Surface element attribute evaluation method and device

Abstract

The invention provides a surface element attribute evaluation method and device, and the method comprises the following steps: 1, determining n distance segments, and enabling the intervals between the distance segments to be equal; 2, aiming at each surface element of the seismic data, storing offset data in the surface element into an offset array; 3, for each surface element, counting the number of offset data included in each distance section; step 4, calculating Simpson coefficients of each surface element according to the number of the offset data; and 5, generating a Simpson coefficient distribution diagram and a histogram according to the Simpson coefficient of each surface element. According to the surface element attribute evaluation method, surface element attribute evaluation is carried out based on the Simpson coefficient, the occurrence probability of the offset in the surface element in different ranges can be quantitatively evaluated, and a more effective analysis means is provided for acquisition designers.

Description

Technical Field

[0001] The invention relates to the technical field of seismic exploration and development, and in particular to a surface element attribute evaluation method and device. Background Art

[0002] In the process of oil and gas exploration and development, the design of a three-dimensional observation system is the first step in the whole process of seismic data acquisition. As the geological targets in the exploration area become increasingly complex, many observation system design schemes are often proposed for a certain geological task during the design of the observation system. However, each scheme is not very different in the macro sense, and it is difficult to measure the quality of an observation system attribute with the naked eye. How to establish a set of quantitative observation system attribute evaluation methods has become a focus of designers.

[0003] Traditional design software mainly provides coverage frequency distribution diagram, offset distribution diagram, rose diagram, etc. Among them, the coverage frequency distribution diagram can be used to describe the distribution of different coverage times in the observation system scheme. Through this distribution diagram, we can understand the proportion and distribution of different coverage times in the observation system scheme. The offset distribution diagram is used to describe the distribution of different offsets in the observation system scheme. The offset refers to the distance between the excitation source and the receiving station in seismic exploration. The distribution of the offset will affect the quality and resolution of the seismic signal. The rose diagram is used to describe the relative relationship between different azimuths and receiving points in the observation system scheme. In seismic exploration, the azimuth refers to the angle between the direction of seismic wave propagation and a certain reference direction, and the receiving point is a device for receiving seismic waves. The rose diagram can better understand the coverage of the observation system and the relationship between different azimuths and receiving points. However, these presentation methods can only provide users with visual results, and cannot provide users with quantitative evaluation of the scheme. Therefore, it is expected to propose a quantitative evaluation method for the design of a three-dimensional observation system. Summary of the invention

[0004] In view of the shortcomings of the above existing methods, the present invention proposes a facet attribute evaluation method, which comprises:

[0005] Step 1: Determine n distance segments so that the intervals between each distance segment are equal;

[0006] Step 2: For each bin of the seismic data, store the offset data in the bin into an offset array;

[0007] Step 3: for each of the bins, counting the number of offset data contained in each distance segment;

[0008] Step 4: Calculate the Simpson coefficient of each bin according to the number of offset data;

[0009] Step 5: Generate a Simpson coefficient distribution graph and a histogram based on the Simpson coefficient of each facet.

[0010] Preferably, storing the offset data within the bin into an offset array comprises:

[0011] sorting the offset data in the bin in ascending order;

[0012] Store the sorted offset data into the offset array offset i Where i represents the offset array i The subscript of , i∈{0,1,2...,m}, m represents the amount of data in the offset array.

[0013] Preferably, the counting of the number of offset data contained in each distance segment comprises:

[0014] Create an offset count array bin j , the offset count array bin j The initial value of each data is set to zero;

[0015] For each data in the offset array, calculate The offset count array bin j The value of the jth data is added by 1, where j represents the gun offset count array bin j The subscript of , j∈{0,1,2...,n}, space represents the interval between each distance segment.

[0016] Preferably, the Simpson coefficient of each bin is calculated according to the following formula:

[0017]

[0018] Among them, D k represents the Simpson coefficient of the kth face element, N k It represents the number of offset data contained in the kth bin.

[0019] Preferably, generating a Simpson coefficient distribution graph according to the Simpson coefficient of each facet comprises:

[0020] A color scale axis in the range of [0,1] is established, and the Simpson coefficient of each facet is correspondingly depicted in a two-dimensional space based on the color scale axis to generate a distribution grid map, that is, a Simpson coefficient distribution map.

