Surface wave inversion method, device, equipment, medium and product

Abstract

The present application relates to the technical field of surface wave inversion, in particular to a surface wave inversion method, device, equipment, medium and product, wherein the method comprises: performing Fourier transform on seismic waveform data to obtain surface wave data; comparing the surface wave data and carrying out surface wave inversion by using the least square criterion in the framework of full waveform inversion; and the seismic waveform data inversion can be carried out based on the surface wave.

Description

Technical Field

[0001] This invention relates to the field of surface wave inversion technology, and particularly to a surface wave inversion method, apparatus, equipment, medium, and product. Background Technology

[0002] Seismic waves include body waves and surface waves. Body waves refer to waves propagating from the Earth's interior and typically include shallow water waves, reflected waves, and refracted waves. Surface waves are seismic waves propagating along velocity interfaces. They are formed by the interference of P-waves and S-waves near the velocity interface, and can also be formed by the interference of S-waves themselves near the interface. Due to high noise and low signal-to-noise ratio in actual data, it is often difficult to accurately separate the contribution of surface waves to inversion. There are technical problems in this field regarding the difficulty of performing seismic waveform data inversion based on surface waves. Summary of the Invention

[0003] This invention provides a surface wave inversion method, apparatus, equipment, medium, and product, which solves the technical problem of difficulty in performing seismic waveform data inversion based on surface waves.

[0004] In a first aspect, the present invention provides a surface wave inversion method, the method comprising: performing Fourier transform on seismic waveform data to obtain surface wave data; and comparing the surface wave data using the least squares criterion within the framework of full waveform inversion to perform surface wave inversion.

[0005] In some embodiments, before the step of performing a Fourier transform on the seismic waveform data to obtain surface wave data, the method further includes: acquiring seismic waveform data, wherein the seismic waveform data includes observation data and synthetic data.

[0006] In some embodiments, the step of comparing surface wave data and performing surface wave inversion within the full waveform inversion framework using the least squares criterion includes: comparing the surface wave data of the observed data and the surface wave data of the synthesized data within the full waveform inversion framework using the least squares criterion, and performing surface wave inversion.

[0007] In some embodiments, seismic waveform data includes a time axis dimension and a spatial axis dimension.

[0008] In some embodiments, the step of performing a Fourier transform on seismic waveform data to obtain surface wave data includes: performing a fast Fourier transform on the seismic waveform data along the time axis to obtain intermediate data; and performing a discrete Fourier transform on the intermediate data along the spatial axis to obtain surface wave data.

[0009] In some embodiments, the Discrete Fourier Transform includes a slowness parameter. In the step of performing a Discrete Fourier Transform on intermediate data along the spatial axis to obtain surface wave data, the slowness parameter is determined based on the surface wave range of the current work area.

[0010] Secondly, the present invention provides a surface wave inversion device, the device comprising: a transformation module for performing Fourier transform on seismic waveform data to obtain surface wave data; and a comparison module for comparing the surface wave data using the least squares criterion within the framework of full waveform inversion to perform surface wave inversion.

[0011] Thirdly, the present invention 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 any of the surface wave inversion methods described above.

[0012] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the surface wave inversion methods described above.

[0013] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the surface wave inversion methods described above.

[0014] This invention provides a surface wave inversion method, apparatus, equipment, medium, and product, wherein the method includes: performing Fourier transform on seismic waveform data to obtain surface wave data; comparing the surface wave data using the least squares criterion within the full waveform inversion framework to perform surface wave inversion; and being able to perform seismic waveform data inversion based on surface waves. Attached Figure Description

[0015] The invention will now be described in more detail with reference to embodiments and the accompanying drawings:

[0016] Figure 1 A schematic flowchart of a surface wave inversion method provided in an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of the structure of a surface wave inversion device provided in an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of common shot gather seismic waveform data is provided as an application example of the present invention;

[0019] Figure 4 A method provided for application examples of the present invention Figure 3 A schematic diagram of surface waves separated from the data;

[0020] Figure 5 A schematic diagram of a real shear wave velocity model provided as an application example of the present invention;

[0021] Figure 6 A method provided for application examples of the present invention based on Figure 5 A schematic diagram of the observation data calculated by the model;

[0022] Figure 7 A schematic diagram of an initial shear wave velocity model provided as an application example of the present invention;

[0023] Figure 8 A method provided for application examples of the present invention based on Figure 7 A schematic diagram of the initial synthetic data obtained from the velocity model calculation in the diagram;

[0024] Figure 9 A method provided as an application example of the present invention is... Figure 6 and Figure 8 The interval display shows a comparison diagram of the initial data obtained;

[0025] Figure 10 A schematic diagram of a shear wave velocity model obtained from the inversion of separated surface waves, provided as an application example of the present invention;

[0026] Figure 11 A method corresponding to the application example of the present invention is provided. Figure 10 A schematic diagram of the final synthesized data;

[0027] Figure 12 A method provided as an application example of the present invention is... Figure 6 The actual data and Figure 11 The final synthesized data intervals are displayed in the diagram showing the comparison of the final data.

[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 present invention and to fully understand and implement the process of how the present invention uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The embodiments of the present invention and the various features therein can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention 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 a 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] Seismic waves include body waves and surface waves. Body waves refer to waves propagating from the Earth's interior and typically include shallow water waves, reflected waves, and refracted waves. Surface waves are seismic waves propagating along velocity interfaces. They are formed by the interference of P-waves and S-waves near the velocity interface, and can also be formed by the interference of S-waves themselves near the interface. Due to high noise and low signal-to-noise ratio in actual data, it is often difficult to accurately separate the contribution of surface waves to inversion. There are technical problems in this field regarding the difficulty of performing seismic waveform data inversion based on surface waves.

