Segmented cubic Hermite interpolation-based post-stack seismic data splicing method along geologic horizon

Abstract

The invention discloses a segmented cubic Hermite interpolation-based post-stack seismic data splicing method along a geologic horizon. The method comprises the steps of obtaining a first data volume and a second data volume; processing the first data body and the second data body into two sets of data bodies with completely consistent coordinate range, sampling interval, total seismic channel number and recording channel length; performing quality control on the two sets of data volumes, and judging whether the two sets of data volumes are suitable for data splicing; if yes, cutting the two sets of data volumes along the same geological horizon, and vertically adding the cut two parts of data seismic channels one by one to obtain a primary spliced data volume; after the primary spliced data volume is subjected to layer leveling processing and segmented Hermite interpolation processing for three times along the spliced layer, layer quality control is carried out until a preset quality control standard is met, and the spliced data volume is output; the problem that for the same block, vertical splicing cannot be achieved through the existing splicing technology by utilizing two sets of post-stack seismic data processed in different periods, different target strata or different processes is solved.

Description

Technical Field

[0001] The present disclosure relates to the field of seismic data processing, and more particularly to a method for splicing post-stack seismic data. Background Art

[0002] The statements in this section only provide background information related to the present disclosure and do not constitute prior art.

[0003] In seismic exploration, the primary task in the seismic acquisition design of each work area is to meet the exploration geological requirements at that time. For the exploration of geological targets at different burial depths, there are often clear target horizons, and all technical applications and parameter optimizations in a single processing will focus on the target horizon. For example, for the first three-dimensional seismic acquisition deployed for the shallow lithologic oil reservoir target within a burial depth of 2000 m in a certain work area, the maximum offset of the observation system usually does not exceed 3300 m. When designing the processing flow, the goal of improving resolution will be emphasized. When selecting the deconvolution parameters, the step size is usually very small. In the processing results, the shallow layer has a wide frequency band and good thin layer imaging, but the imaging quality of the deep layer processed by this set of processes is relatively lower than that of the shallow layer; several years later, if there is a new exploration need for the deep layer, because of the high cost, it is often not possible to re-conduct seismic acquisition for the shallow layer.

[0004] When conducting three-dimensional exploration or unified modeling, it is required to take into account both the middle and shallow layers and the deep layer at the same time. However, due to the above reasons, the existing middle and shallow layer and deep layer data volumes cannot be used. If used, processing is required; if processing is based on the shallow layer data volume, in order to better image the deep complex structure, a larger deconvolution step size will be selected, resulting in significant differences in amplitude, frequency band, and even phase between the middle and shallow layer and deep layer data volumes. At this time, if re-processing is carried out, the time cost is too high and the timeliness is too poor, and vice versa.

[0005] If data splicing technology is used, the existing post-stack seismic data splicing technology is limited to horizontal splicing. There is no relevant report on quickly vertically splicing the middle and shallow layer and deep layer data volumes to meet the needs of rapid mapping and three-dimensional modeling.

[0006] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art. Summary of the Invention

[0007] In view of this, the present disclosure provides a method for splicing post-stack seismic data along geological horizons based on piecewise cubic Hermite interpolation, which solves the problems that in the case of the same block, when using two sets of post-stack seismic data processed at different times, for different target horizons or by different processes for three-dimensional exploration or unified modeling, the existing processing methods cannot meet the high timeliness, and the existing data splicing technology cannot achieve vertical splicing.

[0008] To achieve the above invention object, the post-stack seismic data splicing method based on piecewise cubic Hermite interpolation along geological horizons includes:

[0009] Obtain a first data volume and a second data volume to be spliced;

[0010] Process the first data volume and the second data volume into two data volumes with exactly the same coordinate range, sampling interval, total number of seismic traces, and recording length;

[0011] Perform quality control on the two data volumes to determine whether data splicing is applicable;

[0012] If applicable, cut the two data volumes along the same geological horizon, and vertically add the seismic traces of the two cut parts one by one to obtain a primary spliced data volume;

[0013] After performing layer flattening processing and piecewise cubic Hermite interpolation processing on the primary spliced data volume along the splicing horizon, perform quality control along the layer until it meets the preset quality control standard, and output the spliced data volume.

