A seismic imaging profile extension method and system based on random noise driving

Abstract

The present invention relates to a method and system for seismic imaging profile extension based on random noise. The method comprises: obtaining a seismic reflection wave imaging profile according to original seismic data; performing Fourier transform and normalization processing on each seismic trace in the seismic reflection wave imaging profile in turn to obtain a normalized amplitude spectrum of the seismic reflection wave imaging profile; obtaining a Gaussian random signal according to the normalized amplitude spectrum of the seismic reflection wave imaging profile and a low-pass filter; superimposing the Gaussian random signal on the seismic reflection wave imaging profile to obtain a seismic profile loaded with broadband random noise; performing cross-correlation processing on the seismic profile loaded with broadband random noise and the seismic reflection wave imaging profile to obtain a cross-correlation seismic profile; performing cross-correlation processing on the seismic profile loaded with broadband random noise and the cross-correlation seismic profile to obtain a seismic reflection wave imaging profile after random noise extension. The present invention can improve the bandwidth and resolution of the seismic imaging profile.

Description

Technical Field

[0001] The present invention relates to the technical field of onshore or marine seismic data processing and imaging, and in particular to a random noise-driven seismic imaging profile extension method and system. Background Art

[0002] At present, with the continuous improvement of China's oil and gas and other resource exploration, the focus of exploration has gradually shifted from shallow to deep and ultra-deep layers, and the exploration area has also shifted from simple geological conditions to complex geological conditions. Complex geological conditions often face the dual difficulties and challenges of complex near-surface and complex deep structures. Among them, the strong noise interference of the complex near-surface and the absorption and attenuation of the weathering layer lead to low signal-to-noise ratio and poor quality of shallow seismic reflection waves, while affected by spherical diffusion and strong absorption and attenuation of deep complex media, deep seismic reflection waves often exist in the form of weak signals with low resolution. Therefore, complex geological conditions cause seismic reflection waves to often exist in the form of weak signals with low signal-to-noise ratio and low resolution, affecting the resolution of seismic reflection wave imaging profiles, greatly reducing the application effect of seismic reflection wave imaging technology in actual exploration, and increasing the risk of resource exploitation.

[0003] At the same time, as the exploration of resources such as oil and gas has shifted from simple structural oil and gas exploration to complex structural oil and gas reservoir exploration and lithologic oil and gas reservoir exploration, major oil companies have increasingly higher requirements for the resolution of seismic imaging profiles. Although conventional stacking imaging methods can significantly improve the signal-to-noise ratio of weak seismic reflection waves, they cannot make up for the loss of low- and high-frequency energy and cannot meet the needs of high-resolution exploration. Traditional frequency extension methods based on deconvolution or inverse Q filtering often improve high-frequency components and cannot effectively compensate for low-frequency components. Compared with high-frequency components, low-frequency components have stronger anti-absorption and deeper penetration capabilities, which are more conducive to expanding the bandwidth of effective reflection waves, enhancing the recognition ability of special lithology, and realizing high-fidelity imaging of deep complex structures. However, there are currently few studies and applications on low-frequency energy compensation, and the existing low-frequency compensation methods have certain limitations and cannot meet the needs of high-precision seismic exploration under complex geological conditions; random noise has the characteristics of natural broadband, which can meet the needs of low- and high-frequency energy compensation at the same time. Therefore, in order to obtain wider-bandwidth seismic reflection wave information and imaging profiles under complex wave field conditions, it is still necessary to continue to study and design a stable and reliable seismic data frequency extension method to address the shortcomings of existing methods and technologies and improve the bandwidth and resolution of seismic imaging profiles. Summary of the invention

[0004] The purpose of the present invention is to provide a seismic imaging profile frequency extension method and system based on random noise driving, which can improve the frequency bandwidth and resolution of the seismic imaging profile.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A seismic imaging profile extension method based on random noise driving, comprising:

[0007] Acquiring original seismic data and obtaining a seismic reflection wave imaging profile based on the original seismic data;

[0008] Performing Fourier transformation and normalization processing on each seismic trace in the seismic reflection wave imaging section in turn to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section;

[0009] A Gaussian random signal is obtained according to the normalized amplitude spectrum of the seismic reflection wave imaging section and a low-pass filter; the Gaussian random signal has a broadband characteristic;

[0010] Superimposing the Gaussian random signal on the seismic reflection wave imaging section to obtain a seismic section loaded with broadband random noise;

[0011] Performing cross-correlation processing on the seismic section loaded with broadband random noise and the seismic reflection wave imaging section to obtain a cross-correlation seismic section;

[0012] The seismic profile loaded with broadband random noise and the cross-correlation seismic profile are cross-correlated to obtain a seismic reflection wave imaging profile after random noise frequency extension.

