A seismic acquisition method, apparatus, device, storage medium and computer program
By analyzing the traversal characteristics of seismic data and optimizing the target sampling values based on uniform and regular sampling, the problem of redundant samples in seismic data acquisition was solved, achieving efficient and low-cost acquisition results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-12-28
- Publication Date
- 2026-06-30
Smart Images
Figure CN122307633A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of seismic exploration technology, and in particular to a seismic acquisition method, apparatus, equipment, storage medium, and computer program. Background Technology
[0002] Currently, uniform regular sampling is frequently used when sampling seismic data. Although uniform regular sampling is sufficient to record the target signal, the redundant samples it contains are not necessary, and uniform regular sampling requires a large number of additional samples or receivers, which greatly increases the acquisition cost. Summary of the Invention
[0003] This disclosure provides a seismic acquisition method, apparatus, device, storage medium, and computer program to address the problems of homogeneous and regular patterns with redundant samples and the need for a large number of samples or receiver points.
[0004] In a first aspect, this disclosure provides a seismic acquisition method, comprising: acquiring initial sampling data under uniform and regular conditions; determining traversal characteristics based on the initial sampling data; determining a target sampling value based on the traversal characteristics; and determining a sampling result when the target sampling value meets a preset threshold.
[0005] In some embodiments, when the target sample value does not meet a preset threshold, the initial sample data is updated until the sample result is obtained.
[0006] In some embodiments, the initial sampling data includes irregular sampling and its corresponding sample percentage.
[0007] In some embodiments, the traversal features include: sampling interval, sampling density, and spectral resolution data.
[0008] In some embodiments, determining traversal features based on initial sampled data includes: obtaining the cross-correlation value and entropy value of the initial sampled data; and determining spectral resolution data based on the cross-correlation value and entropy value.
[0009] In some embodiments, determining the target sample value based on ergodic features includes: determining an objective function based on the ergodic features; determining the objective function based on a weighted average of the sampling interval, sampling density, and spectral resolution data; and obtaining the target sample value based on the objective function.
[0010] Secondly, this disclosure provides a seismic acquisition device, comprising: an acquisition unit for acquiring initial sampling data under uniform and regular conditions; a first determination unit for determining traversal characteristics based on the initial sampling data; a second determination unit for determining a target sampling value based on the traversal characteristics; and a third determination unit for determining a sampling result when the target sampling value meets a preset threshold.
[0011] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.
[0012] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.
[0013] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.
[0014] This disclosure provides a seismic acquisition method, apparatus, device, storage medium, and computer program. Under uniform and regular conditions, initial sampling data is acquired; based on the initial sampling data, traversal characteristics are determined; based on the traversal characteristics, target sampling values are determined; and when the target sampling values meet a preset threshold, the sampling result is determined. In this process, the inherent traversal characteristic information of the initial sampling data is fully explored through in-depth analysis. Simultaneously, considering the uncertainty and complexity of the data, traversal calculations are cleverly used to accurately quantify the degree of closeness between the current sampling scheme and the ideal state. Through continuous iterative optimization, the target sampling values gradually approach the optimal solution, thereby efficiently selecting the most representative and informative sampling data, avoiding the acquisition and processing of a large number of redundant samples. Therefore, compared to existing uniform and regular sampling methods that result in redundant data and require a large number of samples, this method significantly reduces the number of samples, simplifies sample data, and reduces redundancy. This improves the efficiency and effectiveness of seismic data acquisition and enhances the adaptability and flexibility of seismic acquisition. Attached Figure Description
[0015] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:
[0016] Figure 1 This is a schematic flowchart of a seismic acquisition method provided in an embodiment of the present disclosure.
[0017] Figure 2 This is a schematic flowchart illustrating the complete seismic acquisition method provided in the embodiments of this disclosure.
[0018] Figure 3 A schematic diagram of one-dimensional regular uniform dense sampling points provided in an embodiment of this disclosure.
[0019] Figure 4 This is a schematic diagram of one-dimensional regular uniform sparse sampling points provided in an embodiment of this disclosure.