[0033] To address the aforementioned technical problem of difficulty in performing seismic waveform data inversion based on surface waves, this invention proposes a surface wave inversion method, apparatus, equipment, medium, and product. The implementation details of this invention are described below. The following content is only for ease of understanding and is not essential for implementing this solution.

[0034] Example 1

[0035] Figure 1 This is a schematic flowchart of a surface wave inversion method provided in an embodiment of this application, as shown below. Figure 1 As shown, in the technical solution of this embodiment, a surface wave inversion method is provided. The method includes: performing Fourier transform on seismic waveform data to obtain surface wave data; and comparing the surface wave data using the least squares criterion within the full waveform inversion framework to carry out surface wave inversion.

[0036] The technical problem to be solved in this embodiment is how to perform seismic waveform data inversion based on surface waves. In the field of seismic data processing, it is quite difficult to extract effective surface wave information from a large amount of seismic waveform data and perform accurate inversion based on it. There is a technical problem in this field that makes it difficult to efficiently utilize surface waves for seismic waveform data inversion.

[0037] In this embodiment, the technical solution first acquires seismic waveform data. This seismic waveform data includes raw waveform information collected under conditions such as natural source earthquakes on land or in the ocean, and artificial source earthquakes, covering multi-dimensional data including time and space. Next, a Fourier transform is performed on the seismic waveform data. First, a Fast Fourier Transform is performed along the time axis, using the expression ∫d(t,x)exp(-i2πft)dt to transform the original common shot gathering seismic waveform data d(t,x) (where t is time and x is the detector coordinates) into intermediate data d1(f,x), where f is the conjugate variable of t, i.e., the frequency. Then, a Discrete Fourier Transform is performed along the spatial axis on the intermediate data, using the expression... s, as the conjugate variable of x, represents the slope or slowness of the seismic event (slowness is the reciprocal of velocity), thus yielding surface wave data. Subsequently, within the framework of full waveform inversion, the least squares criterion was employed, and a data comparison method was constructed. (in For the observed data in the transform domain, To perform surface wave inversion on synthetic data (data in the transform domain), it is possible to systematically extract surface waves from the original seismic waveform data and perform inversion operations.

[0038] The technical solution of this embodiment, through the aforementioned operations of performing a Fast Fourier Transform on the time axis and a Discrete Fourier Transform on the spatial axis on the seismic waveform data, can accurately separate surface wave data from complex raw seismic waveform data. For example, in data acquired on land, the detectors have a specific arrangement, and the source locations are also set accordingly. The raw data contains various body waves, noise, and other interference information. After such transformation, surface waves can be effectively extracted. Then, using the least squares criterion within the full waveform inversion framework for surface wave inversion, the inversion results can be made to better fit the actual situation. In some application examples, the observed data is calculated using a real shear wave velocity model, and then compared with the initial synthetic data and the final synthetic data obtained after inversion using the technical solution of this embodiment. From the comparison of the final data intervals, it can be seen that the synthetic data can fit the observed data very well. This indicates that this technical solution can accurately invert seismic waveform data based on surface waves, thereby providing a reliable basis for constructing related studies such as shallow surface layers and helping to gain a deeper understanding of underground geological structures.

[0039] Example 2

[0040] Based on the above embodiments, before the step of performing Fourier transform on the seismic waveform data to obtain surface wave data, the method further includes: acquiring seismic waveform data, wherein the seismic waveform data includes observation data and synthetic data.

[0041] The technical problem this embodiment aims to solve is how to construct seismic waveform data. In seismic data processing, without a suitable source of seismic waveform data and a reasonable construction method, subsequent operations such as surface wave-based inversion cannot be effectively carried out. There is a technical problem in this field of obtaining complete and compliant seismic waveform data.

[0042] In this embodiment, the acquisition method for seismic waveform data needs to be clearly defined, which includes two parts: observational data and synthetic data. Observational data is data that truly reflects the seismic situation, obtained through actual seismic acquisition in relevant areas such as land or sea. For example, in land acquisition, geophones are arranged in a certain pattern (e.g., a total arrangement of 10 kilometers, geophone spacing of 100 meters, and the epicenter in the center), recording the actual seismic waveform at a certain time sampling interval (e.g., a time sampling interval of 1 millisecond and a recording time of approximately 3 seconds). Synthetic data, on the other hand, is data derived from a predetermined geological model (e.g., a geological model of an elastic isotropic medium, given parameters such as actual shear wave velocity, P-wave velocity, and density) through corresponding calculations and simulations. By acquiring observational data and synthetic data in this way, complete seismic waveform data can be constructed, providing a foundation for subsequent operations such as surface wave-based inversion.

[0043] The technical solution of this embodiment, by limiting the acquisition and construction methods of observational and synthetic data, ensures that the seismic waveform data used is consistent with actual needs and is complete and effective. For example, in practical applications, observational data corresponding to the actual shear wave velocity model is calculated using real geological model parameters. This observational data includes various seismic wave information, such as surface waves, and truly reflects the actual earthquake propagation. Simultaneously, synthetic data is calculated using a set initial model. By comparing the differences between the observational and synthetic data, such as travel time differences, the state of the original data can be clearly understood. The seismic waveform data constructed in this way provides a reliable basis for subsequent surface wave inversion and other operations, ensuring the accuracy of the inversion results and enabling better analysis of the propagation characteristics of seismic waves and underground structures under different geological models.

