Quantitative division method and device for high-frequency cycle sequence, electronic equipment and storage medium

Abstract

The invention relates to a high-frequency cycle sequence quantitative division method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the long-term cycle sequence division of a target sedimentary stratum, and obtaining a long-term cycle sequence and a GR curve; performing discrete wavelet multi-scale decomposition on the GR curve based on the long-term cycle sequence to obtain a decomposition result, and extracting frequency domain information from the decomposition result; performing continuous wavelet analysis on the GR curve based on the long-term cycle sequence to obtain time domain information; according to the frequency domain information and the time domain information, determining a corresponding relation between a high-frequency cycle sequence and a wavelet scale; and performing high-frequency cycle sequence division on the target sedimentary strata based on the corresponding relation to obtain a division result of the high-frequency cycle sequence. According to the invention, the accuracy of quantitative division of the frequency cycle sequence can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of geological exploration technology, specifically to a high-frequency cyclic sequence quantitative classification method, apparatus, electronic device, and storage medium. Background Technology

[0002] Sequence stratigraphy is a discipline that studies the relationships between strata within a genetically related chronostratigraphic framework defined by unconformities and their corresponding conformable surfaces. As a relatively new and comprehensive comparative analysis method, sequence stratigraphy's most significant contribution lies in its ability to effectively delineate and correlate sedimentary strata. It provides a more accurate method for geological age correlation, lithofacies paleogeographic reconstruction, and pre-drilling prediction of source-reservoir-seal assemblages. Currently, it is widely used in many fields such as sedimentary stratigraphic analysis and oil and gas exploration and development, and continues to experience rapid growth.

[0003] High-frequency cycles are controlled by the Milankovitch astronomical cycle. When performing quantitative classification of high-frequency cycles, the premise of quantification is the accurate definition of the astronomical cycle of high-frequency cycles. The key technology is how to accurately identify and locate high-frequency cycles at each level. Currently, the most commonly used method for accurately identifying high-frequency cycles is the application of spectral analysis.

[0004] However, since spectral analysis only extracts frequency features and loses time features, it cannot accurately determine the time position of a certain frequency, and therefore cannot determine the accurate depth of high-frequency cycles. It can only give the average statistical thickness of high-frequency cycles, and the thickness of each cycle must be different. The problem of determining the accurate position of each cycle is something that spectral analysis cannot solve. Therefore, the current method for dividing high-frequency cycle sequences has the problem of inaccurate division. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this disclosure provides a method, apparatus, electronic device and storage medium for quantitative classification of high-frequency cyclic sequence.

[0006] In a first aspect, embodiments of this application provide a method for quantitative segmentation of high-frequency cycle sequences, including:

[0007] Long-term cyclic sequence stratigraphy was performed on the target sedimentary strata to obtain long-term cyclic sequence stratigraphy and GR curves;

[0008] Based on the long-term cyclic sequence, the GR curve is subjected to discrete wavelet multi-scale decomposition to obtain the decomposition result, and frequency domain information is extracted from the decomposition result.

[0009] Continuous wavelet analysis is performed on the GR curve based on the long-term cyclic sequence to obtain time-domain information;

[0010] Based on the frequency domain information and the time domain information, determine the correspondence between high-frequency cyclic sequence and wavelet scale;

[0011] Based on the aforementioned correspondence, the target sedimentary strata were divided into high-frequency cyclic sequence stratigraphy to obtain the high-frequency cyclic sequence stratigraphy results.

[0012] Optionally, the step of performing discrete wavelet multi-scale decomposition on the GR curve based on the long-term cyclic sequence to obtain the decomposition result, and extracting frequency domain information from the decomposition result, includes:

[0013] Using the long-term cyclic sequence as a unit, the GR curve is subjected to discrete wavelet multi-scale decomposition to obtain the decomposition result;

[0014] Identify the low-frequency background signal and the high-frequency interference signal in the decomposition result, and filter the low-frequency background signal and the high-frequency interference signal from the decomposition result to obtain the filtered decomposition result;

[0015] Frequency domain information is extracted from the filtered decomposition results.

[0016] Optionally, extracting frequency domain information from the filtered decomposition result includes:

[0017] Fast Fourier spectrum analysis was performed on the filtered decomposition results to obtain the initial high-frequency cyclotron sequence in the Fourier spectrum.

[0018] If it is determined that the period corresponding to the initial high-frequency cyclic sequence meets the requirements of the Milankovitch astronomical period, then the initial high-frequency cyclic sequence is determined as frequency domain information.

[0019] Optionally, the step of performing continuous wavelet analysis on the GR curve based on the long-term cyclic sequence to obtain time-domain information includes:

[0020] Using the long-term cyclic sequence as a unit, continuous wavelet analysis is performed on the GR curve to obtain multiple wavelet scales;

[0021] Obtain the wavelet coefficient curve corresponding to each of the multiple wavelet scales, and determine the wavelet coefficient curve as the time domain information.

[0022] Optionally, the correspondence between the high-frequency cyclic sequence and the wavelet scale includes:

[0023] The correspondence between the wavelet coefficient curves and the levels with different change periods in the initial high-frequency cyclic sequence.

[0024] Optionally, before performing high-frequency cyclic sequence stratigraphy on the target sedimentary strata based on the correspondence, the method further includes:

[0025] Based on the GR curve, a high-frequency sequence division of the continuous wavelet analysis spectrum is performed to obtain the first division result;

[0026] If the first division result matches the correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the correspondence is executed.

[0027] Optionally, before performing high-frequency cyclic sequence stratigraphy on the target sedimentary strata based on the correspondence, the method further includes:

[0028] Based on the GR curve, high-frequency sequence division of complex wavelet phase spectrum is performed to obtain the second division result;

[0029] If the second division result matches the correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the correspondence is executed.

[0030] Optionally, before performing high-frequency cyclic sequence stratigraphy on the target sedimentary strata based on the correspondence, the method further includes:

[0031] Divide the prosodic layers based on the GR curve;

[0032] Based on the aforementioned prosodic layer, a Fischer diagram is drawn, and high-frequency sequence division is performed according to the Fischer diagram to obtain a third division result;

[0033] If the third division result matches the correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the correspondence is executed.

