Seismic facies diagram generation method and device, equipment and medium

Abstract

The invention belongs to the technical field of seismic exploration data processing, and particularly relates to an amplitude determination method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a target data point in seismic data; determining coherent data points related to the target data points according to the target data points; obtaining a weighting factor of each coherent data point; and determining the weighted amplitude of the target data point according to each weighting factor. And the abnormal amplitude of the common offset domain is filtered and suppressed, so that the final image is clearer.

Description

Technical Field

[0001] The invention belongs to the technical field of earthquake analysis, and specifically relates to a method, device, equipment and medium for generating an earthquake phase diagram. Background Art

[0002] Generally speaking, seismic attributes can only reflect several physical parameters of seismic signals (such as amplitude, phase, frequency, etc.), and cannot fully describe the overall abnormality of seismic signals. The overall abnormality of seismic signals is closely related to the shape of seismic traces. Any change in physical parameters always corresponds to a change in the shape of seismic traces. Therefore, by analyzing the changes in seismic traces, we can understand the changing laws of seismic phases and infer the changing laws of sedimentary phases and lithofacies.

[0003] At present, the more popular seismic phase analysis methods at home and abroad mainly include waveform classification method, seismic attribute feature mapping method and phase division method based on seismic geomorphology. Among them, the waveform classification method is a relatively mature seismic phase analysis method, which has been widely used in lithology prediction, sand body prediction, oil and gas reservoir prediction and other fields. However, the traditional seismic waveform classification method mainly divides seismic phases based on the similarity of seismic trace waveforms in equal time windows, which is only applicable to target layers with stable formation thickness and little change in seismic time thickness. When the formation thickness changes, its seismic time thickness will also change, which means that the equal-length seismic waveforms extracted along the layer do not represent the complete lithology information of the target layer, and may even produce a "time-travel" phenomenon, thus failing to accurately reveal the correspondence between the reservoir and the seismic waveform. In order to solve this problem, it is necessary to develop seismic waveform analysis technology suitable for variable time windows.

[0004] In the application of actual time-varying window waveform classification technology, when facing actual seismic data, the conventional two-dimensional self-organizing feature map network (SOM) and dynamic time warping (DTW) method often have multiple neurons mapping the same type of seismic phase, resulting in large network scale, large amount of calculation, and waveform distortion in the signal resampling process, which is not conducive to the integration and judgment of the final classification results. Therefore, directly using these methods is not suitable for waveform classification technology in the case of large-scale seismic data. Summary of the invention

[0005] In response to the above technical problems, the present invention proposes a method, device, equipment and medium for generating a seismic phase map. The present application obtains seismic data of a target formation and a constant bandwidth of the target formation; converts the seismic data from time domain data to frequency domain data according to the constant bandwidth; obtains a preset classification number range; determines a class spacing variation diagram of the target formation according to the classification number range; determines a target classification number according to the class spacing variation diagram of the classification; and generates a planar seismic phase map of the target formation according to the target classification number. This method takes into account the problems that the correspondence between waveforms and reservoirs cannot be well reflected in the case of equal-thickness time windows and the computational complexity of conventional classification algorithms. It transforms seismic signals of unequal length in the time domain into the frequency domain and selects a suitable constant bandwidth for classification, which meets the actual classification and analysis needs of seismic data.

[0006] To solve the above technical problems, the technical solution adopted by the present invention includes four aspects.

[0007] In a first aspect, a method for generating a seismic phase map is provided, comprising: acquiring seismic data of a target stratum and a constant bandwidth of the target stratum; converting the seismic data from time domain data to frequency domain data according to the constant bandwidth; acquiring a preset classification number range; determining a class spacing versus classification variation graph of the target stratum according to the classification number range; determining a target classification number according to the class spacing versus classification variation graph; and generating a planar seismic phase map of the target stratum according to the target classification number.

[0008] In some embodiments, the method of obtaining the constant bandwidth of the target formation includes: obtaining the duration of the upper seismic reflection of a formation above the target formation; obtaining the duration of the lower seismic reflection of a formation below the target formation; and determining the constant bandwidth based on the duration of the upper seismic reflection and the duration of the lower seismic reflection.

[0009] In some embodiments, determining the constant bandwidth according to the duration of the upper seismic reflection and the duration of the lower seismic reflection includes: determining the maximum time difference between the upper and lower layers of the target formation according to the duration of the upper seismic reflection and the duration of the lower seismic reflection; determining the constant bandwidth according to the maximum time difference between the upper and lower layers.

