Seismic data processing effect automatic evaluation method and device, electronic equipment and medium

Abstract

The application discloses a kind of seismic data processing effect automatic evaluation method, device, electronic equipment and medium.Method includes: reading the seismic data of completion processing, seismic data is including poststack seismic three-dimensional data body of seismic profile, seismic profile includes multiple poststack seismic traces, each seismic trace includes multiple sampling points;According to the average frequency of seismic profile, sampling time interval selects time window length;Through the similarity function of setting global, the similarity between each seismic data unit in all seismic traces is calculated, and the similarity between each seismic data unit in all seismic traces is calculated.The vector formed by all sampling point data in the seismic trace with any sampling point as the center point within the time window length is formed.The sum of all similarity values calculated is used as the processing effect evaluation value of seismic data.It is realized to avoid the influence of human subjective factors on processing effect evaluation, and improve evaluation efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas geophysical exploration, and more particularly to a method and device for automatically evaluating the effect of seismic data processing, an electronic device and a medium. BACKGROUND

[0002] On the basis of the effective seismic data collected, comprehensive and reasonable processing of the seismic data is a crucial step for oil and gas exploration and development. The processing of seismic data is a systematic engineering, such as static correction, seismic data denoising, dynamic correction, pre-stack and post-stack seismic migration, etc. After a series of processing, the post-stack seismic three-dimensional data volume equivalent to self-excitation and self-collection can be obtained. The three-dimensional seismic data volume is the reflection of seismic wave response of underground geological structure, lithology, fluid, etc. In turn, the seismic reflection characteristics of the three-dimensional seismic data volume can be used to predict geological structure, sedimentary evolution, oil and gas distribution, etc. Therefore, the processing of seismic data is an important basis for oil and gas exploration. However, in the process of seismic data processing, different processing methods and parameters need to be selected for repeated parameter adjustment to obtain ideal post-stack seismic data, which will affect the subsequent seismic data interpretation and the final development. The quality of the processing effect of seismic data determines whether the resources can be effectively exploited and utilized. Therefore, it is of great significance to improve the efficiency and quality of resource exploitation to effectively evaluate the processing effect of seismic data and select the best seismic profile.

[0003] At present, the evaluation of the processing effect of seismic data mainly relies on the subjective judgment of the processing personnel. In actual work, the processing of seismic data for each work area often consumes a lot of energy and time of the processing personnel. If the processing result is further evaluated manually, it not only increases the workload of the processing personnel, but also makes it difficult for the human to make accurate judgments on some subtle differences on the seismic profile.

[0004] An existing method and device for evaluating the processing result of seismic data mainly use the synthetic record of seismic wavelet to evaluate the processing result of seismic data, but it focuses on evaluating the quality of the processing result of seismic data for the next step of interpretation, and cannot directly evaluate the processing effect in the processing of seismic data.

[0005] Nowadays, we are in an era of rapid development of artificial intelligence technology. Using computers to liberate human hands and brains is the trend of social development. Letting computers automatically evaluate different seismic profiles obtained by processing personnel and further deploying the next exploration work according to the evaluation results can not only greatly reduce the workload of processing personnel and effectively improve work efficiency, but also is an important manifestation of improving the degree of automation. Therefore, a method for automatically evaluating the processing effect of seismic data is needed. SUMMARY

[0006] The application aims to provide a seismic data processing effect automatic evaluation method, device, electronic equipment and medium, which can avoid the influence of human subjective factors on the processing effect evaluation and improve the evaluation efficiency.

[0007] To achieve the above-mentioned purpose, in the first aspect, the application provides a seismic data processing effect automatic evaluation method, comprising:

[0008] reading the processed seismic data, the seismic data being post-stack seismic three-dimensional data volume including seismic profiles, the seismic profiles including a plurality of post-stack seismic traces, each seismic trace including a plurality of sampling points;

[0009] selecting a time window length according to the average frequency of the seismic profile and the sampling time interval;

[0010] calculating the similarity between each seismic data unit in two different seismic traces in all seismic traces through a set global similarity function, the seismic data unit being a vector formed by all sampling point data within the time window length with any sampling point as the center point in the seismic trace;

[0011] taking the sum of all the calculated similarities as the processing effect evaluation value of the seismic data.

