CN109387872A - Surface-related multiple prediction technique - Google Patents
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Abstract
Disclose a kind of surface-related multiple prediction technique, comprising: based on the big gun collection in total shot gather data to be predicted and first threshold range, form super shot gather data；Using a seismic channel in total shot gather data as seismic channel to be predicted, the common detector gather within the scope of common detector gather and second threshold to be predicted is read in, obtains super geophone station trace gather；It determines pore diameter range and mesh point coordinate, determines the super big gun collection target track and super geophone station trace gather target track of each mesh point, obtain the super big gun collection approximation road road approximate with super geophone station trace gather of each mesh point；It is corrected and sums with convolution, obtain the convolution adjustment road of each mesh point；It sums to each convolution adjustment road, obtains the multiple wave pattern of seismic channel to be predicted；The multiple wave pattern of each seismic channel is obtained, the multiple wave pattern of big gun collection altogether is obtained.The present invention establishes super big gun collection and super geophone station trace gather, and uses substep search strategy, in conjunction with multithreading, improves the efficiency of three-dimensional surface multiple wave prediction.
Description
Technical Field
The invention relates to the field of seismic exploration, in particular to a surface multiple prediction method.
Background
Multiple prediction is a key step in multiple imaging and multiple suppression. The multiple prediction part contained in the free interface multiple prediction (SRME) method developed by Berkhout, Vercshuur and the like is based on the prediction theory of wave equation, adopts a data-driven mode, does not need information of underground structure, can predict all the multiple in the seismic record, and is one of the important methods in the aspect of current multiple suppression.
However, SRME requires full wavefield data, and seismic data collected in the field is not usually full wavefield data, and the original seismic data needs to be subjected to data regularization before prediction, and the quality of data regularization directly determines the quality of prediction, so that data regularization is the key to use of the method. This results in limitations in the use of the method, for example, when the underground structure is particularly complex, it is difficult to determine extrapolation rules, which results in inefficient extrapolation of near offset traces and thus in an inability to accurately predict free surface multiples for small offset traces.
To overcome the problems of SRME in data regularization, Van Deme and Verschuur, Moore and Dragoset et al propose generalized multiple prediction (GSMP) methods. This method does not require a well-regulated data volume to be prepared for the SRME algorithm in advance, which is very large in three dimensions, but only obtains the desired data from the recorded seismic data by minimum neighbor interpolation when needed. Meanwhile, the method can adapt to various acquisition and observation systems and various geological conditions, and can provide better multiple prediction results for common situations such as near offset trace loss, insufficient sampling in the direction of three-dimensional transverse survey lines, pinnate drift of marine exploration and the like. However, the GSMP method involves a large amount of data interpolation work, in the three-dimensional case, a seismic trace of multiples to be predicted may contain tens of thousands of downward-reflecting points within the aperture defined for it, and each point within the aperture requires two minimum neighbor interpolations to search for the desired seismic data associated with shot points and geophone points, respectively. It follows that for a conventional three-dimensional data volume, the minimal neighbor interpolation involved is very significant. How to efficiently find the minimum neighbor seismic channel from the three-dimensional data volume and correct the minimum neighbor seismic channel to obtain ideal seismic data is a key point.
Disclosure of Invention
The invention provides a surface multiple prediction method, which can improve the efficiency of three-dimensional surface multiple prediction by establishing a super shot set and a super demodulator probe gather, adopting a step-by-step search strategy and combining a multithreading technology.
The invention provides a surface multiple prediction method. The method may include: step 1: forming super shot gather data based on the common shot gather data to be predicted and shot gather data of a shot gather within a first threshold range from the common shot gather; step 2: taking one seismic channel in the common shot gather data as a seismic channel to be predicted, reading in a common detection point gather to be predicted and a common detection point gather within a second threshold range from the common detection point gather to be predicted, and obtaining a super detection point gather corresponding to the seismic channel to be predicted; and step 3: determining an aperture range and grid point coordinates for the seismic channel to be predicted, determining a super shot set target channel and a super detection point gather target channel corresponding to each grid point for each grid point in the aperture range, and further acquiring a super shot set approximate channel and a super detection point gather approximate channel corresponding to each grid point; and 4, step 4: carrying out root mean square speed correction and convolution summation on the super-shot gather approximate trace and the super-demodulator point gather approximate trace to obtain a convolution adjustment trace corresponding to each grid point; and 5: summing the convolution adjusting channels corresponding to each grid point to obtain a multiple wave model of the seismic channel to be predicted; step 6: and (3) repeating the steps 2-5 aiming at each seismic channel in the common shot gather data to obtain a multiple wave model of each seismic channel so as to obtain a multiple wave model of the common shot gather.
