Seismic data processing method and device based on three-dimensional prediction deconvolution

Abstract

The invention discloses a seismic data processing method and device based on three-dimensional prediction deconvolution, and the method comprises the steps: determining an operator of three-dimensional prediction deconvolution through employing a pre-built three-dimensional prediction deconvolution model based on the target seismic data of a research region; and performing three-dimensional prediction deconvolution processing on the target seismic data according to the operator of the three-dimensional prediction deconvolution to obtain processed seismic data.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing, and in particular to a seismic data processing method and device based on three-dimensional predictive deconvolution. Background Art

[0002] Multiple waves are a very difficult and thorny problem in seismic data processing, especially in offshore data processing. Therefore, marine seismic data processing urgently needs a technology to suppress shallow water multiple waves and improve data resolution. Predictive deconvolution is different from multiple wave denoising methods such as parabolic Radon transform and SRME. Suppressing multiple waves does not require auxiliary information such as velocity, and the prediction step size can be easily obtained through autocorrelation. In addition, predictive deconvolution has the advantage of simple parameter setting. Predictive deconvolution has become a common method for suppressing multiple waves and has been well applied in eliminating ghost reflections, reverberations or other simple forms of multiple waves. In some seismic data processing methods, one-dimensional predictive deconvolution is used for seismic data processing; therefore, realizing a three-dimensional predictive deconvolution seismic data processing method is a problem that needs to be solved urgently. Summary of the invention

[0003] The present application provides a method and device for processing seismic data based on three-dimensional predictive deconvolution. The method can consider multiple directions of three-dimensional seismic data, predict the operator of three-dimensional predictive deconvolution, and then perform seismic data processing to obtain seismic data that is more spatially connected. At the same time, compared with the seismic data processing method of one-dimensional predictive deconvolution, the resolution of seismic data is improved, and the quality of seismic data processing and offset imaging is improved.

[0004] In a first aspect, the present application provides a method for processing seismic data based on three-dimensional predictive deconvolution, the method comprising:

[0005] Based on the target seismic data in the study area, the operator of 3D predictive deconvolution is determined by using the pre-established 3D predictive deconvolution model;

[0006] The target seismic data is subjected to three-dimensional predictive deconvolution processing according to the three-dimensional predictive deconvolution operator to obtain processed seismic data.

[0007] In an exemplary embodiment, the target seismic data is three-dimensional common detection point seismic gather data.

[0008] In an exemplary embodiment, the pre-established three-dimensional prediction deconvolution model is:

[0009]

[0010] In the above model, d b,m,jp represents the seismic gather data with shot line number b, shot point number m, time direction sample point number j, and common detection point containing multiple waves; b,m,j represents the common detection point seismic gather data with shot line number b, shot point number m, time direction sample point number j, and no multiple waves; f c,n,k is the operator of three-dimensional prediction deconvolution; c represents the sample point number of the three-dimensional prediction operator corresponding to b, n represents the sample point number of the three-dimensional prediction operator corresponding to m, k represents the sample point number of the three-dimensional prediction operator corresponding to j; w is the half-width number of points in the Crossline direction, u is the half-width number of points in the Inline direction; g represents the step size of the three-dimensional prediction deconvolution calculation; d b-c,m-n,j-k It represents the common detection point seismic gather data with shot line number bc, shot point number mn, time direction sample point number jk, and containing multiple waves.

[0011] In an exemplary embodiment, the process of establishing the three-dimensional prediction deconvolution model includes:

[0012] Determine one-dimensional seismic data according to the reflection coefficient and the seismic wavelet; wherein the reflection coefficient is a white noise sequence, and the seismic wavelet is a minimum phase wavelet;

[0013] According to the statistical characteristics of seismic data, a one-dimensional prediction deconvolution model is determined;

[0014] A three-dimensional predictive deconvolution model is determined according to the one-dimensional predictive deconvolution model and the three-dimensional attributes of the three-dimensional seismic data.

[0015] In an exemplary embodiment, determining one-dimensional seismic data according to the reflection coefficient and the seismic wavelet includes:

[0016] According to the reflection coefficient and the seismic wavelet, the one-dimensional seismic data is calculated using the seismic convolution formula, wherein the seismic convolution formula is:

[0017] d(t)=b(t)*f(t)

[0018] In the above formula, d(t) is the seismic data in the time domain, b(t) is the seismic wavelet in the time domain, f(t) is the reflection coefficient in the time domain, and t is the time of the seismic data.