[0013] Optionally, the cross-correlation processing is performed on the seismic profile loaded with broadband random noise and the cross-correlation seismic profile to obtain the seismic reflection wave imaging profile after random noise frequency spreading, specifically including:

[0014] According to the seismic reflection wave imaging section and the seismic section loaded with broadband random noise, the cross-correlation seismic section is subjected to amplitude consistency correction processing to obtain the cross-correlation seismic section after amplitude consistency correction;

[0015] The seismic profile loaded with broadband random noise and the cross-correlation seismic profile corrected for amplitude consistency are cross-correlated to obtain a seismic reflection wave imaging profile after random noise frequency spreading.

[0016] Optionally, the performing Fourier transform and normalization processing on each seismic trace in the seismic reflection wave imaging section in sequence to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section specifically includes:

[0017] Performing Fourier transformation on each seismic trace in the seismic reflection wave imaging section to obtain an amplitude spectrum of each seismic trace;

[0018] Calculating the sum of the amplitude spectra of each of the seismic traces;

[0019] The normalized amplitude spectrum of the seismic reflection wave imaging section is obtained by normalizing the amplitude spectrum of the sum and each seismic trace.

[0020] Optionally, the step of obtaining a Gaussian random signal based on a normalized amplitude spectrum of the seismic reflection wave imaging section and a low-pass filter specifically includes:

[0021] Obtaining the seismic main frequency according to the normalized amplitude spectrum of the seismic reflection wave imaging section;

[0022] A Gaussian random signal is obtained according to the earthquake main frequency and the low-pass filter.

[0023] Optionally, the bandwidth of the low-pass filter is three times the bandwidth of the earthquake main frequency.

[0024] A seismic imaging profile extension system based on random noise driving, comprising:

[0025] An acquisition module, used for acquiring original seismic data and obtaining a seismic reflection wave imaging profile according to the original seismic data;

[0026] A normalization module, used for performing Fourier transform and normalization processing on each seismic trace in the seismic reflection wave imaging section in turn to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section;

[0027] A filtering module, used for obtaining a Gaussian random signal according to a normalized amplitude spectrum of the seismic reflection wave imaging section and a low-pass filter; the Gaussian random signal has a broadband characteristic;

[0028] A noise loading module, used for superimposing the Gaussian random signal on the seismic reflection wave imaging section to obtain a seismic section loaded with broadband random noise;

[0029] A cross-correlation module, used for performing cross-correlation processing on the seismic section loaded with broadband random noise and the seismic reflection wave imaging section to obtain a cross-correlation seismic section;

[0030] The frequency extension module is used to perform cross-correlation processing on the seismic profile loaded with broadband random noise and the cross-correlation seismic profile to obtain a seismic reflection wave imaging profile after random noise frequency extension.

[0031] Optionally, the spectrum extension module specifically includes:

[0032] A correction unit, configured to perform amplitude consistency correction processing on the cross-correlation seismic profile according to the seismic reflection wave imaging profile and the seismic profile loaded with broadband random noise to obtain a cross-correlation seismic profile after amplitude consistency correction;

[0033] The frequency extension unit is used to perform cross-correlation processing on the seismic section loaded with broadband random noise and the cross-correlation seismic section after amplitude consistency correction to obtain a seismic reflection wave imaging section after random noise frequency extension.

[0034] Optionally, the normalization module specifically includes:

[0035] A Fourier transform unit, used for performing Fourier transform on each seismic trace in the seismic reflection wave imaging section to obtain an amplitude spectrum of each seismic trace;

[0036] A calculation unit, used for calculating the sum of the amplitude spectra of each of the seismic traces;

[0037] A normalization unit is used to perform normalization processing on the amplitude spectrum of the sum and each of the seismic traces to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section.

[0038] Optionally, the filtering module specifically includes:

[0039] A seismic main frequency determination unit, used for obtaining the seismic main frequency according to the normalized amplitude spectrum of the seismic reflection wave imaging section;

[0040] The Gaussian random signal determination module is used to obtain a Gaussian random signal according to the earthquake main frequency and a low-pass filter.

