Method and apparatus for predicting multiple waves in seabed nodal multi-component data of water layer

Abstract

This invention discloses a method and apparatus for predicting multiples in seabed node multi-component data. The method includes: obtaining a migration data volume using Kirchhoff pre-stack time migration based on the P-wave component data of the seabed node; constructing a seabed depth model based on interactive picking and time-depth conversion using the migration data volume; acquiring multiple seabed sample points based on the seabed depth model; determining the seabed reflection travel time and distance spread based on the multiple seabed sample points; and predicting multiples in the seabed based on the component data of the seabed node, the seabed reflection travel time, and the distance spread. This method effectively solves the problem that existing SRME methods cannot predict multiples in OBN converted wave data, and can significantly improve the multiple suppression effect of OBN data. Furthermore, it is adaptable to arbitrary water depths and rugged seabed areas, significantly improving the accuracy of multiple prediction.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of seismic data processing and analysis, specifically to a method and apparatus for predicting multiple waves of water layers from multi-component data of seafloor nodes. Background Technology

[0002] To obtain high-quality marine seismic data with wide azimuth, high coverage, and small area features, Ocean Bottom Nodes (OBN) seismic acquisition has developed rapidly in marine seismic exploration. OBN exploration achieves "four-component" exploration, where the P component is the pressure component, mainly responding to the energy of the P-wave pressure component; the Z component is the vertical component, mainly recording the energy of the P-wave vertical component; and the X and Y components are mutually perpendicular horizontal components, mainly recording converted wave energy. The sea surface can be approximated as a free interface (reflection coefficient close to -1), while the seabed is generally a strong wave impedance interface. The majority of multiples contained in the P and Z components are related to the seabed. For the horizontal components X and Y, the P-wave energy reflected downwards from the sea surface is reflected back by the subsurface interface, forming converted wave-type water-layer multiples with amplitudes much larger than interlayer multiples. Since the seismic signals mainly used in oil and gas exploration are single reflections, the prediction and suppression of water-layer multiples in OBN multi-component data has become a key issue in OBN data processing.

[0003] In recent years, multiple removal methods based on wave theory have made significant progress. These methods typically involve two steps: multiple prediction and multiple attenuation. First, the multiple components in the seismic record are predicted using wave theory. Then, various adaptive multiple attenuation methods are applied to remove multiples from the original seismic record based on the predicted multiples. Among these, the Free Interface Multiple Attenuation (SRME) method, based on feedback loop theory, can effectively suppress multiple components in seismic records with almost no prior information and has become one of the preferred methods for 2D seismic data processing, and has been extended to multiple suppression of 3D conventional towed cable data. However, this method has encountered severe challenges in OBN converted wave data processing. Because the seismic signal receiving equipment is deployed on the seabed, OBN acquisition cannot receive primary reflections from the seabed (due to the lack of seabed illumination). Furthermore, the use of observation systems with "few channels, many shots" characteristics results in low and uneven coverage of shallow and mid-situ regions in the received data, and even the loss of reflection signals from some shallow strata. This severely limits the application effect of the data-driven 3D SRME method. The lack of reflected signals will seriously affect the prediction accuracy of multiples. Not only will it be impossible to predict multiples related to the seabed and shallow interface, but it will also produce obvious spatial aliasing. The prediction results will also greatly affect the multiple removal effect of OBN multi-component data. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method and apparatus for predicting multiple wave phases of seafloor node multi-component data of water layers to overcome or at least partially solve the above problems.

[0005] According to one aspect of the present invention, a method for predicting multiple wave phases in seabed node multi-component data is provided, the method comprising:

[0006] Based on the P-wave component data of the seabed nodes, the migration data volume is obtained using Kirchhoff pre-stack time migration.

[0007] Based on the offset data volume, a seabed depth model is constructed through time-depth conversion using interactive picking;

[0008] Multiple seabed sample points are obtained based on the seabed depth model, and the seabed reflection travel time and distance spread are determined based on the multiple seabed sample points.

[0009] Water layer multiple wave prediction is performed based on the component data of the seabed nodes, seabed reflection travel time, and distance spread.

[0010] According to another aspect of the present invention, a device for predicting multiple wave phases of seabed node multi-component data is provided, comprising:

[0011] The offset module is suitable for obtaining the offset data volume by using Kirchhoff pre-stack time offset based on the P-wave component data of the seabed nodes.

