Earthquake imaging method and device based on WSP

Abstract

The invention discloses a WSP-based seismic imaging method and device. The method comprises the steps of S1, acquiring a plurality of CDP dynamic correction gathers; s2, for a certain CDP dynamic calibration trace gather, selecting a standard trace of the CDP dynamic calibration trace gather according to a preset rule; s3, determining correlation coefficients of a standard trace in the CDP dynamic calibration trace gather and other traces; s4, removing the traces of which the correlation coefficients with the standard traces are lower than a set threshold value in the CDP dynamic calibration trace gather to obtain a WSP dynamic calibration trace gather; s5, circulating the steps S2 to S4 until all CDP dynamic correction gathers are converted into WSP dynamic correction gathers; and S6, performing superposition imaging according to each WSP dynamic correction gather. According to the method, the seismic imaging quality is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of seismic exploration, and in particular to a seismic imaging method and device based on WSP. Background Art

[0002] In the past two decades, seismic exploration technology has achieved leapfrog development due to the application of multiple coverage and high-density acquisition technology, and seismic imaging and accuracy have also been significantly improved. However, in the face of the needs of complex geological exploration, especially in the exploration of complex areas, seismic imaging is still stuck in a "bottleneck" stage. Even with up to thousands of coverages, it is difficult to meet the needs of geological interpretation. Local imaging often has problems such as messy waveforms and difficulty in identification and tracking, which seriously limits the further development of exploration work. There is a huge contradiction between the high cost of seismic exploration investment and the slight improvement in imaging quality, which requires us to carry out in-depth scientific and technological innovation rather than simply increase exploration investment.

[0003] The core of multiple coverage is to enhance the intensity of an effective reflection, suppress disordered noise, and improve the signal-to-noise ratio through multiple superpositions of common reflection points. For each coverage signal of the same CDP, after eliminating the influence of various factors, the reflection signal of the same formation should be in phase in the time series. At this time, with the increase in the number of superpositions, the effective reflection energy will gradually increase. However, when the reflection signals of the same formation are out of phase in the time series, or even in opposite phases, the effective signal superposition energy will weaken or become "chaotic", resulting in imaging illusions. There are many reasons for this factor. In the final analysis, the large difference in the reflection sequence of each channel of the same CDP point is the root cause of poor superposition imaging.

[0004] The actual underground structure is complex, and the lateral changes in the underground structure inclination and velocity often cause the actual CDP reflection point to be "dislocated". Figure 1 When the reflection interface is gentle, the reflection signals of the same stratum should be in phase in the time series. At this time, with the increase of the number of stacking, the effective reflection energy will gradually increase; but when the underground structure changes greatly laterally, the receiving channels on the same CDP gather may often come from reflection signals at different locations underground. Traditional CPD stacking technology makes it difficult to achieve in-phase stacking of effective signals in the time domain. Stacking imaging will not be enhanced with the increase of the number of stacking, and the reflection wave group will weaken or become "chaotic", resulting in imaging illusions. Summary of the invention

[0005] The object of the present invention is to provide a seismic imaging method and device based on WSP to improve the quality of seismic imaging.

[0006] In order to solve the above technical problems, the present invention provides a technical solution: a seismic imaging method based on WSP, comprising the following steps:

[0007] S1. Obtain multiple CDP dynamic calibration track sets;

[0008] S2. For a certain CDP dynamic calibration track set, select the standard track of the CDP dynamic calibration track set according to the preset rules;

[0009] S3, determining the correlation coefficient between the standard track and the remaining tracks in the CDP dynamic calibration track set;

[0010] S4, removing the tracks whose correlation coefficients with the standard tracks are lower than a set threshold in the CDP dynamic calibration track set, and obtaining the WSP dynamic calibration track set;

[0011] S5, loop S2 to S4 until all CDP dynamic calibration track sets are converted to WSP dynamic calibration track sets;

[0012] S6. Stack imaging based on the dynamic calibration gathers of each WSP.

[0013] According to the above scheme, the preset rule is: select the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and the offset.