[0010] In some embodiments, determining the graph of the distance between classes of the target stratum as it changes with classification based on the range of classification numbers includes: inputting each classification number within the range of classification numbers into a one-dimensional self-organizing feature mapping network to obtain a model track corresponding to each classification number; determining the sum of the intra-class distances of each classification number based on the model track; and determining the graph of the distance between classes as it changes with classification based on the sum of the intra-class distances of each classification number.

[0011] In some embodiments, determining the graph of how the distance between classes changes with classification based on the sum of the intra-class distances of each classification number includes: obtaining a preset threshold; comparing the sum of the intra-class distances corresponding to each classification number with the preset threshold, and when the sum of the intra-class distances corresponding to any classification number is less than the preset threshold, retaining the sum of the intra-class distances corresponding to the classification number and the model path; and determining the graph of how the distance between classes changes with classification based on the retained sums of the multiple intra-class distances.

[0012] In some embodiments, determining the number of target classifications according to the inter-class distance variation graph includes: determining the sum of distances within the target class according to the inter-class distance variation graph; and determining the number of target classifications according to the sum of distances within the target class.

[0013] In some embodiments, generating a plane seismic phase map of the target formation according to the target classification number includes: determining a target model track according to the target classification number; classifying and processing the frequency domain data according to the target model track and the target classification number using the similarity principle to generate a plane seismic phase map of the target formation.

[0014] In the second aspect, the present application proposes a device for generating a seismic phase map, comprising: a first acquisition module, used to acquire seismic data of a target formation and a constant bandwidth of the target formation; a first execution module, used to convert the seismic data from time domain data to frequency domain data according to the constant bandwidth; a second acquisition module, used to acquire a preset classification number range; a first determination module, used to determine a class spacing variation diagram of the target formation according to the classification number range; a second determination module, used to determine a target classification number according to the class spacing variation diagram of the classification; and a second execution module, used to generate a planar seismic phase map of the target formation according to the target classification number.

[0015] In a third aspect, the present application proposes an electronic device, comprising: a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method described in the first aspect is performed.

[0016] In a fourth aspect, the present application proposes a storage medium, which stores a computer program that can be executed by one or more processors, and the computer program can be used to implement the method described in the first aspect.

[0017] Beneficial effects created by the present invention: The present application obtains seismic data of a target formation and a constant bandwidth of the target formation; converts the seismic data from time domain data to frequency domain data according to the constant bandwidth; obtains a preset classification number range; determines a class spacing versus classification variation diagram of the target formation according to the classification number range; determines the target classification number according to the class spacing versus classification variation diagram; generates a plane seismic phase diagram of the target formation according to the target classification number. This method takes into account the problems that the correspondence between waveforms and reservoirs cannot be well reflected in the case of equal-thickness time windows and the computational complexity of conventional classification algorithms. It transforms seismic signals of unequal length in the time domain into the frequency domain and selects a suitable constant bandwidth for classification, which meets the actual classification and analysis needs of seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. The drawings included are:

[0019] Figure 1 An overall flow chart of a method for generating a seismic phase map provided in an embodiment of the present application;

[0020] Figure 2 A structural block diagram of a seismic phase diagram generation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0023] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0025] Embodiment 1:

[0026] At present, the more popular seismic phase analysis methods at home and abroad mainly include waveform classification method, seismic attribute feature mapping method and phase division method based on seismic geomorphology. Among them, the waveform classification method is a relatively mature seismic phase analysis method, which has been widely used in lithology prediction, sand body prediction, oil and gas reservoir prediction and other fields. However, the traditional seismic waveform classification method mainly divides seismic phases based on the similarity of seismic trace waveforms in equal time windows, which is only applicable to target layers with stable formation thickness and little change in seismic time thickness. When the formation thickness changes, its seismic time thickness will also change, which means that the equal-length seismic waveforms extracted along the layer do not represent the complete lithology information of the target layer, and may even produce a "time-travel" phenomenon, thus failing to accurately reveal the correspondence between the reservoir and the seismic waveform. In order to solve this problem, it is necessary to develop seismic waveform analysis technology suitable for variable time windows.

[0027] In view of the problems existing in the prior art, such as Figure 1 As shown, the present application provides a method for generating a seismic phase map, and the method is applied to an electronic device, and the electronic device can be a server, a mobile terminal, a computer, a cloud platform, etc. The function implemented by the device data processing provided in the embodiment of the present application can be implemented by calling a program code by a processor of the electronic device, wherein the program code can be stored in a computer storage medium, and the method for generating a seismic phase map includes:

[0028] Step S1: Acquire seismic data of a target formation and a constant bandwidth of the target formation.

