Waveform processing method and apparatus, device, and storage medium

Abstract

The present application relates to the technical field of computers, and provides a waveform processing method and apparatus, a device, and a storage medium. The method comprises: acquiring a seismic record, wherein the seismic record comprises a plurality of seismic waves received by a plurality of receiving devices, and the seismic waves are transmitted by a transmitting device; performing spatial partitioning processing on the seismic record to obtain a plurality of sampled wavelets and wavelet information of the sampled wavelets; for any sampled wavelet, determining, on the basis of the wavelet information of the sampled wavelet, a transform operator and a deghosting operator corresponding to the sampled wavelet, and determining, on the basis of the transform operator and the deghosting operator, a target wavelet corresponding to the sampled wavelet, wherein the target wavelet is a deghosted wavelet; and concatenating the target wavelets corresponding to the plurality of sampled wavelets to obtain a ghost suppressed wave. Random noise is effectively attenuated, the problem that effective waves and ghost waves cannot be accurately separated during ghost suppression is further avoided, and the accuracy of ghost suppression is improved.

Description

Waveform processing methods, devices, equipment and storage media

[0001] This application claims priority to Chinese Patent Application No. 202411853767.9, filed on December 16, 2024, entitled "Waveform Processing Method, Apparatus, Device and Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of computer technology, and in particular to a waveform processing method, apparatus, device, and storage medium. Background Technology

[0003] In marine seismic exploration, transmitting and receiving equipment can be installed on the seabed. The transmitting equipment can send seismic waves to the receiving equipment, and earthquake prediction can be made based on the seismic waves received by the receiving equipment.

[0004] In practical applications, the seismic waves received by receiving equipment often include ghost waves reflected from the sea surface. To ensure the accuracy of earthquake prediction, these ghost waves need to be filtered out. One technique used is the Tilted Stacking (TauP) transform to filter out ghost waves. This involves first acquiring a seismic record composed of multiple seismic waves, then transforming the record from the time domain to the TauP domain. Ghost waves are then filtered out in the TauP domain, and finally, the record is converted back from the TauP domain to the time domain to obtain the seismic record with ghost waves removed.

[0005] However, in the above methods, due to the presence of random noise in the seismic records, it is difficult to distinguish ghost waves in the TauP domain from other seismic waves under the interference of random noise, and it is impossible to filter out all ghost waves, resulting in low accuracy of earthquake prediction. Summary of the Invention

[0006] This application provides a waveform processing method, apparatus, device, and storage medium to solve the problem of low accuracy in ghost wave suppression.

[0007] In a first aspect, this application provides a waveform processing method, including:

[0008] The earthquake records are acquired, which include multiple seismic waves received by multiple receiving devices and transmitted by a transmitting device.

[0009] The seismic record is spatially segmented to obtain multiple sampled wavelets and wavelet information for each sampled wavelet. The wavelet information includes the shot-receiver distance and travel time corresponding to the sampled wavelet. The shot-receiver distance is the distance between the transmitting and receiving devices corresponding to the sampled wavelet, and the travel time is the transmission duration of the sampled wavelet.

[0010] For any sampled wavelet, based on the wavelet information of the sampled wavelet, the transform operator and ghost wave operator corresponding to the sampled wavelet are determined, and based on the transform operator and the ghost wave operator, the target wavelet corresponding to the sampled wavelet is determined, wherein the target wavelet is the wavelet after ghost wave filtering.

[0011] The target wavelets corresponding to the multiple sampled wavelets are spliced ​​together to obtain the ghost wave suppressed wave, which is the seismic wave after the ghost wave is suppressed.

[0012] In one possible implementation, determining the target wavelet corresponding to the sampled wavelet based on the transform operator and the ghost wave operator includes:

[0013] Perform multiple iterative processes until the first transformation result and the second transformation result obtained by the iterative processing meet the preset conditions. Then, process the first transformation result to obtain the target wavelet.

[0014] Each iteration includes: determining the Lagrange multipliers and the penalty factor, and determining the first transformation result and the second transformation result based on the transformation operator, the ghost wave operator, the Lagrange multipliers, and the penalty factor.

[0015] In one possible implementation, determining the first transformation result and the second transformation result based on the transformation operator, the ghost wave operator, the Lagrange multiplier, and the penalty factor includes:

[0016] For (F) -1 G H LL H GF+ρI)x=F -1 G H The first transformation result is obtained by solving LFu+ρz-y, and the first transformation result is x;

[0017] The result of the second transformation is determined to be

[0018] Wherein, F is the Fourier operator, G is the ghost wave operator, L is the transform operator, ρ is the penalty factor, y is the Lagrange factor, I is the identity matrix, u is the sampling subwavelet, sgn() is -1 or 1, and λ is the weight parameter of the sparse constraint.

