Method, device and equipment for evaluating quality of seismic image under salt and storage medium

Abstract

The application discloses a salt-underground seismic image quality evaluation method and device, equipment and a storage medium. The salt-underground seismic image quality evaluation method comprises the following steps: acquiring a salt-underground seismic image to be evaluated, and uniformly setting at least two quality detection points in the salt-underground seismic image; determining a linear quality score relationship between an expert score result of a quality detection point and an optimal image attribute based on the expert score result of the quality detection point and an attribute value of the optimal image attribute corresponding to the quality detection point; and determining a predicted expert score result of the quality detection point based on the linear quality score relationship, and performing quality grade evaluation on the salt-underground seismic image to be evaluated. Through the technical solution, objective, rapid and accurate evaluation of a large amount of seismic images is realized.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a method, apparatus, device, and storage medium for quality assessment of subsalt seismic images. Background Technology

[0002] With the continuous deepening of oilfield exploration and development and the rapid development of seismic exploration technology and hardware equipment, seismic image data acquisition has entered a stage of high precision, high density, and high efficiency, and the data volume is getting larger and larger, and the complexity of the data is getting higher and higher.

[0003] Currently, seismic image data quality assessment mainly relies on manual visual evaluation. The massive volume and complex types of data result in low efficiency, high costs, long processing times, and a lack of unified evaluation standards. Furthermore, the presence of numerous human factors leads to inconsistent data evaluations, failing to fully meet the needs of oil and gas field exploration and development. Although quantitative analysis and monitoring software for seismic data quality has been introduced in recent years, its evaluation model still relies on subjective human assessment and cannot adequately account for the differences in seismic image quality caused by different environments and various underground structural conditions.

[0004] Therefore, there is an urgent need to establish a quantitative, accurate, and rapid quality assessment method for seismic image data under different environments and underground geological conditions, so as to provide data support for oilfield exploration and development. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for quality assessment of subsalt seismic images, in order to improve the efficiency of subsalt seismic image assessment and the accuracy of assessment results.

[0006] According to one aspect of the present invention, a method for quality assessment of subsalt seismic images is provided, the method comprising:

[0007] Acquire the subsalt seismic image to be evaluated, and uniformly set at least two quality check points in the subsalt seismic image; wherein, the subsalt seismic image refers to the image acquired by image acquisition of the geological structure covered by the rock salt layer;

[0008] Based on the expert scoring results of the quality inspection points and the attribute values ​​of the optimal image attributes corresponding to the quality inspection points, a linear quality scoring relationship between the expert scoring results of the quality inspection points and the optimal image attributes is determined.

[0009] Based on the linear quality scoring relationship and the expert scoring results of the quality detection points, the quality level of the subsalt seismic images to be evaluated is assessed.

[0010] According to another aspect of the present invention, a quality assessment apparatus for subsalt seismic images is provided, the apparatus comprising:

[0011] The image acquisition module is used to acquire the subsalt seismic image to be evaluated and to uniformly set at least two quality detection points in the subsalt seismic image; wherein, the subsalt seismic image refers to the image acquired by image acquisition of the geological structure covered by the rock salt layer;

[0012] The linear scoring module is used to determine the linear quality scoring relationship between the expert scoring results of the quality inspection points and the attribute value of the optimal image attribute corresponding to the quality inspection points, based on the expert scoring results of the quality inspection points and the attribute value of the optimal image attribute corresponding to the quality inspection points.

[0013] The quality assessment module is used to assess the quality level of the subsalt seismic image to be evaluated based on the linear quality scoring relationship and the expert scoring results of the quality detection points.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the quality assessment method for subsalt seismic images according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the quality assessment method for subsalt seismic images according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the quality assessment method for subsalt seismic images according to any embodiment of the present invention.