[0029] In actual situations, not all strata have constant thickness, and it is common for strata thickness to vary in different ranges at different locations. As strata thickness deteriorates, seismic time thickness will also change, which means that the equal-length seismic waveforms extracted along the layers do not represent the complete lithology information of the target layer, and may even produce a "time-travel" phenomenon, making it impossible for existing technologies to accurately reveal the correspondence between reservoirs and seismic waveforms. In order to solve this problem, it is necessary to develop seismic waveform analysis technology suitable for variable time windows.

[0030] Therefore, in addition to obtaining necessary seismic data, the present application also requires obtaining a constant bandwidth of the target stratum, wherein the constant bandwidth can truly reflect the situation of the target stratum and ensure that the extracted seismic waveform has isochronous significance.

[0031] Therefore, in some embodiments, step S1 of “obtaining a constant bandwidth of a target formation” includes:

[0032] Step S11: Obtaining the seismic reflection duration of the upper layer of the layer above the target layer.

[0033] Step S12: Obtain the seismic reflection duration of the lower layer of the next layer of the target layer.

[0034] Step S13: Determine the constant bandwidth according to the upper layer seismic reflection duration and the lower layer seismic reflection duration.

[0035] The thickness of the target stratum is inconsistent, so we need to use the strata above and below the target stratum to determine the constant bandwidth of the target stratum. The upper seismic reflection duration indicates the seismic reflection duration of the stratum above the target stratum, and the next seismic reflection duration indicates the seismic reflection duration of the stratum below the target stratum. We can determine the constant bandwidth of the target stratum by the seismic reflection duration of the two adjacent strata of the target stratum.

[0036] In some embodiments, step S13 "determining the constant bandwidth according to the upper layer seismic reflection duration and the lower layer seismic reflection duration" includes:

[0037] Step S131: determining the maximum time difference between the upper and lower layers of the target stratum according to the upper layer seismic reflection duration and the lower layer seismic reflection duration.

[0038] Step S132: Determine the constant bandwidth according to the maximum time difference between the upper and lower layers.

[0039] The difference between the seismic reflection time of the upper layer and the seismic reflection time of the lower layer (i.e., the time difference between the upper and lower layers) can represent the seismic time thickness of the target stratum. The maximum time difference between the upper and lower layers can represent the seismic time of the thickest target stratum. After determining the maximum time difference between the upper and lower layers, the constant bandwidth can be determined by the following formula:

[0040]

[0041] In the formula,

[0042] Max h =max(HorTop n -HorBot n )

[0043] Maxh represents the maximum time difference between the upper and lower layers, Len fis a constant bandwidth, HorTop represents the duration of the upper seismic reflection, and HorBot represents the duration of the lower seismic reflection; [x] represents rounding the real number x up to the nearest integer, that is, [x] is the smallest integer not less than x.

[0044] Step S2: Convert the seismic data from time domain data to frequency domain data according to the constant bandwidth.

[0045] Since the thickness of the target strata is inconsistent, the seismic signals of the target earthquake are of unequal length in the time domain, so the seismic signals need to be filtered and transferred to the frequency domain for expression.

[0046] First, initialize the real and imaginary parts of the Fourier transform parameter data dataRe and dataIm, and their data lengths are both Len f . Save the seismic data of the target formation to dataRe, fill in the deficiencies with zeros, and make dataIm zero. Convert the saved time domain data to the frequency domain through fast Fourier transform, obtain the new real and imaginary parts fftRe and fftIm after calculation, and save the results. Expressed as:

[0047] (fftRe,fftIm)=fft(dataRe,dataIm)

[0048]

[0049] Where, fft() stands for Fast Fourier Transform, Cube stands for the saved frequency domain data, l = 1, 2, ..., nLines, nLines stands for the total number of lines, c = 1, 2, ..., nCDPs, nCDPs stands for the number of channels per line, t = 1, 2, ..., Len f / 2 means taking only the first half of the data after transformation.

[0050] Step S3: Obtain a preset range of classification numbers.

[0051] The selection of the number of classifications determines the quality of the final seismic phase map. Improper selection of the number of classifications may result in the final seismic phase map being too rough. Therefore, in this application, a range of classification numbers is preset according to the complexity of the seismic signal, the size of the target layer segment, and the degree of understanding of the seismic data. For example, the range of classification numbers C can be set between 5 and 15.

