CN113534258A - Seismic data fingerprint feature processing method and device - Google Patents
Seismic data fingerprint feature processing method and device Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for processing seismic data fingerprint features, wherein the method comprises the following steps: carrying out image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image; identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data; performing compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area; the method and the device can effectively realize the dimension reduction and the compression coding of the seismic data, and improve the storage efficiency and the calculation efficiency of the seismic data.
Description
Technical Field
The application relates to the field of seismic exploration, in particular to a method and a device for processing seismic data fingerprint features.
Background
The seismic exploration technology is continuously developed, and new acquisition technologies such as single-point high-density, wide-frequency wide-azimuth, multi-wave multi-component and the like are applied in a large scale, so that the seismic data volume is explosively increased, and new challenges are brought to the traditional seismic processing explanation in the aspects of data storage and calculation efficiency. Performing dimension reduction and compression coding on data becomes an important basis for rapidly processing and analyzing seismic data.
The spatial characteristics of the seismic section homomorphic axis have many similarities with the characteristics of human fingerprints, so that the dimension reduction and compression coding of the seismic data can be realized by means of a fingerprint characteristic point identification technology. Fig. 8 shows a human fingerprint picture, and fig. 9 shows a post-stack seismic section. Seismic profiles also have complex data line distributions, similar to the two-dimensional plot of a human fingerprint. It can be seen that the two main differences are:
(1) the human fingerprint takes a central point as a circle center and is in a vortex structure around the central point; the seismic sections are mainly arranged and distributed in sequence by taking the horizontal direction as a parallel axis.
(2) The "ridge" and "groove" textures of human fingerprints are relatively smooth, and the lines of seismic profiles tend to have high frequency spikes.
(3) The human fingerprint has relatively good 'ridge' and 'groove' continuity, and has the characteristics of discontinuous lines, bifurcations and the like locally; the seismic section has more discontinuities of the same-phase axis and also has the characteristics of intersecting and bifurcating of the same-phase axis and the like.
In addition, both have important similarities:
(1) there is a continuous data curve.
(2) The continuous curves are interrupted and branched.
The human fingerprint is determined by individual genetic information, has the characteristic of uniqueness, and can be used as the characteristic of human individual identification. Similarly, the seismic data features are unique, are determined by the subsurface stratigraphic structure and fluid distribution of the exploration area, and can also be used as identification features of the oil and gas distribution of the area.
Human fingerprint identification methods have been known for a long time, and at present, methods based on spatial distribution of fingerprint feature points are mainly used. Human fingerprints are not complete continuous concentric circles, but rather there are a large number of non-continuous local feature points, including discontinuities, bifurcations, etc. The spatial distribution of these feature points constitutes unique feature data representing a human individual. The types of human fingerprint feature points are various, and the most important feature points can be represented by the partial structure shown in fig. 10.
Similarly, there are a large number of local feature points such as discontinuities, branches, etc. on the seismic profile data (as shown in fig. 11). The distribution of these characteristic points is influenced by the underground stratigraphic structure and fluid distribution, and represents the unique characteristics of individual geobodies. In the prior art, main seismic data processing methods (such as Fourier transform, wavelet transform and the like) do not provide a dimensionality reduction and compression coding characterization method based on seismic data feature points, so that the storage efficiency and the calculation efficiency of seismic data are reduced.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a seismic data fingerprint feature processing method and device, which can effectively realize dimension reduction and compression coding of seismic data and improve storage efficiency and calculation efficiency of the seismic data.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides a seismic data fingerprint feature processing method, including:
carrying out image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image;
identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data;
and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
Further, the image processing of the seismic data of the target work area to obtain a corresponding seismic fingerprint image includes:
and processing the seismic data according to a preset computer image processing algorithm to obtain a corresponding seismic fingerprint image, wherein the seismic fingerprint image comprises at least one of the position information of the same phase axis, the intercross information and the local detail bifurcation information.
Further, the identifying the seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data includes:
performing fingerprint feature identification on the seismic fingerprint image according to a preset human fingerprint feature identification algorithm to obtain seismic fingerprint feature points, wherein the seismic fingerprint feature points comprise at least one of end ridge points and bifurcation points;
and carrying out discrete processing on the seismic fingerprint characteristic points according to a preset discrete algorithm to obtain seismic fingerprint characteristic data.