[0044] Example 3

[0045] Based on the above embodiments, the steps of comparing surface wave data and carrying out surface wave inversion within the full waveform inversion framework using the least squares criterion include: comparing the surface wave data of the observed data and the surface wave data of the synthesized data within the full waveform inversion framework using the least squares criterion, and carrying out surface wave inversion.

[0046] The technical problem this embodiment aims to solve is how to compare surface wave data. In the process of surface wave inversion, if the surface wave data cannot be accurately and reasonably compared, it is difficult to measure the accuracy of the inversion and determine whether the inversion results conform to the actual situation. There is a lack of effective methods for comparing surface wave data in this field.

[0047] In this embodiment, the least squares criterion is used to compare surface wave data within the framework of full waveform inversion. The surface wave data in this embodiment includes both observed surface wave data and synthetic surface wave data. The observed surface wave data is extracted from the actually acquired seismic waveform data after undergoing the aforementioned operations such as the Fast Fourier Transform (FFT) on the time axis and the Discrete Fourier Transform (DFT) on the spatial axis, thus accurately reflecting the actual seismic surface wave situation. The synthetic surface wave data is simulated surface wave data calculated based on a set geological model and other conditions. The least squares criterion, i.e., the data comparison method, is used. Surface wave data from the calculation observation data Surface wave data with synthetic data The differences between them are used to perform surface wave inversion. Through the comparison method in this embodiment, the inversion process can be continuously adjusted according to the differences, so that the inversion is more in line with reality.

[0048] The technical solution in this embodiment effectively judges the inversion effect and accuracy by comparing surface wave data using the least squares criterion within the full waveform inversion framework. For example, in actual synthetic numerical experiments, there are real-world scenarios with established shear wave velocity models, P-wave models, and density parameters, from which observational data is calculated. Simultaneously, there is synthetic data under the initial model settings. A comparison of the two initially reveals significant differences in travel time. However, by comparing surface wave data based on the least squares criterion and continuously adjusting according to the comparison results during the inversion process, the synthetic data corresponding to the final inversion result can fit the observational data very well, as clearly demonstrated in the final data interval comparison. This indicates that this comparison method makes surface wave inversion more accurate, better reconstructing underground geological structures based on surface wave information, providing strong support for earthquake-related geological research, and ensuring the scientific validity and effectiveness of the entire surface wave inversion work.

[0049] Example 4

[0050] Based on the above embodiments, the seismic waveform data includes a time axis dimension and a spatial axis dimension.

[0051] The technical problem to be solved in this embodiment is how to construct seismic waveform data. In the earthquake research and data processing stage, seismic waveform data needs to be accurately constructed from multiple dimensions. If the dimensions are not fully considered, it will affect the subsequent work such as surface wave-based processing and inversion. There is a technical problem in the field of insufficient control over the dimensions when constructing seismic waveform data.

[0052] In this embodiment, the technical solution emphasizes that seismic waveform data includes both a time axis and a spatial axis. The time axis dimension reflects that the acquisition of seismic waveform data occurs within a specific time range, with corresponding time sampling intervals. For example, when acquiring data on land, the time sampling interval might be 1 millisecond, with a recording time of approximately 3 seconds. The time recording in this embodiment can fully present the changes in seismic waves over time. The spatial axis dimension involves the deployment of geophones. For instance, geophones are arranged in a specific pattern, with a total length reaching 10 kilometers, a geophone spacing of 100 meters, and the seismic source located in the center. This spatial layout determines the seismic wave reception at different locations, thus reflecting the propagation characteristics of seismic waves from a spatial perspective. By integrating the relevant information from both the time and spatial axes, complete and accurate seismic waveform data can be constructed, providing a reliable data foundation for subsequent surface wave-related operations.

[0053] The technical solution in this embodiment, by clearly defining and constructing the time and spatial dimensions of seismic waveform data, can comprehensively and meticulously reflect the actual propagation of seismic waves. For example, in actual land acquisition examples, based on precise sampling along the time axis, the waveform characteristics of seismic waves at different times can be clearly seen, and the performance of surface waves, volume waves, etc., at different time points can be recorded. From the spatial axis perspective, due to the different positions of the geophones, the propagation differences of seismic waves at different spatial locations can be captured, thereby allowing analysis of the spatial variation patterns of seismic waves during propagation. The seismic waveform data constructed in this way can fully utilize this multi-dimensional information when performing operations such as Fourier transform to extract surface waves and when performing inversion, making surface wave extraction more accurate and the inversion results more consistent with the actual underground geological structure. This plays a crucial role in improving the overall quality of seismic data processing and geological research.

[0054] Example 5

[0055] Based on the above embodiments, the step of performing Fourier transform on seismic waveform data to obtain surface wave data includes: performing fast Fourier transform on the seismic waveform data along the time axis to obtain intermediate data; and performing discrete Fourier transform on the intermediate data along the spatial axis to obtain surface wave data.

[0056] The technical problem to be solved in this embodiment is how to obtain surface wave data by performing Fourier transform on seismic waveform data. When processing seismic waveform data to obtain surface wave data, a scientific, reasonable, and effective Fourier transform process is required; otherwise, it is difficult to accurately separate surface waves from complex seismic waveform data. There is a technical problem in this field of lacking an accurate method for obtaining surface wave data through Fourier transform.

