A data-driven surface roll prediction method, device, electronic device, and system

Abstract

The present invention provides a data-driven surface wave prediction method, device, computer-readable storage medium, and system. The method constructs a cross-arranged shot-and-detection seismic observation system, wherein a source line composed of a number of vertically arranged shot points intersects a detection line composed of a number of horizontally arranged detection points in a cross-shaped manner; seismic data from the cross-arranged shot-and-detection seismic observation system is acquired and converted from the time domain to the frequency domain; the number of shot points on the source line and the number of detection points on the detection line that participate in surface wave prediction are determined, and these are grouped into a number of shot-and-detection pairs; for each group of shot-and-detection pairs, a surface wave prediction result is calculated for each frequency component in the frequency domain seismic data; and the surface wave prediction results for all frequency components of all shot-and-detection pairs are converted from the frequency domain to the time domain to obtain the surface wave prediction results of the shot-and-detection seismic observation system. The present invention does not rely on a near-surface model, does not require human interaction, and can be widely applied to ground seismic data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of ground seismic data processing, and in particular to a data-driven surface wave prediction method, device, computer-readable storage medium and system. Background Art

[0002] In recent years, with the growing demand for high-precision seismic exploration, the requirements for ground seismic data processing have also been increasing. Rayleigh surface waves are the primary interference in ground seismic data. Surface waves generally provide little useful information in exploration seismic data because they mask other, more useful body wave arrivals and are therefore considered noise. Common surface wave separation methods include FK filtering, Radon transform, and dispersion curve inversion. These methods require manual intervention and are labor-intensive. Summary of the Invention

[0003] To address the above problems, embodiments of the present invention provide a data-driven surface roll prediction method, apparatus, computer-readable storage medium, and system.

[0004] In a first aspect, an embodiment of the present invention provides a data-driven surface roll prediction method, the method comprising:

[0005] Construct a cross-arranged shot-detector seismic observation system, in which the source line composed of several vertically arranged shot points and the detection line composed of several horizontally arranged detection points intersect in a cross shape;

[0006] Acquire seismic data from a cross-arranged shot-detector seismic observation system and convert it from the time domain to the frequency domain.

[0007] Determine the number of shot points on the source line and the number of receiver points on the receiver line that participate in surface wave prediction, and group them into several shot-receiver pairs;

[0008] For each shot-detection pair, calculate the surface wave prediction result for each frequency component in the frequency domain seismic data;

[0009] The surface roll prediction results of all frequency components of all shot-detector pairs are converted from the frequency domain to the time domain to obtain the surface roll prediction results of the shot-detector seismic observation system.

[0010] According to an embodiment of the present invention, the step of calculating the surface roll prediction result for each frequency component in the frequency-domain seismic data for each shot-detection pair includes:

[0011] For each shot-detection pair, estimate the shot point location and the detector location;

[0012] Based on the positions of the shot points and the geophones, the surface roll prediction results for each frequency component in the frequency domain seismic data are calculated.

[0013] According to an embodiment of the present invention, based on the positions of the shot points and the receivers, the surface roll prediction result for each frequency component in the frequency domain seismic data is calculated according to the following formula:

[0014]

[0015] where G is the Green's function, which represents the pressure at the previous coordinate position caused by the source at the latter coordinate position, ω is the angular frequency, ρ is the density at the source position, k is a constant, the shot point position is x1, the receiver position is x2, and the two surfaces S′ and S are the receiver boundary and the source boundary, respectively, which are determined by the number of receiver points on the receiver line and the number of shot points on the source line that participate in surface wave prediction, respectively.

[0016] According to an embodiment of the present invention, the number of detection points on the detection line participating in the surface roll prediction is 3 to 7.

[0017] According to an embodiment of the present invention, the number of shot points on the source line participating in surface wave prediction is 3 to 7.

[0018] According to an embodiment of the present invention, the number of detection points on the detection line participating in surface wave prediction is different from the number of shot points on the source line participating in surface wave prediction.

[0019] According to an embodiment of the present invention, in a cross-arranged shot-detector seismic observation system, the detection points on the detection line are located on both sides of the source line.

[0020] In a second aspect, an embodiment of the present invention provides a data-driven surface roll prediction device, comprising:

[0021] A system construction module for constructing a cross-arranged shot-detector seismic observation system, wherein a source line composed of a number of vertically arranged shot points intersects a detection line composed of a number of horizontally arranged detector points in a cross-like manner;

[0022] The data acquisition module is used to acquire the seismic data of the cross-arranged shot-detector seismic observation system and convert it from the time domain to the frequency domain.