Further, the compressing and encoding the seismic fingerprint feature data according to a preset encoding rule to obtain a one-dimensional fingerprint feature point array of the target work area includes:
removing featureless point data in the seismic fingerprint feature data, and converting the seismic fingerprint feature data without the featureless points into a fingerprint feature point sequence with the first identifier and the second identifier as marks;
and performing one-dimensional code conversion on an identification group formed by combining the first identifier and the second identifier in the fingerprint feature point sequence according to a preset coding rule to obtain a one-dimensional fingerprint feature point array.
In a second aspect, the present application provides a seismic data fingerprint feature processing apparatus, including:
the image processing module is used for carrying out image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image;
the fingerprint identification module is used for identifying the seismic fingerprint characteristic points in the seismic fingerprint image according to a preset fingerprint characteristic identification algorithm to obtain seismic fingerprint characteristic data;
and the compression coding module is used for carrying out compression coding on the seismic fingerprint characteristic data according to a preset coding rule to obtain a one-dimensional fingerprint characteristic point array of the target work area.
Further, the image processing module includes:
and the characteristic identification unit is used for processing the seismic data according to a preset computer image processing algorithm to obtain a corresponding seismic fingerprint image, wherein the seismic fingerprint image comprises at least one of the position information of the same phase axis, the intercross information and the local detail bifurcation information.
Further, the fingerprint identification module includes:
the human fingerprint feature recognition unit is used for performing fingerprint feature recognition on the seismic fingerprint image according to a preset human fingerprint feature recognition algorithm to obtain the seismic fingerprint feature points, wherein the seismic fingerprint feature points comprise at least one of end ridge points and bifurcation points;
and the discrete processing unit is used for performing discrete processing on the seismic fingerprint characteristic points according to a preset discrete algorithm to obtain seismic fingerprint characteristic data.
Further, the compression encoding module comprises:
the characteristic point sequence conversion unit is used for removing the featureless point data in the seismic fingerprint characteristic data and converting the seismic fingerprint characteristic data without the featureless points into a fingerprint characteristic point sequence with the first identifier and the second identifier as marks;
and the feature point array conversion unit is used for performing one-dimensional code conversion on the identifier group formed by combining the first identifier and the second identifier in the fingerprint feature point sequence according to a preset coding rule to obtain a one-dimensional fingerprint feature point array.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the seismic fingerprint feature data processing method when executing the program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the seismic fingerprint feature data processing method described herein.
According to the technical scheme, the method and the device for processing the fingerprint characteristics of the seismic data are provided, and the corresponding seismic fingerprint image is obtained by processing the seismic data of the target work area; identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data; and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a seismic data fingerprint feature processing method according to an embodiment of the present application;
FIG. 2 is a second schematic flowchart of a seismic data fingerprint feature processing method according to an embodiment of the present application;
FIG. 3 is a third schematic flowchart of a seismic data fingerprint feature processing method according to an embodiment of the present application;
FIG. 4 is a block diagram of one embodiment of a seismic data fingerprinting device;
FIG. 5 is a second block diagram of an exemplary seismic data fingerprinting apparatus;
FIG. 6 is a third block diagram of a seismic data fingerprint processing apparatus according to an embodiment of the present application;
FIG. 7 is a fourth block diagram of an exemplary seismic data fingerprinting device;
FIG. 8 is a schematic diagram of a human fingerprint image in an embodiment of the present application;
FIG. 9 is a schematic representation of a post-stack seismic section in an embodiment of the present application;
FIG. 10 is a schematic structural diagram of main feature points of human fingerprints in this embodiment of the present application;
FIG. 11 is a schematic diagram of characteristic points of seismic section intersection, end ridges, etc. in an embodiment of the present application;
FIG. 12 is a seismic fingerprint image of a seismic section processed by a computer graphics algorithm according to an embodiment of the present application;
FIG. 13 is a schematic diagram of seismic fingerprint feature points identified by a fingerprint identification algorithm in an embodiment of the present application;
FIG. 14 is a schematic illustration of seismic fingerprint signature data formed after tagging in an embodiment of the present application;
FIG. 15 is a schematic diagram of converting a fingerprint feature point sequence marked with 0 and 1 into a one-dimensional fingerprint feature point array in this embodiment of the present application;
fig. 16 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In consideration of the problems that the dimensionality reduction and compression coding representation methods based on seismic data feature points are not given by main seismic data processing methods (such as Fourier transform, wavelet transform and the like) in the prior art, and further the storage efficiency and the calculation efficiency of seismic data are reduced, the application provides a seismic data fingerprint feature processing method and device, and a corresponding seismic fingerprint image is obtained by carrying out image processing on the seismic data of a target work area; identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data; and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
In order to effectively implement dimension reduction and compression coding of seismic data and improve storage efficiency and calculation efficiency of the seismic data, the application provides an embodiment of a seismic data fingerprint feature processing method, and referring to fig. 1, the seismic data fingerprint feature processing method specifically includes the following contents:
step S101: and carrying out image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image.