[0021] Preferably, generating a Simpson coefficient histogram according to the Simpson coefficient of each bin comprises:

[0022] Define multiple Simpson coefficient ranges and count the number of facets corresponding to each Simpson coefficient range;

[0023] The Simpson coefficient histogram is generated by using the horizontal axis to represent each Simpson coefficient range and the vertical axis to represent the number of bins corresponding to the Simpson coefficient range.

[0024] Another aspect of the present invention provides a facet attribute evaluation device, comprising:

[0025] A preprocessing module, used for determining n distance segments, so that the intervals between each distance segment are equal;

[0026] A data processing module, for storing the offset data in each bin of the seismic data into an offset array;

[0027] A statistical module, for counting the number of offset data contained in each distance segment for each bin;

[0028] A Simpson coefficient calculation module, used for calculating the Simpson coefficient of each bin according to the number of offset data;

[0029] The graphics generation module is used to generate a Simpson coefficient distribution diagram and a histogram according to the Simpson coefficient of each facet.

[0030] Preferably, storing the offset data within the bin into an offset array comprises:

[0031] sorting the offset data in the bin in ascending order;

[0032] Store the sorted offset data into the offset array offset i Where i represents the offset array i The subscript of , i∈{0,1,2...,m}, m represents the amount of data in the offset array.

[0033] Preferably, the counting of the number of offset data contained in each distance segment comprises:

[0034] Create an offset count array bin j , the offset count array bin j The initial value of each data is set to zero;

[0035] For each data in the offset array, calculate The offset count array bin j The value of the jth data is added by 1, where j represents the gun offset count array bin j The subscript of , j∈{0,1,2...,n}, space represents the interval between each distance segment.

[0036] Preferably, the Simpson coefficient of each bin is calculated according to the following formula:

[0037]

[0038] Among them, D k represents the Simpson coefficient of the kth face element, N k It represents the number of offset data contained in the kth bin.

[0039] Preferably, generating a Simpson coefficient distribution graph according to the Simpson coefficient of each facet comprises:

[0040] A color scale axis in the range of [0,1] is established, and the Simpson coefficient of each facet is correspondingly depicted in a two-dimensional space based on the color scale axis to generate a distribution grid map, that is, a Simpson coefficient distribution map.

[0041] Preferably, generating a Simpson coefficient histogram according to the Simpson coefficient of each bin comprises:

[0042] Define multiple Simpson coefficient ranges and count the number of facets corresponding to each Simpson coefficient range;

[0043] The Simpson coefficient histogram is generated by using the horizontal axis to represent each Simpson coefficient range and the vertical axis to represent the number of bins corresponding to the Simpson coefficient range.

[0044] Another aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the facet attribute evaluation method is implemented.

[0045] Another aspect of the present invention provides an electronic device, the electronic device comprising:

[0046] A memory storing executable instructions;

[0047] A processor runs the executable instructions in the memory to implement the facet attribute evaluation method.

[0048] The beneficial effect of the bin attribute evaluation method of the present invention is that the method evaluates bin attributes based on the Simpson coefficient, can quantitatively evaluate the probability of occurrence of intra-bin offsets in different ranges, and provides a more effective analysis method for acquisition designers.

[0049] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0051] Figure 1 A flow chart of a method for evaluating facet attributes according to an embodiment of the present invention is shown.

[0052] Figure 2 A graph of intra-bin offset data is shown according to an exemplary embodiment of the present invention.

[0053] Figure 3 An offset partitioning diagram according to an exemplary embodiment of the present invention is shown.

[0054] Figure 4 A Simpson coefficient distribution graph and a histogram obtained according to an exemplary embodiment of the present invention are shown. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0056] The present invention provides a facet attribute evaluation method, comprising the following steps:

[0057] Step 1: Determine n distance segments so that the intervals between each distance segment are equal;

[0058] Step 2: For each bin of the seismic data, store the offset data in the bin into an offset array;

[0059] Step 3: For each bin, count the number of offset data contained in each distance segment;

[0060] Step 4: Calculate the Simpson coefficient of each bin according to the number of offset data;

[0061] Step 5: Generate a Simpson coefficient distribution graph and a histogram based on the Simpson coefficient of each facet.

[0062] The bin attribute evaluation method of the present invention performs bin attribute evaluation based on the Simpson coefficient, and can quantitatively evaluate the probability of occurrence of intra-bin offsets in different ranges, thus providing a more effective analysis method for acquisition designers.