[0057] In this embodiment, the seismic waveform data is first subjected to a Fast Fourier Transform along the time axis. Using the expression d1(f,x)=∫d(t,x)exp(-i2πft)dt, the original common-shot gathering seismic waveform data d(t,x) (t is time, x is the detector coordinates) is transformed to obtain intermediate data d1(f,x). In this embodiment, f is used as the conjugate variable of t, i.e., frequency. This transformation converts the data from the time domain to the frequency domain, initially extracting frequency-related information. Next, a Discrete Fourier Transform is performed on the intermediate data along the spatial axis, according to the expression... s, as the conjugate variable of x, represents the slope or slowness of the seismic event (slowness is the reciprocal of velocity). Through the transformation in this embodiment, surface wave data that conforms to a specific slowness range can be further filtered from the intermediate data. When performing discrete Fourier transform on the spatial axis, the slowness parameter is usually determined based on the surface wave range of the current work area. For example, it is generally assumed that the surface wave velocity is between 0.4 and 1.2 km / s, and the corresponding slowness parameter range is between 2.5 and 0.83, so as to accurately separate the surface wave data.

[0058] The technical solution in this embodiment, through a two-step operation of performing a Fast Fourier Transform (FFT) along the time axis followed by a Discrete Fourier Transform (DFT) along the spatial axis, can efficiently and accurately extract surface wave data from complex seismic waveform data. For example, in land-based data acquisition scenarios, the raw data contains numerous interference information, including various body wave components and noise. The FFT along the time axis can separate waves of different frequencies that are mixed together in the time domain, filtering out frequency-related information. Then, using the DFT along the spatial axis, combined with an appropriate slowness parameter range, waves whose spatial propagation characteristics conform to the surface wave characteristics can be extracted, thus accurately separating the surface waves. The surface wave data obtained in this way allows for more targeted inversion operations that better reflect the actual underground geological structure, playing a crucial role in improving the accuracy of surface wave-based seismic data processing and related geological research.

[0059] Example 6

[0060] Based on the above embodiments, the Discrete Fourier Transform includes a slowness parameter. In the step of performing a Discrete Fourier Transform on intermediate data along the spatial axis to obtain surface wave data, the slowness parameter is determined based on the surface wave range of the current work area.

[0061] The technical problem to be solved in this embodiment is how to perform Discrete Fourier Transform (DFT). When using DFT to obtain surface wave data from seismic waveform data, determining the slowness parameter is a crucial step. If the slowness parameter cannot be reasonably determined, it is difficult to accurately separate the surface wave data. There is a technical problem in the art of determining the slowness parameter in DFT.

[0062] In the technical solution of this embodiment, in the step of performing a Discrete Fourier Transform (DFT) on intermediate data along a spatial axis to obtain surface wave data, the slowness parameter is determined based on the surface wave range of the current work area. For example, in actual seismic data processing, different work areas have different geological conditions, and the propagation speed range of surface waves is also different. It can usually be assumed that the surface wave velocity is between 0.4 and 1.2 km / s. Based on the relationship that slowness is the reciprocal of velocity, the corresponding slowness parameter range is between 2.5 and 0.83. During the DFT, the expression is used... (where f is the slowness parameter, x is the detector coordinate, and d1(s,x) is the intermediate data). By controlling the slowness parameter within a reasonable range, the transformed result can better match the propagation characteristics of surface waves, thereby accurately separating the surface wave data. This method of determining the slowness parameter based on the actual conditions of the work area ensures the effectiveness of the Discrete Fourier Transform in extracting surface wave data.

[0063] The technical solution of this embodiment, by determining the slowness parameter based on the surface wave range of the current work area and performing Discrete Fourier Transform, can accurately separate surface wave data from intermediate data. For example, in a specific land work area with its unique geological structure, the actual propagation speed of surface waves is around the set range of 0.4 to 1.2 km / s. By accurately setting the corresponding slowness parameter range, when performing Discrete Fourier Transform on the intermediate data, waves that conform to the surface wave characteristics of the work area can be screened out, avoiding inaccurate surface wave data extraction or the mixing of other interfering wave data due to unreasonable slowness parameters. After obtaining accurate surface wave data, it can improve the quality of the entire inversion result when used for subsequent full waveform inversion and other operations, making it more consistent with the actual underground geological structure of the work area.

[0064] Example 7

[0065] Figure 2 This is a schematic diagram of the structure of a surface wave inversion device provided in an embodiment of this application, as shown below. Figure 2As shown, in the technical solution of this embodiment, a surface wave inversion device is provided. The device includes: a transformation module for performing Fourier transform on seismic waveform data to obtain surface wave data; and a comparison module for comparing the surface wave data using the least squares criterion within the full waveform inversion framework to perform surface wave inversion.

[0066] The technical problem to be solved in this embodiment is how to perform seismic waveform data inversion based on surface waves. In the field of seismic data processing, it is quite difficult to extract effective surface wave information from a large amount of seismic waveform data and perform accurate inversion based on it. There is a technical problem in this field that makes it difficult to efficiently utilize surface waves for seismic waveform data inversion.

[0067] In this embodiment, the technical solution first acquires seismic waveform data. This seismic waveform data includes raw waveform information collected under conditions such as natural source earthquakes on land or in the ocean, and artificial source earthquakes, covering multi-dimensional data including time and space. Next, a Fourier transform is performed on the seismic waveform data. First, a Fast Fourier Transform is performed along the time axis, using the expression d1(f,x)=∫d(t,x)exp(-i2πft)dt to transform the original common shot gathering seismic waveform data d(t,x) (where t is time and x is the detector coordinates) into intermediate data d1(f,x), where f is the conjugate variable of t, i.e., the frequency. Then, a Discrete Fourier Transform is performed along the spatial axis on the intermediate data, using the expression... s, as the conjugate variable of x, represents the slope or slowness of the seismic event (slowness is the reciprocal of velocity), thus yielding surface wave data. Subsequently, within the framework of full waveform inversion, the least squares criterion was employed, and a data comparison method was constructed. (in For the observed data in the transform domain, To perform surface wave inversion on synthetic data (data in the transform domain), it is possible to systematically extract surface waves from the original seismic waveform data and perform inversion operations.

