Method and apparatus for identifying three-dimensional tomographic information

Abstract

The present invention discloses a method and apparatus for identifying three-dimensional tomographic information. The method includes: obtaining initial three-dimensional volume data of the third-generation coherence attribute of a three-dimensional seismic data volume in a predetermined area, where the initial three-dimensional volume data has tomographic information; performing median filtering processing on the initial three-dimensional volume data, and determining difference data between the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data; performing histogram equalization processing and threshold adjustment processing on the difference data to enhance the tomographic information; and identifying the tomographic information in the initial three-dimensional volume data according to the data with enhanced tomographic information. By means of the present invention, the quality of seismic data can be improved, the difference between the tomographic area and the background value can be enhanced, and noise pollution can be eliminated, so that the accuracy of tomographic identification can be improved.

Description

Technical Field

[0001] The present invention relates to the field of seismic data processing, and particularly to a method and device for identifying three-dimensional fault information. Background Art

[0002] Faults, fractures, karst caves, etc. are important components of fractured hydrocarbon reservoirs such as carbonate heterogeneous reservoirs, and they play an important role in the hydrocarbon accumulation process. The development status of faults is one of the important factors controlling the "generation, storage, caprock, trapping, migration, and preservation" of reservoir hydrocarbons. Therefore, many large hydrocarbon reservoirs in the world are closely related to the development of faults. At the same time, faults are important hydrocarbon migration channels. Their existence can not only connect dispersed reservoir spaces such as karst cave systems to form large-scale reservoirs, but also provide necessary permeability inside each reservoir space. Therefore, it is of great significance to carry out high-precision fault detection work to support subsequent exploration and development research such as modeling and reservoir simulation.

[0003] Therefore, fault identification is an important part of seismic interpretation work in hydrocarbon exploration. However, conventional post-stack fault detection methods are subject to the influence of seismic data quality, and there have been two aspects of troubles for a long time. On the one hand, in the fractured formation area, due to the low quality of migration imaging, there is cloud-like fuzzy information covering the faults, and the true position of the fault development is not accurately described. On the other hand, the existence of formation dip angles causes crosstalk of fault characteristics in the horizontal direction.

[0004] That is to say, due to the poor quality of seismic data, it is impossible to effectively identify the fault information in the seismic data. Summary of the Invention

[0005] In view of this, the present invention provides a method and device for identifying three-dimensional fault information to solve at least one of the above-mentioned problems.

[0006] According to a first aspect of the present invention, there is provided a method for identifying three-dimensional fault information, the method comprising:

[0007] Obtaining initial three-dimensional volume data of the third-generation coherence attribute of a three-dimensional seismic data volume in a predetermined area, the initial three-dimensional volume data having fault information;

[0008] Performing median filtering processing on the initial three-dimensional volume data, and determining difference data between the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data;

[0009] Performing histogram equalization processing and threshold adjustment processing on the difference data to enhance the fault information;

[0010] Identifying the fault information in the initial three-dimensional volume data according to the data with enhanced fault information.

[0011] According to a second aspect of the present invention, there is provided an apparatus for identifying three-dimensional tomographic information, the apparatus comprising:

[0012] A data acquisition unit for acquiring initial three-dimensional volume data of the third-generation coherence attribute of a three-dimensional seismic data volume in a predetermined area, the initial three-dimensional volume data having tomographic information;

[0013] A median filtering unit for performing median filtering on the initial three-dimensional volume data;

[0014] A difference data determination unit for determining difference data between the three-dimensional volume data after median filtering and the initial three-dimensional volume data;

[0015] An equalization processing unit for performing histogram equalization processing and threshold adjustment processing on the difference data to enhance the tomographic information;

[0016] An identification unit for identifying the tomographic information in the initial three-dimensional volume data according to the data with enhanced tomographic information.

[0017] According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the steps of the above method are implemented.