[0019] In an exemplary embodiment, determining a one-dimensional prediction deconvolution model according to the statistical characteristics of seismic data includes:

[0020] Determine the relationship between seismic data and time based on the statistical characteristics of seismic data;

[0021] A one-dimensional prediction deconvolution model is established based on the determined relationship between seismic data and time;

[0022] Wherein, the one-dimensional prediction deconvolution model is:

[0023]

[0024] In the above one-dimensional predictive deconvolution model, d j represents the seismic gather data of the common detection point with sample point number j in the time direction and containing multiple waves; p j Indicates the seismic gather data of the common detection point with sample number j in the time direction and no multiple waves; d j-k represents the seismic gather data of the common detection point with the sample point number jk in the time direction and containing multiple waves; f k Operator representing one-dimensional predictive deconvolution.

[0025] In a second aspect, an embodiment of the present invention provides a seismic data processing device based on three-dimensional predictive deconvolution, the device comprising: an operator module for determining three-dimensional predictive deconvolution and a seismic data processing module;

[0026] The module for determining the operator of three-dimensional predictive deconvolution is used to determine the operator of three-dimensional predictive deconvolution based on the target seismic data of the study area and using a pre-established three-dimensional predictive deconvolution model;

[0027] The seismic data processing module is used to perform three-dimensional predictive deconvolution processing on the target seismic data according to the three-dimensional predictive deconvolution operator to obtain processed seismic data.

[0028] In an exemplary embodiment, the pre-established three-dimensional prediction deconvolution model is:

[0029]

[0030] In the above model, d b,m,j p represents the seismic gather data with shot line number b, shot point number m, time direction sample point number j, and common detection point containing multiple waves; b,m,j represents the common detection point seismic gather data with shot line number b, shot point number m, time direction sample point number j, and no multiple waves; f c,n,k is the operator of three-dimensional prediction deconvolution; c represents the sample point number of the three-dimensional prediction operator corresponding to b, n represents the sample point number of the three-dimensional prediction operator corresponding to m, k represents the sample point number of the three-dimensional prediction operator corresponding to j; w is the half-width number of points in the Crossline direction, u is the half-width number of points in the Inline direction; g represents the step size of the three-dimensional prediction deconvolution calculation; d b-c,m-n,j-k It represents the common detection point seismic gather data with shot line number bc, shot point number mn, time direction sample point number jk, and containing multiple waves.

[0031] In an exemplary embodiment, the process of establishing the three-dimensional prediction deconvolution model includes:

[0032] Determine one-dimensional seismic data according to the reflection coefficient and the seismic wavelet; wherein the reflection coefficient is a white noise sequence, and the seismic wavelet is a minimum phase wavelet;

[0033] According to the statistical characteristics of seismic data, a one-dimensional prediction deconvolution model is determined;

[0034] A three-dimensional predictive deconvolution model is determined according to the one-dimensional predictive deconvolution model and the three-dimensional attributes of the three-dimensional seismic data.

[0035] In an exemplary embodiment, determining one-dimensional seismic data according to the reflection coefficient and the seismic wavelet includes:

[0036] According to the reflection coefficient and the seismic wavelet, the one-dimensional seismic data is calculated using the seismic convolution formula, wherein the seismic convolution formula is:

[0037] d(t)=b(t)*f(t)

[0038] In the above formula, d(t) is the seismic data in the time domain, b(t) is the seismic wavelet in the time domain, f(t) is the reflection coefficient in the time domain, and t is the time of the seismic data.

[0039] In an exemplary embodiment, determining a one-dimensional prediction deconvolution model according to the statistical characteristics of seismic data includes:

[0040] Determine the relationship between seismic data and time based on the statistical characteristics of seismic data;

[0041] A one-dimensional prediction deconvolution model is established based on the determined relationship between seismic data and time;

[0042] Wherein, the one-dimensional prediction deconvolution model is:

[0043]

[0044] In the above one-dimensional predictive deconvolution model, d j represents the seismic gather data of the common detection point with sample point number j in the time direction and containing multiple waves; p j Indicates the seismic gather data of the common detection point with sample number j in the time direction and no multiple waves; d j-k represents the seismic gather data of the common detection point with the sample point number jk in the time direction and containing multiple waves; f k Operator representing one-dimensional predictive deconvolution.

[0045] In the third aspect, an embodiment of the present invention provides a computer program product with a seismic data processing function based on three-dimensional predictive deconvolution, including a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, it implements the seismic data processing method based on three-dimensional predictive deconvolution described in any one of the above embodiments.