[0019] In one possible implementation, determining the Lagrange multipliers and penalty factors includes:

[0020] In the first iteration, the Lagrange multiplier is 0, and the penalty factor is a preset value;

[0021] During the i-th iteration, ρ i =min(1.1ρ i-1 ,100000), y i =y i-1 +ρ i (x i-1 -z i-1 ), wherein the ρ i-1 ρ is the penalty factor during the (i-1)th iteration. i y is the penalty factor during the i-th iteration. i-1 For the (i-1)th iteration, y is the Lagrange multiplier. i Let x be the Lagrange multiplier during the i-th iteration. i-1 The z is the first transformation result during the (i-1)th iteration. i-1 This is the result of the second transformation during the (i-1)th iteration.

[0022] In one possible implementation, processing the first transformation result to obtain the target wavelet includes:

[0023] The first transformation result is processed according to the Fourier operator and the transformation operator to obtain the target wavelet.

[0024] In one possible implementation, determining the transform operator corresponding to the sampled wavelet based on the wavelet information of the sampled wavelet includes:

[0025] The transform operator is determined based on the following formula and the wavelet information:

[0026] Wherein, L is the transformation operator, ω is the angular frequency, and x is the x-axis. m Let y be the shot-receiver distance corresponding to the m-th receiving device in the x-direction. m Let m be the shot-receiver distance corresponding to the m-th receiving device in the y-direction. For the slowness component corresponding to the k-th sampled wavelet in the x-direction, the Let be the slowness component corresponding to the s-th sampled wavelet in the y-direction.

[0027] In one possible implementation, determining the ghost wave operator corresponding to the sampled wavelet based on the wavelet information of the sampled wavelet includes:

[0028] The ghost wave operator is determined based on the following formula and the wavelet information:

[0029] Wherein, G is the ghost wave operator, and the The ghost wave delay is defined for different slowness components, h is the distance between the receiving device and the sea level, v is the propagation speed of the seismic wave in seawater, and p is the distance between the receiving device and the sea level. x p represents the slowness component of the sampled wavelet in the x-direction. y Let be the slowness component corresponding to the sampled wavelet in the y-direction.

[0030] Secondly, this application provides a waveform processing apparatus, comprising: an acquisition module, a segmentation module, a determination module, and a splicing module, wherein:

[0031] The acquisition module is used to acquire earthquake records, which include multiple seismic waves received by multiple receiving devices and transmitted by a transmitting device;

[0032] The segmentation module is used to perform spatial segmentation processing on the seismic record to obtain multiple sampled wavelets and wavelet information for each sampled wavelet. The wavelet information includes the shot-receiver distance and travel time corresponding to the sampled wavelet. The shot-receiver distance is the distance between the transmitting and receiving devices corresponding to the sampled wavelet, and the travel time is the transmission duration of the sampled wavelet.

[0033] The determining module is used to, for any sampled wavelet, determine the transform operator and ghost wave operator corresponding to the sampled wavelet based on the wavelet information of the sampled wavelet, and determine the target wavelet corresponding to the sampled wavelet based on the transform operator and the ghost wave operator, wherein the target wavelet is the wavelet after filtering out the ghost wave.

[0034] The splicing module is used to splice the target wavelets corresponding to the multiple sampled wavelets to obtain a ghost wave suppressed wave, which is a seismic wave after suppressing the ghost wave.

[0035] In one possible implementation, the determining module is specifically used for:

[0036] Perform multiple iterative processes until the first transformation result and the second transformation result obtained by the iterative processing meet the preset conditions. Then, process the first transformation result to obtain the target wavelet.

[0037] Each iteration includes: determining the Lagrange multipliers and the penalty factor, and determining the first transformation result and the second transformation result based on the transformation operator, the ghost wave operator, the Lagrange multipliers, and the penalty factor.

[0038] In one possible implementation, the determining module is specifically used for:

[0039] For (F) -1 G H LL HGF+ρI)x=F -1 G H The first transformation result is obtained by solving LFu+ρz-y, and the first transformation result is x;

[0040] The result of the second transformation is determined to be

[0041] Wherein, F is the Fourier operator, G is the ghost wave operator, L is the transform operator, ρ is the penalty factor, y is the Lagrange factor, I is the identity matrix, u is the sampling subwavelet, sgn() is -1 or 1, and λ is the weight parameter of the sparse constraint.

[0042] In one possible implementation, the determining module is specifically used for:

[0043] In the first iteration, the Lagrange multiplier is 0, and the penalty factor is a preset value;

[0044] During the i-th iteration, ρ i =min(1.1ρ i-1 ,100000), y i =y i-1 +ρ i (x i-1 -z i-1 ), wherein the ρ i-1 ρ is the penalty factor during the (i-1)th iteration. i y is the penalty factor during the i-th iteration. i-1 For the (i-1)th iteration, y is the Lagrange multiplier. i Let x be the Lagrange multiplier during the i-th iteration. i-1 The z is the first transformation result during the (i-1)th iteration. i-1 This is the result of the second transformation during the (i-1)th iteration.

[0045] In one possible implementation, the determining module is specifically used for:

[0046] The first transformation result is processed according to the Fourier operator and the transformation operator to obtain the target wavelet.

