CN118191935A - Fault identification method and device based on edge detection, electronic equipment and medium - Google Patents
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Abstract
The invention discloses a fault identification method, a fault identification device, electronic equipment and a fault identification medium based on edge detection. The method comprises the following steps: determining seismic imaging data of a stratum corresponding to a target interval; determining a target variance and a target characteristic value of a target area according to target image data of the target area in the seismic imaging data; taking the ratio of the target image characteristic value to the target variance as an edge detection factor of the target area; and updating the seismic image data according to the edge detection factors of each target area to obtain updated seismic image data, and performing fault identification based on the updated seismic image data. According to the method, the small faults are accurately identified by adopting the updated seismic image data obtained by updating the seismic image data according to the edge detection factors according to the target variances and the target feature values of all target areas in the determined seismic imaging data, and the problems of instability, relatively sensitive noise and weak anti-interference capability in the process of identifying the small faults are solved.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration and development, in particular to a fault identification method, device, electronic equipment and medium based on edge detection.
Background
The seismic fault interpretation has important significance for researching the distribution of oil and gas reservoirs and oil and gas migration channels.
Typically fault identification can be achieved using mathematical algorithms or graphic domain algorithms, such as the method of phase separation, curvature attribute methods, etc., which are significant in identifying large faults.
However, when small fault identification is performed, the problems of instability, relatively sensitive noise and weak anti-interference capability exist. Therefore, how to accurately identify small faults is important.
Disclosure of Invention
The invention provides a fault identification method, device, electronic equipment and medium based on edge detection, which are used for solving the problems of instability, relatively sensitive noise and weak anti-interference capability when small fault identification is carried out.
According to an aspect of the present invention, there is provided a fault identification method based on edge detection, the method comprising:
Determining seismic imaging data of a stratum corresponding to a target stratum, wherein the seismic imaging data are image data obtained by pixelating original seismic data;
Determining a target variance and a target characteristic value of a target area according to target image data of the target area in the seismic imaging data; the target area is a circular area in a preset radius with each pixel point in the seismic imaging data as a center, and the target characteristic value is used for describing the image texture characteristics of the target area;
Taking the ratio of the target image characteristic value to the target variance as an edge detection factor of the target area;
and updating the seismic image data according to the edge detection factors of each target area, obtaining updated seismic image data, and performing fault identification based on the updated seismic image data.
According to another aspect of the present invention, there is provided a fault identification device based on edge detection, the device comprising:
The data determining module is used for determining seismic imaging data of a stratum corresponding to a target layer section, wherein the seismic imaging data are image data obtained by pixelating original seismic data;
The parameter determining module is used for determining a target variance and a target characteristic value of a target area according to target image data of the target area in the seismic imaging data; the target area is a circular area in a preset radius with each pixel point in the seismic imaging data as a center, and the target characteristic value is used for describing the image texture characteristics of the target area;
The detection factor determining module is used for taking the ratio of the target image characteristic value to the target variance as an edge detection factor of the target area;
And the identification module is used for updating the seismic image data according to the edge detection factors of each target area, obtaining updated seismic image data and carrying out fault identification based on the updated seismic image data.
According to another aspect of the present invention, there is provided an electronic apparatus including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the fault identification method based on edge detection according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the fault identification method based on edge detection according to any one of the embodiments of the present invention when executed.
According to the technical scheme, the earthquake imaging data of the stratum corresponding to the target interval is determined, the earthquake imaging data are image data obtained by pixelating original earthquake data, and the pixelation process is rapid for subsequent data processing; further, according to the target image data of the target area in the seismic imaging data, the target variance and the target characteristic value of the target area are determined, the division of the target area can be more refined to process the data, then the ratio of the target image characteristic value to the target variance is used as an edge detection factor of the target area, so that the seismic image data is updated according to the edge detection factors of all the target areas to obtain updated seismic image data, and then fault identification is accurately carried out based on the updated seismic image data.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a fault identification method based on edge detection according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of raw seismic data suitable for use in accordance with an embodiment of the invention;
FIG. 3 is a schematic representation of updated seismic image data suitable for use in accordance with an embodiment of the invention;
FIG. 4 is a schematic diagram of a fault identification result applicable in accordance with an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a fault identification device based on edge detection according to an embodiment of the present invention;
Fig. 6 is a schematic structural diagram of an electronic device implementing a fault identification method based on edge detection according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," "third," "candidate," "reference," and the like in the description and in the claims of the invention and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a fault identification method based on edge detection according to an embodiment of the present invention, where the present embodiment is applicable to a fault (especially, a small fault) identification, the method may be performed by a fault identification device based on edge detection, the fault identification device based on edge detection may be implemented in a form of hardware and/or software, and the fault identification device based on edge detection may be configured in any electronic device having a network communication function.