[0046] Compared with the related art, the present application provides a method and device for processing seismic data based on three-dimensional predictive deconvolution, the method comprising: based on the target seismic data of the study area, using a pre-established three-dimensional predictive deconvolution model to determine the operator of three-dimensional predictive deconvolution; performing three-dimensional predictive deconvolution processing on the target seismic data according to the operator of three-dimensional predictive deconvolution to obtain processed seismic data. The present application considers multiple directions of three-dimensional seismic data, predicts the operator of three-dimensional predictive deconvolution, and then performs seismic data processing to obtain seismic data that is more connected in space; at the same time, compared with the one-dimensional predictive deconvolution seismic data processing method, the resolution of seismic data is improved, and the quality of seismic data processing and offset imaging is improved.

[0047] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0049] Figure 1 This is a flow chart of a method for processing seismic data based on three-dimensional predictive deconvolution according to an embodiment of the present application;

[0050] Figure 2 is a schematic diagram comparing a one-dimensional predictive deconvolution operator and a three-dimensional predictive deconvolution operator in some exemplary embodiments;

[0051] Figure 3 A schematic diagram of a seismic data processing device based on three-dimensional predictive deconvolution according to an embodiment of the present application;

[0052] Figure 4 is a schematic diagram comparing the effects of three-dimensional model prediction deconvolution processing in some exemplary embodiments;

[0053] Figure 5 is a schematic diagram of the near-far arrangement before predicting deconvolution in some exemplary embodiments;

[0054] Figure 6It is a schematic diagram of the predicted near-far arrangement effect after deconvolution processing in some exemplary embodiments. DETAILED DESCRIPTION

[0055] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0056] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0057] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0058] The embodiment of the present invention provides a seismic data processing method based on three-dimensional prediction deconvolution, such as Figure 1 As shown, the method includes steps S100-S110, which are specifically as follows:

[0059] Step S100. Based on the target seismic data of the study area, a pre-established three-dimensional prediction deconvolution model is used to determine the operator of three-dimensional prediction deconvolution;

[0060] Step S110: Perform three-dimensional predictive deconvolution processing on the target seismic data according to the three-dimensional predictive deconvolution operator to obtain processed seismic data.

[0061] In this embodiment, a method for suppressing shallow water multiple waves in the process of processing marine seismic data is mainly used, which can theoretically suppress the multiple waves on the seabed surface. Among them, the suppression of shallow water multiple waves, that is, the multiple waves reflected from the seabed in the water depth range of 50 meters to 200 meters, can be effectively suppressed. In deep water, the multiple waves on the seabed no longer meet the periodicity and cannot be effectively suppressed.

[0062] In an exemplary embodiment, the target seismic data is three-dimensional common detection point seismic gather data. The target seismic data can be the common detection point gather after the direct wave is removed. If the common detection point gather after the direct wave is removed is used for convolution, the influence of the primary wave introduced by the convolution of the direct wave and the operator can be avoided.

[0063] In an exemplary embodiment, the pre-established three-dimensional prediction deconvolution model is:

[0064]

[0065] In the above model, d b,m,j p represents the seismic gather data with shot line number b, shot point number m, time direction sample point number j, and common detection point containing multiple waves; b,m,j represents the common detection point seismic gather data with shot line number b, shot point number m, time direction sample point number j, and no multiple waves; f c,n,k is the operator of three-dimensional prediction deconvolution; c represents the sample point number of the three-dimensional prediction operator corresponding to b, n represents the sample point number of the three-dimensional prediction operator corresponding to m, k represents the sample point number of the three-dimensional prediction operator corresponding to j; w is the half-width number of points in the Crossline direction, u is the half-width number of points in the Inline direction; g represents the step size of the three-dimensional prediction deconvolution calculation; d b-c,m-n,j-k It represents the common detection point seismic gather data with shot line number bc, shot point number mn, time direction sample point number jk, and containing multiple waves.

[0066] In an exemplary embodiment, the process of establishing a three-dimensional prediction deconvolution model includes:

[0067] Step 1. Determine one-dimensional seismic data according to the reflection coefficient and the seismic wavelet; wherein the reflection coefficient is a white noise sequence, and the seismic wavelet is a minimum phase wavelet;

[0068] Step 2. Determine a one-dimensional prediction deconvolution model based on the statistical characteristics of the seismic data;

[0069] Step 3: Determine a three-dimensional prediction deconvolution model based on the one-dimensional prediction deconvolution model and the three-dimensional attributes of the three-dimensional seismic data.