[0047] In one possible implementation, the determining module is specifically used for:

[0048] The transform operator is determined based on the following formula and the wavelet information:

[0049] Wherein, L is the transformation operator, ω is the angular frequency, and x is the x-axis. m Let y be the shot-receiver distance corresponding to the m-th receiving device in the x-direction. m Let m be the shot-receiver distance corresponding to the m-th receiving device in the y-direction. For the slowness component corresponding to the k-th sampled wavelet in the x-direction, the Let be the slowness component corresponding to the s-th sampled wavelet in the y-direction.

[0050] In one possible implementation, the determining module is specifically used for:

[0051] The ghost wave operator is determined based on the following formula and the wavelet information:

[0052] Wherein, G is the ghost wave operator, and the The ghost wave delay is defined for different slowness components, h is the distance between the receiving device and the sea level, v is the propagation speed of the seismic wave in seawater, and p is the distance between the receiving device and the sea level. x p represents the slowness component of the sampled wavelet in the x-direction. y Let be the slowness component corresponding to the sampled wavelet in the y-direction.

[0053] Thirdly, embodiments of this application provide a waveform processing apparatus, comprising: at least one processor and a memory; the memory storing computer-executable instructions; the at least one processor executing the computer-executable instructions stored in the memory, causing the at least one processor to perform the waveform processing method as described in the first aspect and various possible designs of the first aspect.

[0054] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the waveform processing method described in the first aspect above and various possible designs of the first aspect.

[0055] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the waveform processing method described in the first aspect and various possible designs of the first aspect. Attached Figure Description

[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0057] Figure 1 is a schematic diagram of the system architecture provided in an embodiment of this application;

[0058] Figure 2 is a schematic flowchart of a waveform processing method provided in an embodiment of this application;

[0059] Figure 3 is a schematic diagram of a process for spatially segmenting earthquake records according to an embodiment of this application;

[0060] Figure 4a is a schematic diagram of the seismic record before ghost wave suppression provided in an embodiment of this application;

[0061] Figure 4b is a schematic diagram of the seismic record after ghost wave suppression provided in an embodiment of this application;

[0062] Figure 4c is a schematic diagram of the seismic record spectrum before and after ghost wave suppression provided in the embodiment of this application;

[0063] Figure 4d is a schematic diagram of the original seismic profile provided in an embodiment of this application;

[0064] Figure 4e is a schematic diagram of the seismic profile after waveform processing provided in the embodiment of this application;

[0065] Figure 4f is a schematic diagram of a seismic profile after processing by other methods provided in the embodiments of this application;

[0066] Figure 5 is a schematic diagram of the solution process for the target wavelet provided in an embodiment of this application;

[0067] Figure 6 is a schematic diagram of the structure of a waveform processing device provided in the embodiment of this application;

[0068] Figure 7 is a schematic diagram of the waveform processing device provided in an embodiment of this application.

[0069] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0070] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0071] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0072] For ease of understanding, the following description, in conjunction with Figure 1, illustrates the principle of ghost wave formation in an embodiment of this application.

[0073] Figure 1 is a schematic diagram of the system architecture provided in an embodiment of this application. Referring to Figure 1, it includes a transmitting device and a receiving device. The transmitting device can transmit seismic waves in multiple directions, some of which are received by the receiving device, and some are reflected after reaching the sea level. The seismic waves received by the receiving device are called effective waves, and the seismic waves reflected from the sea level are called ghost waves.

[0074] The receiving device can receive both valid waves and ghost waves simultaneously. It can predict earthquakes based on the valid waves received by the receiving device. In order to avoid the interference of ghost waves on valid waves, the ghost waves need to be filtered out after the receiving device receives the waves (including valid waves and ghost waves).

[0075] In related technologies, the TauP transform method can be used to filter out ghost waves. First, a seismic record composed of multiple seismic waves needs to be acquired. Then, the seismic record is transformed from the time domain to the TauP domain, where ghost waves are filtered out. Finally, the seismic record is transformed back from the TauP domain to the time domain to obtain the seismic record with ghost waves removed. However, due to the presence of random noise in the seismic record, it is difficult to distinguish ghost waves from other seismic waves in the TauP domain under the interference of random noise, making it impossible to filter out all ghost waves and resulting in low accuracy in earthquake prediction.

[0076] To address the aforementioned technical problems, in this embodiment, when ghost wave filtering of seismic records is required, the acquired seismic records can be spatially segmented to obtain multiple sampled wavelets and their corresponding information. The transform operator and ghost wave operator corresponding to any sampled wavelet are then solved based on the wavelet information. The ghost-filtered wavelet can be obtained from the transform operator and ghost wave operator. Finally, the multiple wavelets are spliced ​​together to obtain the ghost-filtered seismic record. In this process, through spatial segmentation and solving the transform operator and ghost wave operator, ghost wave filtering can be performed on any sampled wavelet. This significantly reduces random noise interference in the spliced ​​seismic record and accurately identifies and filters ghost waves, improving the accuracy of earthquake prediction.