As shown in fig. 1, the fault identification method based on edge detection of the present invention may include the following processes:
S110, determining seismic imaging data of the stratum corresponding to the target layer segment, wherein the seismic imaging data are image data obtained by pixelating the original seismic data.
The original seismic data is data containing fault information representing the target interval corresponding to the stratum, as shown in fig. 2, and the fault refers to a planar destruction or planar rheological zone generated by the rock stratum or rock mass in the crust under the action of stress, and the rock masses on two sides of the fault are obviously displaced.
Specifically, the original seismic data may contain a large amount of data with large data values, and the processing and calculation of the data are not very convenient, so that the data are processed in a pixelated mode, the original seismic data can be converted into data with small data values and convenient for data calculation, and the image texture features are more obvious in display.
Optionally, determining seismic imaging data of the formation corresponding to the target interval includes: acquiring original seismic data of a stratum corresponding to a target interval, and taking the maximum value in the original seismic data as reference data; the ratio of each data in the original seismic data to the reference data is multiplied by a preset value to obtain the seismic imaging data, the preset value of an example can be 255, and the conversion mode of the seismic imaging data I p can adopt the following formula:
Where I 0 is the raw seismic data and max (|I 0 |) is the maximum value in the raw seismic data.
According to the technical scheme, the ratio of each data in the original seismic data to the reference data is multiplied by the preset value to obtain the seismic imaging data, so that the data is simplified, the subsequent processing calculation is facilitated, and the data processing efficiency is improved.
S120, determining a target variance and a target characteristic value of the target area according to target image data of the target area in the seismic imaging data.
The target area is a circular area within a preset radius with each pixel point in the seismic imaging data as a center, and the target feature value is used for describing the image texture feature of the target area. The preset radius may be a radius range where small faults may be detected based on analysis of historical seismic data.
The target area may also be a circular area within a preset radius with a target pixel point in the seismic imaging data as a center, and the target pixel point may be a pixel point determined according to a preset interval and determined by analyzing the historical seismic data.
Specifically, by dividing the seismic imaging data into target areas, the data features of the small faults can be obviously divided into smaller areas, detection omission of the small faults is avoided, and further analysis is performed on target image data of the target areas in the seismic imaging data to obtain target variances and target feature values of the target areas.
Optionally, determining the target variance and the target feature value of the target area according to the target image data of the target area in the seismic image data includes steps A1-A4:
And A1, taking the difference value between the target image data of the target area and the pixel value of the center point of the target area as candidate data of the target area.
Specifically, taking the difference between the target image data of the target area and the pixel value of the center point of the target area as candidate data of the target area, the candidate data may be expressed as: for target image data,/> Is the pixel value of the center point of the target area.
And A2, determining a pixel local discrimination factor of the target area according to the candidate data.
Specifically, the sum of the differences between the candidate data and the pixel local discrimination factor have a certain corresponding relation, the candidate data is obtained, the sum of the differences between the candidate data is determined, and the pixel local discrimination factor of the target area is determined according to the corresponding relation.
Optionally, determining the local pixel discrimination factor of the target area according to the candidate data includes steps B1-B2:
Step B1, determining characteristic parameter information corresponding to candidate data;
Specifically, acquiring target image data of a target area, taking a difference value between the target image data of the target area and a pixel value of a center point of the target area as candidate data of the target area, and if the candidate data is greater than or equal to 0, the candidate data corresponds to first characteristic parameter information, wherein the first characteristic parameter information is 1; if the candidate data is smaller than 0, the candidate data corresponds to second characteristic parameter information, and the second characteristic parameter information is 0.
The specific process of the characteristic parameter information can be expressed by the following formula:
Wherein, X is candidate data of the target area.
And B2, determining a pixel local discrimination factor of the target area according to the characteristic parameter information, wherein the characteristic parameter information is used for representing the value of the pixel local discrimination factor.