[0070] In an exemplary embodiment, determining one-dimensional seismic data according to the reflection coefficient and the seismic wavelet includes:

[0071] According to the reflection coefficient and the seismic wavelet, the one-dimensional seismic data is calculated using the seismic convolution formula, wherein the seismic convolution formula is:

[0072] d(t)=b(t)*f(t)

[0073] In the above formula, d(t) is the seismic data in the time domain, b(t) is the seismic wavelet in the time domain, f(t) is the reflection coefficient in the time domain, and t is the time of the seismic data.

[0074] In an exemplary embodiment, a one-dimensional prediction deconvolution model is determined according to the statistical characteristics of seismic data, including:

[0075] Step 1. Determine the relationship between seismic data and time based on the statistical characteristics of seismic data;

[0076] Step 2. Establish a one-dimensional prediction deconvolution model based on the determined relationship between seismic data and time;

[0077] In this step, based on the assumption that the seismic wavelet is the minimum phase and the reflection coefficient is a white noise sequence, the seismic data can be approximately regarded as a stationary random process, that is, the statistical characteristics of the seismic data are independent of time.

[0078] Wherein, the one-dimensional prediction deconvolution model is:

[0079]

[0080] In the above one-dimensional predictive deconvolution model, d j represents the seismic gather data of the common detection point with sample point number j in the time direction and containing multiple waves; p j Indicates the seismic gather data of the common detection point with sample number j in the time direction and no multiple waves; d j-k represents the seismic gather data of the common detection point with the sample point number jk in the time direction and containing multiple waves; f k represents the operator of one-dimensional predictive deconvolution. Figure 2 The diagram shows a comparison between the one-dimensional predictive deconvolution operator and the three-dimensional predictive deconvolution operator. Figure 2 The figure (a) is a one-dimensional operator. Figure 2 The middle (b) figure is a three-dimensional operator. Gap represents the prediction step length g, which is generally slightly less than the two-way vertical travel time of the water layer (in milliseconds). The prediction operator length Prediction operator width n2op, dx is the detection point interval; z is the water depth; theta is the maximum reflection angle; noise is the added white noise parameter, and its value is generally 1%.

[0081] In step S100, based on the target seismic data of the study area, a pre-established three-dimensional prediction deconvolution model is used to determine the three-dimensional prediction deconvolution operator; wherein the process of determining the three-dimensional prediction deconvolution operator is as follows:

[0082]

[0083]

[0084] In this embodiment, the least square method is used to solve the operator and construct the E(f) function, where the operator f is the independent variable of the function E. Different operators obtain different E(f) function values. When the E(f) function value is the minimum, the operator f that best meets the conditions can be obtained. Because the E(f) function has only one minimum value, its derivative can be set to zero to obtain the corresponding operator f. That is, set this derivative to zero and calculate the operator f of the three-dimensional predictive deconvolution. c,n,k .

[0085] In step S110, the target seismic data is subjected to three-dimensional predictive deconvolution processing according to the three-dimensional predictive deconvolution operator to obtain processed seismic data. The process is as follows: after calculating the three-dimensional predictive deconvolution operator f c,n,k Then, the target earthquake data d b,m,j Subtract the 3D predictive deconvolution operator f c,n,k and earthquake data d b,m,j As a result of the convolution, we can obtain the suppressed multiple waves p b,m,j The subsequent earthquake data.

[0086] The method provided in the embodiments of the present application has the following technical effects:

[0087] 1. It can suppress shallow water multiple waves in ocean data. The method of removing multiple waves by predictive deconvolution can fill the deficiency of the method of suppressing multiple waves in stacking processing.

[0088] 2. This method can consider multiple directions of three-dimensional seismic data, predict the operator of three-dimensional predictive deconvolution, and then process the seismic data to obtain seismic data that is more spatially connected.

[0089] 3. Compared with the one-dimensional predictive deconvolution seismic data processing method, the resolution of seismic data is improved, and the quality of seismic data processing and migration imaging is improved.

[0090] The disclosed embodiment also provides a seismic data processing device based on three-dimensional prediction deconvolution, such as Figure 3 As shown, the device includes: an operator module 310 for determining three-dimensional prediction deconvolution and a seismic data processing module 320;

[0091] The module for determining the operator of three-dimensional predictive deconvolution is used to determine the operator of three-dimensional predictive deconvolution based on the target seismic data of the study area and using a pre-established three-dimensional predictive deconvolution model;

[0092] The seismic data processing module is used to perform three-dimensional predictive deconvolution processing on the target seismic data according to the three-dimensional predictive deconvolution operator to obtain processed seismic data.