[0014] According to the above scheme, the method for selecting the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and the offset distance includes:

[0015] S201, selecting the channel with the highest signal-to-noise ratio in the CDP dynamic calibration channel set as the standard channel;

[0016] S202: If there are multiple channels with the highest signal-to-noise ratio, select the channel with the shortest offset distance as the standard channel.

[0017] According to the above scheme, step S3 includes:

[0018] S301, selecting sample points of a standard channel and another channel according to a set window;

[0019] S302, calculating the Pearson correlation coefficient between the standard channel and the other channels according to the amplitudes of the sample points of the standard channel and the other channels;

[0020] S303, loop S301 to S302 until the Pearson correlation coefficient between the standard channel and all other channels is obtained.

[0021] The present invention also provides a seismic imaging device based on WSP, comprising:

[0022] CDP dynamic calibration track set acquisition module, used to obtain multiple CDP dynamic calibration track sets;

[0023] The standard track selection module is used to select the standard track of the CDP dynamic calibration track set according to the preset rules;

[0024] The correlation coefficient determination module is used to determine the correlation coefficient between the standard track and the remaining tracks in the CDP dynamic calibration track set;

[0025] The WSP dynamic calibration track set acquisition module is used to remove the tracks whose correlation coefficients with the standard tracks in the CDP dynamic calibration track set are lower than the set threshold, and obtain the WSP dynamic calibration track set;

[0026] The stacking imaging module is used to stack the images according to the dynamic calibration gathers of each WSP.

[0027] According to the above scheme, the preset rule is: select the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and the offset.

[0028] According to the above scheme, the method for selecting the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and the offset distance includes:

[0029] In the CDP dynamic calibration track set, the track with the highest signal-to-noise ratio is selected as the standard track;

[0030] If there are multiple channels with the highest signal-to-noise ratio, the channel with the shortest offset distance is selected as the standard channel.

[0031] According to the above scheme, the method for determining the correlation coefficient between the standard track and the remaining tracks in the CDP dynamic calibration track set includes:

[0032] According to the set window, select the sample points of the standard channel and another channel;

[0033] According to the amplitudes of the standard channel and the other sample points of the channel, the Pearson correlation coefficient between the standard channel and the other channels is calculated;

[0034] The above steps are repeated until the Pearson correlation coefficient between the standard channel and all other channels is obtained.

[0035] The present invention also provides a seismic imaging device, the seismic imaging device comprising a memory,

[0036] A processor and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the seismic imaging method as described above.

[0037] The present invention also provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the seismic imaging method described above are implemented.

[0038] The beneficial effects of the present invention are as follows: the present invention utilizes the similar characteristics of the same reflection point underground, and removes the low-similarity disturbance tracks in the CDP dynamic correction track set by setting a threshold. Compared with the traditional CMP stacking technology, the signal-to-noise ratio of the stacked profile is significantly improved locally, the continuity of the reflection layer is enhanced, and the wave group dynamic characteristics are clearer and easier to identify and track, which lays a good foundation for the subsequent pre-stack migration processing and interpretation. At the same time, this method can further improve the convergence of the residual static correction loss of the detection point and the shot point, and the accuracy is more accurate. At the same time, it also enhances the coherence of the velocity spectrum track set, improves the velocity picking accuracy, and improves the quality of the track set.

[0039] Furthermore, the present invention selects the standard track of the CDP dynamic calibration track set by the signal-to-noise ratio and the offset, thereby ensuring the reference of the standard track and improving the effect of subsequent stacking imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the reflection wavelength and reflection point of the complex structure;

[0041] Figure 2 It is a curve diagram showing the change of earthquake waveform over time;

[0042] Figure 3 It is a schematic diagram of the effect of formation dip on seismic waveform;

[0043] Figure 4 is a schematic diagram of the WSP correlation coefficient and waveform changes according to an embodiment of the present invention;

[0044] Figure 5 It is a schematic diagram of a three-layer structural model of lateral dip angle variation of strata established by forward modeling according to an embodiment of the present invention;

[0045] Figure 6 is a schematic diagram comparing a seismic imaging method according to an embodiment of the present invention with a conventional seismic imaging method;

[0046] Figure 7 is a schematic diagram of standard track selection according to an embodiment of the present invention;

[0047] Figure 8 is a schematic diagram of amplitude samples and waveforms according to an embodiment of the present invention;

[0048] Figures 9 to 12 It is a schematic diagram of correlation coefficients of the same standard track with different offsets according to an embodiment of the present invention.