[0020] Figure 5 This is a schematic diagram of a one-dimensional irregular traversal sampling provided in an embodiment of the present disclosure.
[0021] Figure 6 This is a schematic diagram of a one-dimensional, regular, uniform, and densely spaced distribution provided in an embodiment of this disclosure.
[0022] Figure 7 This is a schematic diagram of a one-dimensional regular uniform sparse spacing distribution provided in an embodiment of this disclosure.
[0023] Figure 8 This is a schematic diagram of the one-dimensional irregular traversal spacing distribution provided in an embodiment of this disclosure.
[0024] Figure 9 This is a schematic diagram illustrating the one-dimensional, regular, uniform, and dense information acquisition capability provided in an embodiment of this disclosure.
[0025] Figure 10 A schematic diagram illustrating the one-dimensional regular uniform sparse information acquisition capability provided in this embodiment of the disclosure.
[0026] Figure 11 This is a schematic diagram illustrating the one-dimensional irregular traversal information acquisition capability provided in an embodiment of this disclosure.
[0027] Figure 12 This is a schematic diagram of a seismic acquisition device provided in an embodiment of this disclosure.
[0028] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation
[0029] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.
[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0031] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0032] Currently, uniform regular sampling is frequently used when sampling seismic data. Although uniform regular sampling is sufficient to record the target signal, the redundant samples it contains are not necessary, and uniform regular sampling requires a large number of additional samples or receivers, which greatly increases the acquisition cost.
[0033] Therefore, this disclosure provides a seismic acquisition method that can fully explore the inherent ergonomic characteristics of initial sampling data through in-depth analysis. Simultaneously, considering the uncertainty and complexity of the data, it cleverly utilizes ergonomic calculations to accurately quantify the degree of closeness between the current sampling scheme and the ideal state. Through continuous iterative optimization, the target sampling values gradually approach the optimal solution, thereby efficiently selecting the most representative and informative sampling data and avoiding the acquisition and processing of a large number of redundant samples. Thus, compared to existing uniform regular sampling methods that suffer from redundant data and require a large number of samples, this method significantly reduces the number of samples, simplifies the sample data, and reduces redundancy. It improves the efficiency and effectiveness of seismic data acquisition and enhances the adaptability and flexibility of seismic acquisition. First, please refer to... Figure 1 , Figure 1 This is a schematic flowchart illustrating a seismic acquisition method provided in an embodiment of this disclosure. Figure 1 As shown, the method includes:
[0034] In step S101, under the condition of uniformity, initial sampling data is obtained.
[0035] In this disclosure, the initial sampling data is acquired following a traditional uniform sampling framework. Its purpose is to provide a foundational dataset for subsequent optimization processes. While this dataset contains redundancy, it comprehensively covers the entire seismic information of the target area, laying the groundwork for extracting key, representative samples. It also provides raw material for comparative analysis with traditional sampling methods, allowing for a clear demonstration of the advantages of this method in maintaining information integrity while reducing data volume. Specific details regarding the initial sampling data will be described later in conjunction with embodiments.
[0036] In step S102, traversal features are determined based on the initial sampling data.
[0037] In this disclosure, the initial sampled data can be analyzed from multiple dimensions to obtain traversal characteristics. Spatially, the spacing distribution between sampling points can be accurately calculated, reflecting the spatial density and distribution pattern of the data. From a data density perspective, the number of sampling points per unit area or unit time period can be accurately counted to determine the sampling density. Using spectral analysis techniques, spectral resolution data can be obtained, clarifying the data's ability to distinguish different frequency components in the frequency domain. Simultaneously, cross-correlation analysis methods can be combined to measure the similarity and correlation between different sampled data sequences. Information entropy theory can also be used to assess the uncertainty and complexity of the data, thereby comprehensively determining the traversal characteristics. These characteristics will serve as key bases for subsequently determining target sampled values, fully exploring the potential value and effective information in the initial sampled data, making the optimization of the sampling scheme more scientific and targeted. Specific traversal characteristics will be described later in conjunction with embodiments.