Preferably, determining the approximate trace of the super shot gather comprises: setting an error threshold; acquiring a first error based on offset data and azimuth data of the target trace of the super shot gather and the seismic trace to be predicted; if the first error is smaller than or equal to the error threshold value, acquiring a second error based on the coordinate data of the central points of the super shot gather target trace and the seismic trace to be predicted; and obtaining a total error function based on the first error and the second error, and selecting the seismic trace which enables the total error function to be minimum in the super shot set as a super shot set approximate trace corresponding to the grid point.
Preferably, determining the ultraske point gather approximation trace comprises: setting an error threshold; acquiring a first error based on offset data and azimuth data of the super-detection point gather target trace and the seismic trace to be predicted; if the first error is smaller than or equal to the error threshold value, acquiring a second error based on the coordinate data of the central point of the super-detection point gather target trace and the seismic trace to be predicted; and obtaining a total error function based on the first error and the second error, and selecting the seismic channel with the minimum total error function in the super-demodulator probe channel set as a super-demodulator probe channel set approximate channel corresponding to the grid point.
Preferably, the first error is:
wherein,denotes a first error, hdRepresenting the offset, h, of the target track of the super shot gather or super demodulator probe gatheriRepresenting the offset of seismic traces in a super shot gather or super geophone point gather αdIndicating the azimuth of the target track of the super shot gather or super demodulator probe gather, αiRepresenting azimuth angles of seismic traces in the super shot gather or the super geophone point gather; omegahWeight coefficient, ω, representing offsetαPresentation sideThe weight coefficient for the bit angle, ε, represents the divisor constant.
Preferably, the second error is:
wherein,denotes a second error, xd，ydRespectively representing the abscissa and the ordinate of the central point of the target channel of the super-shot gather or the super-demodulator probe gather; x is the number ofi，yiRepresenting the abscissa and the ordinate of the central point of a seismic channel in the super shot set or the super geophone point channel set; omegax，ωyAnd weight coefficients respectively representing the abscissa of the central point and the ordinate of the central point.
Preferably, the total error function is:
wherein E is2The total error function is represented.
Preferably, for each grid point in the aperture range, the shot point of the seismic channel to be predicted is taken as a shot point, and the grid point is taken as a demodulator probe, so as to obtain the super shot gather target channel.
Preferably, for each grid point in the aperture range, the demodulator probe of the seismic channel to be predicted is taken as a demodulator probe, and the grid point is taken as a shot point, so as to obtain a super-demodulator probe gather target channel.
The invention has the beneficial effects that: the minimum neighbor search is carried out by utilizing the minimum error function of the subentry normalization, the linear search is limited in a reasonable small range in the search process, and the overall efficiency of the three-dimensional surface multiple prediction is improved by adopting a step-by-step search strategy and combining a multithreading technology; the accuracy of the prediction method is verified through a complex three-dimensional theoretical model, and the calculation efficiency is greatly improved; the method can adapt to free surface multiple prediction work under the conditions of three-dimensional large data volume and complex geological structure.
The method of the present invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts.
Fig. 1 shows a flow chart of the steps of a surface multiple prediction method according to the invention.
Fig. 2 shows a three-dimensional GSMP interpolation diagram according to an embodiment of the invention.
FIG. 3 shows a schematic of a super-shot gather and a super-demodulator gather according to one embodiment of the invention.
FIG. 4 shows a schematic of a simulated 2.5-dimensional Smaart simulated shot record according to one embodiment of the invention.
FIG. 5 shows a schematic diagram of a predicted multiples model according to one embodiment of the present invention.
FIG. 6 shows a schematic diagram of a raw data common offset profile, according to one embodiment of the present invention.
FIG. 7 shows a schematic diagram of a predicted multiple model common offset profile according to an embodiment of the invention.
FIG. 8 shows a schematic of a multiple subtracted common offset profile according to one embodiment of the present invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flow chart of the steps of a surface multiple prediction method according to the invention.
The surface multiple prediction method according to the present invention may include:
step 1: and forming super shot set data based on the common shot set data to be predicted and shot set data of the shot set within a first threshold range from the common shot set.