[0063] Example 1

[0064] Figure 1 The flowchart of the facet attribute evaluation method according to an embodiment of the present invention is shown. As shown in the figure, the method includes steps 1 to 5.

[0065] Step 1: Determine n distance segments so that the intervals between each distance segment are equal.

[0066] According to the requirements of analysis accuracy, the shot offset is divided into multiple distance segments. That is, n distance segments are determined so that the intervals between each distance segment are equal. For example, 10 distance segments are determined with an interval of 500 meters, so the shot offset data can be processed within the distance range of 0 to 5000 meters.

[0067] Step 2: For each bin of seismic data, store the offset data within the bin into an offset array.

[0068] In the field of geophysical exploration, an elementary cell refers to a small area in space, usually used to describe a sampling unit or grid unit in seismic exploration data. In three-dimensional seismic exploration, an elementary cell is usually a cubic unit, and its size and shape depend on the sampling density and observation system design.

[0069] The choice of bins has a significant impact on the resolution and quality of seismic data. Smaller bins provide higher resolution but may also introduce more noise and discontinuities. Larger bins provide smoother data but may also sacrifice some resolution.

[0070] When processing seismic data, the size and shape of the bins can be adjusted according to actual needs. For example, in the inversion process, smaller bins can be used to improve resolution, while in filtering or smoothing, larger bins can be used to reduce noise interference.

[0071] For each bin of seismic data, the offset data in the bin is stored in the offset array. Specifically, storing the offset data in the bin in the offset array includes:

[0072] Sort the offset data in the bin from small to large;

[0073] Store the sorted offset data into the offset array offset i Where i represents the offset array i The subscript of , i∈{0,1,2...,m}, m represents the amount of data in the offset array.

[0074] After the above processing, the offset data in each bin is stored in an offset array, and in each offset array, the offset data are arranged in ascending order, so as to prepare for the subsequent counting of the number of offset data contained in each distance segment.

[0075] Step 3: For each bin, count the number of offset data contained in each distance segment.

[0076] The number of offset data included in each distance segment is counted as follows:

[0077] Create an offset count array bin j , bin the offset count array j The initial value of each data is set to zero;

[0078] For each data in the offset array, calculate Bin the offset count array j The value of the jth data is added by 1, where j represents the gun offset count array bin j The subscript of , j∈{0,1,2...,n}, space represents the interval between each distance segment.

[0079] The offset count array is used to reflect the number of offset data contained in each distance segment. Since the offset data have been arranged in ascending order in the previous step, according to the formula The calculated j value indicates which data (i.e., which distance segment) of the offset count array the offset data corresponds to. j The value of the jth data is added by 1, and the number of offset data in the distance segment is accumulated. The above operation is performed for each data in the offset array, and the number of offset data contained in each distance segment can be counted.

[0080] Step 4: Calculate the Simpson coefficient of each bin based on the number of offset data.

[0081] The Simpson coefficient is a statistical indicator used to measure the degree of overall difference, also known as the Simpson coefficient of difference. The Simpson coefficient of a facet can be used to measure the uniformity of the distribution of individuals within the facet. If the distribution of individuals within the facet is more uniform, the Simpson coefficient is closer to 0; if the distribution of individuals within the facet is more uneven, the Simpson coefficient is closer to 1.

[0082] Specifically, the Simpson coefficient of each facet is calculated according to the following formula:

[0083]

[0084] Among them, Dk represents the Simpson coefficient of the kth face element, N k It represents the number of offset data contained in the kth bin.

[0085] Step 5: Generate a Simpson coefficient distribution graph and a histogram based on the Simpson coefficient of each facet.

[0086] Generating a Simpson coefficient distribution graph based on the Simpson coefficient of each facet includes:

[0087] A color scale axis in the range of [0,1] is established, and the Simpson coefficient of each facet is correspondingly depicted in a two-dimensional space based on the color scale axis to generate a distribution grid map, that is, a Simpson coefficient distribution map.

[0088] The color scale axis uses color to indicate the size of the Simpson coefficient. The Simpson coefficient distribution diagram generated based on the color scale axis can more intuitively show the distribution of the Simpson coefficient, thereby providing a more effective analysis method for acquisition designers.