[0038] In step S103, the target sample value is determined based on the traversal features.
[0039] In this disclosure, a carefully designed objective function can be constructed based on the traversal features. This function integrates the various traversal features into a function that can quantify the merits of the current sampling scheme by applying reasonable weighted combinations or mathematical transformations. The target sampling value is then obtained using the objective function. The specific objective function is described later with reference to embodiments.
[0040] In step S104, when the target sampled value meets the preset threshold, the sampling result is determined.
[0041] In this disclosure, the preset threshold can be set by comprehensively considering at least one factor, such as seismic data quality requirements, exploration target accuracy, and cost-effectiveness; no specific limitation is imposed here. When the target sampling value determined through the above steps meets this preset threshold, it means that the current sampling scheme has reached an ideal balance, satisfying the requirements of seismic data acquisition for signal resolution and information integrity, while effectively avoiding increased costs and wasted resources due to oversampling. The sampling results determined at this time can not only include the location and sample data of the finally selected sampling points, but also cover at least one piece of information such as parameter settings and feature analysis results throughout the sampling process, enabling the acquisition of valuable seismic information to be maximized under limited resource conditions, thereby improving the success rate and efficiency of seismic exploration.
[0042] In summary, this process, through in-depth analysis of the initial sampling data, fully explores its inherent traversal characteristics. Simultaneously, considering the uncertainty and complexity of the data, it cleverly utilizes traversal calculations to accurately quantify the degree of closeness between the current sampling scheme and the ideal state. Through continuous iterative optimization, the target sampling values gradually approach the optimal solution, thereby efficiently selecting the most representative and informative sampling data, avoiding the collection and processing of a large number of redundant samples. Therefore, compared to existing uniform regular sampling methods that suffer from redundant data and require a large number of samples, this method significantly reduces the number of samples, simplifies the sample data, and reduces redundancy. This improves the efficiency and effectiveness of seismic data acquisition and enhances the adaptability and flexibility of seismic acquisition.
[0043] The seismic acquisition method disclosed herein will be described in detail below, and also includes:
[0044] If the target sampled value does not meet the preset threshold, update the initial sampled data until the sampled result is obtained.
[0045] In this disclosure, when the target sampled value fails to meet a preset threshold, the initial sampled data can be updated by adjustment. First, based on the difference between the calculated target sampled value and the preset threshold, and the specific performance of the currently traversed features (such as sampling interval, sampling density, and spectral resolution data), the reasons for failing to reach the threshold are analyzed and determined. If it is found that the sparse sampling points in certain areas affect the spectral resolution, resulting in a poor objective function value, then a small number of representative sampling points will be added to these key areas, or the positions of some sampling points will be fine-tuned to make their distribution more reasonable, thereby improving the sampling density and spectral characteristics. Simultaneously, the updated sampled data will be recalculated, and a new target sampled value will be determined by combining these new features, and the comparison process with the preset threshold will be repeated. This iterative update process can continue continuously. Each update is based on a precise analysis and improvement of the results of the previous round, constantly optimizing the distribution and composition of the initial sampling data, gradually approaching the ideal sampling results that meet the preset threshold, and ensuring that the final sampling scheme can not only meet the high-precision requirements of seismic data acquisition, but also minimize unnecessary sample collection, maximize resource utilization and minimize acquisition costs. Through repeated optimization and adjustment, the best balance between data quality and acquisition cost is found, thus providing a set of efficient, reliable and flexible sampling solutions for seismic exploration work.
[0046] The following will describe the specific contents of the initial sampling data disclosed herein:
[0047] The initial sampling data disclosed herein includes: irregular sampling (denoted as Φ), and the percentage of samples corresponding to irregular sampling (denoted as δ, where δ satisfies the following formula: δ=N Φ / N θ (where θ represents uniform, regular, and dense sampling).