Step 2: and taking one seismic channel in the common shot gather data as a seismic channel to be predicted, reading in a common detection point gather to be predicted and a common detection point gather within a second threshold range from the common detection point gather to be predicted, and obtaining a super detection point gather corresponding to the seismic channel to be predicted.
Specifically, the problem of mining optimal approximate data in a seismic data volume is involved when the nearest seismic data is found through minimum neighbor interpolation, the most intuitive data search is linear search, that is, the entire data set is traversed to find the seismic data corresponding to the minimum error value, and when the data volume is large, the linear search consumes much time. FIG. 2 is a schematic diagram of a three-dimensional GSMP interpolation according to an embodiment of the present invention, in which the X and Y coordinate axes represent coordinate axes of field acquisition, the middle black rectangle frame is parallel to a shot-geophone point connecting line SR, which takes M, i.e. the center point of SR, as the center and is the aperture range defined by the seismic trace SR to be predicted, and the dotted line represents an effective downward reflection point set, and the defined aperture range includes all effective downward reflection points; d represents a downward reflection point in the aperture, RD is a seismic channel from the wave detection point to the aperture point D, and the seismic channel which is found in the super common wave detection point gather and is most matched with the super common wave detection point gather is marked by a connecting line of a diamond shape and a triangle.
Finding shot gather data of a shot gather within a first threshold range from the common shot gather in space according to the shot point space position coordinates of the common shot gather data to be predicted, and combining the data of the shots to form super shot gather data; generally, 3x3,5x5,9x9, that is, 9 shots, 25 shots or 81 shots are taken as the center of the shot point to be predicted to form super shot set data, and the number of shots taken can be determined by the skilled person according to the data characteristics, so that the later related search process can be limited to a smaller data range, and the query time is reduced.
One seismic channel in the common shot gather data is used as a seismic channel to be predicted, the spatial coordinates of the wave detection points are obtained from the head of the seismic channel to be predicted, the common wave detection point gather to be predicted and the common wave detection point gather within a second threshold range from the common wave detection point gather to be predicted are read in according to the obtained information of the wave detection points, and the super wave detection point gather corresponding to the seismic channel to be predicted is obtained.
And step 3: determining an aperture range and grid point coordinates for a seismic channel to be predicted, determining a super-shot set target channel and a super-detection point gather target channel corresponding to each grid point for each grid point in the aperture range, and further obtaining a super-shot set approximate channel and a super-detection point gather approximate channel corresponding to each grid point.
In one example, for each grid point in the aperture range, the shot point of the seismic channel to be predicted is taken as a shot point, and the grid point is taken as a demodulator probe, so as to obtain the super-shot gather target channel.
In one example, determining a super shot gather approximate track includes: setting an error threshold; acquiring a first error based on offset data and azimuth data of the target trace of the super shot gather and the seismic trace to be predicted; if the first error is smaller than or equal to the error threshold value, acquiring a second error based on the coordinate data of the central point of the target trace of the super shot gather and the seismic trace to be predicted; and obtaining a total error function based on the first error and the second error, and selecting the seismic trace with the minimum total error function in the super shot set as the super shot set approximate trace corresponding to the grid point.
In one example, for each grid point in the aperture range, a geophone point of a seismic channel to be predicted is taken as a geophone point, and a grid point is taken as a shot point, so that a super-geophone gather target channel is obtained.
In one example, determining a super-demodulator gather approximated trace comprises: setting an error threshold; acquiring a first error based on offset data and azimuth data of a super-detection point gather target channel and a seismic channel to be predicted; if the first error is smaller than or equal to the error threshold value, acquiring a second error based on the coordinate data of the central point of the super-detection point gather target trace and the seismic trace to be predicted; and obtaining a total error function based on the first error and the second error, and selecting the seismic channel with the minimum total error function in the super-demodulator probe channel set as a super-demodulator probe channel set approximate channel corresponding to the grid point.
In one example, the first error is:
wherein,denotes a first error, hdRepresenting the offset, h, of the target track of the super shot gather or super demodulator probe gatheriRepresenting the offset of seismic traces in a super shot gather or super geophone point gather αdIndicating the azimuth of the target track of the super shot gather or super demodulator probe gather, αiRepresenting azimuth angles of seismic traces in the super shot gather or the super geophone point gather; omegahWeight coefficient, ω, representing offsetαThe weight coefficient representing the azimuth, and ε represents the divisor constant, avoiding division by zero.
