Seismic data processing method, apparatus and device, and computer readable storage medium

Abstract

The invention discloses a seismic data processing method, device and equipment and a computer readable storage medium, and belongs to the technical field of exploration, development and mining of oil and gas fields. The method comprises the steps of obtaining seismic data of a target area; for any one trace set in the multiple trace sets, determining the similarity between the trace set data of any trace set and the multiple wave data; determining the similarity between any two pieces of multiple wave data in the multiple wave data; determining a first filter of each multiple data according to the similarity between the trace gather data of any trace gather and each multiple data and the similarity between any two pieces of multiple data; according to the first filters of the multiple data, filtering the multiple data to obtain first data corresponding to the multiple data; and determining primary wave data of the target area according to the trace gather data of any trace gather and the first data corresponding to the multiple wave data. The method improves the accuracy of the determined primary wave data.

Description

Technical Field

[0001] This application relates to the fields of oil and gas field exploration, development, and exploitation technology, and in particular to a method, apparatus, equipment, and computer-readable storage medium for processing seismic data. Background Technology

[0002] In oil and gas exploration, seismic exploration is an effective method. By collecting, processing, and interpreting seismic data, primary wave data is obtained, which is then used to determine the location and reserves of oil and gas, facilitating their extraction.

[0003] In related technologies, seismic data includes multiple wave data and primary wave data. Primary wave data consists of data after a single reflection, while multiple wave data consists of data after multiple reflections. Primary wave data is used to determine the location and reserves of oil and gas, while multiple wave data represents noise within the seismic data. Therefore, after acquiring seismic data, multiple multiple wave data are predicted based on the seismic data. These multiple multiple wave data are then sequentially eliminated to obtain the primary wave data.

[0004] However, in the above-mentioned seismic data processing method, the sequential elimination of multiple multiple wave data is prone to problems of over-elimination or under-elimination, resulting in poor accuracy of the obtained primary wave data, which in turn leads to poor accuracy in determining oil and gas locations and reserves, thus affecting oil and gas extraction. Summary of the Invention

[0005] This application provides a method, apparatus, device, and computer-readable storage medium for processing seismic data, which can be used to solve problems in related technologies. The technical solution is as follows:

[0006] On the one hand, embodiments of this application provide a method for processing seismic data, the method comprising:

[0007] Acquire seismic data for the target area, the seismic data including gather data from multiple gathers;

[0008] For any one of the multiple trace sets, determine the similarity between the trace set data of the any one trace set and each multiple wave data, wherein the multiple wave data is the data predicted based on the trace set data, and any multiple wave data is the data after multiple reflections.

[0009] Determine the similarity between any two multiple wave data points in each of the multiple wave data points;

[0010] Based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data, a first filter for each multiple wave data is determined.

[0011] Based on the first filter of each multiple wave data, the multiple wave data is filtered to obtain the first data corresponding to each multiple wave data. The first data corresponding to any multiple wave data is the data after the first filter of any multiple wave data is filtered by the first filter corresponding to the any multiple wave data.

[0012] Based on the gather data of any gather and the first data corresponding to each multiple wave data, the primary wave data of the target area is determined, and the primary wave data of the target area is used to predict the oil and gas location and oil and gas reserves of the target area.

[0013] In one possible implementation, determining the primary wave data of the target region based on the gather data of any gather and the first data corresponding to each of the multiple wave data includes:

[0014] The first data corresponding to each of the multiple wave data is summed to obtain the first sum value;

[0015] Based on the gather data of any gather and the first sum, determine the primary wave data of any gather;

[0016] Based on the primary wave data of each gather, the primary wave data of the target area are determined.

[0017] In one possible implementation, determining the primary wave data of any given gather based on the gather data of any given gather and the first sum value includes:

[0018] The difference between the gather data of any gather and the first sum is calculated to obtain the first difference value;

[0019] Based on the first difference, the primary wave data of any given gather is determined.

[0020] In one possible implementation, determining the primary wave data of any gather based on the first difference includes:

[0021] The first difference is determined to be the primary wave data of any of the gathers.

[0022] In one possible implementation, determining the primary wave data of any gather based on the first difference includes:

[0023] The first sum is smoothed to obtain a smoothed first sum, which eliminates outliers in the first sum.

[0024] The first difference is smoothed to obtain a smoothed first difference, which eliminates outliers in the first difference.

[0025] Based on the first sum and the first difference after smoothing, an occlusion coefficient is determined, which is used to occlude the valid signal in the gather data of any gather;

[0026] Based on the occlusion coefficient, the gather data of any gather is occluded to obtain the occluded gather data of any gather.

[0027] The primary wave data of any given gather is determined based on the similarity between the gather data after any gather is blocked and any two multiple wave data.

[0028] In one possible implementation, determining the occlusion coefficient based on the first sum and the first difference after smoothing includes:

[0029] The first product is obtained by multiplying the first sum and the first difference after smoothing.

[0030] The first product is smoothed to obtain a smoothed first product, which eliminates outliers in the first product.

[0031] Determine a reference value with e as the base and the first product after smoothing as the exponent;

[0032] The reciprocal of the reference value is determined as the occlusion coefficient.

[0033] In one possible implementation, determining the primary wave data of any given gather based on the similarity between the gather data after any gather obstruction and any two multiple wave data includes:

[0034] Determine the similarity between the gather data after any gather is blocked and the various multiple wave data;

[0035] Based on the similarity between the gather data after any gather is blocked and the various multiple data, and the similarity between any two multiple data, a second filter for each multiple data is determined.

[0036] Based on the second filter of each multiple wave data, the multiple wave data is filtered to obtain the second data corresponding to each multiple wave data. The second data corresponding to any multiple wave data is the data after filtering the multiple wave data by the second filter corresponding to the multiple wave data.

[0037] Based on the gather data of any gather and the second data corresponding to each multiple wave data, the primary wave data of any gather is determined.

[0038] In one possible implementation, determining the primary wave data of any given gather based on the gather data of any given gather and the second data corresponding to each multiple wave data includes:

[0039] The second data corresponding to each of the multiple wave data are summed to obtain the second sum value;

[0040] The difference between the gather data of any gather and the second sum is calculated to obtain the primary wave data of any gather.

[0041] On the other hand, embodiments of this application provide a seismic data processing apparatus, the apparatus comprising:

[0042] The acquisition module is used to acquire seismic data of the target area, the seismic data including gather data of multiple gathers;

[0043] The determination module is used to determine the similarity between the trace data of any trace set and each multiple wave data for any trace set among the plurality of trace sets, wherein the multiple wave data is data predicted based on the trace data, and any multiple wave data is data after multiple reflections.