[0034] Optionally, before performing high-frequency cyclic sequence stratigraphy on the target sedimentary strata based on the correspondence, the method further includes:

[0035] Based on the GR curve, a high-frequency sequence division of the continuous wavelet analysis spectrum is performed to obtain the first division result;

[0036] Based on the GR curve, high-frequency sequence division of complex wavelet phase spectrum is performed to obtain the second division result;

[0037] Based on the GR curve, minor prosodic layers are divided, and Fischer diagrams are drawn based on the minor prosodic layers. High-frequency sequence division is performed based on the Fischer diagrams to obtain a third division result.

[0038] If the first division result, the second division result, and the third division result all match the correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the correspondence is executed.

[0039] Secondly, embodiments of this application provide a high-frequency cyclic sequence quantitative classification device, comprising:

[0040] The first partitioning module is used to perform long-term cycle sequence partitioning of the target sedimentary strata, and obtain long-term cycle sequence and GR curve;

[0041] The extraction module is used to perform discrete wavelet multi-scale decomposition on the GR curve based on the long-term cyclic sequence, obtain the decomposition result, and extract frequency domain information from the decomposition result.

[0042] The wavelet analysis module is used to perform continuous wavelet analysis on the GR curve based on the long-term cyclic sequence to obtain time-domain information.

[0043] The relationship determination module is used to determine the correspondence between high-frequency cyclic sequence and wavelet scale based on the frequency domain information and the time domain information;

[0044] The second partitioning module is used to perform high-frequency cyclic sequence partitioning of the target sedimentary strata based on the correspondence, and obtain the partitioning result of the high-frequency cyclic sequence.

[0045] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory stores a plurality of computer instructions; the processor loads the computer instructions from the memory to execute the steps in the high-frequency cyclic sequence quantitative partitioning method as described in the first aspect.

[0046] Fourthly, embodiments of this application provide a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute the high-frequency cyclic sequence quantitative partitioning method described in the first aspect.

[0047] The technical solution provided by the embodiments of this disclosure can include the following beneficial effects: Long-term cyclic sequence stratigraphy is performed on the target sedimentary strata to obtain long-term cyclic sequences and GR curves; discrete wavelet multi-scale decomposition is performed on the GR curves based on the long-term cyclic sequences to obtain decomposition results, and frequency domain information is extracted from the decomposition results; continuous wavelet analysis is then performed on the GR curves based on the long-term cyclic sequences to obtain time domain information; then, the correspondence between high-frequency cyclic sequences and wavelet scales is determined according to the frequency domain information and the time domain information; finally, high-frequency cyclic sequence stratigraphy is performed on the target sedimentary strata based on the correspondence to obtain the high-frequency cyclic sequence stratigraphy division results. This allows for the division of high-frequency cyclic sequences by combining analysis in both the time and frequency domains, greatly reducing human error during manual division of high-frequency cyclic sequences and improving the accuracy of high-frequency cyclic sequence stratigraphy division.

[0048] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This is a flowchart illustrating a high-frequency cyclic sequence quantitative classification method according to an exemplary embodiment;

[0051] Figure 2 This is a schematic diagram of discrete wavelet multiscale decomposition according to an exemplary embodiment;

[0052] Figure 3 This is a chromatogram of the absolute values ​​of continuous wavelet amplitude coefficients, as illustrated in an exemplary embodiment.

[0053] Figure 4 This is a comparison diagram of wavelet coefficient curves and logging signals at three scales, according to an exemplary embodiment.

[0054] Figure 5 This is a comparison diagram of the partitioning results of wavelet coefficient curve partitioning and continuous wavelet analysis spectrum high-frequency partitioning, based on an exemplary embodiment.

[0055] Figure 6 This is a comparison diagram of wavelet coefficient curve partitioning and complex wavelet phase spectrum partitioning results according to an exemplary embodiment;

[0056] Figure 7 This is a comparison diagram of wavelet coefficient curves for layer division and Fischer plot results, based on an exemplary embodiment.

[0057] Figure 8 This is an implementation flowchart of steps 110 to 150 according to an exemplary embodiment;

[0058] Figure 9 This is a schematic diagram of the structure of a high-frequency cyclic sequence quantitative classification device according to an exemplary embodiment;

[0059] Figure 10 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment. Detailed Implementation

[0060] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0061] Sequence stratigraphy is a discipline that studies the relationships between strata within a genetically related chronostratigraphic framework defined by unconformities and their corresponding conformable surfaces. As a relatively new and comprehensive comparative analysis method, sequence stratigraphy's most significant contribution lies in its ability to effectively delineate and correlate sedimentary strata. It provides a more accurate method for geological age correlation, lithofacies paleogeographic reconstruction, and pre-drilling prediction of source-reservoir-seal assemblages. Currently, it is widely used in many fields such as sedimentary stratigraphic analysis and oil and gas exploration and development, and continues to experience rapid growth. A large portion of the world's oil and gas resources are hosted in carbonate strata, especially in my country, where oil and gas resources are widely distributed in Paleozoic and Mesozoic marine carbonate rocks. Therefore, marine carbonate strata have always been an important part of geological research, and carbonate sequence stratigraphy is currently an international research topic.

[0062] If sequence stratigraphy has made significant progress in analyzing the age, framework, and spatiotemporal distribution of sedimentary strata at the basin scale, providing a theoretical foundation and effective means for basin-scale reservoir prediction, then high-frequency sequence stratigraphy provides a powerful tool for more accurate, detailed, and specific high-precision sequence stratigraphic research, further in-depth oil and gas exploration and development, and precise reservoir description and prediction. Theoretical research and practice have proven that oil and gas exploration has entered a new era characterized by high-frequency sequence stratigraphy, marked by accurate prediction and detailed description.

[0063] High-frequency sequence stratigraphy refers to a series of sedimentary cycles, including orders IV, V, and VI. Zheng Rongcai et al. termed them "medium- and short-term base-level cycles and ultra-short-term base-level cycles." Similar to the "meter-scale cycles" described by Anderson and Mei Mingxiang, it includes the "quasi-sequence" defined by Vail and the "small sequence" referred to by Wang Hongzhen et al. The thickness of high-frequency cycles at outcrops can range from tens of meters to several meters, with a time span from 0.01 to 0.5 Ma.