In one example, the second error is:
wherein,denotes a second error, xd，ydRespectively representing the abscissa and the ordinate of the central point of the target channel of the super-shot gather or the super-demodulator probe gather; x is the number ofi，yiRepresenting the abscissa and the ordinate of the central point of a seismic channel in the super shot set or the super geophone point channel set; omegax，ωyAnd weight coefficients respectively representing the abscissa of the central point and the ordinate of the central point.
In one example, the overall error function is:
wherein E is2The total error function is represented.
And 4, step 4: and carrying out root mean square speed correction and convolution summation on the super shot set approximate trace and the super demodulator probe gather approximate trace to obtain a convolution adjustment trace corresponding to each grid point.
And 5: and summing the convolution adjusting channels corresponding to each grid point to obtain a multiple wave model of the seismic channel to be predicted.
Step 6: and (5) repeating the steps 2-5 aiming at each seismic channel in the common shot gather data to obtain a multiple wave model of each seismic channel and further obtain a multiple wave model of the common shot gather.
Specifically, for the seismic channel to be predicted, a prediction aperture and grid point coordinates are determined, an aperture range is generally defined as a square parallel to the seismic channel to be predicted, the side length is the length of the offset distance of the seismic channel to be predicted, the center of the square is the center point of the seismic channel to be predicted, the square is gridded, and the size of the grid can be the interval size of the seismic channel. Aiming at each grid point in the aperture range, acquiring an ultra-shot gather target channel by taking a shot point of a seismic channel to be predicted as a shot point and taking the grid point as a demodulator probe, and acquiring an ultra-shot gather target channel by taking a demodulator probe of the seismic channel to be predicted as a demodulator probe and taking the grid point as a shot point; and then obtaining a super shot set approximate channel and a super demodulator probe set approximate channel corresponding to each grid point. And carrying out root mean square speed correction and convolution summation on the super shot set approximate trace and the super demodulator probe gather approximate trace to obtain a convolution adjustment trace corresponding to each grid point. And summing the convolution adjusting channels corresponding to each grid point to obtain a multiple wave model of the seismic channel to be predicted. And repeating the steps for each seismic channel in the common shot gather data to obtain a multiple wave model of each seismic channel, and further obtain a multiple wave model of the common shot gather.
Determining the approximate track of the super shot gather comprises the following steps: setting an error threshold, wherein the error threshold is set according to actual conditions; substituting the offset data and the azimuth data of the target trace of the super shot gather and the seismic trace to be predicted into a formula (1) to obtain a first error; if the first error is smaller than or equal to the error threshold, the coordinate data of the central point of the target trace of the super shot gather and the seismic trace to be predicted are substituted into the formula (2) to obtain a second error, if the first error is larger than the error threshold, the seismic trace in the super shot gather is excluded, and the second error related to the trace does not need to be calculated, so that the calculation time is further saved; and substituting the first error and the second error into a formula (3) to obtain a total error function, and selecting the seismic trace which enables the total error function to be minimum in the super shot set as the super shot set approximate trace corresponding to the grid point.
Determining the approximate trace of the super-detection point gather comprises the following steps: setting an error threshold, wherein the error threshold is set according to actual conditions; substituting offset data and azimuth data of a super-detection point gather target channel and a seismic channel to be predicted into a formula (1) to obtain a first error; if the first error is less than or equal to the error threshold, the coordinate data of the central point of the target trace of the super-detection point trace set and the seismic trace to be predicted are substituted into the formula (2) to obtain a second error, if the first error is greater than the error threshold, the seismic trace in the super-detection point trace set is removed, and the second error related to the trace does not need to be calculated, so that the calculation time is further saved; substituting the first error and the second error into a formula (3) to obtain a total error function, and selecting the seismic channel with the minimum total error function in the super-demodulator probe channel set as a super-demodulator probe channel set approximate channel corresponding to the grid point.
FIG. 3 is a diagram illustrating a super shot gather and a super geophone gather according to one embodiment of the present invention, wherein S is a common shot gather to be predicted, represented by black pentagons, and the other black pentagons are shot gathers within a first threshold range from the common shot gather to be predicted, which are combined to form the super shot gather; r is a wave detection point and is represented by a triangle, and a rhombus represents a common detection point gather within a second threshold range from the common detection point gather to be predicted to form a super common detection point gather together; SD and RD are respectively a super-shot set target track and a super-demodulator probe set target track, and a five-pointed star and a triangular black line adjacent to the SD in the figure represent a super-shot set approximate track obtained by searching in a super-shot set; and the black line between the rhombus and the triangle adjacent to RD represents the approximate channel of the super-detection point gather obtained by searching from the super-detection point gather. The linear search is limited in a smaller data range through the super shot set and the super demodulator probe gather, and the efficiency of the linear search can be improved.