[0049] Fig.13 Schematic diagram of the relationship between the signal-to-noise ratio and the similarity coefficient in the same CDP according to an embodiment of the present invention;

[0050] Fig.14This is a schematic diagram of the final processing results of CMP stacking of the piedmont zone of a mountain data in the south;

[0051] Fig.15 is a schematic diagram of superposition results of an imaging method based on WSP according to an embodiment of the present invention;

[0052] Fig.16 It is a flow chart of a seismic imaging method based on WSP according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0054] In the analysis of seismic signals, it is sometimes necessary to study the similarity of more than two signals. As we all know, the waveform of the initial signal emitted by the shot point is known (unlike the controllable vibrator, the waveform of the explosive is affected by many factors, and the excited sub-waves between shots are different and uncontrollable), but due to various geological conditions and factors, the propagation process of the seismic wave and the final signal waveform received are unknown. Since the output record is the convolution of the excited sub-wave and the formation response plus noise, studying the similarity between the sub-wave and each receiving channel is of great guiding significance for us to judge or separate noise.

[0055] Based on the above situation, the present invention proposes a seismic imaging method based on WSP, see Fig.16 In view of the similarity or correlation between the wavelet and the actual received signal, the main steps of the seismic imaging method include: 1. Extraction of stable wavelets (i.e., selection of standard channels); 2. Calculation of similarity (correlation) coefficients between different received channels of the same CDP. Specifically, the seismic imaging method includes the following steps:

[0056] S1. Obtain multiple CDP dynamic calibration track sets;

[0057] S2. For a certain CDP dynamic calibration track set, select the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and offset;

[0058] S3, determining the correlation coefficients between the remaining tracks in the CDP dynamic calibration track set and the standard track;

[0059] S4, removing the tracks whose correlation coefficients with the standard tracks are lower than a set threshold in the CDP dynamic calibration track set, and obtaining the WSP dynamic calibration track set;

[0060] S5, loop S2 to S4 until all CDP dynamic calibration track sets are converted to WSP dynamic calibration track sets;

[0061] S6. Stack imaging based on the dynamic calibration gathers of each WSP.

[0062] S2 specifically includes:

[0063] S201. Select the channel with the highest signal-to-noise ratio in the CDP dynamic calibration channel set as the standard channel (preferred factor);

[0064] S202. If there are multiple channels with the highest signal-to-noise ratio, the channel with the shortest offset distance is selected as the standard channel (secondary selection factor).

[0065] In actual seismic data processing, the characteristics of each common reflection point (CDP) gather are different, which are manifested in the offset size, receiving track position and initial valid sample points of each track. In order to evaluate the similarity between the tracks in the gather, it is necessary to select a standard track (reference track) in each CDP gather for comparison.

[0066] Any continuous digital signal can be regarded as a set of discrete sample point sequences. In seismic discrete signals, any frequency signal can be regarded as a sinusoidal signal. For example, when a digital signal is sampled for 2ms, the number of discrete periods of its sub-wave waveform is:

[0067]

[0068] In discrete-time signal processing, the cross-correlation c xy (τ) is used to measure the similarity of two time series: x(t) and y(t) at two different time delays τ. Usually, a long sequence can be used to represent a short sequence in a specific time window. The standard wavelet sequences mentioned in this article are all x(t), and y(t) is the actual received signal. The two are cross-correlated to obtain the cross-correlation function Cxy(τ), and its cross-correlation within one period is:

[0069]

[0070] Here N is the signal length, and t and τ are discrete time variables.