[0044] The determining module is also used to determine the similarity between any two multiple wave data in each multiple wave data;

[0045] The determining module is further configured to determine a first filter for each multiple wave data based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data.

[0046] The processing module is used to filter the multiple wave data according to the first filter of each multiple wave data to obtain the first data corresponding to each multiple wave data. The first data corresponding to any multiple wave data is the data after the multiple wave data is filtered by the first filter corresponding to the multiple wave data.

[0047] The determining module is further configured to determine the primary wave data of the target area based on the gather data of any gather and the first data corresponding to each multiple wave data, wherein the primary wave data of the target area is used to predict the oil and gas location and oil and gas reserves of the target area.

[0048] In one possible implementation, the determining module is configured to sum the first data corresponding to each of the multiple wave data to obtain a first sum value; determine the primary wave data of any given gather based on the gather data of any given gather and the first sum value; and determine the primary wave data of the target area based on the primary wave data of each gather.

[0049] In one possible implementation, the determining module is configured to perform a subtraction operation on the gather data of any gather and the first sum value to obtain a first difference value; and determine the primary wave data of any gather based on the first difference value.

[0050] In one possible implementation, the determining module is configured to determine that the first difference is the primary wave data of any of the gathers.

[0051] In one possible implementation, the determining module is configured to: smooth the first sum to obtain a smoothed first sum, wherein the smoothed first sum eliminates outliers in the first sum; smooth the first difference to obtain a smoothed first difference, wherein the smoothed first difference eliminates outliers in the first difference; determine an occlusion coefficient based on the smoothed first sum and the smoothed first difference, wherein the occlusion coefficient is used to occlude valid signals in the gather data of any gather; perform occlusion processing on the gather data of any gather based on the occlusion coefficient to obtain occluded gather data of any gather; and determine the primary wave data of any gather based on the similarity between the occluded gather data of any gather and any two multiple wave data.

[0052] In one possible implementation, the determining module is configured to multiply the smoothed first sum and the smoothed first difference to obtain a first product; smooth the first product to obtain a smoothed first product, wherein the smoothed first product eliminates outliers in the first product; determine a reference value with base e and exponent the smoothed first product; and determine the reciprocal of the reference value as the occlusion coefficient.

[0053] In one possible implementation, the determining module is configured to: determine the similarity between the occluded gather data of any gather and each of the multiple wave data; determine a second filter for each of the multiple wave data based on the similarity between the occluded gather data of any gather and each of the multiple wave data, and the similarity between any two of the multiple wave data; filter each of the multiple wave data according to the second filter to obtain second data corresponding to each of the multiple wave data, wherein the second data corresponding to any multiple wave data is the data after filtering the any multiple wave data by the second filter corresponding to the any multiple wave data; and determine the primary wave data of any gather based on the gather data of any gather and the second data corresponding to each of the multiple wave data.

[0054] In one possible implementation, the determining module is used to sum the second data corresponding to each of the multiple wave data to obtain a second sum value; and to subtract the gather data of any gather from the second sum value to obtain the primary wave data of any gather.

[0055] On the other hand, embodiments of this application provide a computer device, the computer device including a processor and a memory, the memory storing at least one piece of program code, the at least one piece of program code being loaded and executed by the processor, so that the computer device implements any of the above-described methods for processing seismic data.

[0056] On the other hand, a computer-readable storage medium is also provided, wherein at least one piece of program code is stored in the computer-readable storage medium, the at least one piece of program code being loaded and executed by a processor to enable a computer to implement any of the above-described methods for processing seismic data.

[0057] On the other hand, a computer program or computer program product is also provided, wherein the computer program or computer program product stores at least one computer instruction, which is loaded and executed by a processor to enable the computer to implement any of the above-described methods for processing seismic data.

[0058] The technical solution provided in this application has at least the following beneficial effects:

[0059] The technical solution provided in this application synchronously calculates filters corresponding to each multiple wave data point based on gather data from any gather in seismic data and multiple multiple wave data predicted based on the gather data from any gather. The filters are then used to filter each multiple wave data point, resulting in filtered first data for each multiple wave data point. Finally, based on the gather data from any gather and the first data corresponding to each multiple wave data point, the primary wave data for the target area is determined. This method performs synchronous adaptive subtraction of multiple multiple wave data points from any gather, avoiding over- or under-elimination during the elimination of multiple wave data, thus resulting in more accurate primary wave data. Since primary wave data is used to determine oil and gas locations and reserves, more accurate primary wave data leads to more accurate determination of oil and gas locations and reserves, thereby improving the accuracy of oil and gas extraction. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic diagram illustrating the implementation environment of a seismic data processing method provided in an embodiment of this application;

[0062] Figure 2 This is a flowchart of a seismic data processing method provided in an embodiment of this application;

[0063] Figure 3 This is a cross-sectional view of gather data of any seismic gather in forward modeling data provided in an embodiment of this application;

[0064] Figure 4 This is a profile of the first multiple wave data of any seismic gather in forward modeling data provided in an embodiment of this application;

[0065] Figure 5 This is a cross-sectional view of the second multiple wave data of any seismic gather in forward modeling data provided in an embodiment of this application;

[0066] Figure 6 This is a cross-sectional view of the third multiple wave data of any seismic gather in forward modeling data provided in an embodiment of this application;

[0067] Figure 7 This is a profile of the primary wave data of any seismic gather of forward modeling data provided in an embodiment of this application;

[0068] Figure 8 This is a profile of the primary wave data of any seismic gather of forward modeling data obtained by three-step adaptive subtraction, as provided in an embodiment of this application.

[0069] Figure 9 This is a profile of primary wave data from any seismic gather of forward modeling data obtained by the method provided in this embodiment of the application.

[0070] Figure 10 This is an original stacked profile of any seismic gather in forward modeling data provided in an embodiment of this application;

[0071] Figure 11 This is a stacked profile of primary wave data from any seismic gather in forward modeling data provided in an embodiment of this application;

[0072] Figure 12 This is a superimposed profile of primary wave data from any seismic gather of forward modeling data obtained through three-step adaptive subtraction, as provided in an embodiment of this application.