[0014] In the present disclosure and possible embodiments, the method for processing the first data volume and the second data volume includes:

[0015] Re-grid the first data volume and the second data volume and perform the same recording length truncation processing, re-sample using a unified step size, and cut off the data outside the coordinates according to the four-point coordinates of the study area to obtain two data volumes with exactly the same coordinate range, sampling interval, total number of seismic traces, and recording length.

[0016] In the present disclosure and possible embodiments, the grid processing is to perform the grid processing on the first data volume and the second data volume using the same seismic processing grid.

[0017] In the present disclosure and possible embodiments, the method for the same recording length truncation processing includes: truncate the two data volumes according to the minimum value of the maximum recording length in the two sets of data.

[0018] In the present disclosure and possible embodiments, the method for the re-sampling processing includes: perform quality control on the sampling intervals of the two data volumes. If they are the same, no processing is performed. If they are different, re-sample using the sampling interval of the set of data with the smallest sampling interval among the two data volumes.

[0019] In the present disclosure and possible embodiments, the method for determining whether data splicing is applicable includes:

[0020] Obtain the bandwidth and dominant frequency of two data volumes with exactly the same coordinate range, sampling interval, total number of seismic traces, and record length. If the difference in bandwidth between the two data volumes is equal to or exceeds 30 Hz, or the difference in dominant frequency is equal to or exceeds 10 Hz, it is determined to be inapplicable.

[0021] In the present disclosure and possible embodiments, the number of nodes N in the Hermite interpolation is determined by the following formula Hermite :

[0022]

[0023] In the formula, : is the average value of the dominant frequencies of the two data volumes, N Hermite is the number of nodes in the Hermite interpolation, in units of pieces, △ A4 is the sampling interval after the data volumes are agreed upon, in units of ms.

[0024] In the present disclosure and possible embodiments, the method for cutting the two data volumes includes:

[0025] Grid and interpolate the same geological horizons corresponding to the two data volumes respectively and record them in the headers of the data volumes, so that the two horizons correspond to the two data volumes respectively. According to geological requirements, cut off the upper or lower part of the corresponding data volume along the horizon.

[0026] In the present disclosure and possible embodiments, the method for vertically adding each seismic trace of the two parts of the cut data includes:

[0027] For the data volume with the upper part retained after cutting, set the values below the T2 horizon to 0. For the data volume with the lower part retained, set the values above the T2 horizon to 0; after cutting, splice each seismic trace, and uniformly truncate the spliced data volume along the minimum record length to obtain the primary spliced data volume.

[0028] In the present disclosure and possible embodiments, during the horizon flattening process, put the shift amount for flattening each seismic trace into the header to obtain the seismic data volume after horizon flattening; and / or,

[0029] The method for the piecewise cubic Hermite interpolation processing includes: according to the number of nodes N Hermite perform piecewise cubic Hermite interpolation, and use the shift amount for flattening in the seismic trace header to obtain the seismic data volume after Hermite interpolation processing.

[0030] In the present disclosure and possible embodiments, the piecewise cubic Hermite interpolation method includes:

[0031] Case A: Assume that f(x) is at the node a ≤ x 0 , x 1,…,x n The function values between ≤ b are f 0 , f 1 ,…, f n Let the polynomial P(x) be the interpolation function of f(x) in the interval {a, b} and its first derivative exists. The function has a first derivative in the interval, that is:

[0032] P(x i ) = f(x i ) = f i , P′(x i ) = f′(x i ) = f i ′, i = 0, 1,…, n. The polynomial P(x) is a polynomial of the highest degree 2n + 1. Two nodes can use a cubic polynomial as the interpolation function to complete interpolation;

[0033] Case B: By analogy with the above case, if the polynomial P(x) has an m - order derivative in the interval {a, b}, then the function P(x) at the nodes x 0 , x 1 ,…, x n needs to satisfy:

[0034]