[0093] In an exemplary embodiment, the pre-established three-dimensional prediction deconvolution model is:

[0094]

[0095] In the above model, d b,m,j p represents the seismic gather data with shot line number b, shot point number m, time direction sample point number j, and common detection point containing multiple waves; b,m,j represents the common detection point seismic gather data with shot line number b, shot point number m, time direction sample point number j, and no multiple waves; f c,n,k is the operator of three-dimensional prediction deconvolution; c represents the sample point number of the three-dimensional prediction operator corresponding to b, n represents the sample point number of the three-dimensional prediction operator corresponding to m, k represents the sample point number of the three-dimensional prediction operator corresponding to j; w is the half-width number of points in the Crossline direction, u is the half-width number of points in the Inline direction; g represents the step size of the three-dimensional prediction deconvolution calculation; d b-c,m-n,j-k It represents the common detection point seismic gather data with shot line number bc, shot point number mn, time direction sample point number jk, and containing multiple waves.

[0096] In an exemplary embodiment, the process of establishing a three-dimensional prediction deconvolution model includes:

[0097] Determine one-dimensional seismic data according to the reflection coefficient and the seismic wavelet; wherein the reflection coefficient is a white noise sequence, and the seismic wavelet is a minimum phase wavelet;

[0098] According to the statistical characteristics of seismic data, a one-dimensional prediction deconvolution model is determined;

[0099] A three-dimensional predictive deconvolution model is determined according to the one-dimensional predictive deconvolution model and the three-dimensional attributes of the three-dimensional seismic data.

[0100] In an exemplary embodiment, determining one-dimensional seismic data according to the reflection coefficient and the seismic wavelet includes:

[0101] According to the reflection coefficient and the seismic wavelet, the one-dimensional seismic data is calculated using the seismic convolution formula, wherein the seismic convolution formula is:

[0102] d(t)=b(t)*f(t)

[0103] In the above formula, d(t) is the seismic data in the time domain, b(t) is the seismic wavelet in the time domain, f(t) is the reflection coefficient in the time domain, and t is the time of the seismic data.

[0104] In an exemplary embodiment, a one-dimensional prediction deconvolution model is determined according to the statistical characteristics of seismic data, including:

[0105] Determine the relationship between seismic data and time based on the statistical characteristics of seismic data;

[0106] A one-dimensional prediction deconvolution model is established based on the determined relationship between seismic data and time;

[0107] Wherein, the one-dimensional prediction deconvolution model is:

[0108]

[0109] In the above one-dimensional predictive deconvolution model, d j represents the seismic gather data of the common detection point with sample point number j in the time direction and containing multiple waves; p j Indicates the seismic gather data of the common detection point with sample number j in the time direction and no multiple waves; d j-k represents the seismic gather data of the common detection point with the sample point number jk in the time direction and containing multiple waves; f k Operator representing one-dimensional predictive deconvolution.

[0110] The disclosed embodiments also provide a computer program product having a seismic data processing function based on three-dimensional predictive deconvolution, including a computer program / instruction. When the computer program / instruction is executed by a processor, the seismic data processing method based on three-dimensional predictive deconvolution described in any one of the above embodiments is implemented.

[0111] Example 1

[0112] This example shows the process of establishing a three-dimensional prediction deconvolution model as follows:

[0113] The first step is to determine the seismic data by assuming that the reflection coefficient sequence is a white noise sequence and the seismic wavelet is of minimum phase.

[0114] Deconvolution is performed under the assumption that the reflection coefficient sequence is a white noise sequence and the seismic wavelet is of minimum phase. If the influence of random noise is ignored, the seismic record x(t) satisfies the following convolution model:

[0115] d(t)=b(t)*f(t)

[0116] In the above formula, d(t) is the seismic data in the time domain, b(t) is the seismic wavelet in the time domain, f(f) is the reflection coefficient in the time domain, and t is the time of the seismic data.

[0117] The second step is to assume that the reflection coefficient sequence is a white noise sequence and the seismic wavelet is the minimum phase. The determined seismic data can be approximately regarded as a stationary random process, that is, the statistical characteristics of the seismic data are independent of time. Under the above assumptions, a one-dimensional predictive deconvolution model is established:

[0118]

[0119] In the above one-dimensional predictive deconvolution model, d j represents the seismic gather data of the common detection point with sample point number j in the time direction and containing multiple waves; p j Indicates the seismic gather data of the common detection point with sample number j in the time direction and no multiple waves; d j-k represents the seismic gather data of the common detection point with the sample point number jk in the time direction and containing multiple waves; f k Operator representing one-dimensional predictive deconvolution.