[0077] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0078] Figure 2 is a flowchart illustrating a waveform processing method provided in an embodiment of this application. Referring to Figure 2, the method may include the following steps:

[0079] S201. Obtain earthquake records.

[0080] The execution subject of this application embodiment can be an electronic device or a waveform processing device installed in an electronic device. The waveform processing device can be implemented by software or by a combination of software and hardware.

[0081] The earthquake record includes multiple seismic waves received by multiple receiving devices, and the seismic waves are emitted by transmitting devices.

[0082] Seismic records include effective waves and ghost waves. Ghost waves are waves that need to be filtered out, while effective waves are waves that need to be retained.

[0083] Transmitting and receiving equipment can be set up within a preset ocean area, and the corresponding seismic records will be the seismic records for that preset ocean area.

[0084] Optionally, multiple transmitting devices and one receiving device can be set up. The receiving device can receive the seismic waves emitted by each transmitting device to obtain seismic records.

[0085] S202. Spatial block processing is performed on the seismic record to obtain multiple sampled wavelets and wavelet information of each sampled wavelet.

[0086] Seismic records can be spatially segmented as follows: a three-dimensional coordinate system is established with a preset location in the ocean as the origin. Seismic waves in the seismic records are mapped in this three-dimensional coordinate system. The three coordinate axes in the three-dimensional coordinate system are cut to obtain multiple three-dimensional spatial blocks. Each three-dimensional spatial block contains N sampled wavelets, where N is an integer greater than or equal to 0.

[0087] In this system, the coordinates of the transmitting and receiving devices in each dimension (e.g., x, y, z) are positive numbers, so that the coordinates of each point in each seismic wave in the seismic record are positive numbers in each dimension of the coordinate system.

[0088] The process of spatial segmentation of earthquake records will be explained below with reference to Figure 3.

[0089] Figure 3 is a schematic diagram of spatial segmentation of seismic records provided in an embodiment of this application. Referring to Figure 3, a coordinate system (xyz coordinate system) is established with the ocean's preset location as the origin. Each seismic wave in the seismic record (not shown in the figure) is mapped in this three-dimensional coordinate system. The three coordinate axes are divided into units of 1 meter each, resulting in multiple three-dimensional spatial blocks (one three-dimensional cube block represents one three-dimensional spatial block in the figure). Since the seismic waves are located in this three-dimensional coordinate system, there may be fragmented seismic waves within the three-dimensional spatial blocks; these fragmented seismic waves are the sampled wavelets.

[0090] The wavelet information includes the shot-receiver distance and travel time corresponding to the sampled wavelet.

[0091] The shot-receiver distance is the distance between the transmitting and receiving devices corresponding to the sampled wavelet. The shot-receiver distance can include both horizontal and vertical distances. The horizontal distance refers to the distance between the transmitting and receiving devices in the horizontal direction; the vertical distance refers to the distance between the transmitting and receiving devices in the vertical direction.

[0092] Travel time is the transmission duration of the sampled wavelet. For example, the three-dimensional spatial block corresponding to the sampled wavelet and the seismic wave containing the sampled wavelet can be determined, and the time required for the seismic wave to be transmitted from the transmitting device to the three-dimensional spatial block can be determined as the travel time of the sampled wavelet.

[0093] S203. For any sampled wavelet, determine the transform operator and ghost wave operator corresponding to the sampled wavelet based on the wavelet information.

[0094] The target wavelet is the wavelet after filtering out the ghost wave.

[0095] Transformation operators are used to transform seismic waves between the time domain and the TauP domain.

[0096] The ghost wave operator is used to simulate the ghost wave effect.

[0097] In practical applications, the shot-receiver distance can be determined based on the positional relationship between the transmitting and receiving equipment; the angular frequency of the sampled wavelet can be determined based on the sampling time and sampling interval; the slowness corresponding to the sampled wavelet can be obtained from the seismic record; and the transform operator and ghost wave operator corresponding to the sampled wavelet can be calculated based on the shot-receiver distance, angular frequency, and slowness.

[0098] The transform operator corresponding to the sampled wavelet can be determined as follows: Based on the following formula and wavelet information, the transform operator is determined:

[0099] Where L is the transformation operator, i is the imaginary part, ω is the angular frequency, and x m Let y be the shot-receiver distance corresponding to the m-th receiving device in the x-direction.m Let be the shot-receiver distance corresponding to the m-th receiving device in the y-direction. Let S be the slowness component corresponding to the k-th sampled wavelet in the x-direction. Let be the slowness component corresponding to the s-th sampled wavelet in the y-direction.

[0100] The ghost wave operator corresponding to the sampled wavelet can be determined as follows: Based on the following formula and wavelet information, the ghost wave operator is determined:

[0101] Wherein, G is the ghost wave operator, and the The ghost wave delay is given by different slowness components, where h is the distance between the receiving device and the sea level, v is the propagation speed of the seismic wave in the sea, and p is the distance between the receiving device and the sea level. x p represents the slowness component of the sampled wavelet in the x-direction. y This represents the slowness component of the sampled wavelet in the y-direction.