Specifically, the difference value between the number of the first characteristic parameter information and the number of the second characteristic parameter information in the characteristic parameter information has a corresponding difference value corresponding relation with the pixel local discrimination factor, after the characteristic parameter information is determined, the number of the first characteristic parameter information and the number of the second characteristic parameter information are respectively determined, the number of the first characteristic parameter information and the number of the second characteristic parameter information are used for obtaining a difference value parameter, and the difference value parameter is used for inquiring the difference value corresponding relation to determine the pixel local discrimination factor of the target area.
Optionally, determining the local pixel discrimination factor of the target area according to the feature parameter information includes steps C1-C3:
Step C1, determining first candidate data corresponding to first preset target image data of a target area, determining second candidate data corresponding to last target image data of the target area, and determining third candidate data corresponding to first target image data of the target area; the target image data of the target area are numbered sequentially according to the positions of the target image data, and the first candidate data, the second candidate data and the third candidate data belong to the candidate data.
Specifically, the first candidate data may be expressed as: The second candidate data may be expressed as: The third candidate data may be expressed as: /(I) Wherein/>For the preset target image data, M is M target image data, and/>For the last target image data,/>For the first one of the target image data,Is the pixel value of the center point of the target area.
And C2, determining a reference factor corresponding to the first preset target image data of the target area according to the characteristic parameter information corresponding to the first candidate data, the characteristic parameter information corresponding to the second candidate data and the characteristic parameter information corresponding to the third candidate data.
Specifically, the characteristic parameter information corresponding to the first candidate data, the characteristic parameter information corresponding to the second candidate data and the characteristic parameter information corresponding to the third candidate data are obtained according to the following formula:
Wherein, X is candidate data of the target area.
Taking the absolute value of the difference between the characteristic parameter information corresponding to the first candidate data and the characteristic parameter information corresponding to the third candidate data as first difference data, taking the absolute value of the difference between the characteristic parameter information corresponding to the second candidate data and the characteristic parameter information corresponding to the third candidate data as second difference data, and taking the sum of the first difference data and the second difference data as a reference factor K m corresponding to the target image data, wherein the reference factor K m can be specifically expressed as:
and C3, summing the reference factors corresponding to each target image data of the target area to obtain the pixel local discrimination factors of the target area.
Specifically, determining a reference factor corresponding to each target image data of the target area, and summing the reference factors corresponding to each target image data of the target area to obtain a pixel local discrimination factor l p of the target area, which can be specifically expressed as:
wherein M is the target image data, and M is the target image data.
And A3, determining a target characteristic value of the target area according to the pixel local discrimination factors, wherein the pixel local discrimination factors have corresponding association relation with the target characteristic value.
Specifically, a pixel local discrimination factor of the target area is obtained, and a target characteristic value of the target area corresponding to the pixel local discrimination factor is queried according to the corresponding relation.
Optionally, determining the target feature value of the target region according to the pixel local discrimination factor includes: acquiring a pixel local discrimination factor, and if the pixel local discrimination factor is larger than a preset factor, taking a reference characteristic value as a target characteristic value, wherein the reference characteristic value is a value obtained by adding one to the number of target image data of a target area; and if the pixel local discrimination factor is smaller than or equal to the preset factor, adding the characteristic parameter information corresponding to the candidate data as a target characteristic value. The specific target characteristic value of the target area determined according to the pixel local discrimination factor can be expressed by the following formula:
Wherein M is M target image data, l p is pixel local discrimination factor, Is candidate data.
In the embodiment, the target characteristic value is determined by adopting the relation between the pixel local discrimination factor and the preset factor, so that the accurate determination of the target characteristic value is realized.
And A4, determining a pixel average value of target image data of the target area, and determining a target variance of the target area according to the pixel average value and the target image data.
Specifically, the target variance S 2 may be determined using the following formula:
Wherein, Is the pixel average value,/>Is target image data.
According to the technical scheme, the difference value between the target image data of the target area and the pixel value of the center point of the target area is used as candidate data of the target area, the pixel local distinguishing factor of the target area is further determined according to the candidate data, then the target characteristic value of the target area is accurately determined according to the corresponding association relation between the pixel local distinguishing factor and the target characteristic value and the pixel local distinguishing factor, and meanwhile the pixel average value of the target image data of the target area is determined, so that the target variance of the target area is determined according to the pixel average value and the target image data, and the edge detection factor of the target area can be conveniently obtained according to the accurate target characteristic value and the target variance.