[0018] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0019] It can be seen from the above technical solutions that by performing median filtering on the acquired initial three-dimensional volume data, determining the difference data between the three-dimensional volume data after median filtering and the initial three-dimensional volume data, and then performing histogram equalization processing and threshold adjustment processing on the difference data to enhance the tomographic information, the tomographic information in the initial three-dimensional volume data can be identified according to the data with enhanced tomographic information. This technical solution improves the quality of seismic data, enhances the difference between the fault area and the background value, and eliminates noise pollution, thereby improving the accuracy of fault identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 is a flowchart of a method for identifying three-dimensional tomographic information according to an embodiment of the present invention;

[0022] Figure 2 is the original coherence attribute profile according to an embodiment of the present invention;

[0023] Figure 3 is the enhanced result diagram of the original coherence attribute profile according to an embodiment of the present invention;

[0024] Figure 4 is the original coherence attribute slice diagram according to an embodiment of the present invention;

[0025] Figure 5 is the enhanced result diagram of the original coherence attribute slice according to an embodiment of the present invention;

[0026] Figure 6 is the structural block diagram of a three-dimensional tomography information recognition device according to an embodiment of the present invention;

[0027] Figure 7 is a schematic block diagram of the system composition of an electronic device 600 according to an embodiment of the present invention. Detailed implementation manners

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] In the process of implementing the present invention, the applicant found the following related technologies:

[0030] A typical representative of fault detection technology is the coherence series technology. The coherence technology emerged in the 1990s and has now developed three generations of algorithms. The first-generation algorithm based on cross-correlation (abbreviated as C1 algorithm) was proposed by Bahorich and Frmer in 1995. The second-generation algorithm that utilizes multi-channel similarity (abbreviated as C2 algorithm) was proposed by Marfurt et al. in 1998. The third-generation coherence algorithm based on eigenstructure (abbreviated as C3 algorithm) was proposed by Gersztenkorn and Marfurt. The application prerequisite of the C1 algorithm is relatively harsh. The defect of the C2 algorithm is that it is sensitive to waveforms and insensitive to lateral amplitude changes. In contrast, the C3 algorithm makes up for the deficiencies of the above two. In the three-dimensional data volume after migration, it calculates the coherence of each sample point with the surrounding data to form a three-dimensional data volume representing coherence, that is, the data coherence within the calculation time window. In this way, it can not only suppress continuity and highlight discontinuity, but also quantitatively reflect the lateral changes of seismic characteristics. The obtained results are more intuitive than the geological interpretation of seismic horizontal slices. This method is mainly applied to more objective and detailed fault interpretation, river channel, sand body and fracture prediction. However, overall, the anti-noise performance of the coherence attribute technology is not good. It is easily contaminated by noise and is more vulnerable to crosstalk caused by inclined strata. Therefore, there is often a situation where the contrast between the fault and the surrounding background is too low, resulting in unclear portrayal of the fault in the resulting image and even affecting the interpreter's judgment of fault interpretation. For example, in the fault-developed area formed by multi-phase tectonic movements, improper setting of migration parameters causes energy dispersion at the fault boundary, resulting in a cloud-like distribution of fault information, etc.

[0031] In view of the above problems, the embodiments of the present invention provide a recognition scheme for three-dimensional fault information. Based on the third-generation coherence attribute three-dimensional data volume, this scheme can improve the fault recognition accuracy in the study area by enhancing the difference between the fault area and the background value and eliminating noise pollution. The following will describe the embodiments of the present invention in detail with reference to the accompanying drawings.

[0032] Figure 1 is a flowchart of the three-dimensional fault information recognition method according to the embodiments of the present invention. As Figure 1 shown, the process includes:

[0033] Step 101, obtain the initial three-dimensional data volume of the third-generation coherence attribute of the three-dimensional seismic data volume in a predetermined area, and the initial three-dimensional data volume has fault information.

[0034] Step 102, perform three-dimensional median filtering on the initial three-dimensional data volume, and determine the difference data between the three-dimensional data volume after median filtering and the initial three-dimensional data volume.

[0035] For example, for a certain point on a three-dimensional volume, its original data is 0.56, and the value of this point after median filtering is 0.64. The difference value is 0.08 (0.64 - 0.56). The difference calculation method here is to directly calculate the difference point by point between the data of the two three-dimensional volumes.

[0036] In one embodiment, the initial difference data can be determined first according to the three-dimensional volume data after median filtering and the initial three-dimensional volume data, and then the initial difference data is normalized to obtain the difference data in the gray scale domain.

[0037] Specifically, normalizing the initial difference data includes: determining the maximum difference value and the minimum difference value according to the initial difference data; normalizing the initial difference data according to the maximum difference value and the minimum difference value.