[0064] High-frequency sequence stratigraphy (HFSE) is based on core samples, outcrops, well logging, and high-resolution seismic profiles. It uses fine sequence division and correlation techniques to predict stratigraphic relationships, establishing a sequence stratigraphic framework for regional, oilfield, and even reservoir-level reservoirs. This framework evaluates the distribution of reservoirs, interlayers, and source layers, scientifically and rationally explaining the genesis and interrelationships of various sedimentary units in both lateral and vertical directions. Through the division of sequences of different scales, identification of base-level cycles, and systems tracts, it systematically links these sequences to the temporal evolution and spatial configuration of sedimentary bodies, making it particularly suitable for current needs in lithologic reservoir exploration and fine-grained exploration and development. Taking the Changxing-Feixianguan Formation carbonate strata in the Liangping area of ​​the Sichuan Basin as an example, high-frequency cycle sea-level changes control the scale of reef-shoal development, thereby controlling the scale of reservoir development. Quantitative high-frequency cycle division technology plays an increasingly important role in fine-grained oil and gas exploration.

[0065] In related technologies, the different methods used in the study of high-frequency cyclic sequence can be summarized into the following five main types:

[0066] Isotope dating: In 1976, Hays used oxygen isotope data from deep-sea sediments to confirm the existence of the Milankovitch sedimentary cycle in the strata.

[0067] Lithofacies-water depth method: Olsen calibrated the water depth levels represented by different lithofacies in the Triassic lacustrine sediments of the Newark Basin in the United States and identified sedimentary cycles in the Mie band; Wang Jianhui et al. determined high-frequency cycles by repeating different lithologies, grain sizes, and biological fossils (clastics); Burn et al. identified the Mie cycles of the Montserrat fan delta in northern Spain by identifying coastline migration and superposition patterns, and discussed the impact on climate, etc.

[0068] Environmental-climate approach: Williams' research on the environmental evolution history of Lake Baikal over the past 4 million years also confirms the existence of clear 100,000-year and 41,000-year cycles.

[0069] Fischer Diagram Method: Su Dechen et al. and Mei Mingxiang et al. applied the Fischer diagram method to analyze carbonate strata and identified Milankovitch cycles. Hu Shouquan et al. applied the Fischer diagram method to analyze continental fan delta strata and identified eccentricity cycles.

[0070] Well logging curve spectrum analysis: Wang Yuyi et al. used the slip trend analysis method to smooth the original data and find the dominant period; Li Fengjie et al. and Wang Honggang et al. used fast Fourier transform to perform spectrum analysis on Gr curves to determine the period of high-frequency cycles and find the ratio between different dominant frequencies to determine the existence of Mie cycles; Fan Guozhang et al. used binary wavelet transform to obtain the main frequency components contained in the curve to study high-frequency cycles; Zhang Zhansong et al. used the sliding window energy spectrum of well logging curves to identify the high-frequency cycle characteristics of formations.

[0071] Among them, high-frequency cycles are controlled by Milankovitch astronomical cycles. The premise of quantification is the accurate definition of the astronomical cycle of high-frequency cycles. The key technology is how to accurately identify and locate high-frequency cycles at each level. Among the related technologies for accurately identifying high-frequency cycles, spectrum analysis methods are frequently used.

[0072] However, because spectral analysis only extracts frequency features and discards time features, it cannot accurately determine the temporal location of a specific frequency, and therefore cannot determine the precise depth of high-frequency cycles. It can only provide the average statistical thickness of high-frequency cycles, but the thickness of each cycle will inevitably differ. Determining the precise location of each cycle is something that spectral analysis cannot accomplish. Furthermore, due to the multi-geological processes and multi-periodic variations in geological processes, the stratigraphic response is actually a cascade process of multiple factors and multiple periods superimposed, plus the interference of random fluctuations caused by uncertainties and local factors. The actual result is that long periods are interfered with by short periods, and short periods are buried in long periods. This increases the difficulty of manually classifying high-frequency cycles and makes the classification of high-frequency cycles highly arbitrary and prone to human error, leading to inaccurate classification.

[0073] To address the aforementioned issues, this embodiment provides a method for quantitative classification of high-frequency cyclic sequence stratigraphy.

[0074] It should be noted that all data in this disclosure was collected in accordance with the relevant data protection laws and policies of the country where the data is located, and with the authorization of the respective device owner.

[0075] Figure 1 This is a flowchart illustrating a high-frequency cycle sequence quantitative partitioning method according to an exemplary embodiment, such as... Figure 1 As shown, the method may include the following steps:

[0076] 110. Long-term cyclic sequence stratigraphy was performed on the target sedimentary strata to obtain long-term cyclic sequence stratigraphy and GR curves.

[0077] In some implementations, long-term cyclic sequence delineation can be performed by querying core, seismic, well logging, and literature survey data of the target sedimentary strata to obtain long-term cyclic sequence and GR curves in advance. These long-term cyclic sequence and GR curves are then stored in an electronic device for direct retrieval when needed.

[0078] It is understood that long-term sequence stratigraphy is usually equivalent to a group or segment in a lithostratigraphic unit and is a large time unit in sequence stratigraphy. Long-term sequence stratigraphy is a common method in this field based on existing data and is not the focus of this embodiment. Therefore, existing division methods can be used and will not be elaborated here.

[0079] Among them, the GR curve, or natural gamma ray logging curve, is a commonly used logging method in geological logging. It is mainly used to analyze the lithology, grain size variation, source supply variation, and sand body modification degree of sedimentary rocks.

[0080] 120. Based on the above long-term cyclic sequence, the above GR curve is subjected to discrete wavelet multi-scale decomposition to obtain the decomposition results, and frequency domain information is extracted from the above decomposition results.

[0081] In some implementations, in step 120, the GR curve is subjected to discrete wavelet multi-scale decomposition based on the long-term cyclic sequence to obtain the decomposition result, and frequency domain information is extracted from the decomposition result, including:

[0082] 121. Using the above long-term cyclic sequence as a unit, perform discrete wavelet multi-scale decomposition on the above GR curve to obtain the decomposition results.

[0083] In this scheme, since the long-term cycles are further divided into short-term cycles to obtain the division result of the high-frequency cycle sequence, each long-term cycle in the long-term cycle sequence can be treated as a unit to perform the operation of discrete wavelet multi-scale decomposition on the above GR curve.

[0084] 122. Identify the low-frequency background signal and high-frequency interference signal in the above decomposition results, and filter the low-frequency background signal and high-frequency interference signal from the above decomposition results to obtain the filtered decomposition results.