[0035] When the interpolation problem satisfying Case A and Case B is called Hermite interpolation, the polynomial P(x) satisfying Case A or Case B is called the Hermite interpolation polynomial, denoted as H k (x), where k is the degree of the polynomial;

[0036] The interpolation polynomial H 3 (x) of the piece - wise cubic Hermite interpolation adopted satisfies the following conditions:

[0037] The function values and derivative values within the two known interpolation nodes x 0 , x 1 are y i = f(x i ), m i f′(x i ), (i = 0, 1). It is required to satisfy H 3 (x i ) = y i , H 3 ′(x i ) = m i , (i = 0, 1). The basis function is used to construct H 3 (x), and H 3 (x) is expressed as:

[0038] H 3(x) = y 0 α 0 (x) + y 1 α 1 (x) + m 0 β 0 (x) + m 1 β 1 (x);

[0039] Where: α 0 (x), α 1 (x), β 0 (x), β 1 (x) is an interpolation basis function, and each term is a polynomial with a degree less than or equal to 3, satisfying the following conditions:

[0040]

[0041] Let α 0 (x) = (a + b(x - x 0 ))(x - x 1 ), where: a and b are undetermined coefficients, and it can be known that:

[0042]

[0043] Using the same method, the polynomial H 3 (x) = y 0 α 0 (x) + y 1 α 1 (x) + m 0 β 0 (x) + m 1 β 1 (x) for each term;

[0044]

[0045] The cubic Hermite interpolation is completed by the above formula to obtain the interpolated value.

[0046] In the present disclosure and possible embodiments, a method for performing layer-by-layer quality control includes:

[0047] The seismic data volume processed by Hermite interpolation is reacted with the migration amount during flattening stored in the trace header to obtain a seismic data volume that restores the actual spatial position of the original data. Along the splicing horizon, attribute extraction is performed on the seismic data volume that restores the actual spatial position of the original data, and quality control is performed based on the maximum peak amplitude attribute.

[0048] In the present disclosure and possible embodiments, when performing the attribute extraction, the upper and lower attribute extraction windows are N Hermite / 10, and the unit is ms.

[0049] In the present disclosure and possible embodiments, the method for determining whether the quality control standard is met is as follows: when performing plane quality control on the maximum peak amplitude attribute, if more than 20% exceeds the average amplitude level, the quality control fails. For those that fail, the layer flattening process, the piecewise cubic Hermite interpolation process, and the in-layer quality control process are repeated until the quality control passes; if in the plane quality control of the maximum peak amplitude attribute, less than 20% exceeds the average amplitude level, the quality control passes.

[0050] The present disclosure has the following beneficial effects:

[0051] The post-stack seismic data splicing method based on piecewise cubic Hermite interpolation along geological horizons in the present disclosure aims at the same block and uses two sets of post-stack seismic data processed in different periods, for different target horizons, or by different processes to perform vertical data splicing, meeting the geological requirements for the exploration and development goals that can take into account shallow-middle and deep layers vertically, enabling rapid splicing of two sets of seismic data volumes for three-dimensional exploration or unified modeling, saving a large amount of seismic acquisition costs, submitting processing results with high efficiency, and effectively solving the problem that the existing data splicing technology cannot achieve vertical splicing. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, the above and other objects, features, and advantages of the present disclosure will become clearer. In the drawings:

[0053] Figure 1 is a flowchart of the post-stack seismic data splicing method based on piecewise cubic Hermite interpolation along geological horizons in the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The following is a description of the present disclosure based on embodiments. However, it should be noted that the present disclosure is not limited to these embodiments. In the following detailed description of the present disclosure, some specific details are described in detail. However, for the parts that are not described in detail, those skilled in the art can also fully understand the present disclosure.

[0055] In addition, those of ordinary skill in the art should understand that the provided drawings are only for illustrating the purposes, features, and advantages of the present disclosure, and the drawings are not actually drawn to scale. At the same time, unless the context clearly requires otherwise, the words "including", "comprising", and similar words throughout the specification and claims should be interpreted as having an inclusive meaning rather than an exclusive or exhaustive meaning; that is, it is the meaning of "including but not limited to".