[0048] These two elements are crucial components of the seismic acquisition method disclosed herein, working together to lay the foundation for subsequent sampling optimization. Irregular sampling breaks away from the traditional uniform, regular, and dense sampling pattern, selecting sample points in an irregular distribution. This approach is more flexible, allowing for targeted sampling of areas or times that are more valuable in reflecting seismic characteristics, thus avoiding the large amount of redundant data that arises with uniform, regular sampling. The percentage of irregular samples allows for precise control over their proportion in the entire sampling process, ensuring that they both demonstrate the advantages of irregular sampling and guarantee that the selected samples, as a whole, have a reasonable correlation and comparison with the information covered by uniform, regular, and dense sampling.
[0049] The following will elaborate on the specific details of the traversal features disclosed herein:
[0050] The traversal features disclosed herein include: sampling interval, sampling density, and spectral resolution data.
[0051] In this disclosure, sampling interval refers to the spatial or time interval between sampling points under irregular sampling patterns. Precise analysis of the sampling interval reveals the density of seismic data distribution at different locations or times, which is crucial for determining signal propagation characteristics and information acquisition in different regions.
[0052] In this disclosure, sampling density characterizes the number of sampling points contained in a unit area or unit time. It intuitively reflects the concentration of data collection, is related to the sampling interval, and describes the distribution characteristics of the data from different perspectives.
[0053] In this disclosure, spectral-resolved data is obtained by performing spectral analysis on sampled data and is used to measure the system's ability to distinguish different frequency components in the frequency domain. Spectral-resolved data reflects the sampling scheme's ability to capture and distinguish these different frequency signals.
[0054] These three elements are closely related and complementary, together forming a comprehensive system describing the characteristics of irregular sampling. Sampling interval and sampling density determine the distribution pattern of the sampled data in the spatiotemporal domain, directly affecting the quality and accuracy of spectral-resolved data. Spectral-resolved data, in turn, provides crucial information for optimizing sampling interval and sampling density from a frequency domain perspective. Analysis of signals at different frequencies can, in turn, guide how to more rationally adjust the distribution of sampling points, i.e., optimize sampling interval and sampling density, to better capture and distinguish seismic signals of various frequency components. Only by comprehensively considering these three ergodic characteristics can a comprehensive evaluation and optimization of irregular sampling be achieved. This ensures that during seismic acquisition, the redundancy problems of traditional uniform regular sampling are avoided, and that the acquired seismic data possesses sufficient resolution and information content in both the spatiotemporal and frequency domains, meeting the needs of seismic exploration under complex geological conditions. This provides a solid and reliable data foundation for subsequent seismic data processing and geological interpretation, driving the development of seismic acquisition technology towards greater efficiency and accuracy.
[0055] The following section will explain in detail how to use initial sampling data to determine traversal features. The methods include:
[0056] Obtain the cross-correlation and entropy values of the initial sampled data;
[0057] Based on the cross-correlation value and entropy value, the spectral resolution data is determined.
[0058] In this disclosure, cross-correlation value is an important indicator used to measure the degree of correlation between different signal sequences in the initial sampled data. Specifically, for the acquired irregular sampled data and the initial sampled data consisting of the corresponding sample percentages, by calculating the cross-correlation value between the seismic signals corresponding to different sampling points, it is possible to reveal the similarity and correlation of these signals in the temporal or spatial dimensions.
[0059] In this disclosure, entropy is a key parameter used from an information theory perspective to characterize the degree of uncertainty of the initial sampled data. Based on the initial sampled data, entropy can be obtained by using calculation methods such as vector entropy to consider the amount of information carried by each sampling point and the degree of disorder of the entire data set.
[0060] In this disclosure, after determining the cross-correlation value and the entropy value, they can be multiplied to obtain the spectral resolution function (denoted as SRF). The specific SRF can satisfy the following formula:
[0061] SRF(Φ)=μ Φ H(|ε Φ |)
[0062] Where Φ represents the irregular sampling pattern, and μ Φ Let ε be the cross-correlation value when sampling Φ irregularly. Φ Let H be the entropy value when sampling Φ irregularly, and let H be a vector ε of length N. Φ The entropy.
