 is the low cut-off frequency of the spark data characteristic wavelet, f d2 is the high cut-off frequency of the spark data characteristic wavelet, is the coefficient of the Newton-Cotes quadrature formula.

8. The method according to claim 7, wherein Based on the first energy value and the second energy value, adjust the amplitude spectra of the air gun data characteristic wavelet and the sparker data characteristic wavelet to have the same energy value through amplitude translation, to obtain the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the sparker data characteristic wavelet, including: The third energy value S' after the amplitude spectrum of the electro - spark data characteristic wavelet is translated by Δ is calculated using the following equation c d :​ Calculate Δ according to the following equation c :[[]]END]] S' d = S q The amplitude spectrum F' of the adjusted feature wavelet of the electrical discharge data is calculated using the following equation d (f k ): F′ d (f k ) = F d (f k ) + Δ c Among them, the translation amount of the amplitude spectrum of the air gun data characteristic wavelet is 0, and the amplitude spectrum F′ of the adjusted air gun data characteristic wavelet q (f k ) = F q (f k ).

9. An apparatus for processing multi-source mixed acquisition data in marine seismic exploration, characterized in that, Including: An air gun seismic data processing unit, used to obtain the preprocessed air gun seismic data, a first velocity model, and the air gun data characteristic wavelet based on the collected air gun seismic data; A sparker seismic data processing unit, used to obtain the time-domain imaging profile of the sparker data and the sparker data characteristic wavelet based on the collected sparker seismic data; A characteristic wavelet integration unit, used to integrate the air gun data characteristic wavelet and the sparker data characteristic wavelet to obtain an integrated wavelet in the time domain; A wave impedance profile acquisition unit, used to perform wave impedance inversion using the preprocessed air gun seismic data, the first velocity model, and the air gun data characteristic wavelet to obtain a wave impedance profile; A reflection coefficient profile acquisition unit, used to perform wave impedance inversion using the wave impedance profile, the time-domain imaging profile of the sparker data, and the sparker data characteristic wavelet to obtain a reflection coefficient profile; A target integration data acquisition unit, configured to perform convolution operation by using the time-domain integration wavelet and the reflection coefficient profile to obtain target integration data; Specifically, the characteristic wavelet integration unit is configured to acquire a first energy value of the amplitude spectrum of the air gun data characteristic wavelet, and acquire a second energy value of the amplitude spectrum of the sparker data characteristic wavelet; Based on the first energy value and the second energy value, adjust the amplitude spectra of the air gun data characteristic wavelet and the sparker data characteristic wavelet to have the same energy value through amplitude translation, so as to obtain the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the sparker data characteristic wavelet; add the adjusted amplitude spectrum of the air gun data characteristic wavelet and the adjusted amplitude spectrum of the sparker data characteristic wavelet to obtain the amplitude spectrum of the integrated wavelet; perform inverse Fourier transform on the amplitude spectrum of the integrated wavelet to obtain the time-domain integration wavelet.

10. An apparatus for processing multi-source mixed acquisition data in marine seismic exploration, characterized in that, It includes: a memory and a processor; The memory is configured to store a program; The processor is configured to execute the program to implement each step of the marine seismic exploration multi-source mixed acquisition data processing method according to any one of claims 1 to 8.

11. A storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, each step of the marine seismic exploration multi-source mixed acquisition data processing method according to any one of claims 1 to 8 is implemented.

Images