[0012] Optionally, the step of selecting the time window length according to the average frequency of the seismic profile and the sampling time interval comprises:

[0013] calculating the average frequency of the entire seismic profile, and obtaining the average period of the seismic profile according to the average frequency;

[0014] obtaining the time window length through the following formula according to the known sampling interval and the average period:

[0015]

[0016] wherein, TW' is the time window length, f is the average frequency, and Δt is the sampling interval.

[0017] Optionally, the global similarity function is:

[0018]

[0019] wherein, TW is the time window length, TW = TW' when TW is an odd number, and TW = TW' + 1 when TW is an even number;

[0020] is the seismic data unit on the i-th seismic trace, indicating all sampling point data with the n1-th sampling point as the center point and the time window length being TW, is the seismic data unit on the i-th seismic trace, indicating all sampling point data with the n1-th sampling point as the center point and the time window length being TW, is a seismic data unit on the jth seismic trace, represents the seismic data unit with the nth2 sampling point on the ith seismic trace as the center point and a time window length of TW, i = 1,..., M-1, j = i+1,..., M, n1 = 1,..., N, n2 = 1,..., N, M is the number of seismic traces in the seismic profile, and N is the number of sampling points on the seismic trace; is all sampling point data with the center point and a time window length of TW, i = 1,..., M-1, j = i+1,..., M, n1 = 1,..., N, n2 = 1,..., N, M is the number of seismic traces in the seismic profile, and N is the number of sampling points on the seismic trace;

[0021] R(i,j,n1,n2,TW) is a similarity between the seismic data units and .

[0022] is a weight, e is a natural constant, and σ = |i-j|.

[0023] Optionally, the calculating of the similarity between each seismic data unit in each two different seismic traces in all seismic traces comprises:

[0024] Step S1: taking the nth1 sampling point on the ith seismic trace as the center point, taking 2 sampling points upward and downward respectively, to obtain a first seismic data unit:

[0025]

[0026]

[0027] Step S2: taking the nth2 (n2 = 1,..., N) sampling point on the jth seismic trace as the center point, taking 2 sampling points upward and downward respectively, to obtain a second seismic data unit:

[0028]

[0029] Step S3: calculating the similarity between the first seismic data unit and the second seismic data unit through the global similarity function.

[0030] Step S4: repeating the above steps S1-S3 to calculate the similarity between each seismic data unit in each two different seismic traces in all seismic traces.

[0031] Optionally, the processing effect evaluation value of the seismic data is calculated through the following formula:

[0032]

[0033] wherein, S is the processing effect evaluation value, R(i,j,n1,n2,TW) is a similarity between the seismic data units and ​​​​​wherein M is the number of seismic traces in the seismic profile, and N is the number of sampling points on the seismic trace.

[0034] In a second aspect, the present application provides an electronic device, comprising:

[0035] at least one processor; and

[0036] a memory in communication with the at least one processor; wherein

[0037] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for automatically evaluating seismic data processing effect according to the first aspect.

[0038] In a third aspect, the present application provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method for automatically evaluating seismic data processing effect according to the first aspect.

[0039] In a fourth aspect, the present application provides a device for automatically evaluating seismic data processing effect, comprising:

[0040] a data reading module configured to read processed seismic data, wherein the seismic data is a post-stack seismic 3D data volume comprising a seismic profile, the seismic profile comprises a plurality of post-stack seismic traces, and each seismic trace comprises a plurality of sampling points;

[0041] a time window length selecting module configured to select a time window length according to an average frequency of the seismic profile and a sampling time interval;

[0042] a similarity calculating module configured to calculate a similarity between each seismic data unit in any two different seismic traces in all seismic traces by using a set global similarity function, wherein the seismic data unit is a vector formed by all sampling point data in the time window length with any sampling point as a center point in the seismic trace;

[0043] a processing effect evaluating module configured to take a sum of all similarities as a processing effect evaluation value of the seismic data.