[0023] The shot-detection pair selection module is used to determine the number of shot points on the source line and the number of detection points on the detection line that participate in surface wave prediction, and to group them into several shot-detection pairs;

[0024] The surface wave analysis module is used to calculate the surface wave amplitude prediction result for each frequency component in the frequency domain seismic data for each shot-detection pair;

[0025] The surface wave prediction summary module is used to convert the surface wave amplitude prediction results of all frequency components of all shot-detection pairs from the frequency domain to the time domain to obtain the surface wave prediction results of the shot-detection seismic observation system.

[0026] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the data-driven surface roll prediction method as described in the first aspect is implemented.

[0027] In a fourth aspect, an embodiment of the present invention provides a data-driven surface roll prediction system, comprising:

[0028] a plurality of shot points and a plurality of geophones, wherein a source line formed by the plurality of vertically arranged shot points and a receiver line formed by the plurality of horizontally arranged receiver points intersect crosswise;

[0029] A processor connected to the detector at the detection point;

[0030] a memory for storing instructions executable by the processor;

[0031] The processor is configured to execute the instructions to implement a data-driven surface roll prediction method as described in the first aspect.

[0032] Compared with the prior art, the above technical solution of the present invention has the following beneficial effects:

[0033] The present invention proposes a practical surface roll prediction method, which realizes the prediction of shot-receiver surface rolls in a cross-arrangement domain, is independent of the near-surface model and does not require manual interaction, and can be widely used in ground seismic data processing and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A schematic diagram showing the principle of cross-arranged field shot-detector wave prediction according to an embodiment of the present invention;

[0036] Figure 2 A flowchart showing the surface roll prediction method according to an embodiment of the present invention is shown;

[0037] Figure 3 A schematic diagram showing seismic data according to an example of an embodiment of the present invention;

[0038] Figure 4 Shows Figure 3 Schematic diagram of surface wave prediction results from seismic data.

[0039] Figure 5 A partial schematic diagram of a surface roll prediction system for implementing a surface roll prediction method according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0041] Example 1

[0042] like Figure 1 As shown, in this embodiment, the data-driven shot detection surface wave prediction method is primarily implemented based on a shot-detection seismic observation system configured in a cross configuration. In this system, a source line consisting of a plurality of vertically arranged shot points and a detection line consisting of a plurality of horizontally arranged detection points intersect in a cross configuration, such that the detection points on the detection line are located on either side of the source line. In this embodiment, the number of detection points on the detection lines on either side of the source line can be the same or different, and this is not limited by the present invention. Furthermore, the number of shot points on the source line and the number of detection points on the detection line can be the same or different, and this is not limited by the present invention.

[0043] like Figure 2 As shown, the shot detection surface wave prediction method provided in this embodiment mainly includes the following steps:

[0044] Construct a cross-arranged shot-detector seismic observation system, in which the source line composed of several vertically arranged shot points and the detection line composed of several horizontally arranged detection points intersect in a cross shape;

[0045] Acquire seismic data from a cross-arranged shot-detector seismic observation system and convert it from the time domain to the frequency domain.

[0046] Determine the number of shot points on the source line and the number of receiver points on the receiver line that participate in surface wave prediction, and group them into several shot-receiver pairs;

[0047] For each shot-check pair, the surface wave prediction result for each frequency component in the frequency domain seismic data is calculated using the following formula:

[0048]

[0049] The surface roll prediction results of all frequency components of all shot-detector pairs are converted from the frequency domain to the time domain to obtain the surface roll prediction results of the shot-detector seismic observation system.

[0050] The working principle of the above method is described in detail below.

[0051] The interchange theorem relates the seismic wave fields of two different states (e.g., due to two different energy source locations):

[0052]

[0053] and

[0054]

[0055] Where p A represents the sound pressure in state A, q A represents the source distribution under state A, v i，A represents the i-th component of the velocity of the particle in state A, and * represents the complex conjugate.

[0056] By replacing one of the states A, B, or C with a Green's function, convolution- and correlation-type expressions can be derived.

[0057] In the convolution-type interchange theorem, the state B is chosen as follows:

[0058]

[0059] The choice of state C in the correlation exchange theorem is:

[0060]

[0061] Where G(x, y) is the Green's function, which represents the pressure at position x caused by the earthquake source at position y, ω represents the angular frequency, ρ represents the density at the earthquake source position, and the partial derivative is is the derivative of the i-th coordinate of the earthquake source position,

[0062] use And the state of selecting equations (3) and (4) is obtained as follows:

[0063]

[0064]

[0065] Here, the prime sign indicates that the integration region in equation (6) is different from that in equation (5).