Optionally, the two-dimensional or three-dimensional seismic data of the target work area may be processed by existing computer image processing algorithms to obtain a seismic fingerprint map, which includes but is not limited to: position information, intercrossing information and local detail bifurcation information of seismic event.
Step S102: and identifying the seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data.
Optionally, the preset fingerprint feature recognition algorithm may be an existing human fingerprint feature recognition algorithm, and is configured to recognize seismic fingerprint feature points in the seismic fingerprint map, where the seismic fingerprint feature points include, but are not limited to, end ridge points and branch points, and valid data after feature point recognition occupies about 1/20 of original data, which greatly reduces data storage capacity.
Optionally, after determining the seismic fingerprint feature points, the seismic fingerprint feature points may be discretized into discrete data capable of data processing, that is, the seismic fingerprint feature data, and the discretization may be implemented by using an existing discretization algorithm.
Specifically, the end ridge point may be marked as 0, the bifurcation point may be marked as 1, and the featureless point may be marked as N, forming the seismic fingerprint feature data.
Step S103: and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
Optionally, the seismic fingerprint feature data is compressed and encoded, the data at the feature-free points are removed, the data at the fingerprint feature points are retained and then converted into a column of fingerprint feature point sequences marked by specific identifiers, and the fingerprint feature point sequences are further converted into a one-dimensional fingerprint feature point array through a preset encoding rule, so that data compression and dimension reduction are realized, and a fingerprint feature point array with a simple data structure is obtained, the encoding mode of the fingerprint feature point array is similar to that of biological DNA molecules, and seismic data processing and analysis can be conveniently performed by using tools such as fingerprint analysis, a biological data analysis method, an artificial intelligence algorithm and the like.
As can be seen from the above description, the method for processing the fingerprint features of the seismic data provided in the embodiment of the present application can obtain a corresponding seismic fingerprint image by performing image processing on the two-dimensional or three-dimensional seismic data of the target work area; identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data; and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
In order to accurately obtain a seismic fingerprint image corresponding to seismic data of a target work area, in an embodiment of the seismic data fingerprint feature processing method of the present application, the following may be specifically included:
processing the two-dimensional or three-dimensional seismic data according to a preset computer image processing algorithm to obtain a corresponding seismic fingerprint map, wherein the seismic fingerprint map comprises but is not limited to: position information, intercrossing information and local detail bifurcation information of seismic event.
In order to structure the seismic fingerprint map into processable seismic fingerprint feature data, in an embodiment of the seismic data fingerprint feature processing method of the present application, referring to fig. 2, the following may be further included:
step S201: and performing fingerprint feature identification on the seismic fingerprint image according to a preset human fingerprint feature identification algorithm to obtain the seismic fingerprint feature points, wherein the seismic fingerprint feature points comprise at least one of end ridge points and bifurcation points.
Step S202: and carrying out discrete processing on the seismic fingerprint characteristic points according to a preset discrete algorithm to obtain seismic fingerprint characteristic data.
Optionally, the preset fingerprint feature recognition algorithm may be an existing human fingerprint feature recognition algorithm, and is configured to recognize seismic fingerprint feature points in the seismic feature data, where the seismic fingerprint feature points include, but are not limited to, end ridge points and branch points, and valid data after feature point recognition occupies about 1/20 of original data, which greatly reduces data storage capacity.
Optionally, after determining the seismic fingerprint feature points, the seismic fingerprint feature points may be discretized into discrete data capable of data processing, that is, the seismic fingerprint feature data, and the discretization may be implemented by using an existing discretization algorithm.
Specifically, the end ridge point may be marked as 0, the bifurcation point may be marked as 1, and the featureless point may be marked as N, forming the seismic fingerprint feature data.
In order to perform compression and dimension reduction processing on processable seismic fingerprint feature data, in an embodiment of the seismic fingerprint feature data processing method of the present application, referring to fig. 3, the following may be further included:
step S301: and removing featureless point data in the seismic fingerprint feature data, and converting the seismic fingerprint feature data without the featureless points into a fingerprint feature point sequence with the first identifier and the second identifier as marks.