[0044] Optionally, the time window length selecting module is specifically configured to:

[0045] calculate an average frequency of the entire seismic profile, and obtain an average period of the seismic profile according to the average frequency;

[0046] obtain the time window length according to a known sampling interval and the average period by using the following formula:

[0047]

[0048] wherein TW' is the length of the time window, f is the average frequency, and Δt is the sampling interval.

[0049] Optionally, the global similarity function is:

[0050]

[0051] wherein TW is the length of the time window, TW = TW' when TW is an odd number, and TW = TW' + 1 when TW is an even number.

[0052] is a seismic data unit on the ith seismic trace, representing all the sampling point data with the nth1 sampling point as the center point and with the length of the time window being TW, is a seismic data unit on the ith seismic trace, representing all the sampling point data with the nth1 sampling point as the center point and with the length of the time window being TW, is a seismic data unit on the jth seismic trace, representing all the sampling point data with the nth2 sampling point as the center point and with the length of the time window being TW, is a seismic data unit on the jth seismic trace, representing all the sampling point data with the nth2 sampling point as the center point and with the length of the time window being TW, i = 1,..., M-1, j = i+1,..., M, n1 = 1,..., N, n2 = 1,..., N, M is the number of seismic traces in the seismic profile, and N is the number of sampling points on the seismic trace.

[0053] R(i,j,n1,n2,TW) is the similarity between the seismic data units and .

[0054] is the weight, e is the natural constant, and σ = |i-j|.

[0055] The present application has the following beneficial effects:

[0056] The method of the present application first selects the length of the time window according to the average frequency and the sampling time interval of the seismic profile, then calculates the similarity between each seismic data unit in each two different seismic traces in all the seismic traces by using the set global similarity function, the seismic data unit is a vector formed by all the sampling point data in the length of the time window with any sampling point as the center point in the seismic trace, and then the sum of all the calculated similarities is taken as the processing effect evaluation value of the seismic data, wherein the self-adaptive selection method of the length of the time window reasonably determines the number of sampling points, avoiding the randomness caused by the artificial selection of the time window, and at the same time, the automatic evaluation of the seismic data processing effect by the global similarity function can greatly reduce the workload of manual evaluation and improve the evaluation efficiency.

[0057] The system of the present application has other characteristics and advantages that will be apparent from and / or set forth in the accompanying drawings and the detailed description that follows, which together serve to explain the particular principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0058] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which like reference characters refer to like parts throughout the figures.

[0059] Figure 1 A flow chart of a method for automatically evaluating seismic data processing results is shown.

[0060] Figure 2 A schematic diagram of seismic data 1 to be evaluated in a method for automatically evaluating seismic data processing results according to an embodiment of the present application is shown.

[0061] Figure 3 A schematic diagram of seismic data 2 to be evaluated in a method for automatically evaluating seismic data processing results according to an embodiment of the present application is shown.

[0062] Figure 4 A schematic diagram of seismic data 3 to be evaluated in a method for automatically evaluating seismic data processing results according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0063] At present, the evaluation of seismic data processing results is mainly subjective judgment of seismic profiles by seismic data processing personnel, which is a time-consuming and laborious work, and more importantly, the subjective judgment of the processing personnel is not accurate for some subtle differences that are difficult to distinguish. Therefore, the present application proposes a judgment method for the computer to automatically evaluate the processing results of seismic data, which can avoid the influence of subjective factors on the evaluation of processing results and effectively improve the evaluation efficiency.

[0064] The global similarity coefficient in the present application is:

[0065] ① Global similarity function

[0066] The similarity of the two seismic data sequences x(n) and y(n) containing N sampling points can be quantitatively expressed as:

[0067]

[0068] wherein, ρ xyThe similarity coefficient between sequences x(n) and y(n) is denoted as |ρ|. xy |≤1.