[0071] For any signal, if waveform 1 is used as the standard signal, its autocorrelation function c xy (τ) reaches its maximum value at τ = 0, because the signal completely coincides with itself. The energy peak of the autocorrelation function can be calculated by c xy(τ) is obtained. When performing a cross-correlation operation between waveform 2 and waveform 1, if the energy reaches its maximum value at τ = 0, it can be considered that the waveforms of the two signals are identical. If the energy extremum appears at other times (0 < τ < T, where T is the time length between adjacent wave peaks / troughs of the seismic wave), it indicates that the similarity between the two signals is not the highest at τ = 0, and the greater the deviation of τ from 0 within one period, the greater the difference between waveform 1 and waveform 2 (see Figure 2 ). There are many methods for selecting the standard wavelet, but a high signal-to-noise ratio, few side lobes, and a stable waveform are important prerequisites for wavelet selection, which can ensure the reliability of calculating the wavelet similarity coefficient. Therefore, the trace with the maximum signal-to-noise ratio in each gather will be used as the standard wavelet (i.e., the standard trace) for that gather.

[0072] The quality of the gather is crucial for the final imaging of the data, and stacking is the most intuitive way to reflect the quality of the gather. For multi-offset data, in the conventional CMP NMO stacking algorithm, it is assumed that the formation is horizontally homogeneous, without considering the influence of complex structures on the propagation of seismic waves. This often causes the correlation between traces to decrease as the complexity of the structure increases for the same reflection point, and this is also the key factor that has not been overcome in CMP stacking in high-steep and complex areas. Good strong reflections can be seen on gathers with different offsets, but there is no good imaging effect after stacking. A large factor is that there is a large non-similarity between traces on the same CDP. To scientifically evaluate the similarity between traces in the same CDP gather, considering the particularity of the actual data in complex areas, signal reference traces with small offsets and relatively high signal-to-noise ratios are selected as much as possible. Based on these conditions, one trace (red) is selected as the standard signal x(t) on each CMP NMO gather, and other traces are used as random signals y(t) (see Figure 7 ).

[0073] S3 specifically includes:

[0074] S301. Select the samples of the standard trace and one of the other traces according to the set window;

[0075] S302. Calculate the Pearson correlation coefficient between the standard trace and the other trace according to the amplitudes of the samples of the standard trace and the other trace;

[0076] S303. Loop S301 to S302 until the Pearson correlation coefficients between the standard trace and all other traces are obtained.

[0077] After obtaining a stable standard wavelet, the cross-correlation can be performed between each received signal and the wavelet in turn, and the similarity coefficient of the waveform can be obtained by the least squares method. First, assume that the waveforms of the two signals are x(t) and y(t) respectively, x(t) is set as the stable standard wavelet, and y(t) is the received signal of each trace. Select an appropriate value of a to make a*y(t) approximate x(t). The specific method is as follows:

[0078] For discrete signals, if the standard signal x(t) is represented by x i and y(t) with y i (i=1,2,....,n) respectively represent, n is the sampling point, if the reference signal has linear characteristics, then there exists:

[0079]

[0080] In the above formula, a is the coefficient to be determined

[0081] Formula (1) can be expressed as:

[0082] y i (x) = a 1 x 1 +a 2 x 2 +…a n x n +ε (2)

[0083] In the above formula, ε is the error between the standard track and the actual track.

[0084] By comparing the target with the standard curve of least squares curve fitting, the unknown coefficient a of each sample point can be obtained. i , so that the objective function is minimized, that is:

[0085]

[0086] According to the signal x i and i The average value of the sample points and the average error of the signal are obtained using the standard deviation and covariance:

[0087]

[0088] In the above formula, σ xy is the covariance, σ x For signal x i Variance, σ y for y i variance.

[0089] Then the correlation coefficient is:

[0090]

[0091] Simplifying formula (7) yields the correlation coefficient of discrete signals:

[0092]

[0093] Then γ is called the similarity coefficient of signals x(t) and y(t), and its value range is 0 to 1.0. i and iWhen they are consistent, it is easy to get γ = 1. It can be seen that the larger γ is, the better the correlation is, and vice versa, the worse the correlation is.