[0038] In an example, the data volume is u, and the value range is [0, 1]. The median filtering result of u is calculated to obtain um; the difference du between the data volume u and the median filtering result um is calculated, where du = um - u; then, du is normalized between [0, 255], and the transformation formula has been provided. For example, the original data at a certain point is 0.56, and the value of this point after median filtering is 0.64. The difference value is 0.08 (0.64 - 0.56), then the value of this point after normalization becomes 23.

[0039] Step 103, perform histogram equalization processing and threshold adjustment processing on the difference data to enhance the tomographic information.

[0040] Specifically, first perform matrix transformation operations on the difference data in the gray scale domain, and perform histogram equalization processing after the matrix transformation operations; then, perform threshold adjustment processing on each pixel in the difference data (U enhanced ) after histogram equalization processing according to a predetermined rule.

[0041] In one embodiment, the matrix transformation algorithm is a global algorithm, and the histogram equalization method is dimension-independent. It can be one-dimensional, two-dimensional, or three-dimensional. To implement the fast application of the histogram algorithm, here the difference data volume (which is three-dimensional, such as a "block" of 5 * 6 * 7) is directly converted into a one-dimensional vector (with a length of 5 * 6 * 7).

[0042] In the case of having wells, if there are wells in the work area, such as Well A inside. The fractures in the target layer of Well A are developed. Suppose the pixel value shown by U at Well A enhanced is 159, then 159 is set as the threshold here. Those greater than this value are set to 255, and those less than or equal to this value retain their original values.

[0043] In the case of no wells, based on geological experience, large faults will have a certain influence range in the lateral direction. U enhanced will gradually increase in the lateral direction away from the fault. At this time, select the U value at an appropriate lateral distance enhanced as the threshold, and then refer to the case of having wells for the subsequent operations.

[0044] The predefined rule here can be to adjust the threshold of each pixel according to the difference between the fault and the background information. The "background" here is the coherence attribute value when the formation is flat and there is no faulting phenomenon, and the value is generally close to 1.

[0045] Step 104, identify the fault information in the initial three-dimensional volume data according to the data after enhancing the fault information.

[0046] Specifically, identify the fault information in the initial three-dimensional volume data according to the time slice data and profile data after enhancing the fault information.

[0047] By performing median filtering on the obtained initial three-dimensional volume data, and determining the difference data between the three-dimensional volume data after median filtering and the initial three-dimensional volume data. Then, perform histogram equalization processing and threshold adjustment processing on the difference data to enhance the fault information. Thus, the fault information in the initial three-dimensional volume data can be identified according to the data after enhancing the fault information. In the embodiments of the present invention, by improving the quality of seismic data, enhancing the difference between the fault area and the background value, and eliminating noise pollution, the accuracy of fault identification can be improved.

[0048] To better understand the present invention, the following gives a specific process example of three-dimensional fault information identification in the embodiments of the present invention:

[0049] 1) Input data: The input data is the three-dimensional volume data U of the third-generation coherence attribute extracted from the three-dimensional seismic data volume. The value range of each pixel in U is [0, 1].

[0050] 2) Preprocess the three-dimensional volume data of the coherence attribute:

[0051] a) Perform median filtering on the attribute volume U to obtain the result U after median filtering m , assuming the dimension of Um is Ntime*Nxline*Ninline, where Ntime (z-axis, time axis), Nxline (x-axis, xline direction), and Ninline (y-axis, inline direction) are the number of time sampling points, the number of xline lines, and the number of inline lines respectively. The size of the filtering window is set according to the scale of the fault. For example, it is set to 3*3*3, 5*5*5, etc.;

[0052] b) Calculate the attribute volume U and the result U of value filtering mThree-dimensional data volume difference ΔU, where ΔU = U - U m ;

[0053] c) Normalize the value range of ΔU to the range of [0, 255] in the gray scale domain. This value range can be adjusted according to the value range distribution of U. The calculation formula is shown in (1), where round is the round-off operator, ΔU min is the minimum value of ΔU, and ΔU max is the maximum value of ΔU

[0054] ΔU normal = round((ΔU - ΔU min ) / (ΔU max - ΔU min ) * 255) (1)

[0055] d) Perform matrix transformation on ΔU normal to generate a vector with a length of Ntime * Nxline * Ninline

[0056] The above preprocessing method plays a key role in maintaining and enhancing the fault boundary characteristics

[0057] 3) Use conventional global histogram equalization to process the generated vector to obtain the result U enhanced .