[0102] S204. Determine the target wavelet corresponding to the sampled wavelet based on the transform operator and the ghost wave operator.

[0103] The target wavelet corresponding to the sampled wavelet can be determined as follows: perform multiple iterations until the first transformation result and the second transformation result satisfy the preset conditions, then process the first transformation result to obtain the target wavelet.

[0104] Each iteration process includes: determining the Lagrange multipliers and penalty factors, and determining the first transformation result and the second transformation result based on the transformation operator, the ghost wave operator, the Lagrange multipliers and the penalty factors.

[0105] The preset condition can be: whether the L2 norm of the first transformation result and the second transformation result is less than a preset threshold.

[0106] The target wavelet can be obtained as follows: The first transformation result is processed using Fourier and transform operators to obtain the target wavelet. For example, the first transformation result can be transformed from the TauP domain to the time domain by performing a domain transformation.

[0107] S205. The target wavelets corresponding to multiple sampled wavelets are spliced ​​together to obtain the ghost wave suppressed wave.

[0108] Among them, the ghost wave suppression wave is the seismic wave after the ghost wave has been suppressed.

[0109] Multiple target wavelets can be spliced ​​together based on the location information of the transmitting and receiving equipment, the gun-receiver distance, and the travel time to obtain a ghost wave suppressed wave.

[0110] The ghost wave filtering process will be explained below with reference to Figures 4a-4f.

[0111] Figures 4a and 4b are schematic diagrams of seismic records before and after ghost wave suppression according to embodiments of this application. Please refer to Figures 4a and 4b. Figure 4a records the waveform changes of seismic waves (including ghost waves) in the seismic record, and Figure 4b records the waveform changes of seismic waves (excluding ghost waves) in the seismic record. Ghost waves can be effectively suppressed.

[0112] Figure 4c is a schematic diagram of the seismic record spectrum before and after ghost wave suppression. As can be seen from Figure 4c, the embodiment of this application effectively eliminates the notch characteristics caused by ghost waves, and the frequency at low frequencies is effectively improved.

[0113] Figures 4d to 4f are seismic profiles before and after ghost wave suppression provided in the embodiments of this application. Figure 4d is the original seismic profile, Figure 4e is the seismic profile after waveform processing according to the embodiments of this application, and Figure 4f is the seismic profile after processing by other methods. Referring to Figures 4d to 4f, it can be seen that the embodiments of this application can effectively attenuate random noise, easily distinguish between effective waves and ghost waves and filter out ghost waves, thereby improving the accuracy of ghost wave suppression.

[0114] It should be noted that the above is merely an example illustrating the waveform processing process and is not a limitation on it. Of course, other relevant parameters can be set for testing, such as the number of transmitting and receiving devices.

[0115] In this embodiment, when ghost wave filtering of seismic records is required, the acquired seismic records can be spatially segmented to obtain multiple sampled wavelets and corresponding wavelet information. Based on the wavelet information, corresponding transform operators and ghost wave operators are obtained, and target wavelets are determined based on these operators. Then, by splicing multiple target wavelets, the ghost-filtered seismic wave is obtained. In this process, through spatial segmentation and solving the transform and ghost wave operators, ghost wave filtering can be performed on multiple target wavelets. This significantly reduces random noise interference in the spliced ​​seismic wave and accurately identifies and filters ghost waves, improving the accuracy of earthquake prediction.

[0116] Based on any of the above embodiments, the solution process for the target wavelet (S204 in the embodiment of Figure 2) will be described in detail below with reference to Figure 5.

[0117] Figure 5 is a schematic diagram of the target wavelet solution process provided in an embodiment of this application. Referring to Figure 5, the method may include:

[0118] S501. Based on the wavelet information of the sampled wavelet, determine the transform operator and ghost wave operator corresponding to the sampled wavelet.

[0119] The execution process of S501 can be found in S203, and will not be repeated here.

[0120] S502, initialize i to 1.

[0121] S503. Determine the Lagrange multipliers and penalty factors in the i-th iteration.

[0122] In the first iteration, the Lagrange multipliers can be initialized to 0, and the penalty factor can be a preset value of 1.

[0123] In the i-th (i > 1) iteration, the penalty factor can be determined by the following formula: ρ i =min(1.1ρ i-1 ,100000), where ρ i-1 ρ is the penalty factor during the (i-1)th iteration. i This is the penalty factor during the i-th iteration.

[0124] In the i-th (i > 1) iteration, the Lagrange multipliers can be determined by the following formula: y i =y i-1 +ρ i (x i-1 -z i-1 ), where y i-1 Let y be the Lagrange multiplier during the (i-1)th iteration. i Let x be the Lagrange multiplier during the i-th iteration. i-1 z is the first transformation result during the (i-1)th iteration. i-1 This is the result of the second transformation during the (i-1)th iteration.

[0125] S504. Based on the transformation operator, the ghost wave operator, and the Lagrange multipliers and penalty factors in the i-th iteration, determine the first transformation result and the second transformation result in the i-th iteration.