[0089] Generating a Simpson coefficient histogram based on the Simpson coefficient of each bin includes:

[0090] Define multiple Simpson coefficient ranges and count the number of facets corresponding to each Simpson coefficient range;

[0091] The horizontal axis represents the range of each Simpson coefficient, and the vertical axis represents the number of bins corresponding to the Simpson coefficient range, thereby generating a Simpson coefficient histogram.

[0092] The Simpson coefficient histogram can intuitively show the number of facets corresponding to each Simpson coefficient range.

[0093] Example 2

[0094] Embodiment 2 provides a method for evaluating facet attributes, comprising the following steps:

[0095] Step 1: Determine n distance segments so that the intervals between each distance segment are equal;

[0096] Step 2: For each bin of the seismic data, store the offset data in the bin into an offset array;

[0097] Step 3: For each bin, count the number of offset data contained in each distance segment;

[0098] Step 4: Calculate the Simpson coefficient of each bin according to the number of offset data;

[0099] Step 5: Generate a Simpson coefficient distribution graph and a histogram based on the Simpson coefficient of each facet.

[0100] In this embodiment, storing the offset data in the bin into the offset array includes:

[0101] sorting the offset data in the bin in ascending order;

[0102] Store the sorted offset data into the offset array offset i Where i represents the offset array i The subscript of , i∈{0,1,2...,m}, m represents the amount of data in the offset array.

[0103] In this embodiment, counting the number of offset data included in each distance segment includes:

[0104] Create an offset count array bin j , bin the offset count array j The initial value of each data is set to zero;

[0105] For each data in the offset array, calculate Bin the offset count array j The value of the jth data is added by 1, where j represents the gun offset count array bin j The subscript of , j∈{0,1,2...,n}, space represents the interval between each distance segment.

[0106] In this embodiment, the Simpson coefficient of each bin is calculated according to the following formula:

[0107]

[0108] Among them, D k represents the Simpson coefficient of the kth face element, N k It represents the number of offset data contained in the kth bin.

[0109] In this embodiment, generating a Simpson coefficient distribution graph according to the Simpson coefficient of each bin includes:

[0110] A color scale axis in the range of [0,1] is established, and the Simpson coefficient of each facet is depicted in two-dimensional space based on the color scale axis to generate a distribution grid map, namely, the Simpson coefficient distribution map.

[0111] In this embodiment, generating a Simpson coefficient histogram according to the Simpson coefficient of each bin includes:

[0112] Define multiple Simpson coefficient ranges and count the number of facets corresponding to each Simpson coefficient range;

[0113] The horizontal axis represents the range of each Simpson coefficient, and the vertical axis represents the number of bins corresponding to the Simpson coefficient range, thereby generating a Simpson coefficient histogram.

[0114] Figure 2 A graph of intra-bin offset data according to this embodiment is shown. Figure 3 The offset division diagram according to this embodiment is shown, and a plurality of distance segments are divided at intervals of 500 meters. Figure 4 The Simpson coefficient distribution diagram and histogram obtained according to the facet attribute evaluation method of this embodiment are shown.

[0115] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0116] Example 3

[0117] This embodiment provides a facet attribute evaluation device, including:

[0118] A preprocessing module, used for determining n distance segments, so that the intervals between each distance segment are equal;

[0119] A data processing module, for storing the offset data in each bin of the seismic data into an offset array;

[0120] A statistical module, for counting the number of offset data contained in each distance segment for each bin;

[0121] A Simpson coefficient calculation module, used for calculating the Simpson coefficient of each bin according to the number of offset data;

[0122] The graphics generation module is used to generate a Simpson coefficient distribution diagram and a histogram according to the Simpson coefficient of each facet.

[0123] In this embodiment, storing the offset data in the bin into the offset array includes:

[0124] Sort the offset data in the bin from small to large;

[0125] Store the sorted offset data into the offset array offset i Where i represents the offset array i The subscript of , i∈{0,1,2...,m}, m represents the amount of data in the offset array.

[0126] In this embodiment, counting the number of offset data included in each distance segment includes:

[0127] Create an offset count array bin j, bin the offset count array j The initial value of each data is set to zero;

[0128] For each data in the offset array, calculate Bin the offset count array j The value of the jth data is added by 1, where j represents the gun offset count array bin j The subscript of , j∈{0,1,2...,n}, space represents the interval between each distance segment.

[0129] In this embodiment, the Simpson coefficient of each bin is calculated according to the following formula:

[0130]

[0131] Among them, D k represents the Simpson coefficient of the kth face element, N k It represents the number of offset data contained in the kth bin.