S130, taking the ratio of the target image characteristic value to the target variance as an edge detection factor of the target area.
Specifically, the following formula can be adopted:
Ep＝fe/S2；
Wherein f e is the target image feature value, and S 2 is the target variance.
The edge detection factors can be used for describing fracture represented by data values in a target area and fracture characteristic conditions, the smaller the edge detection factors are, the more gentle the texture of the stratum corresponding to the layer section is, the less fracture is not generated or generated, the larger the edge detection factors are, the more obvious the texture fluctuation of the stratum corresponding to the layer section is, and the greater the possibility of fracture is.
And S140, updating the seismic image data according to the edge detection factors of the target areas, obtaining updated seismic image data, and performing fault identification based on the updated seismic image data.
Specifically, the updating relation between different edge detection factors and the seismic image data is determined, the updating relation is used for indicating whether the seismic image data of the target area is enlarged, reduced or unchanged, the seismic image data of the target area is updated according to the updating relation and the edge detection factors of the target area, so that updated seismic image data is obtained, and fault identification is performed based on the updated seismic image data.
Optionally, updating the seismic image data according to the edge detection factors of the respective target areas includes: acquiring edge detection factors of all target areas, and setting target image data in the target areas to zero if the edge detection factors are smaller than a preset threshold value; if the edge detection factor is greater than or equal to a preset threshold value, the target image data in the target area is unchanged, so that accurate updating of the seismic image data is realized, accurate acquisition of small fault texture data can be indicated, fault identification can be accurately carried out according to the updated seismic image data, and the fault identification precision of seismic imaging is effectively improved.
For example, fig. 2 is a schematic diagram of original seismic data applicable to the embodiment of the invention, for the data in fig. 2, the data in fig. 2 is divided into a plurality of target areas, and a target variance S 2 and a target eigenvalue l p of the target areas are determined according to target image data of the target areas in the seismic imaging data, which are specifically expressed as:
Wherein M is M target image data, l p is pixel local discrimination factor, For the first preset target image data,/>For the last target image data,/>For the first target image data,/>Pixel value of center point of target area,/>For target image data,/>Is the pixel average value;
Wherein,
Further, the ratio of the target image feature value f e to the target variance S 2 is taken as an edge detection factor of the target area, and may be expressed as: e p＝fe/S2; then judging the sizes of the edge detection factors and a preset threshold value to update the seismic image data, namely setting the target image data in the target area to zero if the edge detection factors are smaller than the preset threshold value; if the edge detection factor is greater than or equal to the preset threshold, the target image data in the target area is unchanged, so that updated seismic image data, such as a schematic diagram of the updated seismic image data shown in fig. 3, is obtained, and fault recognition is performed based on the updated seismic image data to obtain a fault recognition result, such as a schematic diagram of the fault recognition result shown in fig. 4. The effectiveness of the method of the present invention in seismic imaging tomography can be illustrated with reference to fig. 4 and 2.
According to the technical scheme, the earthquake imaging data of the stratum corresponding to the target interval is determined, the earthquake imaging data are image data obtained by pixelating original earthquake data, and the pixelation process is rapid for subsequent data processing; further, according to the target image data of the target area in the seismic imaging data, the target variance and the target characteristic value of the target area are determined, the division of the target area can be more refined to process the data, then the ratio of the target image characteristic value to the target variance is used as an edge detection factor of the target area, so that the seismic image data is updated according to the edge detection factors of all the target areas to obtain updated seismic image data, and then fault identification is accurately carried out based on the updated seismic image data.
Example two
Fig. 5 is a schematic structural diagram of an edge detection-based fault identification device according to an embodiment of the present invention, where the embodiment is applicable to a fault (especially a small fault) identification, the edge detection-based fault identification device may be implemented in hardware and/or software, and the edge detection-based fault identification device may be configured in any electronic device having a network communication function.