[0120] The third step is to derive the following prediction formula based on the one-dimensional prediction deconvolution model:

[0121]

[0122] Based on the above prediction formula, when the seismic data is known, the operator of one-dimensional prediction deconvolution is unknown, and p is obtained; after analysis, the operator f of one-dimensional prediction deconvolution can be obtained first:

[0123]

[0124]

[0125] Among them, i ≥ g;

[0126] R is the autocorrelation function of D: r i =r -i =∑ j d j d j-i .

[0127] Setting this derivative to zero, we get the Toeplitz equation:

[0128]

[0129] Based on the above Toeplitz equation, the operator of one-dimensional predictive deconvolution can be solved.

[0130] The fourth step is to expand the one-dimensional prediction deconvolution to three-dimensional prediction deconvolution, and obtain the three-dimensional prediction deconvolution model as follows:

[0131]

[0132] In the above model, d b,m,j p represents the seismic gather data with shot line number b, shot point number m, time direction sample point number j, and common detection point containing multiple waves; b,m,j represents the common detection point seismic gather data with shot line number b, shot point number m, time direction sample point number j, and no multiple waves; f c,n,k is the operator of three-dimensional prediction deconvolution; c represents the sample point number of the three-dimensional prediction operator corresponding to b, n represents the sample point number of the three-dimensional prediction operator corresponding to m, k represents the sample point number of the three-dimensional prediction operator corresponding to j; w is the half-width number of points in the Crossline direction, u is the half-width number of points in the Inline direction; g represents the step size of the three-dimensional prediction deconvolution calculation; d b-c,m-n,j-k It represents the common detection point seismic gather data with shot line number bc, shot point number mn, time direction sample point number jk, and containing multiple waves.

[0133] Example 2

[0134] The seismic data processing based on 3D predictive deconvolution is performed on the seismic data of an offshore block. The process is as follows:

[0135] Step 1: Collect offshore seismic exploration data to obtain common detection point seismic gathers d b,m,j ;

[0136] Step 2. Determine the operator f of the three-dimensional prediction deconvolution according to the pre-established three-dimensional prediction deconvolution model c,n,k ;

[0137] Among them, the three-dimensional prediction deconvolution model is:

[0138]

[0139] In the above model, d b,m,j p represents the seismic gather data with shot line number b, shot point number m, time direction sample point number j, and common detection point containing multiple waves; b,m,j represents the common detection point seismic gather data with shot line number b, shot point number m, time direction sample point number j, and no multiple waves; f c,n,kis the operator of three-dimensional prediction deconvolution; c represents the sample point number of the three-dimensional prediction operator corresponding to b, n represents the sample point number of the three-dimensional prediction operator corresponding to m, k represents the sample point number of the three-dimensional prediction operator corresponding to j; w is the half-width number of points in the Crossline direction, u is the half-width number of points in the Inline direction; g represents the step size of the three-dimensional prediction deconvolution calculation; d b-c,m-n,j-k It represents the common detection point seismic gather data with shot line number bc, shot point number mn, time direction sample point number jk, and containing multiple waves.

[0140] Step 3. According to the calculated three-dimensional prediction deconvolution operator f c,n,k and earthquake data d b,m,j Convolution is performed to obtain the seismic data p after suppressing multiple waves. b,m,j .

[0141] In order to verify the effect of the seismic data processing method based on 3D predictive deconvolution, a 3D model data containing multiple waves was forward modeled using the wave equation method for predictive deconvolution testing.

[0142] Figure 4 This is a comparison chart of the deconvolution processing effect of three-dimensional model prediction. Figure 4 The three-dimensional model data parameters in are: longitudinal sampling point ns is 251, cmp direction nx is 134, and cmp_line direction ny is 134. The data models used for the three-dimensional prediction deconvolution method are all one-layer models, that is, they contain only one primary wave and the rest are multiple wave signals.

[0143] Figure 4 is a three-dimensional seismic data model containing only one layer, where Figure 4 (a) is a schematic diagram before data processing. Figure 4 (b) is a schematic diagram of the effect after data processing. Since the overall 3D seismic data is not easy to compare and observe, the far array, near array and middle array are extracted from the data for separate comparative analysis. Figure 4 Shown by the dashed line.