[0073] Figure 13 This is a stacked profile of primary wave data from any seismic gather of forward modeling data obtained by the method provided in this application embodiment;

[0074] Figure 14 This is a cross-sectional view of gather data from any seismic gather in actual data provided in the embodiments of this application;

[0075] Figure 15 This is a profile of the first multiple wave data of any seismic gather of actual data provided in the embodiments of this application;

[0076] Figure 16 This is a cross-sectional view of the second multiple wave data of any seismic gather of actual data provided in the embodiments of this application;

[0077] Figure 17 This is a cross-sectional view of the third multiple wave data of any seismic gather of actual data provided in the embodiments of this application;

[0078] Figure 18 This is a profile of the fourth multiple wave data of any seismic gather of actual data provided in the embodiments of this application;

[0079] Figure 19 This is a cross-sectional view of the fifth multiple wave data of any seismic gather of actual data provided in the embodiments of this application;

[0080] Figure 20This is a profile of primary wave data of any seismic gather obtained by five-step adaptive subtraction of actual data, as provided in an embodiment of this application.

[0081] Figure 21 This is a profile of primary wave data of any seismic gather obtained by the method provided in this application embodiment;

[0082] Figure 22 This is an original stacked profile of any seismic gather from actual data provided in the embodiments of this application;

[0083] Figure 23 This is a superimposed profile of primary wave data from any seismic gather of actual data after five-step adaptive subtraction, provided in an embodiment of this application.

[0084] Figure 24 This is a superimposed profile of primary wave data of any seismic gather obtained by the method provided in this application embodiment;

[0085] Figure 25 This is a schematic diagram of the structure of a seismic data processing device provided in an embodiment of this application;

[0086] Figure 26 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application;

[0087] Figure 27 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0088] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0089] It should be noted that the terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0090] In oil and gas exploration, seismic exploration is an effective exploration method. Through methods such as field data acquisition, indoor data processing and interpretation, it obtains information about underground rocks, finds the location and reserves of oil or gas accumulation, and provides important underground images and attribute data for drilling and extraction of oil or gas.

[0091] By generating seismic waves artificially on the Earth's surface or sea surface, these waves penetrate underground rock layers and are reflected back to the surface after encountering interfaces with differences in velocity and density. Receivers (or detectors) placed on the surface receive the reflected seismic waves. These seismic waves, which propagate underground and then reflect back, bring back various physical properties of the underground rock layers. Through data processing, images of the underground rocks can be obtained. By analyzing and processing these images, the properties of various underground rocks can be interpreted, the presence of oil or natural gas can be analyzed, and their location and reserves can be calculated.

[0092] A strong reflective layer at the surface or underground is a strong interface of velocity and density difference. Seismic waves returning from underground are reflected into the underground medium at the surface or underground strong reflective layer, and then reflected back to the surface by the underground medium and received by the geophone. These seismic waves that have undergone multiple reflections are called multiple reflection waves (or multiple waves). The primary reflection wave (or primary wave) is used for imaging the underground medium. At the same time, the received multiple reflection waves, although they contain information for imaging the underground medium, become complicated and difficult to extract the reflection information because they have undergone multiple reflections. Therefore, these multiple reflection waves are generally regarded as correlated noise, which is a kind of noise that interferes with primary wave imaging. These multiple waves need to be removed or suppressed before imaging the primary wave.

[0093] Based on their downward reflection location, multiples are divided into surface multiples and interlayer multiples. Surface multiples are multiples that are emitted downwards and occur at the Earth's surface, while interlayer multiples are multiples that are reflected downwards and occur below the Earth's surface.

[0094] Methods for eliminating or suppressing multiples can be divided into two categories. One category relies on the velocity or periodicity differences between primary and multiple waves, using mathematical transformations to separate or suppress multiples in the transform domain, such as the Radon transform method and predictive deconvolution. The other category relies on the dynamic characteristics of seismic waves, using primary or low-order multiples to predict multiples or higher-order multiples, and then using adaptive subtraction to remove or suppress the predicted multiples from the recorded seismic data. Among these methods, the most effective and widely used method for suppressing surface multiples is SRME (Surface-related multipleelimination), while suppressing interlayer multiples is an extension of the SRME method.

[0095] The extended SRME method is an extension of the SRME method. The SRME method predicts multiples by performing wavelength integration at the Earth's surface, predicting only one multiple data point. However, the wavefield integration surface of the extended SRME method is underground; a strong reflecting layer constitutes one wavefield integration surface. Multiple strong reflecting layers exist underground, meaning multiple inter-layer multiple data points exist. Therefore, there is an adaptive subtraction problem involving multiple multiple data points. Iterative subtraction can be used to eliminate or suppress multiple multiple data points. However, for overlapping portions of multiple multiple data points, there is the problem of over-subtraction or under-subtraction. To more accurately subtract multiple data points, this application proposes a seismic data processing method.

[0096] Figure 1 This is a schematic diagram illustrating the implementation environment of a seismic data processing method provided in an embodiment of this application, such as... Figure 1 As shown, the implementation environment includes a computer device 101, which can be a terminal device or a server; this embodiment does not limit the specific type of device. The computer device 101 is used to execute the seismic data processing method provided in this embodiment.

[0097] Optionally, computer device 101 is a terminal device. A terminal device can be any electronic device that allows human-computer interaction with a user through one or more methods such as a keyboard, touchpad, remote control, voice interaction, or handwriting device. Examples include PCs (Personal Computers), mobile phones, smartphones, PDAs (Personal Digital Assistants), wearable devices, PPCs (Pocket PCs), tablets, smart car systems, smart TVs, smart speakers, and smartwatches.

[0098] A terminal device can refer to one of multiple terminal devices; this embodiment uses only one terminal device as an example. Those skilled in the art will understand that the number of terminal devices can be more or less. For example, there may be only one terminal device, or there may be dozens or hundreds, or even more. This application embodiment does not limit the number or type of terminal devices.

[0099] When computer device 101 is a server, the server can be a single server, a server cluster consisting of multiple servers, or any of the following: a cloud computing platform or a virtualization center. This application embodiment does not limit this. The server and terminal devices communicate via a wired or wireless network. The server has data receiving, data processing, and data sending functions. Of course, the server may also have other functions, which this application embodiment does not limit.

[0100] Those skilled in the art should understand that the above-described terminal devices and servers are merely illustrative examples. Other existing or future terminal devices or servers that are applicable to this application should also be included within the scope of protection of this application, and are hereby incorporated by reference.

[0101] This application provides a method for processing seismic data, which can be applied to the above-mentioned... Figure 1 The implementation environment shown is as follows: Figure 2 The flowchart shown in this embodiment of the present application illustrates a method for processing seismic data. This method can be implemented by... Figure 1 The computer device 101 in the middle performs the operation. For example... Figure 2 As shown, the method includes the following steps 201 to 206.

[0102] In step 201, seismic data for the target area is acquired, including gather data from multiple gathers.

[0103] The earthquake data can be either terrestrial or marine, and this application does not limit the type of earthquake data. Optionally, earthquake data collected by artificial sources excited at the Earth's surface is terrestrial earthquake data, and earthquake data collected by artificial sources excited at the sea surface is marine earthquake data.