According to the method, the efficiency of predicting the three-dimensional surface multiple is improved by establishing the super shot set and the super demodulator probe gather, adopting a step-by-step search strategy and combining a multithreading technology.
Application example
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
Finding shot gather data of a shot gather within a first threshold range from the common shot gather in space according to the shot point space position coordinates of the common shot gather data to be predicted, and combining the data of the shots to form super shot gather data; and taking one seismic channel in the common shot gather data as a seismic channel to be predicted, acquiring a detection point space coordinate from a seismic channel head to be predicted, reading in a common detection point gather to be predicted and a common detection point gather within a second threshold range from the common detection point gather to be predicted according to the acquired detection point information, and acquiring a super detection point gather corresponding to the seismic channel to be predicted.
FIG. 4 shows a schematic of a simulated 2.5-dimensional Smaart simulated shot record according to one embodiment of the invention. The Smart model is a classical model for detecting two-dimensional multiple elimination, the Smart model is expanded to a three-dimensional condition to form a 2.5-dimensional model, and three-dimensional simulation is carried out, wherein the gun lines are vertical to the wave detection line, the gun line distance is 400 m, the gun point interval is 320 m, each gun line is 3 guns, and the total number of the gun lines is 80; the line spacing of the detectors is 80 meters, the point spacing of the detectors is 40 meters, nine receiver lines are received per shot, 151 receiver points are received per detector line, and the point of the gun is located at the position of the middle detector point on the middle detector line, as shown in fig. 4, and the multiple wave development can be seen.
According to the simulated data characteristics, the shot gather data is relatively more, so that 3x3 is adopted when forming the super shot gather, namely, 9 adjacent common shot gather data form a super shot gather; similarly, the analysis data shows that the co-detected point gather data is relatively small, and in order to ensure the accuracy of the prediction, 9x9, namely 81 co-detected point data are adopted to form a super co-detected point gather.
FIG. 5 shows a schematic diagram of a predicted multiples model according to one embodiment of the present invention.
Setting an error threshold, and substituting offset data and azimuth data of the target trace of the super shot gather and the seismic trace to be predicted into a formula (1) to obtain a first error; if the first error is smaller than or equal to the error threshold value, substituting the coordinate data of the central points of the target trace of the super shot gather and the seismic trace to be predicted into a formula (2) to obtain a second error; and substituting the first error and the second error into a formula (3) to obtain a total error function, and selecting the seismic trace which enables the total error function to be minimum in the super shot set as the super shot set approximate trace corresponding to the grid point. Substituting offset data and azimuth data of a super-detection point gather target channel and a seismic channel to be predicted into a formula (1) to obtain a first error; if the first error is smaller than or equal to the error threshold value, substituting the coordinate data of the central point of the super-detection point gather target channel and the seismic channel to be predicted into a formula (2) to obtain a second error; substituting the first error and the second error into a formula (3) to obtain a total error function, and selecting the seismic channel with the minimum total error function in the super-demodulator probe channel set as a super-demodulator probe channel set approximate channel corresponding to the grid point.
And carrying out root mean square speed correction and convolution summation on the super shot set approximate trace and the super demodulator probe gather approximate trace to obtain a convolution adjustment trace corresponding to each grid point. And summing the convolution adjusting channels corresponding to each grid point to obtain a multiple wave model of the seismic channel to be predicted. And repeating the steps for each seismic channel in the common shot gather data to obtain a multiple wave model of each seismic channel, and further obtain a multiple wave model of the common shot gather. As shown in fig. 5, in the case that the linear search ranges are all limited by the super-shot set, the calculation efficiency of predicting the single-shot surface multiples by using 10 threads is improved by 8 times compared with that of a single thread.
FIG. 6 shows a schematic diagram of a raw data common offset profile, according to one embodiment of the present invention.
FIG. 7 shows a schematic diagram of a predicted multiple model common offset profile according to an embodiment of the invention.
FIG. 8 shows a schematic of a multiple subtracted common offset profile according to one embodiment of the present invention.
The co-displacement profile can show the result of multiple prediction more clearly, fig. 6 is a schematic diagram of the original data co-displacement profile according to an embodiment of the present invention, fig. 7 is a schematic diagram of the predicted multiple model co-displacement profile according to an embodiment of the present invention, arrows in the diagrams indicate the positions of the surface multiple at the water bottom and the salt dome in the original data and the predicted result, respectively, and the prediction is accurate by comparison. FIG. 8 is a schematic diagram of a common offset profile after multiple subtraction according to one embodiment of the present invention, where it can be seen that the multiples indicated by the arrows are well suppressed.