[0020] The technical solution of this invention provides a standard seismic image quality assessment method. Considering the differences in seismic data imaging caused by different geological environments and different underground structural conditions, the optimal image attributes are selected for different stratigraphic structures, a linear quality scoring relationship is determined, and further, the quality level of the subsalt seismic image to be evaluated is assessed based on the linear quality scoring relationship. This achieves objective, rapid, and accurate evaluation of massive seismic images, providing data support for the efficient utilization of seismic images and geological exploration and development.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1a This is a flowchart of a method for quality assessment of subsalt seismic images provided in Embodiment 1 of the present invention;

[0024] Figure 1b This is a schematic diagram of the distribution of quality inspection points according to Embodiment 1 of the present invention;

[0025] Figure 2a This is a flowchart of a method for quality assessment of subsalt seismic images provided in Embodiment 2 of the present invention;

[0026] Figure 2b This is a schematic diagram of the level of a cross-sectional image provided according to Embodiment 2 of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of a subsalt seismic image quality assessment device according to Embodiment 3 of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the quality assessment method for subsalt seismic images according to an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Example 1

[0032] Figure 1a This is a flowchart illustrating a method for quality assessment of sub-salt seismic images according to Embodiment 1 of the present invention. This embodiment is applicable to situations involving quality assessment of seismic image data. The method can be executed by a sub-salt seismic image quality assessment device, which can be implemented in hardware and / or software and can be configured in various general-purpose computing devices. Figure 1b As shown, the method includes:

[0033] S110. Obtain the subsalt seismic image to be evaluated, and uniformly set at least two quality detection points in the subsalt seismic image.

[0034] Subsalt seismic images refer to images acquired through image acquisition of geological structures covered by rock salt layers. It should be noted that salt rock, as a special geological body, is associated with 58% of the world's discovered commercial oil and gas fields. Salt rock exhibits strong refractive effects, rapid lateral changes, steep edge dip angles, and the presence of fluid cavities, significantly impacting the imaging quality of seismic data on the target layer beneath it. Subsalt seismic images, obtained through three-dimensional image acquisition of geological layers beneath rock salt, can be used to characterize the structural information of these layers.

[0035] Quality checkpoints can refer to sampling points in subsalt seismic images used to assess the quality of image data.

[0036] Specifically, for the sub-salt seismic image to be evaluated, at least two quality inspection points can be evenly set within the work area corresponding to the sub-salt seismic image, based on a pre-defined work zone (operation area). For example... Figure 1b As shown. By using the set quality detection points, seismic image data within a preset range of the quality detection point is sampled to obtain the image profile corresponding to each quality detection point.

[0037] S120. Based on the expert scoring results of the quality inspection points and the attribute values ​​of the optimal image attributes corresponding to the quality inspection points, determine the linear quality scoring relationship between the expert scoring results of the quality inspection points and the optimal image attributes.

[0038] Among them, the optimal image attribute can refer to the preferred attribute among the image attributes of the subsalt seismic image, which can best represent the image attribute information of the subsalt seismic image.

[0039] Specifically, linear regression can be performed on the expert rating results for each quality detection point in the subsalt seismic image, as well as the attribute value of the optimal image attribute for each quality detection point, to extract the linear relationship between the expert rating results and the optimal image attribute.

[0040] S130. Based on the linear quality scoring relationship, the predicted expert scoring results of the quality detection points are determined, and the quality level of the subsalt seismic image to be evaluated is assessed.

[0041] Specifically, after determining the linear quality scoring relationship between the expert scoring results and the optimal image attributes, the predicted expert scoring result for each quality detection point can be determined based on this linear quality scoring relationship. Then, the quality level of the subsalt seismic image can be evaluated based on the expert scoring result for each quality detection point and the predicted expert scoring result.

[0042] Optionally, based on the linear quality scoring relationship, the predicted expert scoring results of the quality detection points are determined, and the quality level of the subsalt seismic image to be evaluated is assessed. This includes: inputting the attribute value of the optimal image attribute of each quality detection point into the linear quality scoring relationship to determine the predicted expert scoring result of each quality detection point; determining the final score result of each quality detection point based on the predicted expert scoring result and the expert scoring result of each quality detection point in the subsalt seismic image; and determining the final quality level assessment result of the subsalt seismic image based on the final score result of each quality detection point.

[0043] In this embodiment of the invention, the final score of each quality detection point can be determined based on the predicted expert score and predicted score weight of each quality detection point in the sub-salt seismic image, as well as the expert score and expert score weight of each quality detection point in the sub-salt seismic image.

[0044] By combining expert scoring results and predicted expert scoring results to determine the final score of quality inspection points, the credibility and accuracy of the quality inspection point scoring results are improved.