Step S302: and performing one-dimensional code conversion on an identification group formed by combining the first identifier and the second identifier in the fingerprint feature point sequence according to a preset coding rule to obtain a one-dimensional fingerprint feature point array.
Optionally, the seismic fingerprint feature data is compressed and encoded, the data at the feature-free points are removed, the data at the fingerprint feature points are retained and then converted into a column of fingerprint feature point sequences marked by specific identifiers, and the fingerprint feature point sequences are further converted into a one-dimensional fingerprint feature point array through a preset encoding rule, so that data compression and dimension reduction are realized, and a fingerprint feature point array with a simple data structure is obtained, the encoding mode of the fingerprint feature point array is similar to that of biological DNA molecules, and seismic data processing and analysis are conveniently performed by using tools such as fingerprint analysis, a biological data analysis method, an artificial intelligence algorithm and the like.
Specifically, the specific identifier may be a first identifier (e.g. 0) and a second identifier (e.g. 1), and the identification group may be all possible combinations of 0 and 1, such as 00, 11, 01, and 10.
Optionally, after the seismic fingerprint feature data is converted into a series of fingerprint feature point sequences marked with 0 and 1, two adjacent feature points (i, i +1) in the fingerprint feature point sequences are marked as a pair of bases, and then a preset coding rule is introduced to convert the fingerprint feature point sequences into a one-dimensional fingerprint feature point array composed of corresponding elements in the coding rule.
Specifically, the encoding rule may be as shown in table 1:
TABLE 1 encoding rules
| 00 | 11 | 01 | 10 |
| A | T | G | C |
In order to effectively implement dimension reduction and compression coding of seismic data and improve storage efficiency and computational efficiency of seismic data, the present application provides an embodiment of a seismic data fingerprint feature processing apparatus for implementing all or part of the contents of the seismic data fingerprint feature processing method, and referring to fig. 4, the seismic data fingerprint feature processing apparatus specifically includes the following contents:
and the image processing module 10 is used for performing image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image.
And the fingerprint identification module 20 is configured to identify the seismic fingerprint feature points in the seismic fingerprint map according to a preset fingerprint feature identification algorithm, so as to obtain seismic fingerprint feature data.
And the compression coding module 30 is configured to perform compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
As can be seen from the above description, the seismic data fingerprint feature processing apparatus provided in the embodiment of the present application can obtain a corresponding seismic fingerprint image by performing image processing on seismic data of a target work area; identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data; and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
In order to accurately obtain a seismic fingerprint corresponding to seismic data of a target work area, in an embodiment of the seismic data fingerprint feature processing apparatus of the present application, referring to fig. 5, the image processing module 10 includes:
and the feature identification unit 11 is configured to perform feature identification on the seismic data according to a preset computer image processing algorithm to obtain a corresponding seismic fingerprint map, where the seismic fingerprint map includes at least one of event position information, intercross information, and local detail bifurcation information.
In order to be able to structure the seismic fingerprint map into processable seismic fingerprint feature data, in an embodiment of the seismic data fingerprint processing apparatus of the present application, referring to fig. 6, the fingerprint identification module 20 includes:
the human fingerprint feature recognition unit 21 is configured to perform fingerprint feature recognition on the seismic fingerprint map according to a preset human fingerprint feature recognition algorithm to obtain the seismic fingerprint feature points, where the seismic fingerprint feature points include at least one of end ridge points and branch points.
And the discrete processing unit 22 is configured to perform discrete processing on the seismic fingerprint feature points according to a preset discrete algorithm to obtain seismic fingerprint feature data.
In order to perform compression dimension reduction processing on processable seismic fingerprint feature data, in an embodiment of the seismic data fingerprint feature processing apparatus of the present application, referring to fig. 7, the compression encoding module 30 includes:
and the feature point sequence conversion unit 31 is configured to remove feature-free point data in the seismic fingerprint feature data, and convert the seismic fingerprint feature data from which the feature-free points are removed into a fingerprint feature point sequence marked by the first identifier and the second identifier.
The feature point array converting unit 32 is configured to perform one-dimensional code conversion on an identifier group formed by combining the first identifier and the second identifier in the fingerprint feature point sequence according to a preset coding rule, so as to obtain a one-dimensional fingerprint feature point array.