[0041] Optionally, the bandwidth of the low-pass filter is three times the bandwidth of the earthquake main frequency.

[0042] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention obtains a seismic reflection wave imaging profile according to original seismic data; Fourier transform and normalization processing are performed on each seismic trace in the seismic reflection wave imaging profile in turn to obtain a normalized amplitude spectrum of the seismic reflection wave imaging profile; a Gaussian random signal is obtained according to the normalized amplitude spectrum of the seismic reflection wave imaging profile and a low-pass filter; the Gaussian random signal is superimposed on the seismic reflection wave imaging profile to obtain a seismic profile after loading broadband random noise; cross-correlation processing is performed on the seismic profile after loading broadband random noise and the seismic reflection wave imaging profile to obtain a cross-correlation seismic profile; cross-correlation processing is performed on the seismic profile after loading broadband random noise and the cross-correlation seismic profile to obtain a seismic reflection wave imaging profile after random noise spectrum extension, which can improve the frequency bandwidth and resolution of the seismic imaging profile. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 A flow chart of a seismic imaging profile extension method based on random noise driving provided in an embodiment of the present invention;

[0045] Figure 2 It is a seismic reflection wave imaging profile representing the formation pinch-out;

[0046] Figure 3 for Figure 2 Normalized amplitude spectra of the profiles shown;

[0047] Figure 4 is a low-pass filter diagram representing the amplitude spectrum characteristics of random noise;

[0048] Figure 5 This is the seismic reflection wave imaging profile after superimposing broadband random noise;

[0049] Figure 6 for Figure 5 Normalized amplitude spectra of the profiles shown;

[0050] Figure 7 is the cross-correlation seismic profile after amplitude consistency correction;

[0051] Figure 8 This is a profile image of seismic reflection waves after random noise extension.

[0052] Fig. 9 for Figure 8 Normalized amplitude spectrum of the shown profile. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] The purpose of the present invention is to provide a data-driven seismic imaging profile extension method using random noise and amplitude consistency cross-correlation to improve the resolution and fidelity of seismic profiles, realize the seismic identification capability of thin interbedded structures and special lithology bodies under complex geological conditions, and reduce the risk of resource exploitation, in view of the fact that the existing seismic data extension methods cannot achieve good compensation for broadband energy and cannot meet the needs of high-precision seismic exploration under complex geological conditions.

[0056] like Figure 1 As shown, an embodiment of the present invention provides a seismic imaging profile extension method based on random noise driving, comprising:

[0057] Step 101: Acquire original seismic data and obtain a seismic reflection wave imaging profile based on the original seismic data.

[0058] Step 102: Perform Fourier transform and normalization processing on each seismic trace in the seismic reflection wave imaging section in turn to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section.

[0059] Step 103: Obtain a Gaussian random signal according to the normalized amplitude spectrum of the seismic reflection wave imaging section and a low-pass filter; the Gaussian random signal has a broadband characteristic.

[0060] Step 104: superimposing the Gaussian random signal onto the seismic reflection wave imaging section to obtain a seismic section loaded with broadband random noise.

[0061] Step 105: cross-correlation processing is performed on the seismic profile loaded with broadband random noise and the seismic reflection wave imaging profile to obtain a cross-correlation seismic profile.

[0062] Step 106: cross-correlation processing is performed on the seismic profile loaded with broadband random noise and the cross-correlation seismic profile to obtain a seismic reflection wave imaging profile after random noise frequency spreading.

[0063] In practical applications, the cross-correlation processing of the seismic profile loaded with broadband random noise and the cross-correlation seismic profile to obtain the seismic reflection wave imaging profile after random noise frequency spreading specifically includes:

[0064] According to the seismic reflection wave imaging section and the seismic section loaded with broadband random noise, amplitude consistency correction processing is performed on the cross-correlation seismic section to obtain the cross-correlation seismic section after amplitude consistency correction.

[0065] The seismic profile loaded with broadband random noise and the cross-correlation seismic profile corrected for amplitude consistency are cross-correlated to obtain a seismic reflection wave imaging profile after random noise frequency spreading.

[0066] In practical applications, the step of sequentially performing Fourier transform and normalization processing on each seismic trace in the seismic reflection wave imaging section to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section specifically includes:

[0067] Perform Fourier transform on each seismic trace in the seismic reflection wave imaging section to obtain the amplitude spectrum of each seismic trace.