[0045] After determining the final score for each quality detection point, the final score for each quality detection point in the sub-salt seismic image can be statistically analyzed. If the statistical results meet preset quality conditions, the final quality level assessment result of the sub-salt seismic image is determined to be passed. For example, the quality conditions can be set as follows: the average final score of the quality detection points in the sub-salt seismic image is greater than a first preset value; a first proportion of quality detection points have a final score greater than a second preset value; and there are no quality detection points with a final score less than a third preset value. It should be noted that the first, second, and third preset values, as well as the first proportion, can be adaptively set according to those skilled in the art, and the second preset value is greater than the first preset value, and the first preset value is greater than the third preset value.

[0046] Optionally, in one specific implementation, if the average final score of the quality detection points in the subsalt seismic image is greater than 3, and at least 30% of the quality detection points have a final score greater than 4, and there are no quality detection points with a final score less than 1, then the final quality level assessment result of the subsalt seismic image is passed.

[0047] Optionally, the final score can be determined using the following formula:

[0048] Z i =αx i +βy i ;

[0049] Among them, Z i Let x be the final score result for the i-th quality inspection point, α be the predicted score weight for the expert score result, and x be the predicted score weight for the expert score result. i Let β be the predicted expert score for the i-th quality inspection point, and y be the specific scoring weight of the expert score. i This represents the expert score for the i-th quality inspection point.

[0050] The technical solution of this invention provides a standard seismic image quality assessment method. Considering the differences in seismic data imaging caused by different geological environments and different underground structural conditions, the optimal image attributes are selected for different stratigraphic structures, a linear quality scoring relationship is determined, and further, the quality level of the subsalt seismic image to be evaluated is assessed based on the linear quality scoring relationship. This achieves objective, rapid, and accurate evaluation of massive seismic images, providing data support for the efficient utilization of seismic images and geological exploration and development.

[0051] Example 2

[0052] Figure 2aThis is a flowchart of a method for quality assessment of subsalt seismic images provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment provides specific steps for determining the linear quality score relationship between the expert score results of the quality detection points and the attribute values ​​of the optimal image attributes corresponding to those quality detection points, using these expert score results. It should be noted that for parts of this embodiment not described in detail, please refer to the relevant descriptions in other embodiments, which will not be repeated here. Figure 2a As shown, the method includes:

[0053] S210. Obtain the subsalt seismic image to be evaluated, and uniformly set at least two quality detection points in the subsalt seismic image.

[0054] S220. Input the expert scoring results of the quality inspection points and the attribute values ​​of the optimal image attributes corresponding to the quality inspection points into the pre-built linear regression model for linear regression processing to determine the linear quality score relationship between the expert scoring results and the optimal image attributes.

[0055] S230. Based on the linear quality scoring relationship, the predicted expert scoring results of the quality detection points are determined, and the quality level of the subsalt seismic image to be evaluated is assessed.

[0056] Optionally, in this embodiment of the invention, the process of determining the expert scoring result of the quality inspection point includes: scoring the profile image of the quality inspection point at least twice according to the preset expert scoring criteria, and taking the average of the at least two quality scoring results of the quality inspection point as the expert scoring result of the quality inspection point.

[0057] For example, the expert scoring criteria can be shown in Table 1. Based on the image information of the cross-sectional images, the quality inspection points can be divided into five levels. For example, the cross-sectional images for each level can be as follows: Figure 2b As shown.

[0058] Table 1

[0059]

[0060] Optionally, in this embodiment of the invention, the geological structure of historical subsalt seismic images is analyzed, and the optimal image attributes are determined from the image attribute information of the subsalt seismic images in each geological structure.

[0061] The geological structure can include the upper salt layer, the salt layer, and the lower salt layer; the image attribute information can include the signal-to-noise ratio, resolution, and amplitude energy.

[0062] The geological structure to which subsalt seismic images belong includes the overlying salt layer, the salt layer, and the subsalt layer. The heterogeneity of the overlying strata or salt layer causes wavefield distortion, which directly affects the final imaging quality of the subsalt seismic images. Therefore, it is necessary to extract and analyze attributes such as signal-to-noise ratio, resolution, and amplitude energy from the overlying salt strata, the salt layer, and the top of the salt layer. Image attribute information for different structural layers (overlying salt layer, salt layer, and subsalt layer) is determined based on these three aspects. However, the large amount of multidimensional image attribute information can lead to a decrease in the execution efficiency of the processing algorithm. To increase the effectiveness of the analysis task and improve the accuracy of the model, an attribute optimization method is used to screen the optimal image attributes of the subsalt seismic images. Optionally, in this embodiment of the invention, principal component analysis can be used to determine the optimal image attributes.