[0012] The depth module is suitable for constructing a seabed depth model based on offset data volume, interactive picking, and time-depth conversion.

[0013] The calculation module is suitable for obtaining multiple seabed sample points based on the seabed depth model, and determining the seabed reflection travel time and distance diffusion based on the multiple seabed sample points;

[0014] The prediction module is suitable for predicting multiple waves in the water layer based on the component data of the seabed nodes, seabed reflection travel time, and distance spread.

[0015] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0016] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for predicting multiple wave levels in multi-component data of seabed nodes.

[0017] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform an operation corresponding to the above-described method for predicting multiple wave levels in seabed node multi-component data.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising at least one executable instruction that causes a processor to perform operations corresponding to the above-described method for predicting multiple wave levels in seabed node multi-component data.

[0019] The method and apparatus for predicting multiples in multi-component seabed node data provided by embodiments of the present invention effectively solve the problem that existing SRME methods cannot predict multiples in OBN converted wave data, and can significantly improve the multiple suppression effect of OBN data. Furthermore, it is adaptable to any water depth and rugged seabed areas, significantly improving the accuracy of multiple prediction.

[0020] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more obvious and understandable, specific implementation methods of the embodiments of the present invention are described below. Attached Figure Description

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0022] Figure 1 A flowchart of a method for predicting multiple wave phases in seafloor node multi-component data of the present invention is shown.

[0023] Figure 2 A schematic diagram of ray tracing for a "straight ray" model with pre-stack time mirror offset is shown;

[0024] Figure 3 A schematic diagram of the common receiver point gather for the P component is shown;

[0025] Figure 4 This shows a schematic diagram of the offset profile in the P-component recorded mirror offset imaging data volume;

[0026] Figure 5 A schematic diagram of the seabed depth model is shown;

[0027] Figure 6 A schematic diagram of the original P, Z, X, and Y component receiver gathers is shown.

[0028] Figure 7 A schematic diagram of the predicted P, Z, X and Y component water layer multiples is shown;

[0029] Figure 8 A schematic diagram of a multi-component data layer wave multiple prediction device for seabed nodes according to an embodiment of the present invention is shown.

[0030] Figure 9 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. Detailed Implementation

[0031] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0032] Figure 1 A flowchart of a method for predicting multiple wave phases in seafloor node multi-component data according to an embodiment of the present invention is shown, as follows: Figure 1 As shown, the method includes the following steps:

[0033] Step S101: Based on the P-wave component data of the seabed nodes, the offset data volume is obtained using Kirchhoff pre-stack time migration.

[0034] To establish a seafloor depth model, it is necessary to first obtain a migration data volume with good seafloor imaging. However, since seismic signal receiving equipment is deployed on the seafloor, OBN exploration cannot obtain the first reflection wave from the seafloor, i.e., there is no illumination of the seafloor. Therefore, it is impossible to achieve seafloor and shallow strata imaging using the first reflection wave. To solve the seafloor imaging problem, this embodiment is based on the traditional Kirchhoff migration, which is extended to a mirror-image pre-stack time migration. By utilizing the downlink wave (ghost wave at the receiver end) in the P-component data (P-wave component data) of the seafloor nodes, a migration data volume that can be imaged for the seafloor and its underlying strata is obtained.

[0035] Kirchhoff migration is a migration imaging method that, when a migration velocity field is available, can generate seismic profiles from pre-stack seismic data. Kirchhoff migration can be expressed as follows:

[0036]

[0037] Where I(η) represents the offset data volume, η is the coordinate of the profile sample point; ξ is the coordinate of a shot-receiver pair (s, r); w m(ξ, η) are the weighting factors of Kirchhoff integral migration; d(ξ, t) is the time-domain seismic record; ds represents the integral surface element; and τ represents the sum of the travel times of the ray from the shot point to the imaging point and from the imaging point to the receiver point.

[0038] This embodiment achieves time-domain mirror migration based on P-component downlink waves by employing Kirchhoff pre-stack time migration. The migration is calculated using shot gather records, and the ξ pair is replaced by the shot receiver records. The resulting migration is one data point from the imaging gather, as shown below:

[0039]

[0040] Among them, the use of shot inspection records (s i r j ) replace ξ in formula (1) with the coordinates of the shot-receiver pair; g i (η) represents a record in the imaging trace set, where i corresponds to the offset data of the i-th shot record; d i The P-component represents the gun set record, where i is the gun number and j is the track number. Each gun set record contains many tracks, and each track corresponds to a gun number and a track number.