[0126] The first transformation result can be determined as follows: for (F) -1 G H LL H GF+ρI)x=F -1 G H Solving the equation LFu+ρz-y (Formula 1) yields the first transformation result, which is x. The first transformation result can be obtained by solving this equation using the conjugate gradient method.

[0127] The first transformation result can be determined as follows: The second transformation result is determined as follows: (Formula 2) can be solved using the nearest neighbor operator method to obtain the second transformation result.

[0128] Where F is the Fourier operator, G is the ghost wave operator, L is the transform operator, ρ is the penalty factor, y is the Lagrange factor, I is the identity matrix, u is the sampling subwavelet, sgn() is -1 or 1, and λ is the weight parameter of the sparse constraint.

[0129] S505. Determine whether the first transformation result and the second transformation result in the i-th iteration process meet the preset conditions.

[0130] If so, then execute S507.

[0131] If not, then execute S506.

[0132] For example, suppose the result of the first transformation is x i The result of the second transformation is z i In judging x i and z i Is the L2 norm of |x| less than a preset threshold ε, i.e., |x|? i -z i If ||2<ε. Then determine whether the first transformation result and the second transformation result in the i-th iteration process satisfy the preset condition.

[0133] S506, Update i to i+1.

[0134] After S506, S503 is executed.

[0135] S507. Process the first transformation result in the i-th iteration to obtain the target wavelet corresponding to the sampled wavelet.

[0136] In this embodiment, the first and second transformation results are iteratively solved multiple times based on the transformation operator, ghost wave operator, Lagrange multiplier, and penalty factor until the preset conditions are met, thus obtaining the first transformation result under that iteration step. The first transformation result is then transformed based on the Fourier operator and the transformation operator to obtain multiple target wavelets. As shown in the embodiment of Figure 3, ghost wave filtering is performed on any sampled wavelet, effectively attenuating random noise and ensuring that only effective waves are retained in the target wavelet, thereby improving the accuracy of earthquake prediction.

[0137] The derivation process of Formula 1 and Formula 2 in the embodiment of Figure 5 will be explained below.

[0138] The mathematical model is determined as follows: the transformation result of the sampled wavelet is used as the desired output, and the seismic record, ghost wave operator, and transform operator are used as inputs to establish the corresponding mathematical model based on time-domain sparse constraints (Formula 3):

[0139] in, To minimize F -1 λ is the inverse Fourier operator, L is the transform operator, G is the ghost wave operator, F is the Fourier operator, m is the transform result of the sampled wavelet, u is the sampled wavelet, λ is the weight parameter of the sparse constraint, ||·||2 is the L2 norm, and ||·||1 is the L1 norm.

[0140] Multiplying the Fourier operator, ghost wave operator, transform operator, and inverse Fourier operator with the solution matrix m transforms the sampled wavelet transform result m, which does not contain ghost waves in the TauP domain, to the seismic record containing ghost waves in the time domain. The least squares matching method is then used to match the seismic record u, i.e., the data matching term. Based on the weight parameter λ of the sparsity constraint, the transformed result m of the sampled wavelet is subjected to sparsity constraints, i.e., the sparsity constraint term. Combining the data matching term and the sparsity constraint term, the corresponding mathematical model can be determined.

[0141] Based on the matching solution x of the data matching term, the matching solution z of the sparse constraint term, and the equality constraint x = z, Equation 3 is transformed to obtain Equation 4, which can be expressed as follows:

[0142] Based on the Lagrange multipliers and the penalty factor, transforming Equation 4 yields the objective function (Equation 5) that can be used to solve for the target wavelet, which can be expressed as follows:

[0143] After obtaining Formula 5, performing partial derivative operations on x in the equation and setting the partial derivative value to 0, we can obtain Formula 1; performing partial derivative operations on z in the equation and setting the partial derivative value to 0, we can obtain Formula 2.

[0144] Figure 6 is a schematic diagram of a waveform processing device provided in an embodiment of this application. Referring to Figure 6, the waveform processing device 10 includes: an acquisition module 11, a segmentation module 12, a determination module 13, and a splicing module 14, wherein...

[0145] The acquisition module 11 is used to acquire earthquake records, which include multiple seismic waves received by multiple receiving devices and transmitted by a transmitting device;

[0146] The segmentation module 12 is used to perform spatial segmentation processing on the seismic record to obtain multiple sampled wavelets and wavelet information for each sampled wavelet. The wavelet information includes the shot-receiver distance and travel time corresponding to the sampled wavelet. The shot-receiver distance is the distance between the transmitting and receiving devices corresponding to the sampled wavelet, and the travel time is the transmission duration of the sampled wavelet.

[0147] The determining module 13 is used to, for any sampled wavelet, determine the transform operator and ghost wave operator corresponding to the sampled wavelet based on the wavelet information of the sampled wavelet, and determine the target wavelet corresponding to the sampled wavelet based on the transform operator and the ghost wave operator, wherein the target wavelet is the wavelet after filtering out the ghost wave.