[0063] In this embodiment of the invention, after determining the expert rating results for each quality inspection point in the subsalt seismic image and the optimal image attributes of the subsalt seismic image, the stratigraphic thickness of each geological structural layer in the subsalt seismic image can be further extracted. The attribute values ​​of the optimal image attributes (optimal image attributes a, b, and c) and the stratigraphic thickness of each geological structural layer in the subsalt seismic image are normalized and used as independent variables in a linear regression. The expert evaluation results of the quality inspection points are used as the dependent variable in the linear regression to establish a linear regression input data table, as shown in Table 2. Based on this linear regression input data table, a pre-constructed linear regression model is subjected to linear regression processing to solve for the linear quality rating relationship between the expert rating results and the optimal image attributes. It should be noted that the number of independent variables in the linear regression model is the same as the number of independent variables in the linear regression input data table, and the number of optimal image attributes can be adaptively set according to those skilled in the art.

[0064] Alternatively, the linear regression model can be determined using the following formula:

[0065]

[0066] in, To predict expert rating results, a x As a weighting factor, is the independent variable for linear regression.

[0067] Table 2

[0068]

[0069] The technical solution of this invention determines the predicted expert score of quality detection points by establishing a linear quality score relationship between the expert score and the optimal image attributes, thus laying a data foundation for determining the final score of quality detection points from multi-dimensional data.

[0070] Example 3

[0071] Figure 3 This is a structural schematic diagram of a subsalt seismic image quality assessment device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0072] The image acquisition module 310 is used to acquire the subsalt seismic image to be evaluated, and to uniformly set at least two quality detection points in the subsalt seismic image; wherein, the subsalt seismic image refers to the image acquired by image acquisition of the geological structure covered by the rock salt layer;

[0073] The linear scoring module 320 is used to determine the linear quality scoring relationship between the expert scoring results of the quality detection points and the attribute value of the optimal image attribute corresponding to the quality detection points, based on the expert scoring results of the quality detection points and the attribute value of the optimal image attribute corresponding to the quality detection points.

[0074] The quality assessment module 330 is used to assess the quality level of the subsalt seismic image to be assessed based on the linear quality scoring relationship and the expert scoring results of the quality detection points.

[0075] The technical solution of this invention provides a standard seismic image quality assessment method. Considering the differences in seismic data imaging caused by different geological environments and different underground structural conditions, the optimal image attributes are selected for different stratigraphic structures, a linear quality scoring relationship is determined, and further, the quality level of the subsalt seismic image to be evaluated is assessed based on the linear quality scoring relationship. This achieves objective, rapid, and accurate evaluation of massive seismic images, providing data support for the efficient utilization of seismic images and geological exploration and development.

[0076] Optionally, the linear scoring module 320 can be specifically used to: input the expert scoring results of the quality inspection points and the attribute values ​​of the optimal image attributes corresponding to the quality inspection points into a pre-built linear regression model for linear regression processing, and determine the linear quality scoring relationship between the expert scoring results and the optimal image attributes.

[0077] Optionally, the device may also include:

[0078] The expert scoring module is used to score the profile image of the quality inspection point at least twice according to the preset expert scoring criteria, and the average of the at least two quality score results of the quality inspection point is used as the expert score result of the quality inspection point.

[0079] Optionally, the device may also include:

[0080] The optimal attribute module is used to analyze the geological structure of historical subsalt seismic images and determine the optimal image attributes from the image attribute information of the subsalt seismic images in each geological structure; wherein, the geological structure includes the upper salt layer, the salt layer, and the subsalt layer; the image attribute information includes signal-to-noise ratio, resolution, and amplitude energy.

[0081] Optional, the rating assessment module 330 includes:

[0082] The predictive expert scoring result unit is used to input the attribute value of the optimal image attribute of each quality detection point into the linear quality scoring relationship to determine the predictive expert scoring result of each quality detection point.

[0083] The quality assessment unit is used to determine the final score of each quality detection point in the subsalt seismic image based on the predicted expert score and the expert score, and to determine the final quality level assessment result of the subsalt seismic image based on the final score of each quality detection point.

[0084] Optionally, the rating unit can be specifically used to: determine the final rating result of each quality detection point based on the predicted expert rating result and predicted rating weight of each quality detection point in the subsalt seismic image, as well as the expert rating result and expert rating weight of each quality detection point in the subsalt seismic image.