[0056] The overall technical solution of the post-stack seismic data splicing method based on piecewise cubic Hermite interpolation along geological horizons in the present disclosure is as follows: Obtain the first data volume and the second data volume to be spliced; process the first data volume and the second data volume to obtain two data volumes with exactly the same coordinate range, sampling interval, total number of seismic traces, and recording length; perform quality control on the two data volumes to determine whether they meet the data splicing requirements of the present invention; if applicable, cut the two quality-controlled data volumes along the same geological horizon, and vertically add the seismic traces of the two cut parts one by one to obtain a primary spliced data volume; after performing layer flattening processing and piecewise cubic Hermite interpolation processing on the primary spliced data volume along the splicing horizon, perform along-layer quality control until it meets the preset quality control standard, and output the spliced data volume, thereby realizing the data splicing of the first data volume and the second data volume, and providing seismic data with good imaging quality for both shallow and deep layers for three-dimensional exploration or unified modeling.

[0057] The following decomposes and describes the above overall technical solution through specific embodiments for the purpose of elaborating on the method of the present disclosure in detail; the source of the data to be spliced in this embodiment is that in the practice of the Gulong area of the Daqing Oilfield in 2022, taking the seismic data of the Longnan work area as the research object, the data was collected in 1995 and the processing for the buried depth of 1300 m of the middle and shallow Putaohua oil layer group was completed in 2019. The frequency bandwidth of the middle and shallow layers reached 8 - 90 Hz, while the imaging quality of the deep layer was poor; in 2022, the depth-domain modeling and imaging processing for the deep complex structure were completed. The frequency band of the deep layer was 6 - 50 Hz, and the frequency band of the middle and shallow layers was 6 - 80 Hz. The deep structure processed the second time was continuous, and the faults and basement were depicted more clearly, but the resolution of the middle and shallow layers was slightly lower, and the data did not support the tracing and interpretation of small layers.

[0058] Under the new three-dimensional exploration requirements, it is first necessary to unify horizon confirmation and modeling. Therefore, applying the post-stack seismic data splicing method based on piecewise cubic Hermite interpolation along geological horizons in the present disclosure, splice the data volume for the middle and shallow layers in 2019 and the data volume for the deep layer in 2022 to quickly obtain a data volume suitable for unified horizon confirmation and modeling; after analysis, name the data volume for the middle and shallow layers in 2019 as seismic data volume A (equivalent to the first data volume), and name the data volume for the deep layer in 2022 as seismic data volume B (equivalent to the second data volume).

[0059] Figure 1 is the flowchart of the post-stack seismic data splicing method based on piecewise cubic Hermite interpolation along geological horizons in the embodiment of the present disclosure. As shown in Figure 1 The specific steps of applying the splicing method of the present disclosure to splice seismic data volume A and seismic data volume B are as follows:

[0060] S1. Preprocess seismic data volume A and seismic data volume B

[0061] The present disclosure preprocesses seismic data volume A and seismic data volume B according to the conventional techniques in the art. Specifically, the grid of seismic data volume A and seismic data volume B is re-gridded, the same record length is intercepted for processing, and then re-sampled with a unified step size. The data outside the coordinates is removed according to the four-point coordinates of the study area to obtain seismic data volume A4 and seismic data volume B4. At this time, the coordinate ranges, sampling intervals, number of seismic traces, and record lengths of seismic data volume A4 and seismic data volume B4 are exactly the same. The specific steps for preprocessing are as follows:

[0062] The seismic data volume A and seismic data volume B are gridded with the same seismic processing grid to obtain the gridded seismic data A1 and seismic data volume B1;

[0063] Determine the four-point coordinates for splicing according to geological requirements, screen the seismic data A1 and seismic data volume B1, and retain the data within the four-point coordinates to obtain seismic data volume A2 and seismic data volume B2. The total number of seismic traces of seismic data volume A2 and seismic data volume B2 is exactly the same;