[0068] The technical solution of this embodiment, through the aforementioned operations of performing a Fast Fourier Transform on the time axis and a Discrete Fourier Transform on the spatial axis on the seismic waveform data, can accurately separate surface wave data from complex raw seismic waveform data. For example, in data acquired on land, the detectors have a specific arrangement, and the source locations are also set accordingly. The raw data contains various body waves, noise, and other interference information. After such transformation, surface waves can be effectively extracted. Then, using the least squares criterion within the full waveform inversion framework for surface wave inversion, the inversion results can be made to better fit the actual situation. In some application examples, the observed data is calculated using a real shear wave velocity model, and then compared with the initial synthetic data and the final synthetic data obtained after inversion using the technical solution of this embodiment. From the comparison of the final data intervals, it can be seen that the synthetic data can fit the observed data very well. This indicates that this technical solution can accurately invert seismic waveform data based on surface waves, thereby providing a reliable basis for constructing related studies such as shallow surface layers and helping to gain a deeper understanding of underground geological structures.

[0069] Based on the above embodiments, before the step of performing Fourier transform on the seismic waveform data to obtain surface wave data, the apparatus further includes: acquiring seismic waveform data, wherein the seismic waveform data includes observation data and synthetic data.

[0070] The technical problem this embodiment aims to solve is how to construct seismic waveform data. In seismic data processing, without a suitable source of seismic waveform data and a reasonable construction method, subsequent operations such as surface wave-based inversion cannot be effectively carried out. There is a technical problem in this field of obtaining complete and compliant seismic waveform data.

[0071] In this embodiment, the acquisition method for seismic waveform data needs to be clearly defined, which includes two parts: observational data and synthetic data. Observational data is data that truly reflects the seismic situation, obtained through actual seismic acquisition in relevant areas such as land or sea. For example, in land acquisition, geophones are arranged in a certain pattern (e.g., a total arrangement of 10 kilometers, geophone spacing of 100 meters, and the epicenter in the center), recording the actual seismic waveform at a certain time sampling interval (e.g., a time sampling interval of 1 millisecond and a recording time of approximately 3 seconds). Synthetic data, on the other hand, is data derived from a predetermined geological model (e.g., a geological model of an elastic isotropic medium, given parameters such as actual shear wave velocity, P-wave velocity, and density) through corresponding calculations and simulations. By acquiring observational data and synthetic data in this way, complete seismic waveform data can be constructed, providing a foundation for subsequent operations such as surface wave-based inversion.

[0072] The technical solution of this embodiment, by limiting the acquisition and construction methods of observational and synthetic data, ensures that the seismic waveform data used is consistent with actual needs and is complete and effective. For example, in practical applications, observational data corresponding to the actual shear wave velocity model is calculated using real geological model parameters. This observational data includes various seismic wave information, such as surface waves, and truly reflects the actual earthquake propagation. Simultaneously, synthetic data is calculated using a set initial model. By comparing the differences between the observational and synthetic data, such as travel time differences, the state of the original data can be clearly understood. The seismic waveform data constructed in this way provides a reliable basis for subsequent surface wave inversion and other operations, ensuring the accuracy of the inversion results and enabling better analysis of the propagation characteristics of seismic waves and underground structures under different geological models.

[0073] Based on the above embodiments, the steps of comparing surface wave data and carrying out surface wave inversion within the full waveform inversion framework using the least squares criterion include: comparing the surface wave data of the observed data and the surface wave data of the synthesized data within the full waveform inversion framework using the least squares criterion, and carrying out surface wave inversion.

[0074] The technical problem this embodiment aims to solve is how to compare surface wave data. In the process of surface wave inversion, if the surface wave data cannot be accurately and reasonably compared, it is difficult to measure the accuracy of the inversion and determine whether the inversion results conform to the actual situation. There is a technical problem in the art of lacking an effective device for comparing surface wave data.

[0075] In this embodiment, the least squares criterion is used to compare surface wave data within the framework of full waveform inversion. The surface wave data in this embodiment includes both observed surface wave data and synthetic surface wave data. The observed surface wave data is extracted from the actually acquired seismic waveform data after undergoing the aforementioned operations such as the Fast Fourier Transform (FFT) on the time axis and the Discrete Fourier Transform (DFT) on the spatial axis, thus accurately reflecting the actual seismic surface wave situation. The synthetic surface wave data is simulated surface wave data calculated based on a set geological model and other conditions. The least squares criterion, i.e., the data comparison method, is used. Surface wave data from the calculation observation data Surface wave data with synthetic data The differences between them are used to perform surface wave inversion. Through the comparison method in this embodiment, the inversion process can be continuously adjusted according to the differences, so that the inversion is more in line with reality.

[0076] The technical solution in this embodiment effectively judges the inversion effect and accuracy by comparing surface wave data using the least squares criterion within the full waveform inversion framework. For example, in actual synthetic numerical experiments, there are real-world scenarios with established shear wave velocity models, P-wave models, and density parameters, from which observational data is calculated. Simultaneously, there is synthetic data under the initial model settings. A comparison of the two initially reveals significant differences in travel time. However, by comparing surface wave data based on the least squares criterion and continuously adjusting according to the comparison results during the inversion process, the synthetic data corresponding to the final inversion result can fit the observational data very well, as clearly demonstrated in the final data interval comparison. This indicates that this comparison method makes surface wave inversion more accurate, better reconstructing underground geological structures based on surface wave information, providing strong support for earthquake-related geological research, and ensuring the scientific validity and effectiveness of the entire surface wave inversion work.