[0144] Figure 5 This is a schematic diagram of the near-far arrangement before predicting deconvolution. Figure 5 (a) is a close arrangement. Figure 5 (b) is the middle arrangement, Figure 5 Middle (c) is the far arrangement. Figure 6 This is a schematic diagram of the predicted near-far arrangement after deconvolution processing; Figure 6 (a) is a close arrangement. Figure 6 (b) is the middle arrangement, Figure 6 (c) is a long-range arrangement. By comparing the corresponding Figure 5 (a) Near arrangement and Figure 6(a) Nearly aligned for comparison, Figure 5 (b) Arrangement and Figure 6 The alignment in (b) is compared. Figure 5 Middle (c) Far arrangement and Figure 6 The comparison between the near, medium and far arrays shows that the multiple waves of the near, medium and far arrays are all suppressed, and there are some differences in the suppression of the multiple waves of the far, medium and near arrays. When the deconvolution operator is predicted to be convoluted with the seismic data, some noise will be generated at the data boundary. Figure 5 The comparison of the left arrow position in Figure (a) also shows that this is a normal phenomenon of the method. Figure 5 Figure (a) and Figure 6 By comparing the positions of the right arrows in Figure (a), it can be found that the multiple waves are suppressed to a certain extent.

[0145] This example provides a method of determining a three-dimensional predictive deconvolution operator based on target seismic data in a study area using a pre-established three-dimensional predictive deconvolution model; performing three-dimensional predictive deconvolution processing on the target seismic data according to the three-dimensional predictive deconvolution operator to obtain processed seismic data. This application considers multiple directions of three-dimensional seismic data, predicts a three-dimensional predictive deconvolution operator, and then performs seismic data processing to obtain seismic data that is more spatially connected; at the same time, compared with the one-dimensional predictive deconvolution seismic data processing method, it improves the resolution of seismic data and improves the quality of seismic data processing and offset imaging.

[0146] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A seismic data processing method based on three-dimensional predictive deconvolution, characterized in that: The method comprises: Based on the target seismic data in the study area, the operator of 3D predictive deconvolution is determined by using the pre-established 3D predictive deconvolution model; The target seismic data is subjected to three-dimensional predictive deconvolution processing according to the three-dimensional predictive deconvolution operator to obtain processed seismic data.

2. The seismic data processing method based on three-dimensional predictive deconvolution according to claim 1, characterized in that: The target seismic data is three-dimensional common detection point seismic gather data.

3. The seismic data processing method based on three-dimensional predictive deconvolution according to claim 1, characterized in that: The pre-established three-dimensional prediction deconvolution model is: In the above model, d b,m,j p represents the seismic gather data of the common detection point with shot line number b, shot point number m, time direction sample point number j, and multiple waves; b,m,j represents the common detection point seismic gather data with shot line number b, shot point number m, time direction sample point number j, and no multiple waves; f c,n,k is the operator of three-dimensional prediction deconvolution; c represents the sample point number of the three-dimensional prediction operator corresponding to b, n represents the sample point number of the three-dimensional prediction operator corresponding to m, and k represents the sample point number of the three-dimensional prediction operator corresponding to j; w and u are the half-width points in the X and Y directions respectively; g represents the step size of the three-dimensional prediction deconvolution calculation; d b-c,m-n,j-k It represents the common detection point seismic gather data with shot line number bc, shot point number mn, time direction sample point number jk, and containing multiple waves.

4. The seismic data processing method based on three-dimensional predictive deconvolution according to claim 3, characterized in that: The process of establishing the three-dimensional prediction deconvolution model includes: Determine one-dimensional seismic data according to the reflection coefficient and the seismic wavelet; wherein the reflection coefficient is a white noise sequence, and the seismic wavelet is a minimum phase wavelet; According to the statistical characteristics of seismic data, a one-dimensional prediction deconvolution model is determined; A three-dimensional predictive deconvolution model is determined according to the one-dimensional predictive deconvolution model and the three-dimensional attributes of the three-dimensional seismic data.

5. The seismic data processing method based on three-dimensional predictive deconvolution according to claim 4, characterized in that: Determining one-dimensional seismic data according to the reflection coefficient and the seismic wavelet includes: According to the reflection coefficient and the seismic wavelet, the one-dimensional seismic data is calculated using the seismic convolution formula, wherein the seismic convolution formula is: d(t)=b(t)*f(t) In the above formula, d(t) is the seismic data in the time domain, b(t) is the seismic wavelet in the time domain, f(t) is the reflection coefficient in the time domain, and t is the time of the seismic data.