[0094] Relatively flat strata, such as those associated with transgressive sequences, will produce stable similarity values ​​over a large area; high-angle events, when no dip correction is made, will produce low similarity values ​​over a large area. Low signal-to-noise data or missing strata, such as salt domes, will also produce low similarity values ​​over a large area. Detectable vertical fall faults or wavefield distortions are usually clearly identified, but show low similarity over a small area. Similarly, strong changes in geological body properties caused by changes in strata or lithology (such as river channel sand bodies) will also show low similarity over a small area, and abnormal channels, collection "footprints", noise, etc. will affect the similarity value for a certain period of time. In theory, when the geological body is flat and continuous, the similarity between channels is high; when the geological body is inclined and continuous, the similarity is low; when the geological body is discontinuous, it will produce abnormal values. In general, the more complex the geological conditions are, the worse the similarity between traces at the same reflection point is, and the amplitude (positive or negative) and phase of the signal at the same reflection position will have obvious time shifts at the same point (see Figure 3 There are many factors that cause large differences in seismic signal similarity values, mainly including:

[0095] 1) Changes in thickness of the near-surface low-velocity layer and lateral lithologic velocity;

[0096] 2) shallow vertical faults or slip faults;

[0097] 3) Strong changes in seismic characteristic responses due to changes in strata or lithology;

[0098] 4) If the tilt correction is not applied, the large-angle phase axis will also cause the similarity to decrease;

[0099] 5) Missing strata, such as salt domes;

[0100] 6) Low signal-to-noise ratio data, etc.

[0101] See also Figure 4 , where (a) is the standard waveform diagram, and (b) is the forward record and similarity coefficient diagram. It can be seen from the figure that as the "arrow" direction extends, the correlation of the waveform gradually decreases, which is consistent with the actual reflection situation.

[0102] In signal analysis, the cross-correlation function is commonly used to describe the correlation degree between the values of a standard signal x(t) and a random signal y(t) at any two different times t1 and t2. The autocorrelation function describes the correlation degree between the values of a random signal y(t) at any two different times t1 and t2, which is a special case of cross-correlation. In signal processing, the similarity between two signals can be used to describe the correlation degree between them.

[0103] Using WSP to analyze similarity is more reasonable than simply processing signal similarity. WSP can process the similarity between a standard trace and a received trace in the same CDP gather, thereby obtaining a statistical index of the closeness between traces. In machine learning, the distance size is commonly used to distinguish which category it belongs to, that is, those with small and close distances are grouped into one category. The most commonly used are Euclidean distance and Manhattan distance. In the algorithm of the embodiment, the Pearson Correlation Coefficient is used to statistically analyze the similarity between traces.

[0104] The Pearson Correlation Coefficient, usually simply referred to as Pearson correlation or Pearson coefficient, is a statistical index that measures the linear correlation degree between two continuous variables. The algorithm is more complex than the Euclidean distance. The method for calculating similarity in this article is quantitatively realized through the sample amplitudes of the standard trace and the received trace in a specified window, and the calculation formula is as follows:

[0105]

[0106] Different from formula (8), the value of the Pearson correlation coefficient is between -1 and 1. In this embodiment, we use the absolute value of r (|r|) for statistics. When |r| = 1, it means that the two variables are completely correlated. When r = 0, it means that the two variables have no correlation relationship; when 0 < r < 1, it means that the two variables have a certain degree of correlation, and the closer |r| is to 1, the closer the linear relationship between the two variables. The closer |r| is to 0, the weaker the linear correlation between the two variables; generally speaking: |r| < 0.4 is low correlation, 0.4 ≤ |r| < 0.7 is significant correlation, and 0.7 ≤ |r| < 1 is high linear correlation.