[0041] To image the P-component downlink wave (ghost wave reflected from the sea surface), the detector point located on the seabed needs to be mirrored and projected to a symmetrical position above the sea surface, such as... Figure 2 As shown, the x-axis corresponds to sea level, the z-axis corresponds to seabed depth, and s i For the firing point, r j As the detector point, set detector point r j Projecting onto the surface above the sea level yields r′ j It can be based on the new detector point position r′ j Calculate the weighting factor w for Kirchhoff integral bias m With the travel time τ, imaging of the seabed and underlying strata can be achieved. Kirchhoff pre-stack time migration adopts the ray tracing process of the "straight ray" model, and the weighting factor w of Kirchhoff integral migration in formula (2) is used. m (s i r j ′,η) and travel time τ(s) i r j The calculation process of ′, η) can be significantly simplified, and they can be expressed as follows:

[0042]

[0043] Among them, v wThe velocity of the seawater depends on the composition of the seabed medium and is generally between 1450 and 1600; η is the imaging point; l0 is the distance from the shot point to the imaging point; l is the distance from the imaging point to the new receiver point; cosθ represents the tilt factor; θ is the angle between the ray from the imaging point and the new receiver point and the sea level.

[0044] g is obtained from Kirchhoff's pre-stack time offset. i (η) represents the offset data corresponding to a gun number. Figure 3 The data for each shot number in the P-component common receiver gather shown are subjected to Kirchhoff pre-stack time migration, ultimately yielding the overall pre-stack time migration data volume, as follows: Figure 4 The offset profile in the mirror offset imaging data volume shown, wherein, Figure 3 The horizontal axis represents the gun number, and the vertical axis represents the round-trip travel time. Figure 4 The horizontal axis represents the horizontal distance, and the vertical axis represents the round-trip travel time. Among these, Figure 3 and Figure 4 The medium-distance round trip includes travel time on the seabed and travel time in the strata below the seabed.

[0045] Step S102: Based on the offset data volume, construct the seabed depth model through time-depth conversion using interactive picking.

[0046] For interactive picking, picking can be based on preset picking points, such as based on the characteristics of strong energy amplitude and continuous and stable in-phase axis of seabed reflection, based on the characteristics of seabed reflection, based on the offset data volume, and based on... Figure 4 The seismic migration profile shown is based on which pickup points are set at preset intervals (e.g., every 10-50 traces) along the survey line direction to form an initial seafloor reflection interface line. For each seismic trace, the pickup point is precisely placed at the crest / trough of the seafloor reflection wave using methods such as mouse clicks or trackball operations. Built-in software algorithms (e.g., spline interpolation, Kriging interpolation, etc.) are used to generate continuous seafloor interface curves between pickup points to reduce the discrete errors of manual pickup. In 3D seismic data, it is necessary to ensure that the seafloor pickup interfaces of different survey lines (longitudinal and transverse lines) are consistently closed. The picked seafloor data is converted to the depth domain through time-depth conversion. The migration data volume I(x, y, T) is defined, where T represents the amplitude value of the migration data volume at position (x, y) at time t. The seafloor travel time t(x, y) is obtained based on the seafloor reflection amplitude formed by the migration data volume I in space.

[0047] After obtaining the seabed travel time t(x, y) through interactive picking, a seabed depth model is built based on the time-depth conversion. The seabed depth model realizes the time-depth conversion, as shown below:

[0048]

[0049] Where z is the seabed depth, and x and y represent the spatial coordinates of the seabed; v w The seawater velocity is determined based on actual conditions, such as 1500 m / s. This velocity is then combined using seabed depth curves obtained from continuous profiles. Figure 5 The image shows a three-dimensional model of the seabed depth. Figure 5 The mid-seabed depth model includes depths in the X and Y directions as well as longitudinal depth.

[0050] Step S103: Obtain multiple seabed sample points based on the seabed depth model, and determine the seabed reflection travel time and distance diffusion based on the multiple seabed sample points.

[0051] Based on Fermat's principle (ray principle or minimum time principle), the seabed reflection travel time can be further calculated. Fermat's principle states that the time for a seismic wave to travel along the true path is less than the time to travel along any other path, that is, the seismic wave travels along the path with the shortest travel time.