To further explain the present solution, the present application further provides a specific application example of implementing the seismic fingerprint feature data processing method by using the above seismic data fingerprint feature processing apparatus, which specifically includes the following contents:
alternatively, a two-dimensional post-stack seismic section of a work area in the Songliaowan may be selected for analysis. The seismic section has 401 channels, and the target layer section is the yao jia group, the qingshan group and the adjacent stratum, as shown in fig. 9.
The seismic section is processed using a computer image processing algorithm to obtain a seismic fingerprint map, as shown in fig. 12. The position information, the intercrossing information and the local detail bifurcation information of the seismic event are reserved.
Seismic fingerprint feature points are identified on figure 12 based on existing human fingerprint feature identification algorithms. FIG. 13 shows the identified seismic fingerprint feature points, including end ridge points (left image) and bifurcation points (right image). And marking the end ridge point as 0, the bifurcation point as 1 and the featureless point as N to form seismic fingerprint feature data, as shown in FIG. 14. The effective data after feature point recognition occupies 1/20 of the original data, and the data storage capacity is greatly reduced.
And carrying out compression coding on the seismic fingerprint characteristic data, removing data at non-characteristic points, retaining the data at the fingerprint characteristic points, and converting the data into a series of fingerprint characteristic point sequences marked by 0 and 1. According to the fact that two adjacent characteristic points (i, i +1) on the fingerprint characteristic point sequence are marked as a pair of bases, the coding rule shown in the table 1 is introduced:
TABLE 1 encoding rules
| 00 | 11 | 01 | 10 |
| A | T | G | C |
Based on the rules, the sequence of fingerprint feature points is converted into a one-dimensional array of fingerprint feature points consisting of A, T, C, G elements. Fig. 15 shows the process of converting the fingerprint feature point sequence marked with 0 and 1 into a one-dimensional fingerprint feature point array. The data compression and dimension reduction are further realized, a fingerprint feature point array with a simple data structure is obtained, the coding mode of the fingerprint feature point array is similar to that of biological DNA molecules, and seismic data processing and analysis can be conveniently carried out by using tools such as fingerprint analysis, a biological data analysis method, an artificial intelligence algorithm and the like.
As can be seen from the above description, the present application can also achieve at least the following technical effects:
(1) the redundant information of the seismic data is greatly compressed, the data volume is reduced, and the operational efficiency of the seismic data is improved.
(2) The seismic profile is converted into a fingerprint characteristic point array with a simple data structure form, the coding mode of the fingerprint characteristic point array is similar to that of a biological DNA molecule, data processing can be carried out through a mature bioinformatics method, and cross fusion of multiple fields of technologies is achieved.
In order to effectively implement dimension reduction and compression coding of seismic data and improve storage efficiency and computational efficiency of seismic data from a hardware level, the present application provides an embodiment of an electronic device for implementing all or part of contents in the seismic fingerprint feature data processing method, where the electronic device specifically includes the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the seismic fingerprint characteristic data processing device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiment of the seismic fingerprint feature data processing method and the embodiment of the seismic fingerprint feature data processing apparatus in the embodiment, and the contents thereof are incorporated herein, and repeated details are not repeated.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the seismic data fingerprint feature processing method may be performed on the electronic device side as described above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.
The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
Fig. 16 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 16, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 16 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the seismic data fingerprinting method functions may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
step S101: and carrying out image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image.
Step S102: and identifying the seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data.
Step S103: and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
As can be seen from the above description, the electronic device provided in the embodiment of the present application obtains a corresponding seismic fingerprint image by performing image processing on seismic data of a target work area; identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data; and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
In another embodiment, the seismic data fingerprint feature processing apparatus may be configured separately from the central processor 9100, for example, the seismic data fingerprint feature processing apparatus may be configured as a chip connected to the central processor 9100, and the function of the seismic data fingerprint feature processing method may be realized by the control of the central processor.
As shown in fig. 16, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 16; further, the electronic device 9600 may further include components not shown in fig. 16, which can be referred to in the related art.
As shown in fig. 16, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the seismic fingerprint feature data processing method with the execution subject being the server or the client in the foregoing embodiment, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the seismic fingerprint feature processing method with the execution subject being the server or the client in the foregoing embodiment, for example, when the processor executes the computer program, the processor implements the following steps:
step S101: and carrying out image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image.
Step S102: and identifying the seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data.