[0063] Taking vector P as an example, H can satisfy the following formula.
[0064]
[0065] Where i is the sampling sequence number and N is the total number of samples.
[0066] The following section will explain in detail how to use traversal features to determine the target sample value, including:
[0067] The objective function is determined based on ergodic features; the objective function is determined by weighting the sampling interval, sampling density, and spectral resolution data.
[0068] Based on the objective function, the target sample value is obtained.
[0069] In this disclosure, an objective function can be determined by weighting three ergodic features: sampling interval, sampling density, and spectral resolution data. This objective function can be a linear function or a ratio function, with no specific limitation. The key is that it can reasonably integrate these three ergodic features to quantitatively represent the degree of closeness between the current sampling scheme and the ideal state. Then, a suitable optimization algorithm is used to process the objective function; for example, at least one method such as gradient descent, simulated annealing, or genetic algorithm can be used. In this way, based on the objective function and with the help of the corresponding optimization algorithm, the target sample values are accurately obtained, thereby determining the optimal sampling scheme for seismic acquisition work that ensures the quality of acquired data while minimizing redundant samples.
[0070] This disclosure also provides a specific formula for the objective function, which satisfies the following:
[0071] Φ * =min Φ (ω α H(|α Φ -α θ |)+ω β H(|β Φ -β θ |)+μ Φ H(|ε Φ |)
[0072] Where Φ represents the irregular sampling pattern, containing the number of sample points N. Φ θ represents a uniform, regular, dense sampling pattern containing N sample points. θ 1, α Φ and α θ β represents the information sampling capability under different spacing conditions in irregular sampling and uniform regular dense sampling. Φ and β θ ω represents the information sampling capability under different sampling densities: irregular sampling and uniform, regular, dense sampling. α and ω β For the weight, μ Φ and ε Φ These represent the cross-correlation and entropy during irregular sampling, respectively.
[0073] In an exemplary embodiment, Figure 2 This is a schematic flowchart illustrating the complete seismic acquisition method provided in the embodiments of this disclosure. Figure 2 As shown, the method includes:
[0074] Step 1: Define the data collection area;
[0075] Step 2: Design a regular, uniform, and dense sampling pattern;
[0076] Step 3: Set the initial irregular sampling and sample percentage;
[0077] Step 4: Iterate to the k-th irregular sample and sample percentage;
[0078] Step 5: Calculate the traversal sampling attributes:
[0079] Sampling interval, sampling density, and spectral resolution (SRF);
[0080] Step Six: Calculate the objective function value;
[0081] Step 7: Determine if the objective function value is less than the threshold;
[0082] Step 8: If not, continue updating to the (k+1)th irregular sample and repeat the calculation.
[0083] Step 9: Loop until the target function value is less than the threshold, and finally obtain the result of the traversal sampling pattern.
[0084] For example, Figure 3 A schematic diagram of one-dimensional regular uniform dense sampling points provided in an embodiment of this disclosure. Figure 4 This is a schematic diagram of one-dimensional regular uniform sparse sampling points provided in an embodiment of this disclosure. Figure 5 This is a schematic diagram of a one-dimensional irregular traversal sampling provided in an embodiment of the present disclosure. Figure 6 This is a schematic diagram of a one-dimensional, regular, uniform, and densely spaced distribution provided in an embodiment of this disclosure. Figure 7 This is a schematic diagram of a one-dimensional regular uniform sparse spacing distribution provided in an embodiment of this disclosure. Figure 8 This is a schematic diagram of the one-dimensional irregular traversal spacing distribution provided in an embodiment of this disclosure. Figure 9 This is a schematic diagram illustrating the one-dimensional, regular, uniform, and dense information acquisition capability provided in an embodiment of this disclosure. Figure 10 A schematic diagram illustrating the one-dimensional regular uniform sparse information acquisition capability provided in this embodiment of the disclosure. Figure 11 This is a schematic diagram illustrating the one-dimensional irregular traversal information acquisition capability provided in an embodiment of this disclosure.