[0064] Cutoff processing is performed on seismic data volume A2 and seismic data volume B2 according to the minimum value of the maximum record lengths in the two sets of data to obtain seismic data volume A3 and seismic data volume B3. The total number of seismic traces and record lengths of seismic data volume A3 and seismic data volume B3 are the same;

[0065] Quality control the sampling intervals of seismic data volume A3 and seismic data volume B3. If they are the same, no processing is performed. If they are different, resampling processing is performed on seismic data volume A3 and seismic data volume B3 using the sampling interval of the set with the smallest sampling interval among seismic data volume A3 and seismic data volume B3 to obtain seismic data volume A4 and seismic data volume B4. At this time, the sampling interval is △ A4 , with the unit of ms. After this step, the coordinate ranges, sampling intervals, number of seismic traces, and record lengths of seismic data volume A4 and seismic data volume B4 are exactly the same.

[0066] S2. Determine the window range of time-frequency analysis according to geological requirements, and perform time-frequency analysis on the seismic data volume A4 and seismic data volume B4 obtained in S1 respectively to obtain the frequency bandwidth (highest frequency - lowest frequency) of seismic data volume A4 and seismic data volume B4 and and the dominant frequencies of seismic data volume A4 and seismic data volume B4 and Perform quality control using the obtained bandwidth and main frequency. If the difference in bandwidth between the two sets of data is equal to or exceeds 30 Hz, or the difference in main frequency is equal to or exceeds 10 Hz, the quality control fails. In this case, the two sets of data, seismic data volume A4 and seismic data volume B4, cannot be stitched using the method of the present disclosure. If the difference in bandwidth between the two sets of data is less than 30 Hz and the difference in main frequency is less than 10 Hz at the same time, the method of the present disclosure can be used for stitching. After passing the quality control, calculate the Hermite interpolation window size. The specific calculation process is as follows:

[0067] First, calculate the average value of the main frequencies of the two sets of data:

[0068]

[0069] Use the following formula to determine the number of nodes N in the subsequent Hermite interpolation Hermite :

[0070]

[0071] In Equation 2, N Hermite is the number of nodes in the Hermite interpolation, with the unit of "each", and △ A4 is the sampling interval after the data volumes are agreed upon, with the unit of ms.

[0072] The sampling intervals of the two sets of actual data in the application example of the present disclosure are both 1 ms, and the average value of the main frequencies in the large analysis time window from 1000 ms to 3500 ms of the two sets of data is 22 Hz. Therefore, according to Equation (2), the interpolation window obtained is 600 ms in total up and down along the geological horizon. So, the Hermite interpolation nodes obtained in the application example of the present disclosure are 600.

[0073] S3. Grid and interpolate the two sets of data, seismic data volume A4 and seismic data volume B4 in step S1, which respectively correspond to the same geological horizon, and record them in the headers of the data volumes. The two horizons HA and HB are perfectly corresponding to seismic data volume A4 and seismic data volume B4 respectively, and must be the horizon interpretation results of the corresponding data volumes (at this time, the two horizons may have inconsistent shapes or partial time differences). Cut off the lower part of seismic data volume A4 along the HA horizon according to geological requirements, and cut off the upper part of seismic data volume B4 along the HB horizon. Then, perform vertical addition processing on each seismic trace of the two cut-off parts of data to obtain a vertically stitched integrated seismic data volume C (equivalent to the primary stitched data volume).

[0074] After applying the corresponding horizon T2 to the stacked seismic data volume A4 for excision to retain the upper part, after completing the subsurface excision processing along the horizon, all data below T2 in the stacked seismic data volume A4 are set to 0; when applying the horizon T2 to the stacked seismic data volume B4 for excision to retain the deep data in the lower part, all data above T2 in the stacked seismic data volume B4 are 0 after excision; since the horizon T2 corresponds to two sets of data respectively, they are actually likely to be not completely consistent. After excision, splice each seismic trace, and uniformly truncate the spliced data volume along the minimum record length to obtain the vertically spliced integrated seismic data volume C.