[0069] The similarity coefficients mentioned above can only describe the similarity between two adjacent seismic waveforms. This formula is obviously not reasonable enough for judging the similarity between seismic waveforms of different traces within the entire seismic profile.

[0070] A seismic profile contains M stacked seismic traces, each with N sampling points. For any two seismic data points x... i (t1,...,t N ) and x j (t1,...,t N (i,j=1,...,M,i≠j), now we introduce a global similarity function:

[0071]

[0072] Where σ=|ij|, TW represents the n1th sampling point on the i-th seismic trace. The length of the window opened at the center point. Indicated by The seismic data is centered at a point and TW represents the time window length.

[0073] Equation (2), based on Equation (1), considers the time interval and spatial interval between the two center points, and... The similarity coefficient is introduced into the formula as a weight. It can be seen from the weight calculation formula that when the time interval between the center points of two seismic data is too large or the distance between the two seismic traces is too large (i.e., σ is too large), the weight will be too small. This can avoid the waveform similarity. When the time interval or spatial interval between the two center points is too large, the similarity coefficient value is still very large, thus erroneously indicating that the two seismic data are highly similar.

[0074] The similarity coefficient indicates the degree of similarity between seismic data. A larger sum of the final similarity coefficients indicates greater similarity among the seismic data traces within the seismic profile, signifying better data processing. Conversely, a smaller sum indicates poorer data processing. Therefore, using the global similarity function as an indicator for evaluating the effectiveness of seismic data processing is reasonable.

[0075] The invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0076] Example 1

[0077] Figure 1 A flow chart of steps of a seismic data processing effect automatic evaluation method is shown.

[0078] As Figure 1 shown, the present application provides a seismic data processing effect automatic evaluation method, comprising:

[0079] Step S101: reading the completed processed seismic data, the seismic data being a post-stack seismic three-dimensional data volume including seismic profiles, the seismic profile including a plurality of post-stack seismic traces, each seismic trace including a plurality of sampling points;

[0080] Step S102: selecting a time window length according to the average frequency of the seismic profile and the sampling time interval;

[0081] As can be seen from the global similarity function formula (2), the evaluation of the seismic data processing effect is closely related to the time window, and selecting a suitable time window can make the evaluation result more accurate and reliable. In actual work, the time window is often determined according to experience, which has a lot of uncertainty, and adaptive selection of the time window length can effectively avoid this randomness. For seismic data with a sampling interval of Δt, for adaptive selection of the time window length, this step specifically includes:

[0082] Calculate the average frequency f of the entire seismic profile, which is taken as the frequency of the entire profile;

[0083] According to the average frequency, the average period of the seismic profile is obtained

[0084] Since the sampling interval Δt is known, the time window length can be obtained through the following formula:

[0085]

[0086] Wherein, TW' is the time window length, f is the average frequency, Δt is the sampling interval, if the sought time window TW' is an odd number, then the time window TW in the global similarity function is TW'= TW'; otherwise, TW= TW'+1.

[0087] Step S103: calculating the similarity between each seismic data unit in each two different seismic traces in all seismic traces through the set global similarity function, the seismic data unit being a vector formed by all sampling point data in the seismic trace within the time window length with any sampling point as the center point;

[0088] Specifically, the global similarity function is:

[0089]

[0090] Where TW is the time window length. When TW is odd, TW = TW′. When TW is even, TW = TW′ + 1. Let be a seismic data unit on the i-th earthquake trace, representing the data from the n1-th sampling point. Data for all sampled points centered at a time window of length TW. Let j be the seismic data unit on the j-th seismic trace, representing the data from the n2-th sampling point. R(i,j,n1,n2,TW) represents all sampling point data centered at a time window of length TW, where i = 1,...,M-1, j = i+1,...,M, n1 = 1,...,N, n2 = 1,...,N, M is the number of seismic traces in the seismic profile, and N is the number of sampling points on the seismic traces; R(i,j,n1,n2,TW) is the seismic data unit. and The similarity between them; Let be the weight, e be the natural constant, and σ = |ij|.