[0085] The decomposition results typically include low-frequency background signals, high-frequency interference signals, and mid-to-high-frequency signals. The low-frequency background signals and high-frequency interference signals in the decomposition results can be removed, and the mid-to-high-frequency signals can be used as the filtered decomposition results.

[0086] For example, please refer to Figure 2 , Figure 2 A schematic diagram of discrete wavelet multi-scale decomposition is shown. Figure 2In the diagram, s represents the original GR curve. The s curve undergoes a 9th discrete wavelet decomposition, where a9 represents the low-frequency component of the 9th discrete wavelet decomposition, d1 represents the noise component of the logging signal, and d9~d1 represent the high-frequency components of the 9th discrete wavelet decomposition. The original curve s = a9 + d9 + d8 + ... + d1. The logging signal after removing the low-frequency background and high-frequency noise is s - a9 - d1. Then, the... Figure 2 The remaining mid-to-high frequency signals after filtering signals a9 and d1 are used as the filtered decomposition result. In practical applications, the coefficients of the decomposed low-frequency background signal and high-frequency interference signal can be set to zero.

[0087] 123. Extract frequency domain information from the above filtered decomposition results.

[0088] In some implementations, the specific implementation of extracting frequency domain information from the filtered decomposition results in step 123 may include:

[0089] Fast Fourier spectrum analysis was performed on the filtered decomposition results to obtain the initial high-frequency cyclic sequence in the Fourier spectrum.

[0090] If it is determined that the period corresponding to the above-mentioned initial high-frequency cyclic sequence meets the requirements of the Milankovitch astronomical period, then the above-mentioned initial high-frequency cyclic sequence is determined as frequency domain information.

[0091] Using the example above, Fourier transform can be applied to perform spectral analysis on the filtered decomposition result, i.e., the stationary signal (s-a9-d1), to obtain the initial high-frequency cyclotron sequence in the Fourier spectrum.

[0092] Then, the high-frequency cycle sequence can be compared with the Milankovitch period to determine whether the high-frequency cycle obtained above is controlled by the Milankovitch period. If it is controlled by the Milankovitch period, it means that the above high-frequency cycle sequence division scheme is feasible.

[0093] For example, it can be determined whether the period corresponding to the aforementioned initial high-frequency cycle sequence meets the requirements of the Milankovitch astronomical period. A specific implementation method includes: determining the average thickness of each dominant cycle determined based on the correspondence between the high-frequency cycle sequence and the Milankovitch astronomical period. If the thickness ratio of each dominant cycle is similar to the Milankovitch cycle ratio, and the obtained deposition rate is consistent with the deposition rate obtained by integrating other data, then it can be determined that the period corresponding to the aforementioned initial high-frequency cycle sequence meets the requirements of the Milankovitch astronomical period.

[0094] It is understandable that, according to the high-frequency sequence stratigraphy classification scheme, the thickness of strata controlled by long-period eccentricity is determined as the thickness of medium-term sequence stratigraphy, the thickness of strata controlled by short-period eccentricity is determined as the thickness of short-term sequence stratigraphy, and the thickness of strata controlled by precession period is determined as the thickness of ultra-short-term sequence stratigraphy. Fourier spectral analysis only extracts frequency features and discards time features, therefore it cannot accurately determine the time position of a certain frequency, and thus cannot determine the accurate depth of high-frequency cycles. It can only provide the average statistical thickness of high-frequency cycles, but the thickness of each cycle will inevitably differ. Determining the accurate location of each cycle is something that Fourier spectral analysis cannot accomplish. To solve these problems, wavelet transform, which has the ability to perform time-frequency dual-domain analysis, must be used.

[0095] 130. Based on the above long-term cyclic sequence, continuous wavelet analysis is performed on the above GR curve to obtain time-domain information.

[0096] In some embodiments, the specific implementation of performing continuous wavelet analysis on the GR curve based on the long-term cyclic sequence in step 130 to obtain time-domain information may include:

[0097] Using the aforementioned long-term cyclic sequence as a unit, continuous wavelet analysis was performed on the aforementioned GR curve to obtain multiple wavelet scales.

[0098] Obtain the wavelet coefficient curve corresponding to each of the above multiple wavelet scales, and determine the above wavelet coefficient curve as the above time domain information.

[0099] In wavelet analysis, continuous wavelet transform can be used. Wavelengths with high power and relative continuity in the wavelet spectrum represent the thickness of the main formation cycles. Wavelet analysis is a time-frequency analysis method that can localize signals in the time and frequency domains, extracting local features, including frequency and energy variations. Wavelet analysis can be used to identify dominant periods in formations and compare them with the theoretical periods of Mie cycles. Through wavelet analysis, time-frequency characteristic information of formations can be extracted from well logging data, further determining the cycle periods within the formations.

[0100] 140. Based on the above frequency domain information and time domain information, determine the correspondence between high-frequency cyclic sequence and wavelet scale.

[0101] In some implementations, the correspondence between the aforementioned high-frequency cyclic sequence and wavelet scale includes:

[0102] The correspondence between the wavelet coefficient curves and the levels with different variation periods in the initial high-frequency cyclic sequence is shown above.

[0103] Following the example above, after performing continuous wavelet analysis on the GR curve using long-term cycles as units, the wavelet scale range can be determined by analyzing the hierarchy of the amplitude spectrum. If there is a clear scale hierarchy and the range is consistent with the Fourier spectrum analysis results, the correspondence between the wavelet scale and the high-frequency cycle sequence can be determined, wavelet coefficient curves at different scales can be obtained, and then the high-frequency sequence at each level can be divided with reference to the wavelet coefficient curves at different scales.

[0104] In determining whether a distinct size hierarchy exists, large scales correspond to long periods of eccentricity in the Mie cycle, medium scales to short periods of eccentricity in the medium period, and small scales to the precession cycle. A ratio of large-scale cycle thickness to medium-scale thickness to small-scale thickness to the ratio of long periods of eccentricity to short periods of eccentricity in the Mie cycle to the precession cycle is considered distinct; otherwise, it is not.

[0105] As an example, the correspondence between high-frequency cyclic stratigraphy and wavelet scales can be obtained through the following steps:

[0106] A continuous wavelet amplitude spectrum of α at scales of 1–1024 was generated from the GR curve using Morlet wavelets, revealing a distinct dendritic structure. These dendritic structures, in their vertical layers, objectively reflect the depositional cycles of various orders, including long-term and lower-level sequence cycles (orders). The principle for dividing the continuous wavelet amplitude spectrum into layers is that the vertical set exhibits branching; when the branches of the vertical set are uneven, the majority of branches are used as the determining factor.