[0052] Specifically, calculations are performed based on the shot point and receiver point in the shot and receiver records. For example, the shot point and receiver point located on the sea surface are obtained, where the coordinates of the shot point are (x... s y s The coordinates of the receiver point are (x, 0), and (x, 0). r y r , 0), find the ray from the shot point (x s y s ,0) to a certain sample point h on the seabed n (x n y n , z n The first distance And the point h n (x n y n , z n ) to the detector point (x r y r The second distance of ,0) This allows us to calculate the travel time of the rays. The calculation is as follows:

[0053]

[0054] Based on the shot point and its corresponding receiver point, both the shot point and receiver point are located at the sea surface, i.e., at a depth of 0. The coordinates of the shot point are (x... s y s The coordinates of the receiver point are (x, 0), and (x, 0). r y r ,0);h n This represents a seabed sample point in the seabed depth model, where n is the sample point index and the coordinates are (x, y, y). n yn , z n ); h is the distance from the firing point to the seabed sample point. n The first distance, For seabed sample point h n The second distance to the detector point; This indicates the travel time of the corresponding ray. n Seabed sample points at different depths can be taken from the seabed model to cover all depths in the seabed model.

[0055] Formula (5) is repeatedly used to calculate the ray propagation travel time of the shot point and receiver point with respect to all points in the seabed model by taking seabed sample points at different depths. The ray propagation travel time of each point is compared point by point, and the minimum data is taken as the seabed reflection travel time Δt. kr As shown below:

[0056]

[0057] Where, Δt kr When traveling by reflecting off the seabed.

[0058] During the propagation of seismic waves, the wavefront curvature changes continuously, and the energy of seismic waves decreases with increasing propagation distance. Therefore, considering the influence of spherical diffusion on the amplitude of seismic waves, the distance diffusion L is calculated as follows:

[0059] L = l kh +l hr (7)

[0060] When calculating the distance spread, the seabed reflection travel time is first determined, and the sum of the first and second distances corresponding to the seabed reflection travel time is used as the distance spread.

[0061] Step S104: Predict multiple waves of the water layer based on the component data of the seabed nodes, seabed reflection travel time, and distance spread.

[0062] Based on the multi-component data of OBN, namely the common detector point gather d of each component P, Z, X, and Y. com (Where, com takes values ​​from 1 to 4, corresponding to P, Z, X, and Y, a total of 4 components). Using the seabed reflection travel time and distance spread obtained by formulas (5), (6), and (7) above, we can extend and integrate each input channel to obtain the water layer multiple wave prediction recorded by each component, as shown below:

[0063]

[0064] Where, d comFor the input common receiver point gathers of each component, the subscript com indicates the component type, and when the value is 1, 2, 3, or 4, d com These represent the P, Z, X, and Y component records, respectively; m com This represents the predicted water layer multiple wave records for each component, with m on the left side of the formula. com The parameters, x r y r Let x be the spatial coordinates of the receiver point. s y s Let x be the spatial coordinates of the firing point, and t be the seabed travel time; in the right side of the formula, x r y r Let x be the spatial coordinates of the receiver point. k y k For the common receiving point collection d participating in the summation operation com In this context, the shot point coordinates used in the summation operation refer to the coordinates of each shot point used when calculating the offset data volume. Δt ks It is composed of point (x) k y k ,0) to the detector point (x r y r When traveling by reflection from the seabed (0), L ks For point (x) k y k ,0) to the detector point (x r y r The distance diffusion corresponding to 0); Δt ks and L ks It can be calculated using formulas (5)-(7).

[0065] Based on formula (8), input as follows Figure 6 The original P, Z, X, and Y component receiver gathers of the OBN data, shown from left to right, are obtained as follows: Figure 7 The predicted P, Z, X, and Y component water layer multiples are shown from left to right. (Comparison) Figure 6 and Figure 7 It can be seen that, Figure 6 and Figure 7 The travel times of the phase axes of the multiple waves in each component record shown are basically consistent (see...). Figure 6 and Figure 7 The location indicated by the middle arrow demonstrates the effectiveness and accuracy of the predictions in this embodiment. Figure 6 and Figure 7 The horizontal axis represents the gun number, and the vertical axis represents the two-way travel time. This embodiment does not require spatial convolution and mathematical transformation processes, has high computational efficiency, and can be applied to the processing of 3D OBN multi-component data with massive data features, while also providing accurate prediction.