[0068] The sum of the amplitude spectra of the seismic traces is calculated.

[0069] The normalized amplitude spectrum of the seismic reflection wave imaging section is obtained by normalizing the amplitude spectrum of the sum and each seismic trace.

[0070] In practical applications, the Gaussian random signal is obtained according to the normalized amplitude spectrum of the seismic reflection wave imaging section and the low-pass filter, specifically including:

[0071] The seismic main frequency is obtained according to the normalized amplitude spectrum of the seismic reflection wave imaging section.

[0072] A Gaussian random signal is obtained according to the earthquake main frequency and the low-pass filter.

[0073] In practical applications, the bandwidth of the low-pass filter is three times the bandwidth of the earthquake main frequency.

[0074] The embodiment of the present invention further provides a seismic imaging profile extension system based on random noise driving corresponding to the above method, comprising:

[0075] The acquisition module is used to acquire original seismic data and obtain a seismic reflection wave imaging profile based on the original seismic data.

[0076] The normalization module is used to perform Fourier transform and normalization processing on each seismic trace in the seismic reflection wave imaging section in turn to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section.

[0077] The filtering module is used to obtain a Gaussian random signal according to the normalized amplitude spectrum of the seismic reflection wave imaging section and a low-pass filter; the Gaussian random signal has a broadband feature.

[0078] The noise loading module is used to superimpose the Gaussian random signal on the seismic reflection wave imaging section to obtain a seismic section loaded with broadband random noise.

[0079] The cross-correlation module is used to perform cross-correlation processing on the seismic section loaded with broadband random noise and the seismic reflection wave imaging section to obtain a cross-correlation seismic section.

[0080] The frequency extension module is used to perform cross-correlation processing on the seismic profile loaded with broadband random noise and the cross-correlation seismic profile to obtain a seismic reflection wave imaging profile after random noise frequency extension.

[0081] As an optional implementation, the spectrum extension module specifically includes:

[0082] The correction unit is used to perform amplitude consistency correction processing on the cross-correlation seismic section according to the seismic reflection wave imaging section and the seismic section loaded with broadband random noise to obtain the cross-correlation seismic section after amplitude consistency correction.

[0083] The frequency extension unit is used to perform cross-correlation processing on the seismic section loaded with broadband random noise and the cross-correlation seismic section after amplitude consistency correction to obtain a seismic reflection wave imaging section after random noise frequency extension.

[0084] As an optional implementation, the normalization module specifically includes:

[0085] The Fourier transform unit is used to perform Fourier transform on each seismic trace in the seismic reflection wave imaging section to obtain the amplitude spectrum of each seismic trace.

[0086] A calculation unit is used to calculate the sum of the amplitude spectra of each of the seismic traces.

[0087] A normalization unit is used to perform normalization processing on the amplitude spectrum of the sum and each of the seismic traces to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section.

[0088] As an optional implementation, the filtering module specifically includes:

[0089] The seismic main frequency determination unit is used to obtain the seismic main frequency according to the normalized amplitude spectrum of the seismic reflection wave imaging section.

[0090] The Gaussian random signal determination module is used to obtain a Gaussian random signal according to the earthquake main frequency and a low-pass filter.

[0091] As an optional implementation, the bandwidth of the low-pass filter is three times the bandwidth of the earthquake main frequency.

[0092] The embodiment of the present invention also provides a specific process of using the above method to perform the following processing:

[0093] Step 1: Use the original seismic data (observation system data and original seismic data) and conventional seismic data processing methods such as noise suppression, static correction, deconvolution, stacking and migration to obtain seismic reflection wave imaging profiles with high signal-to-noise ratio.

[0094] like Figure 2 The figure shows a seismic reflection wave imaging profile with formation pinch-out characteristics. It can be seen that as the common center point number continues to decrease, the distance between the upper and lower formation reflection interfaces gradually decreases, until after the common center point number 5, the upper and lower formation reflections are completely merged together and cannot be distinguished. The wedge-shaped imaging profile can clearly show the relationship between the seismic resolution and the thickness of the formation. In order to achieve fine exploration of thin-layer structures and complex formations, it is necessary to perform frequency extension processing on the imaging profile to provide high-resolution seismic results for the high-precision interpretation and inversion of subsequent seismic data.