[0058] This conventional global histogram equalization has the characteristics of global optimization and a relatively small risk of local distortion

[0059] 4) Adjust the threshold of U enhanced according to the difference between the fault and the background information to obtain the fault enhancement result

[0060] Through the enhancement processing, the cloudy fault blurred area in the image can be eliminated, highlighting the linear fault structure inside the area

[0061] 5) Extract the time slice data and profile data of the enhanced result U enhanced respectively, and compare them with the original data to identify the fault information

[0062] Based on the three-dimensional volume data of the third-generation coherence attribute results, the embodiments of the present invention perform enhancement processing through targeted preprocessing methods, combined with means such as global histogram equalization and threshold setting, strengthening the linear structure of the fault and improving the recognition accuracy of the fault

[0063] To further understand the embodiments of the present invention, an example is given below. In this example, it is known that the distribution trend of the main faults in the X work area is relatively clear, but due to formation interference, the boundaries of the secondary faults are blurred

[0064] According to an embodiment of the present invention, the process of identifying fault information in the X work area specifically includes:

[0065] 1) Extract the third-generation coherence attribute volume of the 3D post-stack seismic data volume in the X work area;

[0066] 2) Enhance the coherence attribute volume using the technical solution provided in the embodiment of the present invention to obtain a result. For the mapping display, refer to Figures 2 - 5 , where Figure 2 is the original coherence attribute profile diagram, Figure 3 is the enhanced result diagram of the original coherence attribute profile, Figure 4 is the original coherence attribute slice diagram, Figure 5 is the enhanced result diagram of the original coherence attribute slice.

[0067] It can be seen from the comparative analysis of the mapping results that after applying the enhancement method provided in the embodiment of the present invention, the overall fault identification accuracy in the research area has been improved. The enhanced result can clearly show the linear structure of the fault. The embodiment of the present invention improves the accuracy of fault identification.

[0068] Based on a similar inventive concept, the embodiment of the present invention also provides an identification device for 3D fault information. Preferably, this device can be used to implement the process in the above method embodiment.

[0069] Figure 6 is the structural block diagram of the 3D fault information identification device. As Figure 6 shown, the device includes: a data acquisition unit 61, a median filtering unit 62, a difference data determination unit 63, an equalization processing unit 64, and an identification unit 65, where:

[0070] The data acquisition unit 61 is used to acquire the initial 3D volume data of the third-generation coherence attribute of the 3D seismic data volume in a predetermined area, and the initial 3D volume data has fault information;

[0071] The median filtering unit 62 is used to perform median filtering processing on the initial 3D volume data;

[0072] The difference data determination unit 63 is used to determine the difference data between the 3D volume data after median filtering processing and the initial 3D volume data;

[0073] The equalization processing unit 64 is used to perform histogram equalization processing and threshold adjustment processing on the difference data to enhance the fault information;

[0074] The identification unit 65 is used to identify the fault information in the initial 3D volume data according to the data with enhanced fault information.

[0075] Specifically, the recognition unit 65 recognizes the fault information in the initial three-dimensional volume data based on the time slice data and profile data after enhancing the fault information.

[0076] The median filtering unit 62 performs median filtering on the initial three-dimensional volume data obtained by the data acquisition unit 61. The difference data determination unit 63 determines the difference data between the three-dimensional volume data after median filtering and the initial three-dimensional volume data. Then, the equalization processing unit 64 performs histogram equalization processing and threshold adjustment processing on the difference data to enhance the fault information. Thus, the recognition unit 65 can recognize the fault information in the initial three-dimensional volume data based on the data after enhancing the fault information. In the embodiment of the present invention, by improving the quality of seismic data, enhancing the difference between the fault area and the background value, and eliminating noise pollution, the accuracy of fault recognition can be improved.

[0077] Specifically, the above-mentioned difference data determination unit 63 includes: an initial difference determination module and a normalization module, where:

[0078] The initial difference determination module is used to determine the initial difference data according to the three-dimensional volume data after median filtering and the initial three-dimensional volume data;

[0079] The normalization module is used to perform normalization processing on the initial difference data to obtain the difference data in the gray scale domain.