[0085] Will Figure 3 , Figure 4 and Figure 5 The comparison shows that both uniform sparse sampling and traversal sampling are approximately 20% of uniform dense sampling. Figure 6 , Figure 7 and Figure 8 In comparison, it can be seen that the spacing between traversal sampling and uniform dense sampling is similar, both of which can obtain the same information using fewer samples. Figure 9 , Figure 10 and Figure 11 In comparison, it can be seen that traversal sampling can obtain the same amount of information with fewer samples as uniform dense sampling, thus saving costs.
[0086] The comparison of the above figures verifies the superiority of the traversal data collection mode presented in this paper. It can obtain the same amount of information with fewer samples, saving collection costs and improving collection efficiency.
[0087] Based on the above embodiments, this disclosure provides a seismic acquisition device. Figure 12 A schematic diagram of the seismic acquisition device provided in the embodiments of this disclosure, as shown below. Figure 12 As shown, the device 1200 includes:
[0088] Acquisition unit 1201 is used to acquire initial sampling data under uniform conditions.
[0089] The first determining unit 1202 is used to determine the traversal features based on the initial sampled data.
[0090] The second determining unit 1203 is used to determine the target sampled value based on the traversal features.
[0091] The third determining unit 1204 is used to determine the sampling result when the target sampled value meets the preset threshold.
[0092] In some embodiments, when the target sample value does not meet a preset threshold, the initial sample data is updated until the sample result is obtained.
[0093] In some embodiments, the initial sampling data includes irregular sampling and its corresponding sample percentage.
[0094] In some embodiments, the traversal features include: sampling interval, sampling density, and spectral resolution data.
[0095] In some embodiments, determining traversal features based on initial sampled data includes: obtaining the cross-correlation value and entropy value of the initial sampled data; and determining spectral resolution data based on the cross-correlation value and entropy value.
[0096] In some embodiments, determining the target sample value based on ergodic features includes: determining an objective function based on the ergodic features; determining the objective function based on a weighted average of the sampling interval, sampling density, and spectral resolution data; and obtaining the target sample value based on the objective function.
[0097] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.
[0098] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.
[0099] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the method described in the above embodiments.
[0100] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods described in the above embodiments.
[0101] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).
[0102] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.
[0103] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).
[0104] The processor can communicate with external devices via the I / O bus through wired or wireless networks.
[0105] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.
[0106] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0107] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0108] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.
Claims
1. A seismic acquisition method, characterized in that, include: Under uniform and regular conditions, initial sampling data is obtained; Based on the initial sampled data, the traversal features are determined; Based on the traversal features, the target sample value is determined; When the target sampled value meets the preset threshold, the sampling result is determined.
2. The method according to claim 1, characterized in that, Also includes: When the target sampled value does not meet the preset threshold, the initial sampled data is updated until the sampled result is obtained.
3. The method according to claim 1, characterized in that, The initial sampling data includes: irregular sampling and its corresponding sample percentage.
4. The method according to claim 1, characterized in that, The traversal features include: sampling interval, sampling density, and spectral resolution data.
5. The method according to claim 1, characterized in that, The step of determining the traversal features based on the initial sampled data includes: Obtain the cross-correlation and entropy values of the initial sampled data; Based on the cross-correlation value and entropy value, the spectral resolution data is determined.
6. The method according to claim 1, characterized in that, The step of determining the target sample value based on the traversal features includes: Based on the aforementioned traversal characteristics, an objective function is determined; the objective function is determined based on a weighted average of sampling interval, sampling density, and spectral resolution data. The target sample value is obtained based on the objective function.
7. A seismic acquisition device, characterized in that, include: The acquisition unit is used to acquire initial sampling data under uniform conditions. The first determining unit is used to determine the traversal features based on the initial sampled data; The second determining unit is used to determine the target sample value based on the traversal features; The third determining unit is used to determine the sampling result when the target sampled value meets the preset threshold.
8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program / instructions, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.