[0132] In this embodiment, generating a Simpson coefficient distribution graph according to the Simpson coefficient of each bin includes:

[0133] A color scale axis in the range of [0,1] is established, and the Simpson coefficient of each facet is correspondingly depicted in a two-dimensional space based on the color scale axis to generate a distribution grid map, that is, a Simpson coefficient distribution map.

[0134] In this embodiment, generating a Simpson coefficient histogram according to the Simpson coefficient of each bin includes:

[0135] Define multiple Simpson coefficient ranges and count the number of facets corresponding to each Simpson coefficient range;

[0136] The horizontal axis represents the range of each Simpson coefficient, and the vertical axis represents the number of bins corresponding to the Simpson coefficient range, thereby generating a Simpson coefficient histogram.

[0137] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0138] Example 4

[0139] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the aforementioned facet attribute evaluation method is implemented.

[0140] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0141] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0142] Example 5

[0143] This embodiment provides an electronic device, including:

[0144] A memory storing executable instructions;

[0145] The processor runs the executable instructions in the memory to implement the aforementioned facet attribute evaluation method.

[0146] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0147] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0148] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0149] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0150] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0151] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0152] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for evaluating facet attributes, It is characterized in that The method comprises: Step 1: Determine n distance segments so that the intervals between each distance segment are equal; Step 2: For each bin of the seismic data, store the offset data in the bin into an offset array; Step 3: for each of the bins, counting the number of offset data contained in each distance segment; Step 4: Calculate the Simpson coefficient of each bin according to the number of offset data; Step 5: Generate a Simpson coefficient distribution graph and a histogram based on the Simpson coefficient of each facet.

2. The method according to claim 1, It is characterized in that Storing the offset data in the bin into the offset array comprises: sorting the offset data in the bin in ascending order; Store the sorted offset data into the offset array offset i Where i represents the offset array i The subscript of , i∈{0,1,2...,m}, m represents the amount of data in the offset array.

3. The method according to claim 2, It is characterized in that The counting of the number of offset data contained in each distance segment includes: Create an offset count array bin j , the offset count array bin j The initial value of each data is set to zero; For each data in the offset array, calculate The offset count array bin j The value of the jth data is added by 1, where j represents the gun offset count array bin j The subscript of , j∈{0,1,2...,n}, space represents the interval between each distance segment.

4. The method according to claim 3, It is characterized in that The Simpson coefficient for each bin is calculated according to the following formula: Among them, D k represents the Simpson coefficient of the kth face element, N k It represents the number of offset data contained in the kth bin.

5. The method according to claim 1, It is characterized in that Generating a Simpson coefficient distribution diagram according to the Simpson coefficient of each facet includes: A color scale axis in the range of [0,1] is established, and the Simpson coefficient of each facet is correspondingly depicted in a two-dimensional space based on the color scale axis to generate a distribution grid map, namely, the Simpson coefficient distribution map.

6. The method according to claim 1, It is characterized in that Generating a Simpson coefficient histogram based on the Simpson coefficient of each bin includes: Define multiple Simpson coefficient ranges and count the number of facets corresponding to each Simpson coefficient range; The Simpson coefficient histogram is generated by using the horizontal axis to represent each Simpson coefficient range and the vertical axis to represent the number of bins corresponding to the Simpson coefficient range.

7. A facet attribute evaluation device, It is characterized in that include: A preprocessing module, used for determining n distance segments, so that the intervals between each distance segment are equal; A data processing module, for storing the offset data in each bin of the seismic data into an offset array; A statistical module, for counting the number of offset data contained in each distance segment for each bin; A Simpson coefficient calculation module, used for calculating the Simpson coefficient of each bin according to the number of offset data; The graphics generation module is used to generate a Simpson coefficient distribution diagram and a histogram according to the Simpson coefficient of each facet.

8. The device according to claim 7, It is characterized in that Storing the offset data in the bin into the offset array comprises: sorting the offset data in the bin in ascending order; Store the sorted offset data into the offset array offset i Where i represents the offset array i The subscript of , i∈{0,1,2...,m}, m represents the amount of data in the offset array.

9. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating facet attributes according to any one of claims 1 to 6 is implemented.

10. An electronic device, It is characterized in that The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the facet attribute evaluation method according to any one of claims 1 to 6.

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