[0107] In this embodiment, after selecting the standard trace for the CDP NMO gather, for the amplitudes of all traces and the standard trace, the similarity coefficients between each received trace and the standard trace are statistically analyzed using a program. See Figure 8 , where (a) is the standard signal and (b) is the received trace signal. Using the same standard trace to calculate the corresponding correlation coefficients for different offsets (see Figures 9 to 12 ) not only reflects the SNR changes of different offsets but also reflects the temporal variation trend of the strata in the same area.

[0108] The benefit of obtaining the similarity trace header data of the same CDP in the dynamic calibration gather is that it can directly reflect the similarity between traces in the same CDP. Based on this trace header, a threshold is set to select traces with higher similarity to participate in the stacking. Generally speaking, when the similarity coefficient is greater than 0.7, it can be considered that the traces are highly linearly correlated and have good in-phase; when the similarity coefficient is greater than 0.4 and less than 0.7, the traces are significantly correlated; when the similarity coefficient is less than 0.4, the traces are weakly correlated, multiple stacking imaging is poor, and there will be a large time difference in the vertical direction of the same reflection layer, and the stacking is non-in-phase. In the gather, more often, the signal-to-noise ratio is proportional to the similarity coefficient.

[0109] (See Fig.13 ).

[0110] By setting the similarity coefficient threshold of WSP statistics on CDP gathers (this value can be set manually as a hyperparameter, but sufficient coverage times must be maintained), we can screen out the traces with better phase consistency, improve the stacking imaging quality, and improve the data quality. Fig.11 It is the final processing result of CMP stacking of the piedmont zone of a mountain data in the south. The wave group continuity is poor, the formation response characteristics are not clear, and the comparability and tracking of the interpretation are multi-solution and uncertain. Fig.12 It is the result of superposition based on the WSP method. From shallow to deep, the continuity of the wave group is greatly enhanced and improved, and the reflection structure characteristics are clearer.

[0111] In order to demonstrate the difference between the seismic imaging method of this embodiment and the existing seismic imaging method, this embodiment conducts the following comparative test:

[0112] A three-layer structural model with varying lateral inclination angles was established by forward modeling, and the elastic wave mode ( Figure 5 ). See Figure 6 (a) Using traditional CDP stacking, reflection interface 1 and reflection interface 2 basically reflect the underground structural morphology in the stacking time section, but reflection interface 3 is a horizontal flat layer in the depth model, but it is an event axis without a fixed "rule" in the time domain stacking, and the lateral continuity is poor, and the wave group characteristics are not obvious, especially the flat layer under the structural overthrow has the worst reflection, and "aliasing" occurs. Figure 6 (b) After superposition using the WSP technology, reflection interface 1, reflection interface 2, and reflection interface 3 have been significantly improved in terms of wave group characteristics and continuity.

[0113] Imaging of the foreland belt or push structure has always been a difficult point in exploration. The complex structure makes the underground reflection path intricate. There is a large deviation between the theoretical CDP position and the actual reflection point position in the underground in the lateral space. Traditional velocity analysis, static correction and stacking methods are difficult to resolve this contradiction. As the country's demand for energy is getting higher and higher, obtaining high-quality seismic profiles is the basis of exploration and development.

[0114] The stack imaging of complex structural areas has always troubled further exploration, and the stack imaging signal-to-noise ratio increases with the number of drilling coverages, showing a "saturated" state. For reflection gathers in complex areas, the pre-stack gathers of the same underground reflection point are often difficult to achieve uniform in-phase stacking from the perspective of the waveform phase coefficient. When the similarity coefficient between traces is low (less than 0.3), the imaging often shows many and disorderly coinciding axes, and the wave group is difficult to identify. When the similarity coefficient between traces is high (greater than 0.7), the correlation energy is enhanced, and the wave group of stack imaging is often better. Traditional CMP stacking technology cannot identify the actual underground reflection position where the shot inspection received signal comes from the underground on the same CDP, and only relies on theoretical CMP position stacking.