[0052] Step S4: Determine the class spacing versus classification variation diagram of the target stratum according to the classification number range.

[0053] In this application, since the range of classification numbers is preset, it is necessary to select a suitable classification number from a large number of classification numbers within the classification number range, and the class interval change diagram can well characterize the performance quality of seismic data under different classification numbers.

[0054] Therefore, in some embodiments, step S4 of "determining the class spacing versus classification variation diagram of the target stratum according to the classification number range" includes:

[0055] Step S41: input each classification number within the classification number range into a one-dimensional self-organizing feature mapping network to obtain a model path corresponding to each classification number.

[0056] In the present application, each classification number within the classification number range is first input into a one-dimensional self-organizing feature mapping network, so that the self-organizing feature mapping network classifies the frequency domain data according to the input classification number, and obtains a model of the frequency domain data determined by the input classification number output by the self-organizing feature mapping network.

[0057] Because in the application of actual time-varying window waveform classification technology, based on the conventional two-dimensional self-organizing feature map network (SOM) and dynamic time warping (DTW) method, when facing actual seismic data, multiple neurons often map the same type of seismic phase, so that the network scale is huge, the amount of calculation is large, and there are problems of waveform distortion in the signal resampling process, which is not conducive to the integration and judgment of the final classification result, so these methods are directly used. It is not suitable for the waveform classification technology under the situation of large-scale seismic data. That is, in the application of the common two-dimensional Kohonen (self-organizing feature map network) network structure, multiple neurons often map the same type of seismic phase, thereby causing the network scale to be huge and the algorithm convergence speed to be slow, which is not conducive to the integration and judgment of the final classification result. Therefore, in the present embodiment, we have improved the original two-dimensional Kohonen network according to the resolution of seismic data and the requirements of seismic phase classification, and changed it to use a structure with fewer neurons and a one-dimensional network as the output layer to meet the needs of practical applications.

[0058] The improved one-dimensional Kohonen (self-organizing feature map network) network is different from the two-dimensional array in the structure of the output layer. It adopts a linear topology and is composed of one-dimensional output nodes. The network still adjusts the domain and value of weight correction according to the responsiveness of the output layer neurons to the input neurons, effectively reducing the scale of the network and reducing the complexity.

[0059] Step S42: Determine the sum of the intra-class distances of each classification number according to the model.

[0060] Step S43: determining the distance between classes and the change graph of the classification according to the sum of the intra-class distances of each classification number.

[0061] When classifying frequency domain waveforms, we use the same method as time domain classification, calculating the sum of the intra-class distances ERR for each classification number, and finally generating a graph of the distance between classes changing with classification. It can be expressed as:

[0062]

[0063] Among them, ERR is the sum of the intra-class distances obtained, is a sample belonging to the i-th category, i=1,…,K, K∈(5,15) represents the current number of categories, j=1,…,n(i), () is the weight vector corresponding to this type.

[0064] In some embodiments, step S43 "determining the distance between classes and classification variation diagram according to the sum of the intra-class distances of each classification number" includes:

[0065] Step S431: Obtain a preset threshold.

[0066] Step S432: Compare the sum of the intra-class distances corresponding to each classification number with the preset threshold value. When the sum of the intra-class distances corresponding to any classification number is less than the preset threshold value, retain the sum of the intra-class distances corresponding to the classification number and the model path.

[0067] Step S433: determining the class distance variation graph according to the sum of the multiple retained intra-class distances.

[0068] However, in actual operation, not the sum of the intra-class distances corresponding to each classification number can be used to generate a diagram of the class distance variation with classification. After all, the intra-class distances corresponding to some classification numbers are too large. Even if they are used to generate a diagram of the class distance variation with classification, they will not be selected. Instead, the amount of calculation will be increased. Therefore, there is a preset threshold in the application. The sum of the intra-class distances corresponding to each classification number is filtered through the preset threshold, and the model paths and intra-class distances whose sum of intra-class distances is less than the preset threshold are retained, and the diagram of the class distance variation with classification is generated based on the retained sum of the intra-class distances.

[0069] Step S5: Determine the target number of categories according to the graph showing the variation of the distance between categories with the category.

[0070] In some embodiments, step S5 "determining the target number of classifications according to the class distance variation diagram" includes:

[0071] Step S51: determining the sum of the distances within the target class according to the class distance variation diagram.

[0072] Step S52: Determine the target classification number according to the sum of the distances within the target class.