[0104] This application does not limit the method of acquiring seismic data of the target area in its embodiments. Optionally, seismic data acquisition is the first and most important step in oil and gas seismic exploration engineering, and an indispensable piece of equipment in this step is a seismic signal receiving and recording system. That is, seismic data of the target area is acquired through a seismic signal receiving and recording system.

[0105] In step 202, for any one of the multiple trace sets, the similarity between the trace set data of any one trace set and the multiple wave data is determined.

[0106] In one possible implementation, for any given gather among multiple gathers, the multiple wave data of that gather are predicted based on the gather data of that gather. That is, the multiple wave data is the data predicted based on the gather data, and any single multiple wave data is the data after multiple reflections. For example, the gather data of any gather is D1(x,t), and the multiple multiple wave data are: M1(x,t), M2(x,t), ..., M... m (x, t). Where x is the number of channels in any channel set, t is the number of time samples, there are n time samples in total, n is an integer greater than or equal to 1, and m is the number of multiple wave data, m is an integer greater than or equal to 1. For example, there are 6 multiple wave data, with a total of 2000 time samples.

[0107] The embodiments of this application do not limit the process of determining the similarity between the gather data of any gather and each multiple wave data. Optionally, the similarity between the gather data of any gather and each multiple wave data can be determined according to the following formula (1).

[0108]

[0109] In the above formula (1), C i Let D1(x,τ) represent the similarity between the gather data of any gather and the i-th multiple wave data, where D1(x,τ) is the data at time τ in the gather data of the x-th gather (any gather), and M is the similarity between the gather data of any gather and the multiple wave data of the i-th gather. i (x, t+τ) represents the data at time t+τ in the i-th multiple data of the x-th gather (any gather), where x is the gather number, t is the time sample, and n is the number of time samples. The value of i ranges from 1 to m, where m is the number of multiple data.

[0110] In step 203, the similarity between any two multiple data points in each multiple data point is determined.

[0111] The embodiments of this application do not limit the process of determining the similarity between any two multiple wave data in each multiple wave data. Optionally, the similarity between any two multiple wave data can be determined according to the following formula (2).

[0112]

[0113] In the above formula (2), C ij For the similarity between the i-th multiple data and the j-th multiple data, M i (x,τ) represents the data at time τ in the i-th multiple data of the x-th gather (any gather), M j (x, t+τ) represents the data at time t+τ in the j-th multiple data of the x-th gather (any gather), where x is the gather number, t is the time sample, and n is the number of time samples. The values ​​of i and j are both from 1 to m, where m is the number of multiple data.

[0114] In step 204, the first filter for each multiple wave data is determined based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data.

[0115] In one possible implementation, the process of determining the first filter for each multiple wave data based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data, includes: constructing a set of filter equations based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data; solving the set of filter equations to obtain the first filter for each multiple wave data.

[0116] The process of solving the filter equations to obtain the first filter for each multiple wave data includes: solving the filter equations using the conjugate gradient algorithm to obtain the first filter for each multiple wave data.

[0117] The conjugate gradient algorithm is a method between the steepest descent method and Newton's method. It only requires first-order derivative information, overcoming the slow convergence of the steepest descent method while avoiding the need to store, compute, and invert the Hessian matrix in Newton's method. The conjugate gradient algorithm is not only one of the most useful methods for solving large linear equation systems but also one of the most efficient algorithms for solving large nonlinear optimization problems. Among various optimization algorithms, the conjugate gradient algorithm is a very important one. Its advantages include low storage requirements, non-convergence, high stability, and no need for any external parameters.

[0118] Optionally, the filter equation set constructed based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data, is shown in the following formula (3).

[0119]

[0120] In the above formula (3), C 11 For the similarity between the first multiple data points and the first multiple data points, C 12 To determine the similarity between the first and second multiple data sets, C 1m For the similarity between the first multiple data and the m-th multiple data, C 21 To determine the similarity between the second and first multiple data sets, C 22 For the similarity between the second multiple data points, C 2m For the similarity between the second multiple data and the m-th multiple data, C m1 For the similarity between the m-th multiple data point and the first multiple data point, C m2 For the similarity between the m-th multiple data and the second multiple data, C mmLet C1 be the similarity between the m-th multiple data point and the m-th multiple data point; C2 be the similarity between the gather data of any gather and the first multiple data point; and C3 be the similarity between the gather data of any gather and the second multiple data point. m f1 represents the similarity between the gather data of any gather and the m-th multiple data; f2 represents the first filter corresponding to the first multiple data, f3 represents the first filter corresponding to the second multiple data, and f4 represents the similarity between the gather data of any gather and the m-th multiple data. m This is the first filter corresponding to the m-th multiple wave data.

[0121] By solving the above formula (3), the first filters f1, f2, ..., f1 corresponding to each multiple wave data can be obtained. m .

[0122] In step 205, the multiple wave data is filtered according to the first filter of each multiple wave data to obtain the first data corresponding to each multiple wave data.

[0123] The first data corresponding to any multiple wave data is the data after the multiple wave data has been filtered by the first filter corresponding to the multiple wave data.

[0124] The embodiments of this application do not limit the process of filtering each multiple wave data according to the first filter of each multiple wave data to obtain the first data corresponding to each multiple wave data. Optionally, according to the first filter of each multiple wave data, the multiple wave data is filtered according to the following formula (4) to obtain the first data corresponding to each multiple wave data.

[0125]

[0126] In the above formula (4), Q i M is the first data corresponding to the i-th multiple wave data. i (x, τ) represents the data at time τ in the i-th multiple data of the x-th gather, f i (t-τ) represents the data in the first filter of the i-th multiple wave data at time t-τ, x is the channel number of any gather, t is the time sample, and n is the number of time samples.

[0127] In step 206, the primary wave data of the target area is determined based on the gather data of any gather and the first data corresponding to each multiple wave data.

[0128] The primary wave data for the target area refers to the seismic wave after one reflection. This primary wave data is used to predict the location and reserves of oil and gas in the target area.

[0129] In one possible implementation, the process of determining the primary wave data of the target area based on the gather data of any gather and the first data corresponding to each multiple wave data includes: summing the first data of each multiple wave data to obtain a first sum value; determining the primary wave data of any gather based on the gather data of any gather and the first sum value; and determining the primary wave data of the target area based on the primary wave data of each gather.

[0130] Optionally, the process of determining the primary wave data of any given gather based on the gather data and the first sum value includes: performing a subtraction operation on the gather data and the first sum value of any given gather to obtain a first difference value; and determining the primary wave data of any given gather based on the first difference value.