[0115] Through the theory of WSP stacking imaging principle and based on the waveform similarity algorithm, the present invention has carried out model forward modeling and application of actual data on this basis. WSP technology utilizes the similar characteristics of the same reflection point underground and removes the low-similarity interference channels. Compared with the traditional CMP stacking, the signal-to-noise ratio of the stacking profile is significantly improved locally, the continuity of the reflection layer is enhanced, the dynamic characteristics of the wave group are clearer and easier to identify and track, and a good foundation is laid for the subsequent pre-stack migration processing and interpretation. At the same time, WSP technology can further improve the convergence of the residual static correction loss of the detection point and the shot point, and the accuracy is more accurate. At the same time, it also enhances the coherence of the velocity spectrum gather, improves the velocity picking accuracy, and improves the quality of the gather. The method has a good development prospect.

[0116] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A seismic imaging method based on WSP, characterized in that: The following steps are involved: S1. Obtain multiple CDP dynamic calibration track sets; S2. For a certain CDP dynamic calibration track set, select the standard track of the CDP dynamic calibration track set according to the preset rules; S3, determining the correlation coefficient between the standard track and the remaining tracks in the CDP dynamic calibration track set; S4, removing the tracks whose correlation coefficients with the standard tracks are lower than a set threshold in the CDP dynamic calibration track set, and obtaining the WSP dynamic calibration track set; S5, loop S2 to S4 until all CDP dynamic calibration track sets are converted to WSP dynamic calibration track sets; S6. Stack imaging based on the dynamic calibration gathers of each WSP.

2. The WSP-based seismic imaging method according to claim 1, characterized in that: The preset rule is: select the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and the offset.

3. The WSP-based seismic imaging method according to claim 2, characterized in that: The method for selecting the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and the offset distance comprises: S201, selecting the channel with the highest signal-to-noise ratio in the CDP dynamic calibration channel set as the standard channel; S202: If there are multiple channels with the highest signal-to-noise ratio, select the channel with the shortest offset distance as the standard channel.

4. The WSP-based seismic imaging method according to claim 1, characterized in that: Step S3 includes: S301, selecting sample points of a standard channel and another channel according to a set window; S302, calculating the Pearson correlation coefficient between the standard channel and the other channels according to the amplitudes of the sample points of the standard channel and the other channels; S303, loop S301 to S302 until the Pearson correlation coefficient between the standard channel and all other channels is obtained.

5. A seismic imaging device based on WSP, characterized in that: include: CDP dynamic calibration track set acquisition module, used to obtain multiple CDP dynamic calibration track sets; The standard track selection module is used to select the standard track of the CDP dynamic calibration track set according to the preset rules; The correlation coefficient determination module is used to determine the correlation coefficient between the standard track and the remaining tracks in the CDP dynamic calibration track set; The WSP dynamic calibration track set acquisition module is used to remove the tracks whose correlation coefficients with the standard tracks in the CDP dynamic calibration track set are lower than the set threshold, and obtain the WSP dynamic calibration track set; The stacking imaging module is used to stack the images according to the dynamic calibration gathers of each WSP.

6. The WSP-based seismic imaging device according to claim 1, characterized in that: The preset rule is: select the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and the offset.

7. The WSP-based seismic imaging device according to claim 6, characterized in that: The method for selecting the standard track of the CDP dynamic calibration track set according to the signal-to-noise ratio and the offset distance comprises: In the CDP dynamic calibration track set, the track with the highest signal-to-noise ratio is selected as the standard track; If there are multiple channels with the highest signal-to-noise ratio, the channel with the shortest offset distance is selected as the standard channel.

8. The WSP-based seismic imaging device according to claim 5, characterized in that: The method for determining the correlation coefficient between the standard track and the remaining tracks in the CDP dynamic calibration track set comprises: According to the set window, select the sample points of the standard channel and another channel; According to the amplitudes of the standard channel and the other sample points of the channel, the Pearson correlation coefficient between the standard channel and the other channels is calculated; The above steps are repeated until the Pearson correlation coefficient between the standard channel and all other channels is obtained.

9. A seismic imaging device, characterized in that: The seismic imaging device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the seismic imaging method according to any one of claims 1 to 4.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the seismic imaging method according to any one of claims 1 to 4 are implemented.

Images