[0091] This step specifically includes the following steps:

[0092] Step S201: Using the n1th sampling point on the i-th seismic trace Taking the center point as the starting point, take the following values ​​upwards and downwards respectively. The first seismic data unit was obtained by sampling points (with 0 representing undefined values).

[0093] Meta (vector):

[0094]

[0095] Step S202: Using the n2th (n2 = 1, ..., N) sampling point on the j-th seismic data... Taking the center point as the starting point, take the following values ​​upwards and downwards respectively. The second seismic data unit (vector) is obtained by sampling points (with zero values ​​for those without):

[0096]

[0097] Step S203: Calculate the similarity between the first seismic data unit and the second seismic data unit using a global similarity function;

[0098] Step S204: Repeat steps S1-S3 above to calculate the similarity between seismic data units in every two different seismic traces. This involves calculating the similarity between the vector x obtained in steps S201 and S202. i and x j Substitute these values ​​into the global similarity function formula to calculate the global similarity function R(i,j,n1,n2,TW).

[0099] Step S104: taking the sum of all the similarity values calculated as the processing effect evaluation value of the seismic data.

[0100] Specifically, the processing effect evaluation value of the seismic data is calculated by the following formula:

[0101]

[0102] wherein S is the processing effect evaluation value, R(i,j,n1,n2,TW) is the similarity between the seismic data units and , M is the number of seismic traces in the seismic profile, and N is the number of sampling points on the seismic trace.

[0103] Taking S as the processing effect evaluation value of the seismic data;

[0104] For different seismic profiles obtained by processing the same seismic data, different processing effect evaluation values are obtained by applying the above steps S101-S104 respectively, and the size of the seismic data processing effect evaluation value is compared, the larger the S value is, the better the processing effect is; the smaller the S value is, the worse the processing effect is.

[0105] Figures 2-4 The same seismic data to be evaluated in a work area obtained by three different processing methods is shown respectively, and the automatic evaluation method of the seismic data processing effect of the embodiment and the professional seismic data processing personnel are used to automatically evaluate the processing effect of the three seismic data, and the evaluation result comparison shown in Table 1 is obtained:

[0106] Table 1 Comparison of manual evaluation and automatic evaluation results

[0107]

[0108]

[0109] From Table 1, it can be seen that:

[0110] The automatic evaluation value of the seismic data processing effect of Figure 1 is S1=1.0416;

[0111] The automatic evaluation value of the seismic data processing effect of Figure 2 is S2=6.8356;

[0112] The automatic evaluation value of the seismic data processing effect of Figure 3 is S3=7.2665;

[0113] Since S1 Figure 3 , it is determined that the seismic data processing effect of is the best.

[0114] Professional seismic data processing personnel also determine Figure 3 the processing effect is the best, Figure 2 the processing effect is the second, Figure 1 the processing effect is the worst, it can be seen that the evaluation result obtained by the automatic evaluation method of the embodiment is reasonable and effective.

[0115] In summary, the method of the present application can effectively evaluate the processing effect of seismic data, select the best processing profile, and improve the efficiency of oil and gas exploration and development. Based on adaptive selection of time window length, the present application uses a global similarity function as an evaluation index of seismic data processing effect, designs an evaluation algorithm, and automatically evaluates the seismic data processing effect. The final evaluation result is consistent with the manual evaluation result. The automatic evaluation of seismic data processing effect improves the evaluation efficiency and improves the evaluation accuracy. The automatic evaluation of seismic data processing effect realizes the automation of seismic data processing effect evaluation, avoids the current evaluation of seismic data processing effect mainly relying on qualitative judgment of people, cannot be quantitatively analyzed, has low efficiency and is inevitably affected by some subjective factors.