As shown in fig. 5, the fault recognition device based on edge detection of the present invention includes:
the data determining module 210 is configured to determine seismic imaging data of a stratum corresponding to the target interval, where the seismic imaging data is image data obtained by pixelating original seismic data;
a parameter determining module 220, configured to determine a target variance and a target feature value of a target area according to target image data of the target area in the seismic imaging data; the target area is a circular area in a preset radius with each pixel point in the seismic imaging data as a center, and the target characteristic value is used for describing the image texture characteristics of the target area;
A detection factor determining module 230, configured to take a ratio of the target image feature value to the target variance as an edge detection factor of the target region;
the identifying module 240 is configured to update the seismic image data according to the edge detection factors of the respective target areas, obtain updated seismic image data, and perform fault identification based on the updated seismic image data.
On the basis of the above embodiment, optionally, the data determining module is configured to:
Acquiring original seismic data of a stratum corresponding to a target interval, and taking the maximum value in the original seismic data as reference data;
And multiplying the ratio of each datum in the original seismic data to the reference datum by a preset value to obtain the seismic imaging data.
On the basis of the above embodiment, optionally, the parameter determining module includes:
A candidate data determining unit configured to use a difference value between target image data of the target area and a pixel value of a center point of the target area as candidate data of the target area;
a factor determining unit, configured to determine a local pixel discriminating factor of the target area according to the candidate data;
The characteristic value determining unit is used for determining a target characteristic value of the target area according to the pixel local distinguishing factor, and the pixel local distinguishing factor and the target characteristic value have a corresponding association relation;
And the variance determining unit is used for determining a pixel average value of target image data of the target area and determining the target variance of the target area according to the pixel average value and the target image data.
On the basis of the above embodiment, optionally, the factor determining unit includes:
the characteristic parameter information determining unit is used for determining characteristic parameter information corresponding to the candidate data;
the pixel local discrimination factor determining unit is used for determining a pixel local discrimination factor of the target area according to the characteristic parameter information, wherein the characteristic parameter information is used for representing the value of the pixel local discrimination factor;
correspondingly, the characteristic parameter information determining unit is used for:
If the candidate data is greater than or equal to 0, the candidate data corresponds to first characteristic parameter information, and the first characteristic parameter information is 1;
and if the candidate data is smaller than 0, the candidate data corresponds to second characteristic parameter information, and the second characteristic parameter information is 0.
On the basis of the above embodiment, optionally, the pixel local discrimination factor determining unit is configured to:
Determining first candidate data corresponding to first preset target image data of the target area, determining second candidate data corresponding to last target image data of the target area, and determining third candidate data corresponding to first target image data of the target area; the target image data of the target area are numbered sequentially according to the position of the target image data, and the first candidate data, the second candidate data and the third candidate data belong to the candidate data;
determining a reference factor corresponding to a first preset target image data of the target area according to the characteristic parameter information corresponding to the first candidate data, the characteristic parameter information corresponding to the second candidate data and the characteristic parameter information corresponding to the third candidate data;
And summing the reference factors corresponding to each target image data of the target region to obtain the pixel local discrimination factors of the target region.
On the basis of the above embodiment, optionally, the feature value determining unit is configured to:
If the pixel local discrimination factor is larger than a preset factor, taking the reference characteristic value as a target characteristic value, wherein the reference characteristic value is a value obtained by adding one to the number of target image data of a target area;
And if the pixel local discrimination factor is smaller than or equal to a preset factor, adding the characteristic parameter information corresponding to the candidate data to obtain a target characteristic value.
On the basis of the above embodiment, optionally, the identification module is configured to:
If the edge detection factor is smaller than a preset threshold value, setting the target image data in the target area to zero;
And if the edge detection factor is greater than or equal to a preset threshold value, the target image data in the target area is unchanged.
The fault identification device based on edge detection provided by the embodiment of the invention can execute the fault identification method based on edge detection provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 6 is a schematic structural diagram of an electronic device that may be used to implement the fault identification method based on edge detection according to the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, 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 meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as a fault recognition method based on edge detection.
In some embodiments, the edge detection-based fault identification method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described fault identification method based on edge detection may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the fault identification method based on edge detection in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out 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, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here 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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A fault identification method based on edge detection, the method comprising:
Determining seismic imaging data of a stratum corresponding to a target stratum, wherein the seismic imaging data are image data obtained by pixelating original seismic data;
Determining a target variance and a target characteristic value of a target area according to target image data of the target area in the seismic imaging data; the target area is a circular area in a preset radius with each pixel point in the seismic imaging data as a center, and the target characteristic value is used for describing the image texture characteristics of the target area;
Taking the ratio of the target image characteristic value to the target variance as an edge detection factor of the target area;
and updating the seismic image data according to the edge detection factors of each target area, obtaining updated seismic image data, and performing fault identification based on the updated seismic image data.