[0066] Furthermore, after obtaining the multiple wave prediction results, multiple wave components in each component data can be eliminated based on the multiple wave prediction results, such as through adaptive attenuation methods, which will not be elaborated here.

[0067] The method for predicting multiples in multi-component seabed node data provided in this invention effectively solves the problem that existing SRME methods cannot predict multiples in OBN converted wave data, and can significantly improve the multiple suppression effect of OBN data. Furthermore, it is adaptable to any water depth and rugged seabed areas, significantly improving the accuracy of multiple prediction.

[0068] Figure 8 A schematic diagram of the structure of the multi-component data water layer multiple wave prediction device for seabed nodes provided in an embodiment of the present invention is shown. Figure 8 As shown, the device includes:

[0069] The offset module 810 is adapted to obtain the offset data volume by using Kirchhoff pre-stack time offset based on the P-wave component data of the seabed node.

[0070] The depth module 820 is suitable for constructing a seabed depth model based on offset data volume, interactive picking, and time-depth conversion.

[0071] The calculation module 830 is suitable for obtaining multiple seabed sample points based on the seabed depth model, and determining the seabed reflection travel time and distance diffusion based on the multiple seabed sample points.

[0072] The prediction module 840 is suitable for predicting water layer multiples based on the component data of the seabed nodes, seabed reflection travel time, and distance spread.

[0073] Optionally, the offset module 810 is further adapted to:

[0074] Using Kirchhoff pre-stack time migration, the migration data of each shot trajectory was obtained based on the P-component data of the seabed nodes and the shot receiver records.

[0075] The offset data of each artillery path are integrated to obtain the overall offset data volume.

[0076] Optionally, the depth module 820 is further adapted to:

[0077] Based on the offset data volume, pick-up points are set according to a preset interval, and interactive picking is performed to determine the time of seabed travel;

[0078] A seabed depth model is constructed by performing time-depth conversion based on seabed travel time and seawater velocity; the seabed depth model is determined by the product of seabed travel time and seawater velocity.

[0079] Optionally, the computing module 830 is further adapted to:

[0080] Multiple seabed sample points at different depths were obtained based on the seabed depth model;

[0081] For any seabed sample point, calculate the first distance between the seabed sample point and the shot point in the shot detection record, and the second distance between the sample point and the receiver point; obtain the ray propagation travel time based on the first distance, the second distance, and the seawater velocity;

[0082] Based on the obtained propagation travel times of each ray, the minimum ray propagation travel time is determined as the seabed reflection travel time.

[0083] Optionally, the computing module 830 is further adapted to:

[0084] The distance spread is obtained by summing the first and second distances.

[0085] Optionally, the prediction module 840 is further adapted to:

[0086] Based on the shot point and receiver point of the common receiver point gather of each component data of the seabed node, determine the seabed reflection travel time and distance spread of the shot point and receiver point.

[0087] Multiple waves in the water layer are predicted based on the seabed reflection travel time of the shot point, receiver point, and the shot point and receiver point, as well as the distance spread and seabed travel time.

[0088] The descriptions of the above modules refer to the corresponding descriptions in the method embodiments, and will not be repeated here.

[0089] This invention also provides a non-volatile computer storage medium storing at least one executable instruction that can perform the operation corresponding to the multi-component data layer multiple wave prediction method for seabed nodes in any of the above method embodiments.

[0090] This application provides a computer program product, which includes at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the seabed node multi-component data water layer multiple wave prediction method in any of the above method embodiments.

[0091] Figure 9 The diagram illustrates the structure of a computing device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0092] like Figure 9 As shown, the computing device may include: a processor 902, a communication interface 904, a memory 906, and a communication bus 908.

[0093] in:

[0094] The processor 902, communication interface 904, and memory 906 communicate with each other via communication bus 908.

[0095] The communication interface 904 is used to communicate with other network elements such as clients or other servers.

[0096] The processor 902 is used to execute program 910, which can specifically execute the relevant steps in the above embodiment of the multi-component data water layer multiple wave prediction method for seabed nodes.

[0097] Specifically, program 910 may include program code that includes computer operation instructions.

[0098] Processor 902 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0099] Memory 906 is used to store program 910. Memory 906 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0100] Specifically, program 910 can be used to cause processor 902 to execute the seabed node multi-component data water layer multiple wave prediction method in any of the above method embodiments. The specific implementation of each step in program 910 can be found in the corresponding descriptions of the steps and units in the above embodiments of seabed node multi-component data water layer multiple wave prediction, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0101] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the embodiments of the present invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of the present invention.