[0075] S4. Perform horizon flattening on the vertically spliced integrated seismic data volume C obtained in step S3 along the geological horizon HA, store the migration amount during flattening of each seismic trace into the trace header to obtain the horizon-flattened seismic data volume D.

[0076] Perform horizon flattening on the seismic data volume C along the horizon T2 to obtain the horizon-flattened seismic data volume D. At this time, write the migration amount (including positive values, negative values, and zero values) during flattening of each trace into the trace header information of this trace. This step of operation can be implemented in any processing and interpretation software.

[0077] S5. For the horizon-flattened seismic data volume D obtained in step S4, according to the number of nodes N obtained in step 2 Hermite , perform piecewise cubic Hermite interpolation, and apply the migration amount during horizon flattening in the seismic trace header to obtain the seismic data volume E after Hermite interpolation processing.

[0078] Among them, the principle and method of piecewise cubic Hermite interpolation are as follows:

[0079] Case A: Let the function values of f(x) between the nodes a ≤ x 0 , x 1 , …, x n ≤ b be f 0 , f 1 , …, f n . Let the polynomial P(x) be the interpolation function of f(x) within the interval {a, b} and its first derivative exists. The function has a first derivative within the interval, that is:

[0080] P(x i ) = f(x i ) = f i , P′(x i ) = f′(x i ) = f i ′, i = 0, 1, …, n. The polynomial P(x) is a polynomial with the highest degree of 2n + 1. Two nodes can use a cubic polynomial as the interpolation function to complete interpolation.

[0081] Case B: By analogy with the above case, if the polynomial P(x) has m-order derivatives in the interval {a, b}, then the function P(x) at the nodes x 0 , x 1 , …, x n needs to satisfy:

[0082]

[0083] When the interpolation problem that satisfies both Case A and Case B is called Hermite interpolation, and the polynomial P(x) that satisfies Case A or Case B is called the Hermite interpolation polynomial, denoted as H k (x), where k is the degree of the polynomial.

[0084] The interpolation polynomial H 3 (x) of the piecewise cubic Hermite interpolation adopted by the present invention satisfies the following conditions:

[0085] First, the function values and derivative values within the two known interpolation nodes x 0 , x 1 are y i = f(x i ), m i f′(x i ), (i = 0, 1), and it is required to satisfy H 3 (x i ) = y i , H′ 3 (x i ) = m i , (i = 0, 1). Using the basis functions to construct H 3 (x), H 3 (x) can be expressed as:

[0086] H 3 (x) = y 0 α 0 (x) + y 1 α 1 (x) + m 0 β 0 (x) + m 1 β 1 (x); (4)

[0087] In formula (4): α 0 (x), α 1 (x), β 0 (x), β 1 (x) are interpolation basis functions, and each term is a polynomial with a degree less than or equal to 3, satisfying the following conditions:

[0088]

[0089] Let α 0 (x) = (a + b(x - x 0 ))(x - x 1 ) 2 , where a and b are undetermined coefficients. It can be known that:

[0090]

[0091] Using the same method, each term of the polynomial H 3 (x) = y 0 α 0 (x) + y 1 α 1 (x) + m 0 β 0 (x) + m 1 β 1 (x) can be obtained respectively.

[0092]

[0093] The cubic Hermite interpolation is completed by Equation (7) to obtain the interpolated value.

[0094] The seismic data volume D of the Longnan work area is processed by piecewise cubic Hermite interpolation. After such processing, the outliers and anomalies near T2 are suppressed, and the seismic data volume E after Hermite interpolation processing is obtained.

[0095] S6. Apply the horizontal shift amount stored in the trace header when flattening the layer obtained in step S5 to the seismic data volume E after Hermite interpolation processing to obtain the seismic data volume F.

[0096] Apply the horizontal shift amount in the trace header to the seismic data volume E to obtain the seismic data volume F that restores the actual spatial position of the original data.