[0080] In an embodiment, the above-mentioned normalization module specifically includes: a difference value determination sub-module, which is used to determine the maximum difference value and the minimum difference value according to the initial difference data; a normalization sub-module, which is used to perform normalization processing on the initial difference data according to the maximum difference value and the minimum difference value.

[0081] In the specific implementation process, the above-mentioned equalization processing unit 64 specifically includes: a histogram equalization module and a threshold adjustment module, where:

[0082] The histogram equalization module is used to perform matrix transformation operations on the difference data in the gray scale domain and perform histogram equalization processing on the matrix transformation operations;

[0083] The threshold adjustment module is used to perform threshold adjustment processing on each pixel in the difference data after histogram equalization processing according to a predetermined rule.

[0084] For the specific execution processes of the above units, modules, and sub-modules, reference can be made to the descriptions in the above method embodiments, which will not be elaborated here.

[0085] In actual operation, the above units, modules, and sub-modules can be combined or set individually, and the present invention is not limited thereto.

[0086] This embodiment also provides an electronic device, which may be a desktop computer, a tablet computer, a mobile terminal, etc., and this embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to the above method embodiment and the embodiment of the three-dimensional tomographic information recognition device, and the content thereof is incorporated herein, and the repeated parts will not be described again.

[0087] Figure 7 It is a schematic block diagram of the system composition of the electronic device 600 according to an embodiment of the present invention. As Figure 7 shown, the electronic device 600 may include a central processing unit 100 and a memory 140; the memory 140 is coupled to the central processing unit 100. It should be noted that this figure is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0088] In one embodiment, the three-dimensional tomographic information recognition function may be integrated into the central processing unit 100. Among them, the central processing unit 100 may be configured to perform the following controls:

[0089] Obtain the initial three-dimensional volume data of the third-generation coherence attribute of the three-dimensional seismic data volume in a predetermined area, and the initial three-dimensional volume data has tomographic information;

[0090] Perform median filtering processing on the initial three-dimensional volume data, and determine the difference data between the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data;

[0091] Perform histogram equalization processing and threshold adjustment processing on the difference data to enhance the tomographic information;

[0092] Identify the tomographic information in the initial three-dimensional volume data according to the data after the tomographic information is enhanced.

[0093] As can be seen from the above description, the electronic device provided by the embodiment of the present application, by performing median filtering processing on the obtained initial three-dimensional volume data, and determining the difference data between the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data, and then performing histogram equalization processing and threshold adjustment processing on the difference data to enhance the tomographic information, so that the tomographic information in the initial three-dimensional volume data can be identified according to the data after the tomographic information is enhanced. The embodiment of the present invention improves the quality of seismic data, enhances the difference between the tomographic area and the background value, and eliminates noise pollution, thereby improving the accuracy of tomographic identification.

[0094] In another embodiment, the three-dimensional tomographic information recognition device may be separately configured from the central processing unit 100. For example, the three-dimensional tomographic information recognition device may be configured as a chip connected to the central processing unit 100, and the three-dimensional tomographic information recognition function is realized through the control of the central processing unit.

[0095] As Figure 7 shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processing unit 130, a display 160, and a power supply 170. It should be noted that the electronic device 600 does not necessarily have to include all the components shown in Figure 7 ; in addition, the electronic device 600 may further include components not shown in Figure 7 . Reference may be made to the prior art.

[0096] As Figure 7 shown, the central processing unit 100, sometimes also referred to as a controller or operation control, may include a microprocessor or other processor devices and / or logic devices. The central processing unit 100 receives inputs and controls the operations of the various components of the electronic device 600.

[0097] Among them, the memory 140, for example, may be one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. It can store the above-mentioned information related to failures, and can also store programs for executing relevant information. And the central processing unit 100 can execute the programs stored in the memory 140 to implement information storage or processing, etc.

[0098] The input unit 120 provides inputs to the central processing unit 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to supply power to the electronic device 600. The display 160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0099] The memory 140 may be a solid-state memory. For example, it may be a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that stores information even when powered off, can be selectively erased, and has more data. Examples of such a memory are sometimes referred to as EPROMs, etc. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142, and the application / function storage unit 142 is used to store application programs and function programs or the processes for operating the electronic device 600 through the central processing unit 100.

[0100] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0101] The communication module 110 is a transmitter / receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter / receiver) 110 is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0102] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that the sound can be recorded on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

[0103] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned three-dimensional fault information identification method are implemented.