[0066] Substituting equation (6) into equation (5), and using the gun-check interchange G(x, x2) = G(x2, 2), we have:

[0067]

[0068] Using Green's functions between point x' on boundary S', point x on boundary S, and point x2, equation (7) expresses the relationship between the pressure at x2 caused by the source distribution defined by q(x) and the pressure on boundary S.

[0069] If q(x) = δ(x-x1) and p(x) = G(x, x1), then formula (7) is simplified to

[0070]

[0071] This formula indicates that the surface wave prediction record with the shot point position x1 and the receiver position x2 can be estimated by integrating the two surfaces S′ and S.

[0072] Based on the above principles, the prediction of shot-detector surface waves can be realized in the cross-arrangement domain.

[0073] The technical effect of the shot-detector wave prediction method of the present invention is illustrated below by using a specific example.

[0074] Reference Figure 2 , the source line where the shot points are located in the cross arrangement domain is taken as the source boundary, and the detection line where the detector is located is taken as the receiving boundary. Figure 3 The seismic data shown reconstruct the shot-to-receive (x1-x2) surface waves.

[0075] The specific implementation steps are as follows:

[0076] (1) Obtain seismic data from a cross-arranged shot-detector seismic observation system (e.g. Figure 3 shown);

[0077] (2) transforming seismic data from the time domain to the frequency domain using fast Fourier transform;

[0078] (3) Determine the source aperture parameter, i.e., the number of shot points in the surface integral S (3 to 7); determine the receiver aperture parameter, i.e., the number of receiver points in the surface integral S′ (3 to 7);

[0079] (4) For a set of shot detection pairs, select a frequency component;

[0080] (5) Select the source position as x1 and the detector position as x2, and use formula (8) to output the surface wave prediction record under the frequency component;

[0081] (6) For other frequency components, execute step (5);

[0082] (7) Repeat steps (4) to (6) for other gun inspection pairs;

[0083] (8) Transform the surface roll prediction records of all shot detection pairs from the frequency domain to the time domain to obtain the entire surface roll prediction results (such as Figure 4 shown).

[0084] Figure 3 It is the original ground seismic data, with a total of 263 channels and a channel spacing of 50 meters. Figure 4 Schematic diagram of the surface roll prediction results obtained using the method of the present invention. Figure 4 It can be seen that the predicted noise model is in good agreement with the actual data.

[0085] Example 2

[0086] The following are embodiments of the apparatus of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the apparatus embodiments of the present invention, please refer to the method embodiments of the present invention.

[0087] A data-driven surface roll prediction device, comprising:

[0088] An observation system construction module is used to construct a cross-arranged shot-detector seismic observation system, in which a source line composed of a number of vertically arranged shot points and a detection line composed of a number of horizontally arranged detection points intersect in a cross shape;

[0089] The seismic data acquisition module is used to acquire the seismic data of the cross-arranged shot-detection seismic observation system and convert it from the time domain to the frequency domain.

[0090] The shot-detection pair selection module is used to determine the number of shot points on the source line and the number of detection points on the detection line that participate in surface wave prediction, and to group them into several shot-detection pairs;

[0091] The surface wave analysis module is used to calculate the surface wave amplitude prediction result for each frequency component in the frequency domain seismic data for each shot-detection pair;

[0092] The surface wave prediction summary module is used to convert the surface wave amplitude prediction results of all frequency components of all shot-detection pairs from the frequency domain to the time domain to obtain the surface wave prediction results of the shot-detection seismic observation system.

[0093] Example 4

[0094] This embodiment provides a computer-readable medium having a computer program stored thereon. When the program is executed by a processor, the steps of the data-driven surface roll prediction method described in the above embodiment are implemented.

[0095] It should be noted that the present invention can implement all or part of the processes in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. Of course, there are other types of readable storage media, such as quantum memory, graphene memory, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0096] Example 5

[0097] Figure 5 FIG. 1 is a schematic diagram of the structure of a data-driven surface roll prediction system according to an embodiment of the present invention. Figure 5 As shown, at the hardware level, the system includes a processor in addition to a detector, and optionally an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the system may also include other hardware required for other services.

[0098] The processor, network interface, and memory can be interconnected via an internal bus, such as an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. These buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the diagram uses only line segments, but this does not imply that there is only one bus or only one type of bus.

[0099] The memory is configured to store a program. Specifically, the program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it. The processor executes the program stored in the memory to perform all steps of the aforementioned data-driven surface roll prediction method.

[0100] The communication bus mentioned in the above devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, only a single thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus. The communication interface is used for communication between the above systems and other devices.

[0101] Bus comprises hardware, software or both, for above-mentioned parts are coupled together.For example, bus can comprise accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more above these combinations.In suitable case, bus can comprise one or more buses.Although the embodiment of the present invention describes and shows specific bus, the present invention considers any suitable bus or interconnection.