[0097] For the obtained seismic data volume F, attribute extraction is performed along the geological horizon HA, and the upper and lower attribute extraction windows are N Hermite / 10, with the unit of ms. Quality control is performed on the maximum peak amplitude attribute along the HA layer of the seismic data volume F. If the maximum peak amplitude attribute fails the quality control when it is greater than or equal to 20% above the average amplitude level in the plane, repeat steps S4 to S6 until the quality control passes; if the maximum value amplitude attribute in the plane quality control is less than 20% above the average amplitude level, the quality control passes.

[0098] In the application example of the present disclosure, layer-by-layer quality control is performed on the seismic data volume F near T2. Attribute extraction is performed with a full window of 60 ms along T2, 30 ms above and below respectively, and then the quality control process of step S6 is performed until the preset quality control standard is met.

[0099] S7. Output the result data, that is, output the spliced seismic data volume after splicing along the HA horizon.

[0100] The above-described embodiments are only for expressing the implementation manners of the present disclosure, and the description thereof is relatively specific and detailed. However, it should not be construed as a limitation on the scope of the patent of the present disclosure. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present disclosure, several deformations, equivalent replacements, improvements, etc. can be made, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the patent of the present disclosure shall be subject to the appended claims.

Claims

1. A method for splicing post-stack seismic data along geological horizons based on piecewise cubic Hermite interpolation, characterized in that, it includes: Obtain a first data volume and a second data volume to be spliced; Process the first data volume and the second data volume into two data volumes with exactly the same coordinate range, sampling interval, total number of seismic traces, and recording length; Perform quality control on the two data volumes to determine whether data splicing is applicable; If applicable, cut the two data volumes along the same geological horizon, and vertically add the seismic traces of the two cut parts one by one to obtain a primary spliced data volume; After performing layer flattening processing and piecewise cubic Hermite interpolation processing on the primary spliced data volume along the splicing horizon, perform quality control along the layer until it meets the preset quality control standard, and output the spliced data volume.

2. The post-stack seismic data splicing method according to claim 1, characterized in that, The method for processing the first data volume and the second data volume includes: Re-grid the first data volume and the second data volume and perform the same recording length truncation processing, re-sample using a unified step size, and cut off the data outside the coordinates according to the four-point coordinates of the study area to obtain two data volumes with exactly the same coordinate range, sampling interval, total number of seismic traces, and recording length.

3. The post-stack seismic data splicing method according to claim 2, characterized in that: For the grid processing, the first data volume and the second data volume are subjected to the grid processing using the same seismic processing grid; And / or, The method for the same recording length truncation processing includes: truncating the two data volumes according to the minimum value of the maximum recording lengths in the two sets of data; and / or, The method for the re-sampling processing includes: quality control the sampling intervals of the two data volumes, if they are the same, no processing is performed, if they are different, re-sample using the sampling interval of the data volume with the smallest sampling interval in the two data volumes.

4. The post-stack seismic data splicing method according to any one of claims 1-3, characterized in that, The method for determining whether data splicing is applicable includes: Obtain the frequency bandwidth and main frequency of two data volumes with exactly the same coordinate range, sampling interval, total number of seismic traces, and recording length. If the difference in the frequency bandwidths of the two data volumes is equal to or exceeds 30 Hz, or the difference in the main frequencies is equal to or exceeds 10 Hz, it is determined as not applicable.

5. The post-stack seismic data splicing method according to claim 4, characterized in that, Determine the number of nodes \(N\) in Hermite interpolation using the following formula Hermite : In the formula, is the average value of the main frequencies of two data bodies, and N Hermite is the number of nodes in the Hermite interpolation, with the unit of piece, and △ A4 is the sampling interval after the data bodies are unified, with the unit of ms.

6. The post-stack seismic data splicing method according to claim 5, characterized in that: The method for cutting the two data volumes includes: Grid and interpolate the same geological horizon corresponding to the two data volumes and record them in the headers of the data volumes, so that the two horizons correspond to the two data volumes respectively, and cut off the upper or lower parts of the corresponding data volumes along the horizon according to geological requirements; and / or, The method for vertically adding the seismic traces of the two cut parts one by one includes: For the data volume that retains the upper part after cutting, set the part below the T2 horizon to 0; for the data volume that retains the lower part, set the part above the T2 horizon to 0. After excision, splice each seismic trace, and uniformly truncate the spliced data volume along the minimum recording length to obtain the primary spliced data volume.