[0077] Based on the above embodiments, the seismic waveform data includes a time axis dimension and a spatial axis dimension.

[0078] The technical problem to be solved in this embodiment is how to construct seismic waveform data. In the earthquake research and data processing stage, seismic waveform data needs to be accurately constructed from multiple dimensions. If the dimensions are not fully considered, it will affect the subsequent work such as surface wave-based processing and inversion. There is a technical problem in the field of insufficient control over the dimensions when constructing seismic waveform data.

[0079] In this embodiment, the technical solution emphasizes that seismic waveform data includes both a time axis and a spatial axis. The time axis dimension reflects that the acquisition of seismic waveform data occurs within a specific time range, with corresponding time sampling intervals. For example, when acquiring data on land, the time sampling interval might be 1 millisecond, with a recording time of approximately 3 seconds. The time recording in this embodiment can fully present the changes in seismic waves over time. The spatial axis dimension involves the deployment of geophones. For instance, geophones are arranged in a specific pattern, with a total length reaching 10 kilometers, a geophone spacing of 100 meters, and the seismic source located in the center. This spatial layout determines the seismic wave reception at different locations, thus reflecting the propagation characteristics of seismic waves from a spatial perspective. By integrating the relevant information from both the time and spatial axes, complete and accurate seismic waveform data can be constructed, providing a reliable data foundation for subsequent surface wave-related operations.

[0080] The technical solution in this embodiment, by clearly defining and constructing the time and spatial dimensions of seismic waveform data, can comprehensively and meticulously reflect the actual propagation of seismic waves. For example, in actual land acquisition examples, based on precise sampling along the time axis, the waveform characteristics of seismic waves at different times can be clearly seen, and the performance of surface waves, volume waves, etc., at different time points can be recorded. From the spatial axis perspective, due to the different positions of the geophones, the propagation differences of seismic waves at different spatial locations can be captured, thereby allowing analysis of the spatial variation patterns of seismic waves during propagation. The seismic waveform data constructed in this way can fully utilize this multi-dimensional information when performing operations such as Fourier transform to extract surface waves and when performing inversion, making surface wave extraction more accurate and the inversion results more consistent with the actual underground geological structure. This plays a crucial role in improving the overall quality of seismic data processing and geological research.

[0081] Based on the above embodiments, the step of performing Fourier transform on seismic waveform data to obtain surface wave data includes: performing fast Fourier transform on the seismic waveform data along the time axis to obtain intermediate data; and performing discrete Fourier transform on the intermediate data along the spatial axis to obtain surface wave data.

[0082] The technical problem to be solved in this embodiment is how to obtain surface wave data by performing Fourier transform on seismic waveform data. When processing seismic waveform data to obtain surface wave data, a scientific, reasonable, and effective Fourier transform process is required; otherwise, it is difficult to accurately separate surface waves from complex seismic waveform data. There is a technical problem in the field of lacking a device that can accurately obtain surface wave data through Fourier transform.

[0083] In this embodiment, the seismic waveform data is first subjected to a Fast Fourier Transform along the time axis. Using the expression d1(f,x)=∫d(t,x)exp(-i2πft)dt, the original common-shot gathering seismic waveform data d(t,x) (t is time, x is the detector coordinates) is transformed to obtain intermediate data d1(f,x). In this embodiment, f is used as the conjugate variable of t, i.e., frequency. This transformation converts the data from the time domain to the frequency domain, initially extracting frequency-related information. Next, a Discrete Fourier Transform is performed on the intermediate data along the spatial axis, according to the expression... s, as the conjugate variable of x, represents the slope or slowness of the seismic event (slowness is the reciprocal of velocity). Through the transformation in this embodiment, surface wave data that conforms to a specific slowness range can be further filtered from the intermediate data. When performing discrete Fourier transform on the spatial axis, the slowness parameter is usually determined based on the surface wave range of the current work area. For example, it is generally assumed that the surface wave velocity is between 0.4 and 1.2 km / s, and the corresponding slowness parameter range is between 2.5 and 0.83, so as to accurately separate the surface wave data.

[0084] The technical solution in this embodiment, through a two-step operation of performing a Fast Fourier Transform (FFT) along the time axis followed by a Discrete Fourier Transform (DFT) along the spatial axis, can efficiently and accurately extract surface wave data from complex seismic waveform data. For example, in land-based data acquisition scenarios, the raw data contains numerous interference information, including various body wave components and noise. The FFT along the time axis can separate waves of different frequencies that are mixed together in the time domain, filtering out frequency-related information. Then, using the DFT along the spatial axis, combined with an appropriate slowness parameter range, waves whose spatial propagation characteristics conform to the surface wave characteristics can be extracted, thus accurately separating the surface waves. The surface wave data obtained in this way allows for more targeted inversion operations that better reflect the actual underground geological structure, playing a crucial role in improving the accuracy of surface wave-based seismic data processing and related geological research.

[0085] Based on the above embodiments, the Discrete Fourier Transform includes a slowness parameter. In the step of performing a Discrete Fourier Transform on intermediate data along the spatial axis to obtain surface wave data, the slowness parameter is determined based on the surface wave range of the current work area.

[0086] The technical problem to be solved in this embodiment is how to perform Discrete Fourier Transform (DFT). When using DFT to obtain surface wave data from seismic waveform data, determining the slowness parameter is a crucial step. If the slowness parameter cannot be reasonably determined, it is difficult to accurately separate the surface wave data. There is a technical problem in the art of determining the slowness parameter in DFT.