Step S103: and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application obtains a corresponding seismic fingerprint map by performing image processing on seismic data of a target work area; identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data; and carrying out compression coding on the seismic fingerprint feature data according to a preset coding rule to obtain a one-dimensional fingerprint feature point array of the target work area.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A seismic data fingerprinting feature processing method, characterized in that the method comprises:
carrying out image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image;
identifying seismic fingerprint feature points in the seismic fingerprint image according to a preset fingerprint feature identification algorithm to obtain seismic fingerprint feature data;
and carrying out compression coding on the seismic fingerprint characteristics according to a preset coding rule to obtain a one-dimensional fingerprint characteristic point array of the target work area.
2. The method for processing the seismic data fingerprint features of claim 1, wherein the image processing of the seismic data of the target work area to obtain the corresponding seismic fingerprint image comprises:
and processing the seismic data according to a preset computer image processing algorithm to obtain a corresponding seismic fingerprint image, wherein the seismic fingerprint image comprises at least one of the position information of the same phase axis, the intercross information and the local detail bifurcation information.
3. The method for processing the fingerprint features of the seismic data according to claim 1, wherein the identifying the seismic fingerprint feature points in the seismic fingerprint map according to a preset fingerprint feature identification algorithm to obtain the seismic fingerprint feature data comprises:
performing fingerprint feature identification on the seismic fingerprint image according to a preset human fingerprint feature identification algorithm to obtain seismic fingerprint feature points, wherein the seismic fingerprint feature points comprise at least one of end ridge points and bifurcation points;
and carrying out discrete processing on the seismic fingerprint characteristic points according to a preset discrete algorithm to obtain seismic fingerprint characteristic data.
4. The seismic data fingerprint feature processing method according to claim 1, wherein the compressing and encoding the seismic fingerprint feature data according to a preset encoding rule to obtain the one-dimensional fingerprint feature point array of the target work area comprises:
removing featureless point data in the seismic fingerprint feature data, and converting the seismic fingerprint feature data without the featureless points into a fingerprint feature point sequence with the first identifier and the second identifier as marks;
and performing one-dimensional code conversion on an identification group formed by combining the first identifier and the second identifier in the fingerprint feature point sequence according to a preset coding rule to obtain a one-dimensional fingerprint feature point array.
5. A seismic data fingerprinting apparatus, characterized by comprising:
the image processing module is used for carrying out image processing on the seismic data of the target work area to obtain a corresponding seismic fingerprint image;
the fingerprint identification module is used for identifying the seismic fingerprint characteristic points in the seismic fingerprint image according to a preset fingerprint characteristic identification algorithm to obtain seismic fingerprint characteristic data;
and the compression coding module is used for carrying out compression coding on the seismic fingerprint characteristic data according to a preset coding rule to obtain a one-dimensional fingerprint characteristic point array of the target work area.
6. The seismic data fingerprinting device of claim 5, wherein the image processing module includes:
and the characteristic identification unit is used for processing the seismic data according to a preset computer image processing algorithm to obtain a corresponding seismic fingerprint image, wherein the seismic fingerprint image comprises at least one of the position information of the same phase axis, the intercross information and the local detail bifurcation information.
7. The seismic data fingerprinting device of claim 5, wherein the fingerprinting module includes:
the human fingerprint feature recognition unit is used for performing fingerprint feature recognition on the seismic fingerprint image according to a preset human fingerprint feature recognition algorithm to obtain the seismic fingerprint feature points, wherein the seismic fingerprint feature points comprise at least one of end ridge points and bifurcation points;
and the discrete processing unit is used for performing discrete processing on the seismic fingerprint characteristic points according to a preset discrete algorithm to obtain seismic fingerprint characteristic data.
8. The seismic data fingerprinting device of claim 5, wherein the compression encoding module includes:
the characteristic point sequence conversion unit is used for removing the featureless point data in the seismic fingerprint characteristic data and converting the seismic fingerprint characteristic data without the featureless points into a fingerprint characteristic point sequence with the first identifier and the second identifier as marks;
and the feature point array conversion unit is used for performing one-dimensional code conversion on the identifier group formed by combining the first identifier and the second identifier in the fingerprint feature point sequence according to a preset coding rule to obtain a one-dimensional fingerprint feature point array.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of seismic data fingerprint processing of any one of claims 1 to 4.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the seismic data fingerprinting method according to any one of claims 1 to 4.
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| RU2013113136A (en) * | 2013-03-26 | 2014-10-10 | Некоммерческое партнерство "Российско -Индийский Научно-Технологический Центр", | SMALL BOTTOM SEISMIC MODULE |
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