7. The post-stack seismic data splicing method according to claim 6, characterized in that: During the horizon flattening process, put the shift amount for flattening each seismic trace into the trace header to obtain the horizon-flattened seismic data volume; and / or, The method for piecewise cubic Hermite interpolation processing includes: according to the number of nodes N Hermite perform piecewise cubic Hermite interpolation, and reflect the layer flattening shift amount in the seismic trace header to obtain the seismic data volume processed by Hermite interpolation.

8. The post-stack seismic data splicing method according to claim 7, characterized in that the piecewise cubic Hermite interpolation method includes: Case A: Let the function values of \(f(x)\) at the nodes \(a\leq x\) 0 , \(x\) 1 , …, \(x\) n \(\leq b\) be \(f_0, f_1, \ldots, f\) n . Let the polynomial \(P(x)\) be the interpolation function of \(f(x)\) in the interval \(\{a, b\}\) and there exists its first derivative. The function has a first derivative in the interval, that is: P(x i ) = f(x i ) = f i , P'(x i ) = f'(x i ) = f i ', i = 0, 1, …, n, the polynomial P(x) is For a polynomial of the highest degree 2n + 1, interpolation can be completed using a cubic polynomial as the interpolation function with two nodes; Case B: By analogy with the above case, if the polynomial P(x) has m-order derivatives in the interval {a, b}, then the function P(x) at the nodes x0, x1, …, x n needs to satisfy: The interpolation problem that satisfies both Case A and Case B is called Hermite interpolation. The polynomial P(x) that satisfies either Case A or Case B is called the Hermite interpolation polynomial, denoted as H k (x), where k is the degree of the polynomial; The interpolation polynomial H 3 (x) of piecewise cubic Hermite interpolation used satisfies the following conditions: For two known interpolation nodes \(x_0, x\) 1 The function values and derivative values within are \(y\) i = f(x i ), m i f′(x i ), (i = 0, 1), and it is required to satisfy H 3 (x i ) = y i , H 3 ′(x i ) = mi, (i = 0, 1), and construct H 3 (x) using basis functions. H 3 (x) is expressed as: H 3 (x) = y 0 α 0 (x) + y 1 α 1 (x) + m 0 β 0 (x) + m 1 β 1 (x); Where: α 0 (x), α 1 (x), β 0 (x), β 1 (x) are interpolation basis functions, and each term is a polynomial with a degree less than or equal to 3, satisfying the following conditions: Let α 0 (x) = (a + b(x - x 0 ))(x - x 1 ), where: a and b are undetermined coefficients, and it can be known that: 2 ​ The polynomial H is obtained separately by the same method 3 (x) = y 0 α 0 (x) + y 1 α 1 (x) + m 0 β 0 (x) + m 1 β 1 (x) for each term; The cubic Hermite interpolation is completed by the above formula to obtain the interpolated value.

9. The post-stack seismic data splicing method according to claim 8, characterized in that the method for performing horizon-based quality control includes: For the seismic data volume processed by Hermite interpolation, react with the shift amount for flattening stored in the trace header during flattening to obtain the seismic data volume that restores the actual spatial position of the original data. Along the splicing horizon, extract the attributes of the seismic data volume that restores the actual spatial position of the original data, and perform quality control based on the maximum peak amplitude attribute.

10. The post-stack seismic data splicing method according to claim 9, characterized in that: When performing the attribute extraction, the upper and lower attribute extraction windows are N Hermite / 10, with the unit being ms; and / or, The method for determining whether it meets the quality control standard is as follows: during the plane quality control of the maximum peak amplitude attribute, if it is greater than or equal to 20% and exceeds the average amplitude level, the quality control fails. For those that fail, repeat the horizon flattening process, the piecewise cubic Hermite interpolation process, and the horizon-based quality control process until the quality control passes; if it is less than 20% and exceeds the average amplitude level during the plane quality control of the maximum peak amplitude attribute, the quality control passes.

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