[0073] Usually, as the number of categories increases, the distance between classes gradually decreases. Theoretically, the point where the ERR is the smallest is the optimal number of categories, but it is difficult to find this point clearly in practical applications. In this embodiment, we usually find the point where the curve monotonically decreases and tends to be flat on the curve of the distance between classes changing with the number of categories, which is the appropriate number of categories. Therefore, we can determine the sum of the distances within the target class through the diagram of the distance between classes changing with the classification. Since the sum of the distances within the class is in a corresponding relationship with the number of categories, the target number of categories can be determined by the sum of the distances within the target class.

[0074] Step S6: generating a planar seismic phase map of the target stratum according to the target classification number.

[0075] In some embodiments, step S6 of "generating a plane seismic phase map of the target stratum according to the target classification number" includes:

[0076] Step S61: Determine the target model path according to the target classification number.

[0077] Step S62: using the similarity principle to classify the frequency domain data according to the target model trace and the target classification number, and generating a plane seismic phase map of the target stratum.

[0078] Since the model traces corresponding to the classification numbers in the class spacing versus classification change diagram are retained in step S432, we can determine the target model trace by the target classification number. After determining the target model trace, the seismic data converted into frequency domain data can be classified and processed using the similarity principle according to the target model trace and the target classification number, thereby obtaining a suitable plane seismic phase map of the target stratum.

[0079] Therefore, considering the problems that the correspondence between waveform and reservoir cannot be well reflected in the case of equal-thickness time window and the computational complexity of conventional classification algorithms, this application adopts the method of transforming the seismic signals of unequal length in the time domain into the frequency domain, selecting a suitable constant bandwidth for classification, and at the same time, changing the existing two-dimensional Kohonen network into a one-dimensional Kohonen network structure. This can not only meet the actual classification and analysis needs of seismic data, but also effectively control the network scale, reduce complexity and improve classification effect.

[0080] Embodiment 2:

[0081] Based on the foregoing embodiments, an embodiment of the present application provides a device for generating a seismic phase diagram. The modules included in the device and the units included in each module can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0082] like Figure 2 As shown, a device for generating a seismic phase diagram includes: a first acquisition module 1, a first execution module 2, a second acquisition module 3, a first determination module 4, a second determination module 5 and a second execution module 6.

[0083] The first acquisition module 1 is used to acquire the seismic data of the target stratum and the constant bandwidth of the target stratum. The first execution module 2 is used to convert the seismic data from time domain data to frequency domain data according to the constant bandwidth. The second acquisition module 3 is used to acquire a preset classification number range. The first determination module 4 is used to determine the class spacing with classification change diagram of the target stratum according to the classification number range. The second determination module 5 is used to determine the target classification number according to the class spacing with classification change diagram. The second execution module 6 is used to generate a plane seismic phase map of the target stratum according to the target classification number.

[0084] In some embodiments, the first acquisition module 1 includes: a third acquisition module, a fourth acquisition module and a third determination module.

[0085] The third acquisition module is used to acquire the duration of the upper seismic reflection of the layer above the target layer. The fourth acquisition module is used to acquire the duration of the lower seismic reflection of the layer below the target layer. The third determination module is used to determine the constant bandwidth according to the duration of the upper seismic reflection and the duration of the lower seismic reflection.

[0086] In some embodiments, the third determination module includes: a fourth determination module and a fifth determination module.

[0087] The fourth determination module is used to determine the maximum time difference between the upper and lower layers of the target stratum according to the upper layer seismic reflection duration and the lower layer seismic reflection duration. The fifth determination module is used to determine the constant bandwidth according to the maximum time difference between the upper and lower layers.

[0088] In some embodiments, the first determination module 4 includes: a third execution module, a sixth determination module and a seventh determination module.

[0089] The third execution module is used to input each classification number within the classification number range into the one-dimensional self-organizing feature map network to obtain a model path corresponding to each classification number. The sixth determination module is used to determine the sum of the intra-class distances of each classification number according to the model path. The seventh determination module is used to determine the inter-class distance variation diagram according to the sum of the intra-class distances of each classification number.

[0090] In some embodiments, the seventh determination module includes: a fifth acquisition module, a fourth execution module and an eighth determination module.

[0091] The fifth acquisition module is used to obtain a preset threshold. The fourth execution module is used to compare the sum of the intra-class distances corresponding to each classification number with the preset threshold. When the sum of the intra-class distances corresponding to any classification number is less than the preset threshold, the sum of the intra-class distances corresponding to the classification number and the model path are retained. The eighth determination module is used to determine the inter-class distance variation diagram according to the retained sum of the multiple intra-class distances.