[0148] The splicing module 14 is used to splice the target wavelets corresponding to the multiple sampled wavelets to obtain a ghost wave suppressed wave, which is a seismic wave after suppressing the ghost wave.

[0149] The waveform processing apparatus provided in this application embodiment can execute the method shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0150] In one possible implementation, the determining module 13 is specifically used for:

[0151] Perform multiple iterative processes until the first transformation result and the second transformation result obtained by the iterative processing meet the preset conditions. Then, process the first transformation result to obtain the target wavelet.

[0152] Each iteration includes: determining the Lagrange multipliers and the penalty factor, and determining the first transformation result and the second transformation result based on the transformation operator, the ghost wave operator, the Lagrange multipliers, and the penalty factor.

[0153] In one possible implementation, the determining module 13 is specifically used for:

[0154] For (F) -1 G H LL H GF+ρI)x=F -1 G H The first transformation result is obtained by solving LFu+ρz-y, and the first transformation result is x;

[0155] The result of the second transformation is determined to be

[0156] Wherein, F is the Fourier operator, G is the ghost wave operator, L is the transform operator, ρ is the penalty factor, y is the Lagrange factor, I is the identity matrix, u is the sampling subwavelet, sgn() is -1 or 1, and λ is the weight parameter of the sparse constraint.

[0157] In one possible implementation, the determining module 13 is specifically used for:

[0158] In the first iteration, the Lagrange multiplier is 0, and the penalty factor is a preset value;

[0159] During the i-th iteration, ρ i =min(1.1ρ i-1 ,100000), y i =y i-1 +ρ i (x i-1 -z i-1 ), wherein the ρ i-1 ρ is the penalty factor during the (i-1)th iteration. i y is the penalty factor during the i-th iteration. i-1 For the (i-1)th iteration, y is the Lagrange multiplier. i Let x be the Lagrange multiplier during the i-th iteration. i-1 The z is the first transformation result during the (i-1)th iteration. i-1 This is the result of the second transformation during the (i-1)th iteration.

[0160] In one possible implementation, the determining module 13 is specifically used for:

[0161] The first transformation result is processed according to the Fourier operator and the transformation operator to obtain the target wavelet.

[0162] In one possible implementation, the determining module 13 is specifically used for:

[0163] The transform operator is determined based on the following formula and the wavelet information:

[0164] Wherein, L is the transformation operator, ω is the angular frequency, and x is the x-axis. m Let y be the shot-receiver distance corresponding to the m-th receiving device in the x-direction. m Let m be the shot-receiver distance corresponding to the m-th receiving device in the y-direction. For the slowness component corresponding to the k-th sampled wavelet in the x-direction, the Let be the slowness component corresponding to the s-th sampled wavelet in the y-direction.

[0165] In one possible implementation, the determining module 13 is specifically used for:

[0166] The ghost wave operator is determined based on the following formula and the wavelet information:

[0167] Wherein, G is the ghost wave operator, and the The ghost wave delay is defined for different slowness components, h is the distance between the receiving device and the sea level, v is the propagation speed of the seismic wave in seawater, and p is the distance between the receiving device and the sea level. x p represents the slowness component of the sampled wavelet in the x-direction. y Let be the slowness component corresponding to the sampled wavelet in the y-direction.

[0168] The waveform processing device provided in this embodiment can be used to execute the waveform processing method shown in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0169] Figure 7 is a schematic diagram of the waveform processing device provided in an embodiment of this application. As shown in Figure 7, the waveform processing device 20 may include: a transceiver 21, a processor 22, and a memory 23.

[0170] Processor 22 executes computer execution instructions stored in memory, causing processor 22 to perform the scheme in the above embodiments. Processor 22 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may 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, or discrete hardware components.

[0171] The memory 23 is connected to the processor 22 via the system bus and completes communication between them. The memory 23 is used to store computer program instructions.

[0172] Transceiver 21 can be used to obtain the task to be run and the configuration information of the task to be run.

[0173] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory (NVM).

[0174] This application also provides a chip for executing instructions, which is used to execute the waveform processing method described in the above embodiments.

[0175] This application also provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the waveform processing method described in the above embodiments.

[0176] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and when the at least one processor executes the computer program, it can implement the technical solution of the waveform processing method in the above embodiments.

[0177] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0178] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0179] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0180] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0181] It should be understood that the aforementioned processor can be a CPU, or other general-purpose processors, DSPs, ASICs, etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules within the processor.

[0182] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0183] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0184] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0185] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside within an ASIC. Alternatively, the processor and storage medium can exist as discrete components within an electronic control unit or mainframe device.