[0116] Embodiment 2

[0117] An automatic seismic data processing effect evaluation device, comprising:

[0118] A data reading module is configured to read the processed seismic data, wherein the seismic data is a post-stack seismic three-dimensional data volume including a seismic profile, the seismic profile includes a plurality of post-stack seismic traces, and each seismic trace includes a plurality of sampling points.

[0119] A time window length selection module is configured to select a time window length according to an average frequency of the seismic profile and a sampling time interval.

[0120] A similarity calculation module is configured to calculate the similarity between each seismic data unit in each two different seismic traces in all seismic traces by using a set global similarity function, wherein the seismic data unit is a vector formed by all sampling point data within the time window length and centered on any sampling point in the seismic trace.

[0121] A processing effect evaluation module is configured to take the sum of all similarities as the processing effect evaluation value of the seismic data.

[0122] In the embodiment, the time window length selection module is specifically configured to:

[0123] Calculate the average frequency of the entire seismic profile, and obtain the average period of the seismic profile according to the average frequency.

[0124] According to the known sampling interval and average period, the time window length is obtained by the following formula:

[0125]

[0126] wherein TW' is the length of the time window, f is the average frequency, and Δt is the sampling interval.

[0127] Optionally, the global similarity function is:

[0128]

[0129] wherein TW is the length of the time window, TW = TW' when TW is an odd number, and TW = TW' + 1 when TW is an even number;

[0130] is a seismic data unit on the ith seismic trace, representing all the sampling points with the nth1sampling point as the center point and a time window length of TW, is a seismic data unit on the jth seismic trace, representing all the sampling points with the nth2sampling point as the center point and a time window length of TW, i = 1,..., M-1, j = i+1,..., M, n1= 1,..., N, n2= 1,..., N, M is the number of seismic traces in the seismic profile, and N is the number of sampling points on the seismic trace;

[0131] R(i,j,n1,n2,TW) is a similarity between the seismic data units and .

[0132] is a weight, e is a natural constant, and σ = |i-j|.

[0133] In this embodiment, the similarity calculation module is specifically configured to:

[0134] Step S1: taking the nth1sampling point on the ith seismic trace as the center point, taking sampling points upward and downward respectively to obtain a first seismic data unit:

[0135]

[0136]

[0137] Step S2: taking the nth2(n2= 1,..., N) sampling point on the jth seismic trace as the center point, taking sampling points upward and downward respectively to obtain a second seismic data unit:

[0138]

[0139] ​​​Step S3: calculating the similarity between the first seismic data unit and the second seismic data unit through a global similarity function;

[0140] Step S4: repeating the above steps S1-S3 to calculate the similarity between each seismic data unit in each two different seismic traces in all seismic traces.

[0141] In this embodiment, the processing effect evaluation module calculates the processing effect evaluation value of the seismic data through the following formula:

[0142]

[0143] Wherein, S is the processing effect evaluation value, R(i,j,n1,n2,TW) is the similarity between the seismic data units and M is the number of seismic traces in the seismic profile, and N is the number of sampling points on the seismic trace.

[0144] Embodiment 3

[0145] An electronic device, the electronic device comprising:

[0146] at least one processor; and,

[0147] a memory connected with the at least one processor in communication; wherein,

[0148] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the seismic data processing effect automatic evaluation method of embodiment 1.

[0149] The electronic device according to the embodiments of the present disclosure comprises a memory and a processor, and the memory is used to store non-transitory computer readable instructions. Specifically, the memory can include one or more computer program products, which can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0150] The processor can be a central processing unit (CPU) or other forms of processing units with data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer readable instructions stored in the memory.

[0151] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment can also include well-known structures such as a communication bus, an interface, and the like, which should also be included in the protection scope of the present disclosure.

[0152] The detailed description of the present embodiment can refer to the corresponding description in the foregoing embodiments, which will not be repeated here.

[0153] Embodiment 4

[0154] A non-transitory computer readable storage medium stores computer instructions for causing a computer to execute the seismic data processing effect automatic evaluation method of embodiment 1.