[0107] The single-well GR curve was obtained by performing a 1:1:1024 scale continuous wavelet transform on the Morlet wavelet, as shown below. Figure 3 The chromatogram of the absolute values ​​of the continuous wavelet amplitude coefficients is shown.

[0108] from Figure 3 From this, we can see that the color change from red to blue represents a decrease in the absolute value of the wavelet coefficients. After continuous wavelet transform, the well logging curve becomes a function in the depth-scale domain of two-dimensional space. From... Figure 3 As can be seen, different scales exhibit different periodicities, with three distinct periodic zones: 900–700, 300–100, and 40. Taking the midpoints, which are 800, 200, and 40 respectively, the wavelet coefficient curves for these three scales are calculated. Figure 4 The image shows a comparison of wavelet coefficient curves and logging signals at three different scales.

[0109] according to Figure 4It can be seen from the relationship between the scale factor α and the period T, and the signal sampling interval, that the 800-scale wavelet coefficient curve theoretically corresponds to a period of 100m; the 200-scale wavelet coefficient curve theoretically corresponds to a period of 25m; and the 40-scale wavelet coefficient curve theoretically corresponds to a period of 5m. This is exactly consistent with the high-frequency cyclotron average statistical thickness of 100m, 25m, and 5m periods determined in Fourier spectrum analysis.

[0110] Depend on Figure 4 The continuous wavelet coefficient curves show that the 800-scale wavelet coefficient curve contains 3 periods, with a formation thickness of 350m (total thickness) / 3 (number of periods) = 116.7m per period; the 200-scale wavelet coefficient curve contains 12 periods, with a thickness of 350m (total thickness) / 12 (number of periods) = 29.2m per period; and the 40-scale wavelet coefficient curve contains 56 periods, with a thickness of 350m (total thickness) / 56 (number of periods) = 6.3m per period. The thickness ratio of each period is 1:0.25:0.05, which is consistent with the ratio of the long eccentricity period: short eccentricity period: precession period of the Mie cycle. Combining the above two points, it is believed that there is a correspondence between wavelet scales and high-frequency cycles: the 800-scale wavelet coefficient period corresponds to the medium-term cycle; the 200-scale wavelet coefficient period corresponds to the short-term cycle; and the 40-scale wavelet coefficient period corresponds to the ultra-short-term cycle. This allows us to obtain the correspondence between high-frequency cyclic sequence and wavelet scale.

[0111] 150. Based on the above correspondence, high-frequency cyclic sequence stratigraphy was performed on the target sedimentary strata to obtain the high-frequency cyclic sequence stratigraphy results.

[0112] In some embodiments, prior to step 150, the method may further include:

[0113] Based on the above GR curve, the high-frequency sequence division of the continuous wavelet analysis spectrum is performed to obtain the first division result;

[0114] If the first division result matches the above correspondence, then the above steps of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence are executed.

[0115] For example, please refer to Figure 5 , Figure 5 A comparison diagram showing the partitioning results of wavelet coefficient curve partitioning and continuous wavelet analysis spectrum high-frequency partitioning is presented.

[0116] Among them, Figure 4 The obtained Morlet continuous wavelet coefficient curves at three scales of 800, 200, and 40 are compared with Figure 5The high-frequency sequence divisions obtained from continuous wavelet analysis are largely consistent, demonstrating a definite correspondence between high-frequency cyclic sequences and wavelet scales: a800 corresponds to medium-term cyclic sequences; a200 to short-term cyclic sequences; and a40 to ultra-short-term cyclic sequences. Therefore, the steps for high-frequency cyclic sequence division of the target sedimentary strata based on this correspondence can be continued.

[0117] In other embodiments, prior to step 150, the method may further include:

[0118] Based on the above GR curve, high-frequency sequence division of complex wavelet phase spectrum is performed to obtain the second division result.

[0119] If the second division result matches the above correspondence, then the above steps of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence are performed.

[0120] For example, please refer to Figure 6 , Figure 6 A comparison diagram showing the results of wavelet coefficient curve division of the hierarchical sequence and complex wavelet phase spectrum division is presented.

[0121] Among them, Figure 4 The Morlet continuous wavelet coefficient curves obtained at three scales of 800, 200 and 40 are consistent with the complex wavelet phase spectrum sequence division, which also proves that there is a definite correspondence between high-frequency cyclic sequence and wavelet scale: a800 corresponds to medium-term cyclic sequence; a200 corresponds to short-term cyclic sequence; a40 corresponds to ultra-short-term cyclic sequence.

[0122] Among them, Figure 6 In the chromatogram, blue represents -π, and red represents π. The blue-red abrupt change surface represents the phase transition point from -π to π, corresponding to the GR maximum. Phase transition points at different scales represent different levels of sequence interfaces. Connecting phase transition points at different scales forms the phase transition line, reflecting the changing trends of sequence interfaces at different levels. The red-to-gray midline, also known as the yellow region, represents the phase zero point where the phase equals 0, corresponding to the GR minimum. Phase zero points at different scales represent different levels of sequence interfaces. Connecting phase zero points at different scales forms the phase zero line, reflecting the changing trends of sequence interfaces at different levels.

[0123] In other embodiments, prior to step 150, the method may further include:

[0124] Based on the above GR curve, sub-prosodic layers are divided.

[0125] Based on the above minor prosodic layers, a Fischer diagram was drawn, and based on the above Fischer diagram, a high-frequency sequence division was performed to obtain the third division result.

[0126] If the third division result matches the above correspondence, then the above steps of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence are performed.

[0127] The Fischer plot method, based on the high-frequency sedimentary cycles in well logging curves, studies the spatial superposition patterns of sedimentary cycles and the changing trends of sedimentary base levels, providing an objective and applicable method for establishing sedimentary base level variation curves. The Fischer plot is an effective method for identifying long-term base level cycles. It typically uses the cycle number as the x-axis and the cumulative average thickness offset as the y-axis, representing the cumulative difference between the thickness of each cycle and the average thickness of all cycles. Specifically, it calculates the net accumulation by subtracting the average thickness of all high-frequency cycle sequences from the thickness of the high-frequency cycle sequence. The cumulative net accumulation of all preceding cycles is then plotted on a graph with the cycle number as the x-axis.