[0087] In the technical solution of this embodiment, in the step of performing a Discrete Fourier Transform (DFT) on intermediate data along a spatial axis to obtain surface wave data, the slowness parameter is determined based on the surface wave range of the current work area. For example, in actual seismic data processing, different work areas have different geological conditions, and the propagation speed range of surface waves is also different. It can usually be assumed that the surface wave velocity is between 0.4 and 1.2 km / s. Based on the relationship that slowness is the reciprocal of velocity, the corresponding slowness parameter range is between 2.5 and 0.83. During the DFT, the expression is used... (where s is the slowness parameter, x is the detector coordinate, and d1(f,x) is the intermediate data). By controlling the slowness parameter within a reasonable range, the transformed result can better match the propagation characteristics of surface waves, thereby accurately separating the surface wave data. This method of determining the slowness parameter based on the actual conditions of the work area ensures the effectiveness of the Discrete Fourier Transform in extracting surface wave data.

[0088] The technical solution of this embodiment, by determining the slowness parameter based on the surface wave range of the current work area and performing Discrete Fourier Transform, can accurately separate surface wave data from intermediate data. For example, in a specific land work area with its unique geological structure, the actual propagation speed of surface waves is around the set range of 0.4 to 1.2 km / s. By accurately setting the corresponding slowness parameter range, when performing Discrete Fourier Transform on the intermediate data, waves that conform to the surface wave characteristics of the work area can be screened out, avoiding inaccurate surface wave data extraction or the mixing of other interfering wave data due to unreasonable slowness parameters. After obtaining accurate surface wave data, it can improve the quality of the entire inversion result when used for subsequent full waveform inversion and other operations, making it more consistent with the actual underground geological structure of the work area.

[0089] Example 8

[0090] In the technical solution of this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of any of the surface wave inversion methods described in the above embodiments.

[0091] In the technical solution of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of any of the surface wave inversion methods described in the above embodiments.

[0092] In the technical solution of this embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the surface wave inversion methods described above.

[0093] 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 performing the methods in the above embodiments. The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0094] 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.

[0095] In addition, the computer device may also include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., a keyboard, mouse, speakers, etc.). The processor can communicate with external devices via the I / O bus through a wired or wireless network. 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, when executed by the processor, perform the steps of the various functions and / or methods in the embodiments described herein.

[0096] Example 9

[0097] Based on the above embodiments, this embodiment provides an application example.

[0098] This application example provides a surface wave separation and inversion method. This invention belongs to the field of data processing for natural and artificial source earthquakes, specifically involving a surface wave extraction method and its inversion algorithm, representing fundamental research in inversion algorithms.

[0099] Seismic waves include body waves and surface waves. Body waves are waves that propagate from the Earth's interior and typically include shallow water waves, reflected waves, and refracted waves. Surface waves are seismic waves that propagate along velocity interfaces. They are formed by the interference of P-waves and S-waves near the velocity interface. Surface waves can also be formed near the interface by the interference of S-waves with each other.

[0100] Due to interference, surface waves exhibit exponential attenuation in the direction perpendicular to the interface, causing their energy to propagate only near the interface. Among various surface waves, surface waves from the Earth's surface are frequently encountered in exploration seismic studies and large earthquakes because seismic instruments are typically deployed on the surface. The Earth's surface is a surface of strong velocity variation; seismic wave velocities in air are 340 m / s, while in rock, they can reach thousands. Because surface waves propagate only near the interface, using surface waves to invert shallow subsurface layers is a feasible approach.

[0101] Due to significant noise, real-world data often makes it difficult to accurately separate the contribution of surface waves to the inversion process. In large earthquakes, surface wave dispersion curve inversion is a popular method. This involves first extracting the dispersion curve, then assuming a one-dimensional subsurface medium for forward modeling. Some algorithms employ pseudo-two-dimensional or pseudo-three-dimensional methods in the inversion. However, inversion based solely on surface wave dispersion curves struggles to reconstruct highly accurate geological models. The fundamental reason is that fitting the dispersion curve does not equate to fitting the waveform.

[0102] This invention mainly focuses on the inversion of surface waves. First, the surface waves are separated in the transform domain, and then the underground velocity structure is constructed by waveform inversion of the separated surface waves.

[0103] To address the technical problem of low signal-to-noise ratio in land data, which makes surface wave inversion difficult, this invention employs a strategy of separating surface waves in the transform domain and then performing inversion. This invention is not targeted at any specific region, but rather belongs to the category of fundamental methodological research.

[0104] For a common shot set seismic waveform data, this invention first performs a fast Fourier transform along the time axis:

[0105] d1(f,x)=∫d(t,x)exp(-i2πft)dt (1)

[0106] Where d(t,x) represents the original common shot gather seismic waveform data, t represents time, and x represents the geophone coordinates. f is the conjugate variable of t, which physically represents frequency. d1(f,x) represents the common shot gather seismic data after Fast Fourier Transform, i.e., intermediate data.

[0107] Considering that detectors may not always be arranged in a perfectly regular pattern, this invention performs a discrete Fourier transform along the spatial axis:

[0108]

[0109] Here, s is the conjugate variable of x, and its physical meaning is the slope of the earthquake event, or the slowness (slowness is the reciprocal of the velocity). This is the common shot gather seismic data after discrete Fourier transform, i.e., surface wave data. To achieve surface wave separation, the range of the slowness parameter is crucial. It is usually based on the surface wave range of the current work area. It can usually be assumed that the surface wave velocity is between 0.4 and 1.2 km / s, which corresponds to a slowness parameter range of 2.5 to 0.83.

[0110] After separating the surface waves, a data comparison method is established using the least squares criterion within the framework of full waveform inversion:

[0111]

[0112] in, For the observed data in the transform domain, This refers to the synthesized data in the transform domain. After constructing the data comparison method, surface wave inversion can be performed within the full waveform inversion framework.