[0095] Step 2: Using the seismic imaging profile obtained in step 1, Figure 2 Each vertical line in the diagram is called a seismic trace, representing each piece of seismic data) is transformed by Fourier to obtain its amplitude spectrum, and the amplitude spectra of all seismic traces are superimposed and normalized to obtain the normalized amplitude spectrum of the seismic profile.

[0096] Figure 3 for Figure 2 From the normalized amplitude spectrum of the seismic section shown, we can see that the bandwidth of actual seismic data is very limited, which reduces the resolution and fidelity of seismic exploration.

[0097] Step 3: Use the normalized amplitude spectrum of the seismic profile to estimate the earthquake main frequency information Fm.

[0098] like Figure 2 The main frequency information of the seismic profile shown is about 20 Hz. When the frequency is less than 20 Hz, the low-frequency information of the seismic reflection wave decays sharply; when the frequency is greater than 20 Hz, the high-frequency information of the seismic reflection wave also decays smoothly to 60 Hz.

[0099] Step 4: Using the estimated earthquake main frequency information Fm, design a low-pass filter with a width three times that of the earthquake main frequency, use the bandwidth of the low-pass filter to represent the bandwidth of the random signal, and generate a Gaussian random signal with broadband characteristics.

[0100] like Figure 4 The figure shows a low-pass filter with a trapezoidal shape. It can be seen that the main inflection point frequencies of the filter are related to the estimated earthquake main frequency information Fm. Compared with the normalized amplitude spectrum of the original seismic signal, the amplitude spectrum of the random signal represented by the low-pass filter not only contains higher high-frequency components, but also has lower frequency components. Therefore, the random signal has the characteristics of a broadband signal.

[0101] Step 5: superimpose the broadband random signal calculated in step 4 onto the seismic reflection wave imaging profile obtained in step 1 to obtain a seismic profile loaded with broadband random noise.

[0102] like Figure 5As shown in the figure, the random noise with strong amplitude destroys the continuity of the seismic reflection event axis, resulting in a significant reduction in the signal-to-noise ratio of the entire seismic section.

[0103] Step 6. Compare and check the original seismic profile and the final seismic profile: Use the seismic profile after loading broadband random noise to perform Fourier transform on each seismic data to obtain its amplitude spectrum, and superimpose and normalize the amplitude spectra of all seismic channels to obtain the normalized amplitude spectrum of the seismic profile after loading broadband random noise, and check the correctness of noise loading from the frequency domain perspective.

[0104] Figure 6 is the normalized amplitude spectrum of the seismic profile after loading broadband random noise, and Figure 3 By comparison, it can be seen that after noise loading, the main frequency of the entire seismic profile is still 20 Hz, but the bandwidth is greatly expanded, with the low-frequency component reaching below 1 Hz and the high-frequency component expanding to 60 Hz, until it decays to 80 Hz representing the main frequency components of the four earthquakes, which is consistent with the Figure 4 The frequency band of the low pass filter shown is consistent.

[0105] Step 7: Using the high signal-to-noise ratio seismic profile obtained in step 1 and the seismic profile loaded with broadband random noise calculated in step 5, cross-correlation processing is performed to obtain a cross-correlation seismic profile representing energy intensity. The formula for obtaining the cross-correlation seismic profile is:

[0106] C(i,ω)=N(i,ω)S(i,ω) *

[0107] Among them, * represents the complex conjugate, i represents the common center point number, and ω represents the angular frequency. S(i,ω) represents the high signal-to-noise ratio seismic profile of the i-th common center point number obtained in step 1, N(i,ω) represents the seismic profile of the i-th common center point number generated in step 5 after loading broadband random noise, and C(i,ω) represents the cross-correlation seismic profile of the i-th common center point number generated in step 7.

[0108] Step 8: Using the high signal-to-noise ratio seismic profile obtained in step 1, the seismic profile after loading broadband random noise calculated in step 5, and the cross-correlation seismic profile calculated in step 7, amplitude consistency correction is performed to obtain the cross-correlation seismic profile after amplitude consistency correction. The formula for obtaining the cross-correlation seismic profile is:

[0109]

[0110] Among them, || represents the amplitude spectrum, w represents the white noise coefficient, and H(i,ω) represents the cross-correlation seismic profile after amplitude consistency correction of the i-th common center point number generated in step eight.