[0092] In some embodiments, the second determination module 5 includes: a ninth determination module and a tenth determination module.

[0093] The ninth determination module is used to determine the sum of the distances within the target class according to the class distance variation diagram. The tenth determination module is used to determine the number of target classifications according to the sum of the distances within the target class.

[0094] In some embodiments, the second execution module 6 includes: an eleventh determination module and a fifth execution module.

[0095] The eleventh determination module is used to determine the target model trace according to the target classification number. The fifth execution module is used to classify the frequency domain data according to the target model trace and the target classification number using the similarity principle to generate a plane seismic phase map of the target stratum.

[0096] Each module in the above-mentioned device for generating a seismic phase diagram can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the device in the form of hardware, or can be stored in the memory in the processing device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic, which is only a logical function division, and there may be other division methods in actual implementation.

[0097] Embodiment 3:

[0098] The third aspect provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a method for generating a seismic phase map when executing the computer program.

[0099] Embodiment 4:

[0100] A fourth aspect provides a storage medium, which stores a computer program that can be executed by one or more processors. The computer program can be used to implement the steps of any method for generating a seismic phase map in the first aspect.

[0101] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0102] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0103] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0105] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0106] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0107] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0108] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0109] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for generating a seismic phase map, characterized in that: include: Acquire seismic data of a target formation and a constant bandwidth of the target formation; Converting the seismic data from time domain data to frequency domain data according to the constant bandwidth; Get the preset range of classification numbers; Determine a graph of the distance between classes and the change of classification of the target stratum according to the range of the classification number; Determine the target classification number according to the class distance variation diagram; A planar seismic phase map of the target stratum is generated according to the target classification number.

2. The method according to claim 1, characterized in that The step of obtaining a constant bandwidth of a target formation includes: Obtaining the seismic reflection duration of an upper layer of a layer above the target layer; Obtaining the seismic reflection duration of a lower layer of a layer below the target layer; The constant bandwidth is determined according to the upper layer seismic reflection duration and the lower layer seismic reflection duration.

3. The method according to claim 2, characterized in that The step of determining the constant bandwidth according to the upper layer seismic reflection duration and the lower layer seismic reflection duration comprises: Determine the maximum time difference between the upper and lower layers of the target stratum according to the upper layer seismic reflection duration and the lower layer seismic reflection duration; The constant bandwidth is determined according to the maximum time difference between the upper and lower layers.

4. The method according to claim 1, characterized in that The step of determining the class spacing versus classification variation diagram of the target stratum according to the classification number range includes: Input each classification number within the classification number range into a one-dimensional self-organizing feature mapping network to obtain a model path corresponding to each classification number; Determine the sum of the intra-class distances of each classification number according to the model; The graph of the distance between classes changing with classification is determined according to the sum of the intra-class distances of each classification number.

5. The method according to claim 4, characterized in that The step of determining the distance between classes and the change of classification according to the sum of the intra-class distances of each classification number includes: Get the preset threshold value; Compare the sum of the intra-class distances corresponding to each classification number with the preset threshold value, and when the sum of the intra-class distances corresponding to any classification number is less than the preset threshold value, retain the sum of the intra-class distances corresponding to the classification number and the model path; The graph of the distance between classes changing with classification is determined according to the sum of the multiple retained intra-class distances.

6. The method according to claim 5, characterized in that Determining the target classification number according to the class distance variation diagram includes: Determine the sum of the distances within the target class according to the class distance variation diagram; The target classification number is determined according to the sum of the distances within the target class.

7. The method according to claim 4, characterized in that The step of generating a plane seismic phase map of the target stratum according to the target classification number comprises: Determine the target model according to the target classification number; The frequency domain data is classified and processed according to the target model trace and the target classification number by using the similarity principle to generate a plane seismic phase map of the target stratum.

8. A device for generating a seismic phase diagram, characterized in that: include: A first acquisition module, used for acquiring seismic data of a target formation and a constant frequency bandwidth of the target formation; A first execution module, configured to convert the seismic data from time domain data to frequency domain data according to the constant bandwidth; The second acquisition module is used to obtain a preset classification number range; A first determination module is used to determine a graph of the variation of the inter-class distance and the classification of the target stratum according to the classification number range; A second determination module is used to determine the target classification number according to the class distance variation diagram; The second execution module is used to generate a plane seismic phase map of the target stratum according to the target classification number.

9. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is executed.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors, and the computer program can be used to implement the method according to any one of claims 1 to 7.

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