[0155] The computer readable storage medium according to the embodiments of the present disclosure has non-transitory computer readable instructions stored thereon. When the non-transitory computer readable instructions are run by a processor, all or part of the steps of the method of the embodiments of the present disclosure are executed.

[0156] The computer readable storage medium described above includes, but is not limited to, an optical storage medium (for example, CD-ROM and DVD), a magneto-optical storage medium (for example, MO), a magnetic storage medium (for example, magnetic tape or a mobile hard disk), a medium with a built-in rewritable non-volatile memory (for example, a memory card), and a medium with a built-in ROM (for example, a ROM cartridge).

[0157] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for automatically evaluating the effect of seismic data processing, characterized in that, The method comprises the following steps: reading seismic data, the seismic data being post-stack seismic three-dimensional data volume including seismic profiles, the seismic profiles including a plurality of post-stack seismic traces, each of the seismic traces including a plurality of sampling points; selecting a time window length according to an average frequency of the seismic profiles and a sampling time interval; calculating similarity between each seismic data unit in each two different seismic traces in all seismic traces by using a global similarity function, the seismic data unit being a vector formed by all sampling point data within the time window length and centered at any sampling point in the seismic trace; taking a sum of all calculated similarities as a processing effect evaluation value of the seismic data; the step of selecting the time window length according to the average frequency of the seismic profiles and the sampling time interval comprises: calculating an average frequency of the entire seismic profiles, and obtaining an average period of the seismic profiles according to the average frequency; obtaining the time window length according to a known sampling interval and the average period by using the following formula: wherein, is the window length, is the average frequency, is the sampling interval; the global similarity function is: ， wherein, is the window length, when is odd, when is even, ; is the seismic data unit on the i-th trace, which represents all the sample point data with the i-th sample point as the center point and the time window length of is the seismic data unit on the i-th trace, which represents all the sample point data with the i-th sample point as the center point and the time window length of is the number of traces in the seismic profile, is the number of sample points on the trace.​​​​​​​​​​​​ for a seismic data unit with similarity between is a weight, is a natural constant, ; the step of calculating the similarity between each seismic data unit in each two different seismic traces in all seismic traces comprises: Step S1: Using the first The first on the earthquake data sampling points Taking the center point as the starting point, take the following values ​​upwards and downwards respectively. The first seismic data unit is obtained by sampling points: ； Step S2: With the first The first on the earthquake data sampling points Taking the center point as the starting point, take the following values ​​upwards and downwards respectively. The second seismic data unit is obtained by sampling points: step S3: calculating the similarity between the first seismic data unit and the second seismic data unit by using the global similarity function; step S4: repeating the steps S1-S3 to calculate the similarity between each seismic data unit in each two different seismic traces in all seismic traces.

2. The method according to claim 1, wherein the processing effect evaluation value of the seismic data is calculated by using the following formula: wherein, is a processing effect evaluation value, is a seismic data unit is a similarity between , is a number of seismic traces in a seismic profile, is a number of sampling points on a seismic trace.

3. The device for automatically evaluating the effect of seismic data processing according to any one of claims 1-2, characterized in that, The method comprises the following steps: reading seismic data, the seismic data being post-stack seismic three-dimensional data volume including seismic profiles, the seismic profiles including a plurality of post-stack seismic traces, each of the seismic traces including a plurality of sampling points; selecting a time window length according to an average frequency of the seismic profiles and a sampling time interval; calculating similarity between each seismic data unit in each two different seismic traces in all seismic traces by using a global similarity function, the seismic data unit being a vector formed by all sampling point data within the time window length and centered at any sampling point in the seismic trace; taking a sum of all calculated similarities as a processing effect evaluation value of the seismic data; 4. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for automatically evaluating processing effect of seismic data according to any one of claims 1-2.

5. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium stores computer instructions for causing a computer to perform the method for automatically evaluating processing effect of seismic data according to any one of claims 1-2.

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