[0113] The technology designed in this invention is for separating and inverting surface waves in land data. The separation effect and inversion results demonstrate that surface waves are indeed effectively separated and inverted.

[0114] Figure 3 To collect seismic waveform data. Figure 4 for Figure 3 The surface waves separated from the data. Figure 5 The model represents a true shear wave velocity, which is uniform throughout and has a magnitude of 2.0 km / s. Figure 6 According to Figure 5 The observation data calculated by the model. Figure 7 The initial shear wave velocity model is uniform everywhere and has a magnitude of 1.8 km / s. Figure 8 According to Figure 7 The initial synthetic data were obtained from the velocity model calculation. Figure 9 For initial data comparison, by Figure 6 and Figure 8 The intervals are displayed. Figure 10This is the shear wave velocity model obtained from the inversion of the separated surface waves. Figure 11 For corresponding Figure 10 The final synthesized data. Figure 12 For the final data comparison, by Figure 6 The actual data and Figure 11 The final synthetic data intervals are shown in the image.

[0115] The first application example of this invention demonstrates surface wave separation, including... Figure 3 and Figure 4 . Figure 3 This data was collected from actual land-based sources. The geophones were arranged over a total distance of 10 kilometers, with a spacing of 100 meters between them; the seismic source was located in the middle. The time sampling interval was 1 millisecond, and the recording time was approximately 3 seconds. Figure 4 The surface wave separated after applying the algorithm of the present invention has a velocity scan range of 0.4 to 1.2 km / s.

[0116] The second application example of this invention demonstrates its application in full-waveform inversion. Here, a synthetic numerical experiment is used, and the geological model is an elastic isotropic medium. Figure 5 It is a true shear wave velocity model with a magnitude of 2.0 km / s, a true longitudinal wave model with a magnitude of 3.7 km / s, and a true density of 2400 kg / m^3. Figure 6 The data displayed is observational data, from... Figure 5 The actual model was calculated. Among them, the stronger signal is the surface wave. In the initial model, the P-wave velocity and density are kept constant, and the shear wave velocity model is as follows: Figure 7 As shown, the speed is 1.8 km / s. The initial synthesized data are as follows: Figure 7 As shown. To further illustrate the discrepancy between the observed data and the initial synthetic data, Figure 9 The method of comparing the intervals between observed data and synthetic data is shown, and it can be seen that there are significant differences in the travel time of the data. Figure 10 To apply the shear wave velocity model obtained by the algorithm of this invention, Figure 11 For the final synthesized data, Figure 12 For the final data interval comparison. From Figure 12 As can be seen, the synthetic data has perfectly matched the observed data.

[0117] Surface waves propagate roughly along the velocity discontinuity interface, with their energy concentrated only near the interface. Therefore, surface wave inversion in exploration seismic data or large earthquakes offers unique advantages for constructing shallow surface data. Land data acquisition typically lacks the high signal-to-noise ratio of ocean data, and surface wave waveform inversion is often unstable. This invention employs a separation-then-inversion strategy. First, the original common-shot data undergoes a Fast Fourier Transform (FFT) along the time axis and a Discrete Fourier Transform (DFT) along the spatial axis. During the DFT along the spatial axis, the range of slowness can be limited, thus achieving separation. After separation, the least squares criterion is used for data comparison, and surface wave waveform inversion is performed within the framework of full waveform inversion.

[0118] In the embodiments provided by this invention, 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 the present invention. 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.

[0119] It should be noted that, in this invention, 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.

[0120] While the embodiments disclosed in this invention are as described above, the above content is merely for the purpose of facilitating understanding of this invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed in this invention; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A surface wave inversion method, characterized in that, The method includes: Fourier transform is performed on the seismic waveform data to obtain surface wave data; Within the framework of full waveform inversion, the least squares criterion is used to compare surface wave data and carry out surface wave inversion.

2. The surface wave inversion method according to claim 1, characterized in that, Before the step of performing a Fourier transform on the seismic waveform data to obtain surface wave data, the method further includes: Seismic waveform data is acquired, wherein the seismic waveform data includes observational data and synthetic data.

3. The surface wave inversion method according to claim 2, characterized in that, The steps for performing surface wave inversion within the full waveform inversion framework by comparing surface wave data using the least squares criterion include: Within the framework of full waveform inversion, the least squares criterion is used to compare the surface wave data of the observed data and the surface wave data of the synthetic data to carry out surface wave inversion.

4. The surface wave inversion method according to claim 1, characterized in that, The seismic waveform data includes both time and spatial dimensions.

5. The surface wave inversion method according to claim 4, characterized in that, The step of performing a Fourier transform on the seismic waveform data to obtain surface wave data includes: The seismic waveform data is subjected to a fast Fourier transform along the time axis to obtain intermediate data; The intermediate data is subjected to a discrete Fourier transform along the spatial axis to obtain surface wave data.

6. The surface wave inversion method according to claim 5, characterized in that, The discrete Fourier transform includes a slowness parameter. In the step of performing a discrete Fourier transform on the intermediate data along the spatial axis to obtain surface wave data, the slowness parameter is determined based on the surface wave range of the current work area.

7. A surface wave inversion device, characterized in that, The device includes: The transformation module is used to perform Fourier transform on seismic waveform data to obtain surface wave data; The comparison module is used to compare surface wave data and perform surface wave inversion within the framework of full waveform inversion using the least squares criterion.

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 surface wave inversion 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 surface wave inversion method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the surface wave inversion method according to any one of claims 1 to 6.

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