[0085] The subsalt seismic image quality assessment device provided in this embodiment of the invention can execute the subsalt seismic image quality assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0086] Example 4

[0087] Figure 4 A schematic diagram of an electronic device 410 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0088] like Figure 4As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0089] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0090] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as quality assessment methods for subsalt seismic images.

[0091] In some embodiments, the subsalt seismic image quality assessment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the subsalt seismic image quality assessment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the subsalt seismic image quality assessment method by any other suitable means (e.g., by means of firmware).

[0092] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0095] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0096] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0097] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0098] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0099] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for quality assessment of subsalt seismic images, characterized in that, include: Acquire the subsalt seismic image to be evaluated, and uniformly set at least two quality check points in the subsalt seismic image; wherein, the subsalt seismic image refers to the image acquired by image acquisition of the geological structure covered by the rock salt layer; Based on the expert scoring results of the quality inspection points and the attribute values ​​of the optimal image attributes corresponding to the quality inspection points, a linear quality scoring relationship between the expert scoring results of the quality inspection points and the optimal image attributes is determined. Based on the linear quality scoring relationship, the predicted expert scoring results of the quality detection points are determined, and the quality level of the subsalt seismic image to be evaluated is assessed.

2. The method according to claim 1, characterized in that, Based on the expert ratings of the quality inspection points and the attribute values ​​of the optimal image attributes corresponding to those points, a linear quality rating relationship between the expert ratings of the quality inspection points and the optimal image attributes is determined, including: The expert ratings for quality inspection points, along with the attribute values ​​of the optimal image attributes corresponding to those points, are input into a pre-built linear regression model for linear regression processing to determine the linear quality rating relationship between the expert ratings and the optimal image attributes.

3. The method according to any one of claims 1-2, characterized in that, The process for determining the expert scoring results of the quality inspection points includes: The profile image of the quality inspection point is scored at least twice according to the preset expert scoring criteria. The average of the at least two quality score results of the quality inspection point is taken as the expert score result of the quality inspection point.

4. The method according to claim 1, characterized in that, Before determining the linear quality score relationship between the expert scores for quality inspection points and the optimal image attributes, the following steps are also included: By analyzing the geological structure of historical subsalt seismic images, the optimal image attributes are determined from the image attribute information of the subsalt seismic images in each geological structure; wherein, the geological structure includes the upper salt layer, the salt layer, and the subsalt layer; the image attribute information includes signal-to-noise ratio, resolution, and amplitude energy.

5. The method according to claim 1, based on the linear quality scoring relationship, determines the predicted expert scoring results of the quality detection points, and performs quality level assessment on the subsalt seismic image to be evaluated, including: The attribute value of the optimal image attribute at each quality detection point is input into the linear quality scoring relationship to determine the predicted expert score result for each quality detection point. Based on the predicted expert scores and expert scores for each quality checkpoint in the subsalt seismic image, the final score for each quality checkpoint is determined, and the final quality level assessment result of the subsalt seismic image is determined based on the final score for each quality checkpoint.

6. The method according to claim 5, characterized in that, The process of determining the final score for each quality detection point based on the predicted expert score and the expert score for each quality detection point in the subsalt seismic image includes: Based on the predicted expert scores and predicted score weights for each quality detection point in the subsalt seismic image, as well as the expert scores and expert score weights for each quality detection point in the subsalt seismic image, the final score for each quality detection point is determined.

7. A quality assessment device for subsalt seismic images, characterized in that, include: The image acquisition module is used to acquire the subsalt seismic image to be evaluated and to uniformly set at least two quality detection points in the subsalt seismic image; wherein, the subsalt seismic image refers to the image acquired by image acquisition of the geological structure covered by the rock salt layer; The linear scoring module is used to determine the linear quality scoring relationship between the expert scoring results of the quality inspection points and the attribute value of the optimal image attribute corresponding to the quality inspection points, based on the expert scoring results of the quality inspection points and the attribute value of the optimal image attribute corresponding to the quality inspection points. The quality assessment module is used to assess the quality level of the subsalt seismic image to be evaluated based on the linear quality scoring relationship and the expert scoring results of the quality detection points.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the quality assessment method for subsalt seismic images according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for quality assessment of subsalt seismic images according to any one of claims 1-6.

10. A computer program product, characterized in that, The method includes a computer program that, when executed by a processor, implements the quality assessment method for subsalt seismic images according to any one of claims 1-6.

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