[0186] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A waveform processing method characterized by, include: The earthquake records are acquired, which include multiple seismic waves received by multiple receiving devices and transmitted by a transmitting device. The seismic record is spatially segmented to obtain multiple sampled wavelets and wavelet information for each sampled wavelet. The wavelet information includes the shot-receiver distance and travel time corresponding to the sampled wavelet. The shot-receiver distance is the distance between the transmitting and receiving devices corresponding to the sampled wavelet, and the travel time is the transmission duration of the sampled wavelet. For any sampled wavelet, based on the wavelet information of the sampled wavelet, the transform operator and ghost wave operator corresponding to the sampled wavelet are determined, and based on the transform operator and the ghost wave operator, the target wavelet corresponding to the sampled wavelet is determined, wherein the target wavelet is the wavelet after ghost wave filtering. The target wavelets corresponding to the multiple sampled wavelets are spliced ​​together to obtain the ghost wave suppressed wave, which is the seismic wave after the ghost wave is suppressed.

2. The method of claim 1, wherein, Determining the target wavelet corresponding to the sampled wavelet based on the transform operator and the ghost wave operator includes: Perform multiple iterative processes until the first transformation result and the second transformation result obtained by the iterative processing meet the preset conditions. Then, process the first transformation result to obtain the target wavelet. Each iteration includes: determining the Lagrange multipliers and the penalty factor, and determining the first transformation result and the second transformation result based on the transformation operator, the ghost wave operator, the Lagrange multipliers, and the penalty factor.

3. The method of claim 2, wherein, Determining the first transformation result and the second transformation result based on the transformation operator, the ghost wave operator, the Lagrange multiplier, and the penalty factor includes: to (F -1 G H LL H GF+ρI)x=F -1 G H LFu+ρz-y, the first transformation result being the x; determining the second transform result as Wherein, F is the Fourier operator, G is the ghost wave operator, L is the transform operator, ρ is the penalty factor, y is the Lagrange factor, I is the identity matrix, u is the sampling subwavelet, sgn() is -1 or 1, and λ is the weight parameter of the sparse constraint.

4. The method according to claim 2 or 3, characterized in that, Determining the Lagrange multipliers and penalty factors includes: In the first iteration, the Lagrange multiplier is 0, and the penalty factor is a preset value; At the i-th iteration process, p i = min(1.1 p i-1 , 100000), y i = y i-1 + p i (x i-1 - z i-1 ), wherein the p i-1 is a penalty factor at the (i-1)-th iteration process, the p i is a penalty factor at the i-th iteration process, the y i-1 is a Lagrange multiplier at the (i-1)-th iteration process, the y i is a Lagrange multiplier at the i-th iteration process, the x i-1 is a first transformation result at the (i-1)-th iteration process, and the z i-1 is a second transformation result at the (i-1)-th iteration process.

5. The method according to any one of claims 2-4, characterized in that, The first transformation result is processed to obtain the target wavelet, including: The first transformation result is processed according to the Fourier operator and the transformation operator to obtain the target wavelet.

6. The method according to any one of claims 1 to 5, characterized in that, Based on the wavelet information of the sampled wavelet, the transform operator corresponding to the sampled wavelet is determined, including: The transform operator is determined according to the following formula and the wavelet information: wherein the L is the transform operator, the ω is the angular frequency, the x m is the corresponding offset distance in the x direction of the mth receiving device, the y m is the corresponding offset distance in the y direction of the mth receiving device, the For the slowness component corresponding to the kth sub-sampling wavelet in the x direction, the kth sub-sampling wavelet in the x direction is given by Let be the slowness component corresponding to the s-th sampled wavelet in the y-direction.

7. The method according to any one of claims 1 to 6, characterized in that, Based on the wavelet information of the sampled wavelet, the ghost wave operator corresponding to the sampled wavelet is determined, including: The ghost wave operator is determined according to the following formula and the wavelet information: wherein G is the ghost wave operator, and for the different slowness components, h is the distance between the receiving device and sea level, v is the propagation speed of the seismic wave in the sea water, p x is the slowness component corresponding to the x direction of the received sub-wave y is the slowness component corresponding to the y direction of the received sub-wave 8. A waveform processing device, characterized by comprising: include: The module includes an acquisition module, a segmentation module, a determination module, and a splicing module, among which: The acquisition module is used to acquire earthquake records, which include multiple seismic waves received by multiple receiving devices and transmitted by a transmitting device; The segmentation module is used to perform spatial segmentation processing on the seismic record to obtain multiple sampled wavelets and wavelet information for each sampled wavelet. The wavelet information includes the shot-receiver distance and travel time corresponding to the sampled wavelet. The shot-receiver distance is the distance between the transmitting and receiving devices corresponding to the sampled wavelet, and the travel time is the transmission duration of the sampled wavelet. The determining module is used to, for any sampled wavelet, determine the transform operator and ghost wave operator corresponding to the sampled wavelet based on the wavelet information of the sampled wavelet, and determine the target wavelet corresponding to the sampled wavelet based on the transform operator and the ghost wave operator, wherein the target wavelet is the wavelet after filtering out the ghost wave. The splicing module is used to splice the target wavelets corresponding to the multiple sampled wavelets to obtain a ghost wave suppressed wave, which is a seismic wave after suppressing the ghost wave.

9. A waveform processing device, characterized by comprising: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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