[0111] like Figure 7The cross-correlation seismic profile after amplitude consistency correction is shown. It can be seen that since the original high signal-to-noise ratio seismic profile and the seismic profile after loading broadband random noise are both seismic reflection wave fields containing the same main frequency components, the cross-correlation seismic profile after amplitude consistency correction is similar to the autocorrelation seismic profile, and the amplitude maximum appears at time zero, and the random noise shows the characteristics of oscillation attenuation along the positive and negative semi-axes at time zero.

[0112] Step 9: Using the seismic profile loaded with broadband random noise obtained in step 5 and the cross-correlation profile after amplitude consistency correction calculated in step 8, further cross-correlation processing is performed to obtain the seismic reflection wave imaging profile after random noise spectrum extension. The formula for obtaining the cross-correlation profile is:

[0113] I(i,ω)=N(i,ω)H(i,ω) *

[0114] Among them, I(i,ω) represents the seismic reflection wave imaging profile after the random noise extension of the i-th common center point number generated in step 9, and H(i,ω) * represents the complex conjugate of the cross-correlation seismic profile after amplitude consistency correction for the i-th common center point number generated in step eight.

[0115] like Figure 8 The figure shows the seismic reflection wave imaging profile obtained by random noise extension in step 9. It can be seen that the two sets of seismic reflection interfaces showing wedge-shaped characteristics are restored with high signal-to-noise ratio. Figure 2 In comparison, the distinction between the upper and lower strata reflection interfaces can be extended to the common center point number 4; at the same time, the sub-wave sidelobes caused by the limited frequency of the original seismic signal are compressed, and the resolution of the entire seismic profile is significantly improved.

[0116] Step 10. Compare the difference in frequency band between the original profile and the final profile, and check the effect of frequency band expansion from the frequency domain: perform Fourier transform on each seismic data in the seismic reflection wave imaging profile after random noise spreading obtained in step 9, obtain its amplitude spectrum, and superimpose and normalize the amplitude spectra of all seismic traces to obtain the normalized amplitude spectrum of the seismic reflection wave imaging profile after random noise spreading, and check the frequency band width of the entire seismic reflection wave imaging profile after cross-correlation processing from the frequency domain perspective.

[0117] Fig. 9 is the normalized amplitude spectrum of the seismic reflection wave imaging profile after random noise extension, and Figure 3 By comparison, it can be seen that after broadband processing, the bandwidth of the entire seismic profile, especially the low-frequency component, has been greatly expanded. Figure 6By comparison, the entire spectrum becomes relatively smooth, indicating that the random noise representing non-effective seismic reflection frequencies is suppressed, thereby ensuring a significant improvement in the signal-to-noise ratio and resolution of the entire seismic profile.

[0118] The beneficial technical effects of the present invention are: using a high signal-to-noise ratio seismic imaging profile to calculate the main frequency range of seismic data; on this basis, designing a random noise low-pass filter to generate a random noise signal with broadband characteristics, and superimposing it on the initial high signal-to-noise ratio seismic profile; then, using amplitude consistency cross-correlation processing to generate an amplitude-corrected cross-correlation profile; finally, using cross-correlation to generate a seismic imaging profile after random noise frequency extension, achieving high-resolution seismic detection based on random noise driving under complex media conditions, and improving the bandwidth and resolution of the seismic imaging profile.

[0119] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0120] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A random noise driven seismic imaging profile extension method, characterized in that: include: Acquiring original seismic data and obtaining a seismic reflection wave imaging profile based on the original seismic data; Performing Fourier transformation and normalization processing on each seismic trace in the seismic reflection wave imaging section in turn to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section; A Gaussian random signal is obtained according to the normalized amplitude spectrum of the seismic reflection wave imaging section and a low-pass filter; the Gaussian random signal has a broadband characteristic; Superimposing the Gaussian random signal on the seismic reflection wave imaging section to obtain a seismic section loaded with broadband random noise; Performing cross-correlation processing on the seismic section loaded with broadband random noise and the seismic reflection wave imaging section to obtain a cross-correlation seismic section; The seismic profile loaded with broadband random noise and the cross-correlation seismic profile are cross-correlated to obtain a seismic reflection wave imaging profile after random noise frequency extension.