2. The method of claim 1, wherein determining seismic imaging data for a formation corresponding to a target interval comprises:
Acquiring original seismic data of a stratum corresponding to a target interval, and taking the maximum value in the original seismic data as reference data;
And multiplying the ratio of each datum in the original seismic data to the reference datum by a preset value to obtain the seismic imaging data.
3. The method of claim 1, wherein determining the target variance and target eigenvalue of the target area from target image data of the target area in the seismic image data comprises:
Taking the difference value between the target image data of the target area and the pixel value of the center point of the target area as candidate data of the target area;
Determining a pixel local discrimination factor of the target area according to the candidate data;
Determining a target characteristic value of the target area according to the pixel local discrimination factors, wherein the pixel local discrimination factors have corresponding association relations with the target characteristic value;
and determining a pixel average value of target image data of the target area, and determining a target variance of the target area according to the pixel average value and the target image data.
4. A method according to claim 3, wherein determining a pixel local discrimination factor for the target region from the candidate data comprises:
determining characteristic parameter information corresponding to the candidate data;
Determining a pixel local discrimination factor of the target area according to the characteristic parameter information, wherein the characteristic parameter information is used for representing the value of the pixel local discrimination factor;
correspondingly, determining the characteristic parameter information corresponding to the candidate data comprises the following steps:
If the candidate data is greater than or equal to 0, the candidate data corresponds to first characteristic parameter information, and the first characteristic parameter information is 1;
and if the candidate data is smaller than 0, the candidate data corresponds to second characteristic parameter information, and the second characteristic parameter information is 0.
5. The method of claim 4, wherein determining the local pixel discrimination factor for the target area based on the feature parameter information comprises:
Determining first candidate data corresponding to first preset target image data of the target area, determining second candidate data corresponding to last target image data of the target area, and determining third candidate data corresponding to first target image data of the target area; the target image data of the target area are numbered sequentially according to the position of the target image data, and the first candidate data, the second candidate data and the third candidate data belong to the candidate data;
determining a reference factor corresponding to a first preset target image data of the target area according to the characteristic parameter information corresponding to the first candidate data, the characteristic parameter information corresponding to the second candidate data and the characteristic parameter information corresponding to the third candidate data;
And summing the reference factors corresponding to each target image data of the target region to obtain the pixel local discrimination factors of the target region.
6. The method according to claim 4 or 5, wherein determining the target feature value of the target region from the pixel local discrimination factor comprises:
If the pixel local discrimination factor is larger than a preset factor, taking the reference characteristic value as a target characteristic value, wherein the reference characteristic value is a value obtained by adding one to the number of target image data of a target area;
And if the pixel local discrimination factor is smaller than or equal to a preset factor, adding the characteristic parameter information corresponding to the candidate data to obtain a target characteristic value.
7. The method of claim 1, wherein updating the seismic image data based on the edge detection factors for each target region comprises:
If the edge detection factor is smaller than a preset threshold value, setting the target image data in the target area to zero;
And if the edge detection factor is greater than or equal to a preset threshold value, the target image data in the target area is unchanged.
8. A fault identification device based on edge detection, the device comprising:
The data determining module is used for determining seismic imaging data of a stratum corresponding to a target layer section, wherein the seismic imaging data are image data obtained by pixelating original seismic data;
The parameter determining module is used for determining a target variance and a target characteristic value of a target area according to target image data of the target area in the seismic imaging data; the target area is a circular area in a preset radius with each pixel point in the seismic imaging data as a center, and the target characteristic value is used for describing the image texture characteristics of the target area;
The detection factor determining module is used for taking the ratio of the target image characteristic value to the target variance as an edge detection factor of the target area;
And the identification module is used for updating the seismic image data according to the edge detection factors of each target area, obtaining updated seismic image data and carrying out fault identification based on the updated seismic image data.
9. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the edge detection-based fault identification method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the edge detection based fault identification method of any one of claims 1-7 when executed.
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