[0128] For example, please refer to Figure 7 , Figure 7 The diagram shows a comparison between the wavelet coefficient curves used to divide the sequence and the Fischer plot results.

[0129] Specifically, the hierarchical intervals were determined from the Morlet continuous wavelet amplitude spectrum, and the correspondence between the Morlet continuous wavelet scale and high-frequency cycles was determined by combining the results with Fourier spectrum analysis. Then, the sequence was divided with reference to wavelet coefficient curves at different scales. The division results were compared and analyzed with Fischer plot results and complex wavelet phase spectra. The analysis results showed that there is a correspondence between wavelet scale and high-frequency cycle sequence level: the wavelet coefficient curve at scale a800 corresponds to the intermediate cycle; the wavelet coefficient curve at scale a200 corresponds to the short-term cycle; and the wavelet coefficient curve at scale a40 corresponds to the ultra-short-term cycle. The comparative verification results show that the above correspondence between the high-frequency cycle sequence and wavelet scale is correct and effective.

[0130] In some other embodiments, prior to step 150, the method may further include:

[0131] Based on the above GR curve, sub-prosodic layers are divided.

[0132] Based on the above minor prosodic layers, a Fischer diagram was drawn, and based on the above Fischer diagram, a high-frequency sequence division was performed to obtain the third division result.

[0133] If the third division result matches the above correspondence, then the above steps of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence are performed.

[0134] In some other embodiments, prior to step 150, the method may further include:

[0135] Based on the above GR curve, the high-frequency sequence of the continuous wavelet analysis spectrum is divided to obtain the first division result.

[0136] Based on the above GR curve, high-frequency sequence division of complex wavelet phase spectrum is performed to obtain the second division result.

[0137] Based on the above GR curves, minor prosodic layers are divided, and Fischer diagrams are drawn based on the above minor prosodic layers. High-frequency sequence division is then performed based on the above Fischer diagrams to obtain the third division result.

[0138] If the first division result, the second division result, and the third division result all match the above correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence is executed.

[0139] For details on the matching process between the first partitioning result, the second partitioning result, the third partitioning result, and the corresponding relationship, please refer to [the relevant documentation / reference]. Figures 5 to 7 Therefore, I will not elaborate further here.

[0140] As can be seen, the high-frequency cyclic sequence stratigraphy method of this implementation obtains long-term cyclic sequences and GR curves by performing long-term cyclic sequence stratigraphy on the target sedimentary strata; then, based on the long-term cyclic sequence stratigraphy, the GR curves are subjected to discrete wavelet multi-scale decomposition to obtain the decomposition results, and frequency domain information is extracted from the decomposition results; next, continuous wavelet analysis is performed on the GR curves based on the long-term cyclic sequence stratigraphy to obtain time domain information; then, based on the frequency domain information and the time domain information, the correspondence between the high-frequency cyclic sequence stratigraphy and the wavelet scale is determined; finally, based on the above correspondence, the target sedimentary strata are stratigraphed using high-frequency cyclic sequences to obtain the high-frequency cyclic sequence stratigraphy classification results. This method combines analysis from both the time and frequency domains to classify high-frequency cyclic sequences, greatly reducing human error in manual high-frequency cyclic sequence stratigraphy and improving the accuracy of high-frequency cyclic sequence stratigraphy classification.

[0141] As an example, in practical applications, the specific implementation process of steps 110 to 150 above can be as follows: Figure 8 As shown, according to Figure 8 The high-frequency cyclic sequence quantitative classification method of this embodiment can be implemented through the following steps:

[0142] Step 1: Conduct long-term cyclic sequence stratigraphy based on core, seismic, well logging, and literature research data.

[0143] Step 2: Using long-term cycle sequences as units, the GR curve is decomposed using discrete wavelet multi-scale decomposition. The coefficients of the decomposed low-frequency background signal and high-frequency interference signal are set to zero, and then fast Fourier spectrum analysis is performed to obtain the high-frequency cycle sequences. The average thickness of each dominant cycle is determined based on the correspondence between the high-frequency cycle sequences and the Milankovitch astronomical cycles. If the thickness ratio of each dominant cycle is similar to the Milankovitch cycle ratio, and the obtained deposition rate is consistent with the deposition rate obtained from other data, then the partitioning scheme in Step 2 is feasible. The identification results of the high-frequency cycles obtained in Step 2 correspond to the initial high-frequency cycle sequences in the above embodiments.

[0144] Step 3: Perform continuous wavelet analysis on the GR curve using long-term cycles as units. Determine the wavelet scale range by analyzing the hierarchical nature of the amplitude spectrum. If there is a clear scale hierarchy and the range is consistent with the Fourier spectrum analysis results, then determine the correspondence between the wavelet scale and the high-frequency cycle sequence. Obtain the wavelet coefficient curves at different scales, and then divide the high-frequency sequence at each level by referring to the wavelet coefficient curves at different scales.

[0145] Step 4 involves performing continuous wavelet analysis on the GR curve to identify the superposition mode of the cycles and the sequence boundary, then performing high-frequency sequence division. The division result is compared with the sequence division scheme obtained in Step 3. For a detailed comparison process in Step 4, please refer to [link to Step 4]. Figure 5 .

[0146] Step 5: Based on the seafloor and sequence boundary identified by the complex wavelet phase spectrum of the GR curve, high-frequency sequence delineation is performed, and the delineation result is compared with the sequence delineation scheme obtained in Step 3. For a detailed comparison process in Step 5, please refer to [link to Step 5]. Figure 6 .

[0147] Step 6: Divide the minor prosodic layers based on the GR curves, draw Fischer diagrams, and perform high-frequency sequence division based on the high-frequency tolerance space variation characteristics analyzed by the Fischer diagrams. Compare the division results with the sequence division scheme obtained in Step 3. For a detailed comparison process in Step 6, please refer to [link to Step 6]. Figure 7 .

[0148] Step 7: If the comparison results of steps 4, 5, and 6 are consistent, then the correspondence between wavelet coefficient curves at different scales and different high-frequency cycles is determined. This allows for the quantitative classification of high-frequency cycles based on wavelet coefficient curves at different scales.