[0102] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0103] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. In a specific embodiment, the memory is a non-volatile solid-state memory. In a specific embodiment, the memory includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0104] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0105] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0106] The devices, apparatuses, systems, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, an in-vehicle human-computer interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0107] Although the present invention provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When an actual device or terminal product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0108] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1The steps for the function specified in one or more boxes.

[0111] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0112] Each embodiment in this specification is described in a related manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device, system, and readable storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For related portions, reference can be made to the descriptions of the method embodiments.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A data-driven surface roll prediction method, characterized in that: include: Construct a cross-arranged shot-detector seismic observation system, in which the source line composed of several vertically arranged shot points and the detection line composed of several horizontally arranged detection points intersect in a cross shape; Acquire seismic data from a cross-arranged shot-detector seismic observation system and convert it from the time domain to the frequency domain. Determine the number of shot points on the source line and the number of receiver points on the receiver line that participate in surface wave prediction, and group them into several shot-receiver pairs; For each shot-detection pair, calculate the surface wave prediction result for each frequency component in the frequency domain seismic data; Converting the surface roll prediction results of all frequency components of all shot-detector pairs from the frequency domain to the time domain to obtain the surface roll prediction results of the shot-detector seismic observation system; Based on the positions of the shot points and the receivers, the surface wave prediction results for each frequency component in the frequency domain seismic data are calculated according to the following formula: Where, is the Green's function, which represents the pressure at the previous coordinate position caused by the earthquake source at the latter coordinate position. represents the angular frequency, represents the density at the source location, k is a constant, and the shot point location is , the detector position is , two sides and They are the receiver boundary and the source boundary, respectively, which are determined by the number of receiver points on the receiver line and the number of shot points on the source line that participate in surface wave prediction.

2. The data-driven surface roll prediction method according to claim 1, wherein: For each shot-check pair, the surface wave prediction results for each frequency component in the frequency domain seismic data are calculated, including: For each shot-detection pair, estimate the shot point location and the detector location; Based on the positions of the shot points and the geophones, the surface roll prediction results for each frequency component in the frequency domain seismic data are calculated.

3. The data-driven surface roll prediction method according to claim 1, wherein: The number of detection points on the detection line participating in surface roll prediction is 3 to 7.

4. The data-driven surface roll prediction method according to claim 1, wherein: The number of shot points on the source line participating in surface wave prediction is 3 to 7.

5. The data-driven surface roll prediction method according to claim 1, wherein: The number of detection points on the detection line participating in surface wave prediction is different from the number of shot points on the source line participating in surface wave prediction.

6. The data-driven surface roll prediction method according to claim 1, wherein: In a cross-arranged shot-detector seismic observation system, the detection points on the detection line are distributed on both sides of the source line.

7. A data-driven surface roll prediction device, characterized in that: include: An observation system construction module is used to construct a cross-arranged shot-detector seismic observation system, in which a source line composed of a number of vertically arranged shot points and a detection line composed of a number of horizontally arranged detection points intersect in a cross shape; The seismic data acquisition module is used to acquire the seismic data of the cross-arranged shot-detection seismic observation system and convert it from the time domain to the frequency domain. The shot-detection pair selection module is used to determine the number of shot points on the source line and the number of detection points on the detection line that participate in surface wave prediction, and to group them into several shot-detection pairs; The surface wave analysis module is used to calculate the surface wave amplitude prediction result for each frequency component in the frequency domain seismic data for each shot-detection pair; A surface wave prediction summary module is used to convert the surface wave amplitude prediction results of all frequency components of all shot-detection pairs from the frequency domain to the time domain to obtain the surface wave prediction results of the shot-detection seismic observation system; The surface wave analysis module is used for: Based on the positions of the shot points and the receivers, the surface wave prediction results for each frequency component in the frequency domain seismic data are calculated according to the following formula: Where, is the Green's function, which represents the pressure at the previous coordinate position caused by the earthquake source at the latter coordinate position. represents the angular frequency, represents the density at the source location, k is a constant, and the shot point location is , the detector position is , two sides and They are the receiver boundary and the source boundary, respectively, which are determined by the number of receiver points on the receiver line and the number of shot points on the source line that participate in surface wave prediction.

8. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, a data-driven surface roll prediction method according to any one of claims 1 to 6 is implemented.

9. A data-driven surface roll prediction system comprising: a plurality of shot points and a plurality of geophones, wherein a source line formed by the plurality of vertically arranged shot points and a receiver line formed by the plurality of horizontally arranged receiver points intersect crosswise; A processor connected to the detector at the detection point; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement a data-driven surface roll prediction method according to any one of claims 1 to 6.

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