[0102] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0103] Similarly, it should be understood that, in order to streamline the embodiments of the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0104] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0105] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0106] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0107] It should be noted that the above embodiments are illustrative of the present invention and not restrictive of the invention, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the present invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for predicting multiple wave phases in seabed node multi-component data, characterized in that the method... include: The offset data volume is obtained by using Kirchhoff pre-stack time migration based on the downflow wave in the P-wave component data of the seabed node. The Kirchhoff pre-stack time offset is a mirror image of the pre-stack time offset. Based on the offset data volume, a seabed depth model is constructed through time-depth conversion using interactive picking. Multiple seabed sample points are obtained based on the seabed depth model. The seabed reflection travel time and distance spread are determined based on these multiple seabed sample points. Specifically, multiple seabed sample points at different depths are obtained based on the seabed depth model. For any seabed sample point, a first distance between the seabed sample point and the shot point in the shot receiver record, and a second distance between the seabed sample point and the receiver point are calculated. The ray propagation travel time is obtained based on the first distance, the second distance, and the seawater velocity. Based on the obtained ray propagation travel times, the minimum ray propagation travel time is determined as the seabed reflection travel time. The distance spread is obtained based on the sum of the first distance and the second distance. Water layer multiple wave prediction is performed based on the component data of the seabed nodes, the seabed reflection travel time, and the distance spread.

2. The method according to claim 1, characterized in that, The step of obtaining the migration data volume based on the P-wave component data of the seabed nodes using Kirchhoff pre-stack time migration further includes: Using Kirchhoff pre-stack time migration, the migration data of each shot trajectory was obtained based on the P-component data of the seabed nodes and the shot receiver records. The offset data of each artillery path are integrated to obtain the overall offset data volume.

3. The method according to claim 1, characterized in that, The step of constructing a seabed depth model based on the offset data volume, using interactive picking and time-depth conversion, further includes: Based on the offset data volume, pick-up points are set at preset intervals to perform interactive picking and determine the underwater travel time. A seabed depth model is constructed by performing time-depth conversion based on the seabed travel time and the seawater velocity; wherein the seabed depth model is determined based on the product of the seabed travel time and the seawater velocity.

4. The method according to claim 1, characterized in that, The prediction of water layer multiples based on the component data of the seabed nodes, the seabed reflection travel time, and the distance spread further includes: Based on the shot point and receiver point of the common receiver point gather of each component data of the seabed node, determine the seabed reflection travel time and distance spread of the shot point and receiver point. Multiple waves in the water layer are predicted based on the seabed reflection travel time of the shot point, receiver point, and the shot point and receiver point, as well as the distance spread and seabed travel time.

5. A device for predicting multiple wave phases in seabed node multi-component data, characterized in that, The device includes: The offset module is adapted to obtain the offset data volume by using Kirchhoff pre-stack time offset based on the downflow wave in the P-wave component data of the seabed node; the Kirchhoff pre-stack time offset is a mirrored pre-stack time offset. A depth module is adapted to construct a seabed depth model based on the offset data volume, using interactive picking and time-depth conversion. The calculation module is adapted to acquire multiple seabed sample points based on the seabed depth model, and to determine the seabed reflection travel time and distance spread based on the multiple seabed sample points; wherein, multiple seabed sample points at different depths are acquired based on the seabed depth model; for any seabed sample point, a first distance between the seabed sample point and the shot point in the shot receiver record, and a second distance between the seabed sample point and the receiver point are calculated; the ray propagation travel time is obtained based on the first distance, the second distance, and the seawater velocity; based on the obtained ray propagation travel times, the minimum ray propagation travel time is determined as the seabed reflection travel time; and the distance spread is obtained based on the sum of the first distance and the second distance. The prediction module is adapted to predict water layer multiples based on the component data of the seabed nodes, the seabed reflection travel time, and the distance spread.

6. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the seabed node multi-component data water layer multiple wave prediction method as described in any one of claims 1-4.

7. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the seabed node multi-component data water layer multiple wave prediction method as described in any one of claims 1-4.

8. A computer program product, characterized in that, It includes at least one executable instruction that causes the processor to perform the operation corresponding to the seabed node multi-component data water layer multiple wave prediction method as described in any one of claims 1-4.

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