[0149] In summary, the high-frequency cycle quantitative segmentation method provided in this embodiment is theoretically based on the Milankovitch astronomical cycle, with quantitative segmentation predicated on an accurate definition of the astronomical cycle of the high-frequency cycle. The key technical challenge lies in accurately identifying and locating each level of the high-frequency cycle. Previous methods for accurately identifying high-frequency cycles have largely relied on spectral analysis. However, spectral analysis only extracts frequency features and discards time features, thus failing to accurately determine the temporal position of a specific frequency and consequently the precise depth of the high-frequency cycle. It can only provide the average statistical thickness of the high-frequency cycle, but the thickness of each cycle will inevitably differ. Determining the precise location of each cycle is a problem that spectral analysis cannot solve. This invention employs multiple techniques, including time-domain analysis, frequency-domain analysis, and time-frequency dual-domain analysis, to segment the high-frequency cycle sequence. The results are cross-validated, resulting in a high degree of objectivity and accuracy in the quantitative segmentation of the high-frequency cycle sequence. This method offers high objectivity and accuracy in the quantitative segmentation of the high-frequency cycle sequence, significantly reducing the arbitrariness inherent in manual high-frequency sequence segmentation.

[0150] To better implement the above methods, this application also provides a high-frequency cyclic sequence quantitative classification device, which can be integrated into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, or personal computer; the server can be a single server or a server cluster composed of multiple servers.

[0151] For example, in this embodiment, the method of the present application will be described in detail by taking the high-frequency cyclic sequence quantitative classification device specifically integrated into an electronic device.

[0152] For example, such as Figure 9 As shown, the high-frequency cyclic sequence quantitative classification device may include

[0153] The first partitioning module is used to perform long-term cycle sequence partitioning of the target sedimentary strata, and obtain long-term cycle sequence and GR curve;

[0154] The extraction module is used to perform discrete wavelet multi-scale decomposition on the GR curve based on the long-term cyclic sequence, obtain the decomposition result, and extract frequency domain information from the decomposition result.

[0155] The wavelet analysis module is used to perform continuous wavelet analysis on the GR curve based on the long-term cyclic sequence to obtain time-domain information.

[0156] The relationship determination module is used to determine the correspondence between high-frequency cyclic sequence and wavelet scale based on the above frequency domain information and the above time domain information.

[0157] The second partitioning module is used to perform high-frequency cyclic sequence partitioning of the target sedimentary strata based on the above correspondence, and to obtain the partitioning results of the high-frequency cyclic sequence.

[0158] In some implementations, the extraction module is specifically used for:

[0159] Using the aforementioned long-term cyclic sequence as a unit, the aforementioned GR curve is subjected to discrete wavelet multi-scale decomposition to obtain the decomposition results;

[0160] Identify the low-frequency background signal and high-frequency interference signal in the above decomposition results, and filter the low-frequency background signal and high-frequency interference signal from the above decomposition results to obtain the filtered decomposition results;

[0161] Frequency domain information is extracted from the filtered decomposition results.

[0162] In some implementations, the extraction module is further used for:

[0163] Fast Fourier spectrum analysis was performed on the above filtered decomposition results to obtain the initial high-frequency cyclotron sequence in the Fourier spectrum.

[0164] If it is determined that the period corresponding to the above-mentioned initial high-frequency cyclic sequence meets the requirements of the Milankovitch astronomical period, then the above-mentioned initial high-frequency cyclic sequence is determined as frequency domain information.

[0165] In some implementations, the wavelet analysis module is specifically used for:

[0166] Using the aforementioned long-term cycle sequence as a unit, continuous wavelet analysis was performed on the aforementioned GR curve to obtain multiple wavelet scales;

[0167] Obtain the wavelet coefficient curve corresponding to each of the above multiple wavelet scales, and determine the above wavelet coefficient curve as the above time domain information.

[0168] In some implementations, the relationship determination module is specifically used for:

[0169] The correspondence between the wavelet coefficient curves and the levels with different variation periods in the initial high-frequency cyclic sequence is shown above.

[0170] In some embodiments, the device further includes a verification module, which is used for:

[0171] Based on the above GR curve, the high-frequency sequence division of the continuous wavelet analysis spectrum is performed to obtain the first division result;

[0172] If the first division result matches the above correspondence, then the above steps of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence are executed.

[0173] In some embodiments, the device further includes a verification module, which is used for:

[0174] Based on the above GR curve, high-frequency sequence division of complex wavelet phase spectrum is performed to obtain the second division result;

[0175] If the second division result matches the above correspondence, then the above steps of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence are performed.

[0176] In some embodiments, the device further includes a verification module, which is used for:

[0177] Based on the above GR curves, sub-prosodic layers are divided;

[0178] Based on the above minor prosodic layers, Fischer diagrams were drawn, and high-frequency sequence divisions were performed according to the above Fischer diagrams to obtain the third division result;

[0179] If the third division result matches the above correspondence, then the above steps of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence are performed.

[0180] In some embodiments, the device further includes a verification module, which is also used for:

[0181] Based on the above GR curve, the high-frequency sequence division of the continuous wavelet analysis spectrum is performed to obtain the first division result;

[0182] Based on the above GR curve, high-frequency sequence division of complex wavelet phase spectrum is performed to obtain the second division result;

[0183] Based on the above GR curves, minor prosodic layers are divided, and Fischer diagrams are drawn based on the above minor prosodic layers. High-frequency sequence division is then performed based on the above Fischer diagrams to obtain the third division result.

[0184] If the first division result, the second division result, and the third division result all match the above correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the above correspondence is executed.

[0185] In practice, each of the above units can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units, please refer to the previous method embodiments, which will not be repeated here.

[0186] This application also provides an electronic device, such as... Figure 10 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically:

[0187] The electronic device may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a power supply 403, an input module 404, and a communication module 405. Those skilled in the art will understand that... Figure 10 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0188] The processor 401 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 402, and by calling data stored in the memory 402, it performs various functions and processes data, thereby performing overall detection of the electronic device. In some embodiments, the processor 401 may include one or more processing cores; in some embodiments, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 401.

[0189] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and high-frequency cyclic sequence quantitative partitioning by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function, etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0190] The electronic device also includes a power supply 403 that supplies power to the various components. In some embodiments, the power supply 403 can be logically connected to the processor 401 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 403 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0191] The electronic device may also include an input module 404, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0192] The electronic device may also include a communication module 405. In some embodiments, the communication module 405 may include a wireless module. The electronic device can perform short-range wireless transmission through the wireless module of the communication module 405, thereby providing users with wireless broadband Internet access.