2. The random noise driven seismic imaging profile extension method according to claim 1, characterized in that: The cross-correlation processing is performed on the seismic profile loaded with broadband random noise and the cross-correlation seismic profile to obtain the seismic reflection wave imaging profile after random noise frequency spreading, specifically comprising: According to the seismic reflection wave imaging section and the seismic section loaded with broadband random noise, the cross-correlation seismic section is subjected to amplitude consistency correction processing to obtain the cross-correlation seismic section after amplitude consistency correction; The seismic profile loaded with broadband random noise and the cross-correlation seismic profile corrected for amplitude consistency are cross-correlated to obtain a seismic reflection wave imaging profile after random noise frequency spreading.

3. The random noise driven seismic imaging profile extension method according to claim 1, characterized in that: The step of sequentially performing Fourier transformation and normalization processing on each seismic trace in the seismic reflection wave imaging section to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section specifically includes: Performing Fourier transformation on each seismic trace in the seismic reflection wave imaging section to obtain an amplitude spectrum of each seismic trace; Calculating the sum of the amplitude spectra of each of the seismic traces; The normalized amplitude spectrum of the seismic reflection wave imaging section is obtained by normalizing the sum of the amplitude spectra and the amplitude spectra of each seismic trace.

4. The random noise driven seismic imaging profile extension method according to claim 1, characterized in that: The step of obtaining a Gaussian random signal based on the normalized amplitude spectrum of the seismic reflection wave imaging section and a low-pass filter specifically includes: Obtaining the seismic main frequency according to the normalized amplitude spectrum of the seismic reflection wave imaging section; A Gaussian random signal is obtained according to the earthquake main frequency and the low-pass filter.

5. The random noise driven seismic imaging profile extension method according to claim 1, characterized in that: The bandwidth of the low-pass filter is three times the bandwidth of the earthquake main frequency.

6. A seismic imaging profile extension system based on random noise driving, characterized in that: include: An acquisition module, used for acquiring original seismic data and obtaining a seismic reflection wave imaging profile according to the original seismic data; A normalization module, used for performing Fourier transform and normalization processing on each seismic trace in the seismic reflection wave imaging section in turn to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section; A filtering module, used for obtaining a Gaussian random signal according to a normalized amplitude spectrum of the seismic reflection wave imaging section and a low-pass filter; the Gaussian random signal has a broadband characteristic; A noise loading module, used for superimposing the Gaussian random signal on the seismic reflection wave imaging section to obtain a seismic section loaded with broadband random noise; A cross-correlation module, used for performing cross-correlation processing on the seismic section loaded with broadband random noise and the seismic reflection wave imaging section to obtain a cross-correlation seismic section; The frequency extension module is used to perform cross-correlation processing on the seismic profile loaded with broadband random noise and the cross-correlation seismic profile to obtain a seismic reflection wave imaging profile after random noise frequency extension.

7. The random noise driven seismic imaging profile extension system according to claim 6, characterized in that: The spectrum extension module specifically includes: A correction unit, configured to perform amplitude consistency correction processing on the cross-correlation seismic profile according to the seismic reflection wave imaging profile and the seismic profile loaded with broadband random noise to obtain a cross-correlation seismic profile after amplitude consistency correction; The frequency extension unit is used to perform cross-correlation processing on the seismic section loaded with broadband random noise and the cross-correlation seismic section after amplitude consistency correction to obtain a seismic reflection wave imaging section after random noise frequency extension.

8. The random noise driven seismic imaging profile extension system according to claim 6, characterized in that: The normalization module specifically includes: A Fourier transform unit, used for performing Fourier transform on each seismic trace in the seismic reflection wave imaging section to obtain an amplitude spectrum of each seismic trace; A calculation unit, used for calculating the sum of the amplitude spectra of each of the seismic traces; A normalization unit is used to obtain a normalized amplitude spectrum of the seismic reflection wave imaging section by performing normalization processing on the sum of the amplitude spectra and the amplitude spectra of each seismic trace.

9. The random noise driven seismic imaging profile extension system according to claim 6, characterized in that: The filtering module specifically includes: A seismic main frequency determination unit, used for obtaining the seismic main frequency according to the normalized amplitude spectrum of the seismic reflection wave imaging section; The Gaussian random signal determination module is used to obtain a Gaussian random signal according to the earthquake main frequency and a low-pass filter.

10. The random noise driven seismic imaging profile extension system according to claim 6, characterized in that: The bandwidth of the low-pass filter is three times the bandwidth of the earthquake main frequency.

Images