[0131] The first difference is obtained by subtracting the data of any set and the first sum according to the following formula (5).

[0132] P(x,t)=D1(x,t)-N(x,t) Formula (5)

[0133] In the above formula (5), P(x,t) is the first difference, D1(x,t) is the data of any gather, and N(x,t) is the first sum.

[0134] Optionally, embodiments of this application provide the following two implementation methods to determine the primary wave data of any gather based on the first difference.

[0135] Method 1: Determine the first difference as the primary wave data of any gather.

[0136] Method 2: Smooth the first sum to obtain a smoothed first sum. Smooth the first difference to obtain a smoothed first difference. Determine the occlusion coefficient based on the smoothed first value and the smoothed first difference. Based on the occlusion coefficient, occlude the gather data of any gather to obtain the occluded gather data of any gather. Determine the primary wave data of any gather based on the similarity between the occluded gather data of any gather and any two multiple wave data.

[0137] The smoothed first sum eliminates outliers, providing smooth and balanced data for subsequent calculations; similarly, the smoothed first difference eliminates outliers, providing smooth and balanced data for subsequent calculations. The occlusion coefficient is used to block the effective signal in the gather data of any gather; the effective signal refers to the primary wave data.

[0138] In one possible implementation, the process of smoothing the first sum to obtain a smoothed first sum includes: applying a smoothing algorithm to smooth the first sum in both time and space directions to obtain a smoothed first sum. For example, let the smoothed first sum be denoted as Ns(x, t).

[0139] The process of smoothing the first difference to obtain a smoothed first difference includes: using a smoothing algorithm to smooth the first difference in both time and space directions to obtain a smoothed first difference. For example, let the smoothed first difference be denoted as Ps(x, t).

[0140] The smoothing algorithm can be any type, and this application does not limit it. For example, the smoothing algorithm can be a Bayesian smoothing method.

[0141] In one possible implementation, the process of determining the occlusion coefficient based on the smoothed first sum and the smoothed first difference includes: multiplying the smoothed first sum and the smoothed first difference to obtain a first product; smoothing the first product to obtain a smoothed first product, which eliminates outliers and provides smooth and balanced data for subsequent calculations; determining a reference value with base e and exponent of the smoothed first product; and determining the reciprocal of the reference value as the occlusion coefficient.

[0142] Optionally, the process of multiplying the smoothed first sum and the smoothed first difference to obtain the first product includes: determining the product of the absolute value of the smoothed first sum and the absolute value of the smoothed first difference to obtain the first product. For example, the first product is denoted as R1(x, t).

[0143] The process of smoothing the first product to obtain a smoothed first product includes: applying a smoothing algorithm to smooth the first product in both time and space directions to obtain a smoothed first product. For example, let the smoothed first product be denoted as R2(x, t).

[0144] Alternatively, the reference value can be determined according to the following formula (6).

[0145]

[0146] In the above formula (6), R3(x,t) is the reference value, R2(x,t) is the first product after smoothing, and e is an infinite non-repeating constant.

[0147] Alternatively, the shading coefficient can be determined according to the following formula (7).

[0148]

[0149] In the above formula (7), R4(x,t) is the occlusion coefficient and R3(x,t) is the reference value.

[0150] In one possible implementation, after determining the occlusion coefficient in the above process, the process of occluding the gather data of any gather according to the occlusion coefficient to obtain the occluded gather data of any gather includes: determining that the product between the gather data of any gather and the occlusion coefficient is the occluded gather data of any gather.

[0151] Optionally, based on the occlusion coefficient, the gather data of any gather can be occluded according to the following formula (8) to obtain the occluded gather data of any gather.

[0152] D2(x,t)=D1(x,t)*R4(x,t) Formula (8)

[0153] In the above formula (8), D2(x,t) is the gather data after any gather is blocked, D1(x,t) is the gather data of any gather, and R4(x,t) is the blocking coefficient.

[0154] In one possible implementation, the process of determining the primary wave data of any given gather based on the similarity between the gather data after any gather is obscured and any two multiple wave data includes: determining the similarity between the gather data after any gather is obscured and each multiple wave data; determining a second filter for each multiple wave data based on the similarity between the gather data after any gather is obscured and each multiple wave data, and the similarity between any two multiple wave data; filtering each multiple wave data based on the second filter to obtain the second data corresponding to each multiple wave data, wherein the second data corresponding to any multiple wave data is the data after filtering the any multiple wave data by the second filter corresponding to the any multiple wave data; and determining the primary wave data of any gather based on the gather data of any gather and the second data corresponding to each multiple wave data.

[0155] For example, the second filters for acquiring each multiple wave data are as follows:

[0156] The process of determining the similarity between the gathered data after any gathered is obstructed and the data of each multiple is similar to the process of determining the similarity between the gathered data of any gathered and the data of each multiple in step 202 above. The process of determining the second filter for each multiple based on the similarity between the gathered data after any gathered is obstructed and the data of each multiple, and the similarity between any two multiples, is similar to the process of determining the first filter for each multiple based on the similarity between the gathered data of any gathered and the data of each multiple, and the similarity between any two multiples, in step 204 above. The embodiments of this application will not be described in detail here.

[0157] Based on the second filter of each multiple wave data, the multiple wave data is filtered according to the following formula (9) to obtain the second data corresponding to each multiple wave data.

[0158]

[0159] In the above formula (9), This is the second data corresponding to the i-th multiple wave data. Let i be the data at time τ from the i-th multiple wave data of any given set. The data in the second filter for the i-th multiple wave data at time t-τ.

[0160] In one possible implementation, the process of determining the primary wave data of any given gather based on the gather data of any given gather and the second data corresponding to each multiple wave data includes: summing the second data corresponding to each multiple wave data to obtain a second sum; and subtracting the gather data of any given gather from the second sum to obtain the primary wave data of any given gather.

[0161] Optionally, the gather data and the second sum of any gather can be subtracted according to the following formula (10) to obtain the first wave data of any gather.

[0162]

[0163] In the above formula (10), Let D(x, t) be the primary wave data for any gather, and D(x, t) be the gather data for any gather. This is the second sum.

[0164] The aforementioned method simultaneously calculates filters corresponding to each multiple wave data point based on gather data from any seismic gather and multiple multiple wave data predicted from that gather data. Each multiple wave data point is then filtered using these filters to obtain the first filtered data. Finally, based on the gather data from any gather and the first data points corresponding to each multiple wave data point, the primary wave data for the target area is determined. This method performs synchronous adaptive subtraction of multiple multiple wave data points from any gather data point, avoiding over- or under-elimination during the elimination of multiple wave data, thus resulting in more accurate primary wave data. Since primary wave data is used to determine oil and gas locations and reserves, more accurate primary wave data leads to more accurate determination of oil and gas locations and reserves, thereby improving the accuracy of oil and gas extraction.