6. The method for processing seismic data based on three-dimensional predictive deconvolution according to claim 4, characterized in that: Determining a one-dimensional prediction deconvolution model according to the statistical characteristics of the seismic data includes: Determine the relationship between seismic data and time based on the statistical characteristics of seismic data; A one-dimensional prediction deconvolution model is established based on the determined relationship between seismic data and time; Wherein, the one-dimensional prediction deconvolution model is: In the above one-dimensional predictive deconvolution model, d j represents the seismic gather data of the common detection point with sample point number j in the time direction and containing multiple waves; p j Indicates the seismic gather data of the common detection point with sample number j in the time direction and no multiple waves; d j-k represents the seismic gather data of the common detection point with the sample point number jk in the time direction and containing multiple waves; f k Operator representing one-dimensional predictive deconvolution.

7. A seismic data processing device based on three-dimensional predictive deconvolution, characterized in that: The device comprises: an operator module for determining three-dimensional prediction deconvolution and a seismic data processing module; The module for determining the operator of three-dimensional predictive deconvolution is used to determine the operator of three-dimensional predictive deconvolution based on the target seismic data of the study area and using a pre-established three-dimensional predictive deconvolution model; The seismic data processing module is used to perform three-dimensional predictive deconvolution processing on the target seismic data according to the three-dimensional predictive deconvolution operator to obtain processed seismic data.

8. The seismic data processing device based on three-dimensional prediction deconvolution according to claim 7, characterized in that: The pre-established three-dimensional prediction deconvolution model is: In the above model, d b,m,j p represents the seismic gather data of the common detection point with shot line number b, shot point number m, time direction sample point number j, and multiple waves; b,m,j represents the common detection point seismic gather data with shot line number b, shot point number m, time direction sample point number j, and no multiple waves; f c,n,k is the operator of three-dimensional prediction deconvolution; c represents the sample point number of the three-dimensional prediction operator corresponding to b, n represents the sample point number of the three-dimensional prediction operator corresponding to m, k represents the sample point number of the three-dimensional prediction operator corresponding to j; w is the half-width number of points in the Crossline direction, u is the half-width number of points in the Inline direction; g represents the step size of the three-dimensional prediction deconvolution calculation; d b-c,m-n,j-k It represents the common detection point seismic gather data with shot line number bc, shot point number mn, time direction sample point number jk, and containing multiple waves.

9. The seismic data processing device based on three-dimensional prediction deconvolution according to claim 8, characterized in that: The process of establishing the three-dimensional prediction deconvolution model includes: Determine one-dimensional seismic data according to the reflection coefficient and the seismic wavelet; wherein the reflection coefficient is a white noise sequence, and the seismic wavelet is a minimum phase wavelet; According to the statistical characteristics of seismic data, a one-dimensional prediction deconvolution model is determined; A three-dimensional predictive deconvolution model is determined according to the one-dimensional predictive deconvolution model and the three-dimensional attributes of the three-dimensional seismic data.

10. The seismic data processing device based on three-dimensional prediction deconvolution according to claim 4, characterized in that: Determining one-dimensional seismic data according to the reflection coefficient and the seismic wavelet includes: According to the reflection coefficient and the seismic wavelet, the one-dimensional seismic data is calculated using the seismic convolution formula, wherein the seismic convolution formula is: d(t)=b(t)*f(t) In the above formula, d(t) is the seismic data in the time domain, b(t) is the seismic wavelet in the time domain, f(f) is the reflection coefficient in the time domain, and t is the time of the seismic data.

11. The seismic data processing device based on three-dimensional prediction deconvolution according to claim 10, characterized in that: Determining a one-dimensional prediction deconvolution model according to the statistical characteristics of the seismic data includes: Determine the relationship between seismic data and time based on the statistical characteristics of seismic data; A one-dimensional prediction deconvolution model is established based on the determined relationship between seismic data and time; Wherein, the one-dimensional prediction deconvolution model is: In the above one-dimensional predictive deconvolution model, d j represents the seismic gather data of the common detection point with sample point number j in the time direction and containing multiple waves; p j Indicates the seismic gather data of the common detection point with sample number j in the time direction and no multiple waves; d j-k represents the seismic gather data of the common detection point with the sample point number jk in the time direction and containing multiple waves; f j Operator representing one-dimensional predictive deconvolution.

12. A computer program product having a seismic data processing function based on three-dimensional predictive deconvolution, comprising a computer program / instructions, characterized in that: When the computer program / instruction is executed by a processor, the seismic data processing method based on three-dimensional predictive deconvolution described in any one of claims 1 to 6 is implemented.

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