[0193] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 401 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 402 according to the following computer instructions, and the processor 401 runs the application programs stored in the memory 402, thereby realizing the various functions in the embodiments of this application.

[0194] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0195] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by computer instructions, or by controlling related hardware with computer instructions. The computer instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0196] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of computer instructions that can be loaded by a processor to execute the steps in any of the high-frequency cyclic sequence quantitative partitioning methods provided in embodiments of this application.

[0197] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0198] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the above embodiments.

[0199] Since the computer instructions stored in the storage medium can execute the steps in any of the high-frequency cyclic sequence quantitative partitioning methods provided in the embodiments of this application, the beneficial effects that any of the high-frequency cyclic sequence quantitative partitioning methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0200] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0201] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0202] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for quantitative classification of high-frequency cyclic sequence stratigraphy, characterized in that, include: Long-term cyclic sequence stratigraphy was performed on the target sedimentary strata to obtain long-term cyclic sequence stratigraphy and GR curves; Based on the long-term cyclic sequence, the GR curve is subjected to discrete wavelet multi-scale decomposition to obtain the decomposition result, and frequency domain information is extracted from the decomposition result. Continuous wavelet analysis is performed on the GR curve based on the long-term cyclic sequence to obtain time-domain information; Based on the frequency domain information and the time domain information, determine the correspondence between high-frequency cyclic sequence and wavelet scale; Based on the aforementioned correspondence, the target sedimentary strata were divided into high-frequency cyclic sequence stratigraphy to obtain the high-frequency cyclic sequence stratigraphy results.

2. The method according to claim 1, characterized in that, The discrete wavelet multi-scale decomposition of the GR curve based on the long-term cyclic sequence is performed to obtain the decomposition result, and frequency domain information is extracted from the decomposition result, including: Using the long-term cyclic sequence as a unit, the GR curve is subjected to discrete wavelet multi-scale decomposition to obtain the decomposition result; Identify the low-frequency background signal and the high-frequency interference signal in the decomposition result, and filter the low-frequency background signal and the high-frequency interference signal from the decomposition result to obtain the filtered decomposition result; Frequency domain information is extracted from the filtered decomposition results.

3. The method according to claim 2, characterized in that, Extracting frequency domain information from the filtered decomposition results includes: Fast Fourier spectrum analysis was performed on the filtered decomposition results to obtain the initial high-frequency cyclotron sequence in the Fourier spectrum. If it is determined that the period corresponding to the initial high-frequency cyclic sequence meets the requirements of the Milankovitch astronomical period, then the initial high-frequency cyclic sequence is determined as frequency domain information.

4. The method according to claim 3, characterized in that, The continuous wavelet analysis of the GR curve based on the long-term cyclic sequence to obtain time-domain information includes: Using the long-term cyclic sequence as a unit, continuous wavelet analysis is performed on the GR curve to obtain multiple wavelet scales; Obtain the wavelet coefficient curve corresponding to each of the multiple wavelet scales, and determine the wavelet coefficient curve as the time domain information.

5. The method according to claim 4, characterized in that, The correspondence between the high-frequency cyclic sequence and the wavelet scale includes: The correspondence between the wavelet coefficient curves and the levels with different change periods in the initial high-frequency cyclic sequence.

6. The method according to claim 1, characterized in that, Before performing high-frequency cyclic sequence stratigraphy on the target sedimentary strata based on the aforementioned correspondence, the method further includes: Based on the GR curve, a high-frequency sequence division of the continuous wavelet analysis spectrum is performed to obtain the first division result; If the first division result matches the correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the correspondence is executed.

7. The method according to claim 1, characterized in that, Before performing high-frequency cyclic sequence stratigraphy on the target sedimentary strata based on the aforementioned correspondence, the method further includes: Based on the GR curve, high-frequency sequence division of complex wavelet phase spectrum is performed to obtain the second division result; If the second division result matches the correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the correspondence is executed.

8. The method according to claim 1, characterized in that, Before performing high-frequency cyclic sequence stratigraphy on the target sedimentary strata based on the aforementioned correspondence, the method further includes: Divide the prosodic layers based on the GR curve; Based on the aforementioned prosodic layer, a Fischer diagram is drawn, and high-frequency sequence division is performed according to the Fischer diagram to obtain a third division result; If the third division result matches the correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the correspondence is executed.

9. The method according to any one of claims 1 to 8, characterized in that, Before performing high-frequency cyclic sequence stratigraphy on the target sedimentary strata based on the aforementioned correspondence, the method further includes: Based on the GR curve, a high-frequency sequence division of the continuous wavelet analysis spectrum is performed to obtain the first division result; Based on the GR curve, high-frequency sequence division of complex wavelet phase spectrum is performed to obtain the second division result; Based on the GR curve, minor prosodic layers are divided, and Fischer diagrams are drawn based on the minor prosodic layers. High-frequency sequence division is performed based on the Fischer diagrams to obtain a third division result. If the first division result, the second division result, and the third division result all match the correspondence, then the step of performing high-frequency cyclic sequence division of the target sedimentary strata based on the correspondence is executed.

10. A high-frequency cyclic sequence stratigraphy quantitative classification device, characterized in that, include: The first partitioning module is used to perform long-term cycle sequence partitioning of the target sedimentary strata, and obtain long-term cycle sequence and GR curve; The extraction module is used to perform discrete wavelet multi-scale decomposition on the GR curve based on the long-term cyclic sequence, obtain the decomposition result, and extract frequency domain information from the decomposition result. The wavelet analysis module is used to perform continuous wavelet analysis on the GR curve based on the long-term cyclic sequence to obtain time-domain information. The relationship determination module is used to determine the correspondence between high-frequency cyclic sequence and wavelet scale based on the frequency domain information and the time domain information; The second partitioning module is used to perform high-frequency cyclic sequence partitioning of the target sedimentary strata based on the correspondence, and obtain the partitioning result of the high-frequency cyclic sequence.

11. An electronic device, characterized in that, It includes a processor and a memory, the memory storing multiple computer instructions; the processor loads the computer instructions from the memory to perform the steps in the high-frequency cyclic sequence quantitative partitioning method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The storage medium stores multiple instructions, which are adapted to be loaded by a processor to execute the high-frequency cyclic sequence quantitative partitioning method according to any one of claims 1 to 9.

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