[0165] Figure 3 This is a cross-sectional view of gather data from any seismic gather in forward modeling data provided in an embodiment of this application. Figure 4 This is a profile of the first multiple wave data of any seismic gather in forward modeling data provided in an embodiment of this application. Figure 5 This is a profile of the second multiple wave data of any seismic gather in forward modeling data provided in an embodiment of this application. Figure 6 This is a profile of the third multiple wave data of any seismic gather in forward modeling data provided in an embodiment of this application. Figure 7 This is a profile of the primary wave data of any seismic gather from forward modeling data provided in this application embodiment. Figure 8 This is a profile of the primary wave data of any seismic gather obtained from forward modeling data after three steps of adaptive subtraction, as provided in an embodiment of this application. Figure 9 This is a profile of primary wave data from any seismic gather of forward modeling data obtained by the method provided in this embodiment of the application. Figure 10 This is the original stacked profile of any seismic gather in forward modeling data provided in an embodiment of this application. Figure 11 This is a stacked profile of primary wave data from any seismic gather in forward modeling data, provided in an embodiment of this application. Figure 12 This is a stacked profile of primary wave data from any seismic gather of forward modeling data obtained through three-step adaptive subtraction, as provided in an embodiment of this application. Figure 13 This is a stacked profile of primary wave data from any seismic gather of forward modeling data obtained by the method provided in this embodiment of the application. Figure 8 and Figure 9It can be seen that the profile of the primary wave data of any seismic gather obtained by three-step adaptive subtraction of forward modeling data is basically consistent with the profile of the primary wave data of any seismic gather obtained by the method provided in the embodiments of this application. Figure 12 and Figure 13 It is evident that the stacked profile of the primary wave data of any seismic gather obtained by three-step adaptive subtraction of forward modeling data is basically consistent with the stacked profile of the primary wave data of any seismic gather obtained by the method provided in the embodiments of this application. However, there are multiple wave data residues in the profile and stacked profile of the primary wave data of any seismic gather obtained by three-step adaptive subtraction of forward modeling data within 4000-8000 ms. The method provided in the embodiments of this application can completely eliminate the multiple wave data. The profile and stacked profile of the primary wave data after eliminating the multiple wave data are basically consistent with the actual profile and stacked profile of the primary wave data. In other words, the elimination effect of the method provided in the embodiments of this application is better.

[0166] Figure 14 This is a cross-sectional view of gather data from any seismic gather in actual data provided in the embodiments of this application; Figure 15 This is a profile of the first multiple wave data of any seismic gather of actual data provided in the embodiments of this application; Figure 16 This is a cross-sectional view of the second multiple wave data of any seismic gather of actual data provided in the embodiments of this application; Figure 17 This is a cross-sectional view of the third multiple wave data of any seismic gather of actual data provided in the embodiments of this application; Figure 18 This is a profile of the fourth multiple wave data of any seismic gather of actual data provided in the embodiments of this application; Figure 19 This is a cross-sectional view of the fifth multiple wave data of any seismic gather of actual data provided in the embodiments of this application; Figure 20 This is a profile of primary wave data of any seismic gather obtained by five-step adaptive subtraction of actual data, as provided in an embodiment of this application. Figure 21 This is a profile of primary wave data of any seismic gather obtained by the method provided in this application embodiment; Figure 22 This is an original stacked profile of any seismic gather from actual data provided in the embodiments of this application; Figure 23 This is a superimposed profile of primary wave data from any seismic gather of actual data after five-step adaptive subtraction, provided in an embodiment of this application. Figure 24 This is a stacked profile of primary wave data from any seismic gather obtained using the method provided in this embodiment of the application. Figure 20 and Figure 23 It is evident that, after five steps of adaptive subtraction, the profile and stacked profile of any seismic gather obtained from actual data contain residual primary wave data and damaged primary wave data. Figure 21 and Figure 24 As can be seen, the method provided in this application provides a profile and overlay profile of the primary wave data of any seismic gather from actual data, which can effectively remove the multiple wave data. The primary wave data after suppressing the multiple wave data has high fidelity, effectively suppresses the multiple wave data in the seismic data, improves the data quality, and meets the needs of processing actual seismic data.

[0167] Figure 25 The diagram shown is a structural schematic of a seismic data processing device provided in an embodiment of this application. Figure 25 As shown, the device includes:

[0168] The acquisition module 2501 is used to acquire seismic data of the target area, which includes gather data from multiple gathers.

[0169] The determination module 2502 is used to determine the similarity between the trace data of any trace and each multiple wave data for any trace of any trace among multiple traces. The multiple wave data is the data predicted based on the trace data, and any multiple wave data is the data after multiple reflections.

[0170] The determination module 2502 is also used to determine the similarity between any two multiple wave data in each multiple wave data;

[0171] The determination module 2502 is also used to determine the first filter for each multiple wave data based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data.

[0172] Processing module 2503 is used to filter each multiple wave data according to the first filter of each multiple wave data to obtain the first data corresponding to each multiple wave data. The first data corresponding to any multiple wave data is the data after filtering any multiple wave data by the first filter corresponding to any multiple wave data.

[0173] The determination module 2502 is also used to determine the primary wave data of the target area based on the gather data of any gather and the first data corresponding to each multiple wave data. The primary wave data of the target area is used to predict the oil and gas location and oil and gas reserves of the target area.

[0174] In one possible implementation, the determining module 2502 is used to sum the first data corresponding to each multiple wave data to obtain a first sum value; determine the primary wave data of any given gather based on the gather data of any gather and the first sum value; and determine the primary wave data of the target area based on the primary wave data of each gather.

[0175] In one possible implementation, the determining module 2502 is used to perform a subtraction operation on the gather data of any gather and a first sum to obtain a first difference; and to determine the primary wave data of any gather based on the first difference.

[0176] In one possible implementation, the determining module 2502 is used to determine the first difference as primary wave data for any gather.

[0177] In one possible implementation, the determining module 2502 is used to smooth the first sum to obtain a smoothed first sum, which eliminates outliers in the first sum; to smooth the first difference to obtain a smoothed first difference, which eliminates outliers in the first difference; to determine an occlusion coefficient based on the smoothed first sum and the smoothed first difference, which is used to occlude valid signals in the gather data of any gather; to perform occlusion processing on the gather data of any gather based on the occlusion coefficient to obtain occluded gather data of any gather; and to determine the primary wave data of any gather based on the similarity between the occluded gather data of any gather and any two multiple wave data.