In conclusion, the method improves the efficiency of predicting the three-dimensional surface multiple waves by establishing the super shot set and the super wave detection point gather, adopting a step-by-step search strategy and combining a multithreading technology.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (8)
1. A method of surface multiple prediction, comprising:
step 1: forming super shot gather data based on the common shot gather data to be predicted and shot gather data of a shot gather within a first threshold range from the common shot gather;
step 2: taking one seismic channel in the common shot gather data as a seismic channel to be predicted, reading in a common detection point gather to be predicted and a common detection point gather within a second threshold range from the common detection point gather to be predicted, and obtaining a super detection point gather corresponding to the seismic channel to be predicted;
and step 3: determining an aperture range and grid point coordinates for the seismic channel to be predicted, determining a super shot set target channel and a super detection point gather target channel corresponding to each grid point for each grid point in the aperture range, and further acquiring a super shot set approximate channel and a super detection point gather approximate channel corresponding to each grid point;
and 4, step 4: carrying out root mean square speed correction and convolution summation on the super-shot gather approximate trace and the super-demodulator point gather approximate trace to obtain a convolution adjustment trace corresponding to each grid point;
and 5: summing the convolution adjusting channels corresponding to each grid point to obtain a multiple wave model of the seismic channel to be predicted;
step 6: and (3) repeating the steps 2-5 aiming at each seismic channel in the common shot gather data to obtain a multiple wave model of each seismic channel so as to obtain a multiple wave model of the common shot gather.
2. The surface multiples prediction method of claim 1, wherein determining a hypercoal gather approximate track comprises:
setting an error threshold;
acquiring a first error based on offset data and azimuth data of the target trace of the super shot gather and the seismic trace to be predicted;
if the first error is smaller than or equal to the error threshold value, acquiring a second error based on the coordinate data of the central points of the super shot gather target trace and the seismic trace to be predicted;
and obtaining a total error function based on the first error and the second error, and selecting the seismic trace which enables the total error function to be minimum in the super shot set as a super shot set approximate trace corresponding to the grid point.
3. The surface multiples prediction method of claim 1, wherein determining a super-detector gather approximated trace comprises:
setting an error threshold;
acquiring a first error based on offset data and azimuth data of the super-detection point gather target trace and the seismic trace to be predicted;
if the first error is smaller than or equal to the error threshold value, acquiring a second error based on the coordinate data of the central point of the super-detection point gather target trace and the seismic trace to be predicted;
and obtaining a total error function based on the first error and the second error, and selecting the seismic channel with the minimum total error function in the super-demodulator probe channel set as a super-demodulator probe channel set approximate channel corresponding to the grid point.
4. The surface multiple prediction method of claim 2 or 3, wherein the first error is:
wherein,denotes a first error, hdRepresenting the offset, h, of the target track of the super shot gather or super demodulator probe gatheriRepresenting the offset of seismic traces in a super shot gather or super geophone point gather αdIndicating the azimuth of the target track of the super shot gather or super demodulator probe gather, αiRepresenting azimuth angles of seismic traces in the super shot gather or the super geophone point gather; omegahWeight coefficient, ω, representing offsetαThe weight coefficient for azimuth and epsilon a divisor constant.
5. The surface multiple prediction method of claim 4, wherein the second error is:
wherein,denotes a second error, xd，ydRespectively representing the abscissa and the ordinate of the central point of the target channel of the super-shot gather or the super-demodulator probe gather; x is the number ofi，yiRepresenting the abscissa and the ordinate of the central point of a seismic channel in the super shot set or the super geophone point channel set; omegax，ωyAnd weight coefficients respectively representing the abscissa of the central point and the ordinate of the central point.
6. The surface multiple prediction method of claim 5, wherein the overall error function is:
wherein E is2The total error function is represented.
7. The surface multiple prediction method according to claim 1, wherein for each grid point within the aperture range, a super-shot gather target trace is obtained with a shot point of the seismic trace to be predicted as a shot point and the grid point as a demodulator probe.
8. The surface multiple prediction method according to claim 1, wherein for each grid point within the aperture range, a super-geophone gather target trace is acquired with the geophone point of the seismic trace to be predicted as a geophone point and the grid point as a shot point.
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