[0104] In summary, the embodiment of the present invention is based on the three-dimensional volume data of the third-generation coherent attribute results, and enhances the linear structure of the fault through targeted preprocessing methods combined with conventional global histogram equalization, threshold setting and other means, which is an effective fault identification technology. The advantages of the embodiment of the present invention include the following aspects:

[0105] (1) The enhancement method can eliminate the cloudy fault fuzzy area in the image and highlight the linear fault structure inside the area; (2) The calculation process is simple, easy to implement, and has high calculation efficiency; (3) It has the ability to mine some implicit fault information; (4) The conventional global histogram equalization processing method has the characteristics of global optimization and the risk of local distortion is low.

[0106] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. Many features and advantages of these embodiments are apparent from this detailed description, and thus the claims are intended to cover all such features and advantages that fall within the true spirit and scope of these embodiments. In addition, since many modifications and variations are readily contemplated by those skilled in the art, the embodiments of the present invention are not to be limited to the exact construction and operation illustrated and described, but may cover all suitable modifications and equivalents that fall within its scope.

[0107] Those skilled in the art will appreciate that embodiments of the present invention may be provided as a method, system, 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 implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

[0108] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations 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 device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device create means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0111] In the present invention, specific embodiments are used to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for identifying three-dimensional tomographic information, characterized in that, the method includes: Obtaining the initial three-dimensional volume data of the third-generation coherence attribute of the three-dimensional seismic data volume in a predetermined area, and the initial three-dimensional volume data has tomographic information; Performing median filtering processing on the initial three-dimensional volume data, and determining the difference data between the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data; Performing histogram equalization processing and threshold adjustment processing on the difference data to enhance the tomographic information; Identifying the tomographic information in the initial three-dimensional volume data according to the data after the tomographic information is enhanced; The identifying the tomographic information in the initial three-dimensional volume data according to the data after the tomographic information is enhanced includes: Identifying the tomographic information in the initial three-dimensional volume data according to the time slice data and profile data after the tomographic information is enhanced.

2. The method according to claim 1, characterized in that, Determining the difference data between the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data includes: Determining the initial difference data according to the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data; Performing normalization processing on the initial difference data to obtain the difference data in the gray scale domain.

3. The method according to claim 2, characterized in that, Performing normalization processing on the initial difference data includes: Determining the maximum difference value and the minimum difference value according to the initial difference data; Performing normalization processing on the initial difference data according to the maximum difference value and the minimum difference value.

4. The method according to claim 2, characterized in that, Performing histogram equalization processing and threshold adjustment processing on the difference data includes: Performing matrix transformation operation on the difference data in the gray scale domain, and performing histogram equalization processing after the matrix transformation operation; Performing threshold adjustment processing on each pixel in the difference data after histogram equalization processing according to a predetermined rule.

5. A device for identifying three-dimensional tomographic information, characterized in that, the device includes: A data acquisition unit for acquiring the initial three-dimensional volume data of the third-generation coherence attribute of the three-dimensional seismic data volume in a predetermined area, and the initial three-dimensional volume data has tomographic information; A median filtering unit for performing median filtering processing on the initial three-dimensional volume data; A difference data determination unit for determining the difference data between the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data; An equalization processing unit for performing histogram equalization processing and threshold adjustment processing on the difference data to enhance the tomographic information; An identification unit for identifying the tomographic information in the initial three-dimensional volume data according to the data after the tomographic information is enhanced; The identification unit is specifically used for identifying the tomographic information in the initial three-dimensional volume data according to the time slice data and profile data after the tomographic information is enhanced.

6. The device according to claim 5, characterized in that, The difference data determination unit includes: An initial difference determination module for determining the initial difference data according to the three-dimensional volume data after median filtering processing and the initial three-dimensional volume data; A normalization module, configured to perform normalization processing on the initial difference data to obtain difference data in the grayscale domain.

7. The apparatus according to claim 6, wherein, the normalization module includes: a difference value determination sub-module, configured to determine a maximum difference value and a minimum difference value according to the initial difference data; a normalization sub-module, configured to perform normalization processing on the initial difference data according to the maximum difference value and the minimum difference value.

8. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, when the processor executes the program, the steps of the method according to any one of claims 1 to 4 are implemented.

9. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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