[0178] In one possible implementation, module 2502 is used to perform product processing on the smoothed first sum and the smoothed first difference to obtain a first product; to perform smooth processing on the first product to obtain a smoothed first product, the smoothed first product eliminating outliers of the first product; to determine a reference value with e as the base and the smoothed first product as the exponent; and to determine the reciprocal of the reference value as the occlusion coefficient.

[0179] In one possible implementation, the determining module 2502 is used to determine the similarity between the gather data after any gather is blocked and each multiple wave data; based on the similarity between the gather data after any gather is blocked and each multiple wave data, and the similarity between any two multiple wave data, a second filter is determined for each multiple wave data; based on the second filter for each multiple wave data, the multiple wave data is filtered to obtain the second data corresponding to each multiple wave data, wherein the second data corresponding to any multiple wave data is the data after the multiple wave data is filtered by the second filter corresponding to the multiple wave data; based on the gather data of any gather and the second data corresponding to each multiple wave data, the primary wave data of any gather is determined.

[0180] In one possible implementation, the determining module 2502 is used to sum the second data corresponding to each multiple wave data to obtain a second sum value; and to subtract the gather data and the second sum value of any gather to obtain the primary wave data of any gather.

[0181] The aforementioned device synchronously calculates filters corresponding to each multiple wave data point based on gather data from any seismic gather and multiple multiple wave data predicted from that gather data. It then filters each multiple wave data point using these filters, obtaining the first filtered data for each multiple wave data point. Finally, based on the gather data from any gather and the first data for each multiple wave data point, it determines the primary wave data for the target area. By synchronously and adaptively subtracting multiple multiple wave data points from any gather data point, the device avoids the problems of over- or under-elimination during the elimination of multiple wave data, thus resulting in more accurate primary wave data. Since primary wave data is used to determine oil and gas locations and reserves, more accurate primary wave data leads to more accurate determination of oil and gas locations and reserves, thereby improving the accuracy of oil and gas extraction.

[0182] It should be understood that the above-described apparatus is only illustrated by the division of the functional modules described above when implementing its functions. In practical applications, the functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0183] Figure 26 This illustration shows a structural block diagram of a terminal device 2600 provided in an exemplary embodiment of this application. The terminal device 2600 can be any electronic device product capable of human-computer interaction with a user through one or more methods such as a keyboard, touchpad, remote control, voice interaction, or handwriting device. Examples include PCs (Personal Computers), mobile phones, smartphones, PDAs (Personal Digital Assistants), wearable devices, PPCs (Pocket PCs), tablet computers, smart car systems, smart TVs, smart speakers, and smartwatches.

[0184] Typically, terminal device 2600 includes a processor 2601 and a memory 2602.

[0185] Processor 2601 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 2601 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 2601 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 2601 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, processor 2601 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0186] The memory 2602 may include one or more computer-readable storage media, which may be non-transitory. The memory 2602 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 2602 is used to store at least one instruction, which is executed by the processor 2601 to implement the seismic data processing method provided in the method embodiments of this application.

[0187] In some embodiments, the terminal device 2600 may optionally include a peripheral device interface 2603 and at least one peripheral device. The processor 2601, memory 2602, and peripheral device interface 2603 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 2603 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: radio frequency circuitry 2604, display screen 2605, camera assembly 2606, audio circuitry 2607, and power supply 2608.

[0188] Peripheral device interface 2603 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 2601 and memory 2602. In some embodiments, processor 2601, memory 2602 and peripheral device interface 2603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 2601, memory 2602 and peripheral device interface 2603 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0189] The radio frequency (RF) circuit 2604 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 2604 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 2604 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 2604 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 2604 can communicate with other terminal devices through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 2604 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0190] Display screen 2605 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 2605 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 2601 for processing. In this case, display screen 2605 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 2605, disposed on the front panel of terminal device 2600; in other embodiments, there may be at least two display screens 2605, disposed on different surfaces of terminal device 2600 or in a folded design; in still other embodiments, display screen 2605 may be a flexible display screen, disposed on a curved or folded surface of terminal device 2600. Furthermore, display screen 2605 may also be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 2605 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0191] The camera assembly 2606 is used to acquire images or videos. Optionally, the camera assembly 2606 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal device 2600, and the rear-facing camera is located on the back of the terminal device 2600. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 2606 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0192] The audio circuit 2607 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 2601 for processing, or input to the radio frequency circuit 2604 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the terminal device 2600. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 2601 or the radio frequency circuit 2604 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 2607 may also include a headphone jack.

[0193] Power supply 2608 is used to power the various components in terminal device 2600. Power supply 2608 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 2608 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0194] In some embodiments, the terminal device 2600 further includes one or more sensors 2609. The one or more sensors 2609 include, but are not limited to: an accelerometer 2610, a gyroscope 2611, a pressure sensor 2612, an optical sensor 2613, and a proximity sensor 2614.

[0195] Accelerometer 2610 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by terminal device 2600. For example, accelerometer 2610 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 2601 can control display screen 2605 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 2610. Accelerometer 2610 can also be used for games or for acquiring user motion data.

[0196] The gyroscope sensor 2611 can detect the orientation and rotation angle of the terminal device 2600. The gyroscope sensor 2611 can work in conjunction with the accelerometer sensor 2610 to collect the user's 3D movements on the terminal device 2600. Based on the data collected by the gyroscope sensor 2611, the processor 2601 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0197] The pressure sensor 2612 can be disposed on the side bezel of the terminal device 2600 and / or on the lower layer of the display screen 2605. When the pressure sensor 2612 is disposed on the side bezel of the terminal device 2600, it can detect the user's grip signal on the terminal device 2600, and the processor 2601 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 2612. When the pressure sensor 2612 is disposed on the lower layer of the display screen 2605, the processor 2601 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 2605. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0198] Optical sensor 2613 is used to collect ambient light intensity. In one embodiment, processor 2601 can control the display brightness of display screen 2605 based on the ambient light intensity collected by optical sensor 2613. Specifically, when the ambient light intensity is high, the display brightness of display screen 2605 is increased; when the ambient light intensity is low, the display brightness of display screen 2605 is decreased. In another embodiment, processor 2601 can also dynamically adjust the shooting parameters of camera assembly 2606 based on the ambient light intensity collected by optical sensor 2613.

[0199] The proximity sensor 2614, also known as a distance sensor, is typically installed on the front panel of the terminal device 2600. The proximity sensor 2614 is used to detect the distance between the user and the front of the terminal device 2600. In one embodiment, when the proximity sensor 2614 detects that the distance between the user and the front of the terminal device 2600 is gradually decreasing, the processor 2601 controls the display screen 2605 to switch from a screen-on state to a screen-off state; when the proximity sensor 2614 detects that the distance between the user and the front of the terminal device 2600 is gradually increasing, the processor 2601 controls the display screen 2605 to switch from a screen-off state to a screen-on state.

[0200] Those skilled in the art will understand that Figure 26 The structure shown does not constitute a limitation on the terminal device 2600, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0201] Figure 27The schematic diagram of the server provided in this application embodiment shows that the server 2700 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 2701 and one or more memories 2702, wherein the one or more memories 2702 store at least one piece of program code, which is loaded and executed by the one or more processors 2701 to implement the seismic data processing methods provided in the above-described method embodiments. Of course, the server 2700 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server 2700 may also include other components for implementing device functions, which will not be elaborated here.

[0202] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one piece of program code that is loaded and executed by a processor to enable a computer to implement any of the above-described methods for processing seismic data.

[0203] Optionally, the aforementioned computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0204] In an exemplary embodiment, a computer program or computer program product is also provided, which stores at least one computer instruction, which is loaded and executed by a processor to enable the computer to implement any of the above-described methods for processing seismic data.

[0205] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the earthquake data involved in this application was obtained with full authorization.

[0206] It should be understood that "multiple" as used in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0207] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0208] The above description is merely an exemplary embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for processing seismic data, characterized in that, The method includes: Acquire seismic data for the target area, the seismic data including gather data from multiple gathers; For any one of the multiple trace sets, determine the similarity between the trace set data of the any one trace set and each multiple wave data, wherein the multiple wave data is the data predicted based on the trace set data, and any multiple wave data is the data after multiple reflections. Determine the similarity between any two multiple wave data points in each of the multiple wave data points; Based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data, a first filter for each multiple wave data is determined. Based on the first filter of each multiple wave data, the multiple wave data is filtered to obtain the first data corresponding to each multiple wave data. The first data corresponding to any multiple wave data is the data after the first filter of any multiple wave data is filtered by the first filter corresponding to the any multiple wave data. Based on the gather data of any gather and the first data corresponding to each multiple wave data, the primary wave data of the target area is determined, and the primary wave data of the target area is used to predict the oil and gas location and oil and gas reserves of the target area.

2. The method according to claim 1, characterized in that, Determining the primary wave data of the target region based on the gather data of any gather and the first data corresponding to each multiple wave data includes: The first data corresponding to each of the multiple wave data is summed to obtain the first sum value; Based on the gather data of any gather and the first sum, determine the primary wave data of any gather; Based on the primary wave data of each gather, the primary wave data of the target area are determined.

3. The method according to claim 2, characterized in that, Determining the primary wave data of any given gather based on the gather data of any given gather and the first sum value includes: The difference between the gather data of any gather and the first sum is calculated to obtain the first difference value; Based on the first difference, the primary wave data of any given gather is determined.

4. The method according to claim 3, characterized in that, The step of determining the primary wave data of any gather based on the first difference includes: The first difference is determined to be the primary wave data of any of the gathers.

5. The method according to claim 3, characterized in that, The step of determining the primary wave data of any gather based on the first difference includes: The first sum is smoothed to obtain a smoothed first sum, which eliminates outliers in the first sum. The first difference is smoothed to obtain a smoothed first difference, which eliminates outliers in the first difference. Based on the first sum and the first difference after smoothing, an occlusion coefficient is determined, which is used to occlude the valid signal in the gather data of any gather; Based on the occlusion coefficient, the gather data of any gather is occluded to obtain the occluded gather data of any gather. The primary wave data of any given gather is determined based on the similarity between the gather data after any gather is blocked and any two multiple wave data.

6. The method according to claim 5, characterized in that, The step of determining the occlusion coefficient based on the first sum and the first difference after smoothing includes: The first product is obtained by multiplying the first sum and the first difference after smoothing. The first product is smoothed to obtain a smoothed first product, which eliminates outliers in the first product. Determine a reference value with e as the base and the first product after smoothing as the exponent; The reciprocal of the reference value is determined as the occlusion coefficient.

7. The method according to claim 5, characterized in that, The step of determining the primary wave data of any given gather based on the similarity between the gather data after any gather is blocked and any two multiple wave data includes: Determine the similarity between the gather data after any gather is blocked and the various multiple wave data; Based on the similarity between the gather data after any gather is blocked and the various multiple data, and the similarity between any two multiple data, a second filter for each multiple data is determined. Based on the second filter of each multiple wave data, the multiple wave data is filtered to obtain the second data corresponding to each multiple wave data. The second data corresponding to any multiple wave data is the data after filtering the multiple wave data by the second filter corresponding to the multiple wave data. Based on the gather data of any gather and the second data corresponding to each multiple wave data, the primary wave data of any gather are determined.

8. The method according to claim 7, characterized in that, Determining the primary wave data of any given gather based on the gather data of any given gather and the second data corresponding to each multiple wave data includes: The second data corresponding to each of the multiple wave data are summed to obtain the second sum value; The difference between the gather data of any gather and the second sum is calculated to obtain the primary wave data of any gather.

9. A seismic data processing device, characterized in that, The device includes: The acquisition module is used to acquire seismic data of the target area, the seismic data including gather data of multiple gathers; The determination module is used to determine the similarity between the trace data of any trace set and each multiple wave data for any trace set among the plurality of trace sets, wherein the multiple wave data is data predicted based on the trace data, and any multiple wave data is data after multiple reflections. The determining module is also used to determine the similarity between any two multiple wave data in each multiple wave data; The determining module is further configured to determine a first filter for each multiple wave data based on the similarity between the gather data of any gather and each multiple wave data, and the similarity between any two multiple wave data. The processing module is used to filter the multiple wave data according to the first filter of each multiple wave data to obtain the first data corresponding to each multiple wave data. The first data corresponding to any multiple wave data is the data after the multiple wave data is filtered by the first filter corresponding to the multiple wave data. The determining module is further configured to determine the primary wave data of the target area based on the gather data of any gather and the first data corresponding to each multiple wave data, wherein the primary wave data of the target area is used to predict the oil and gas location and oil and gas reserves of the target area.

10. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one piece of program code, which is loaded and executed by the processor to enable the computer device to implement the seismic data processing method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to enable the computer to implement the seismic data processing method as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, The computer program product stores at least one computer instruction, which is loaded and executed by a processor to enable the computer to implement the seismic data processing method as described in any one of claims 1 to 8.

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