Water and land inspection data calibration method and device, terminal and storage medium

Abstract

The application provides a water-land detection data calibration method and device, a terminal and a storage medium, and belongs to the technical field of oil and gas exploration. The method comprises the following steps: determining a first cross-correlation sequence, a second cross-correlation sequence, a third cross-correlation sequence, a first autocorrelation sequence and a second autocorrelation sequence based on space-time domain water detection data and space-time domain land detection data; determining a first tensor and a second tensor based on the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence and the second autocorrelation sequence; obtaining a target calibration operator vector by iteratively solving a calibration operator characteristic equation constructed based on the first tensor and the second tensor; and calibrating the space-time domain land detection data based on the target calibration operator vector. The above technical solution simultaneously considers the changes of water-land detection data in the time and space directions, improves the calibration precision, and realizes the separation of the upgoing wave field and the downgoing wave field.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, terminal and storage medium for calibrating land and water survey data. Background Technology

[0002] By recording data at the same location in the ocean using both underwater and land-based geophones, we can obtain both underwater and land-based data for that location. Because the recording mechanisms of the two types of geophones differ, they exhibit different characteristics regarding virtual reflections and seawater reverberation multiples at the same location. For example, the amplitudes of virtual reflections and seawater reverberation multiples recorded by the two types of geophones are different, their polarities are opposite, and they differ by a constant proportional to the seabed reflection coefficient; this constant is the calibration operator. Therefore, determining how to establish this calibration operator to calibrate both the underwater and land-based data is a problem that needs to be solved.

[0003] Currently, the calibration operator is typically determined using a scanning method. By pre-setting a calibration operator range and scanning step size, a series of calibration operator values ​​are generated using the scanning method. Then, the sum of water and land survey data is calculated, and the autocorrelation function is calculated on the data sum. The maximum variance modulus is then calculated from the autocorrelation function, and finally, the calibration operator value is determined from the maximum maximum variance modulus value.

[0004] The problem with the above technical solution is that it only considers the time domain when determining the calibration operator and requires a lot of calculation, resulting in low calibration accuracy and high calibration difficulty, which in turn leads to low exploration accuracy. Summary of the Invention

[0005] This application provides a method, apparatus, terminal, and storage medium for calibrating land and sea survey data. It considers the temporal and spatial variations of the data, improving calibration accuracy and enabling the separation of the uplink and downlink wave fields. This eliminates interference from seawater hum and virtual reflection multiples, thereby improving exploration accuracy. The technical solution is as follows:

[0006] On the one hand, a method for calibrating land and water inspection data is provided, the method comprising:

[0007] Based on spatiotemporal water and land inspection data, a first cross-correlation sequence, a second cross-correlation sequence, a third cross-correlation sequence, a first autocorrelation sequence, and a second autocorrelation sequence are determined. Elements in the first cross-correlation sequence represent the correlation between water and land inspection data at the same spatial sampling point in the spatial domain. Elements in the second cross-correlation sequence represent the correlation between water and land inspection data at the same spatial sampling point in the time-space domain. Elements in the third cross-correlation sequence represent the correlation between water and land inspection data after removing direct waves at the same spatial sampling point in the time-space domain. Elements in the first autocorrelation sequence represent the correlation between land inspection data at the same spatial sampling point at different times in the time-space domain. Elements in the second autocorrelation sequence represent the correlation between land inspection data after removing direct waves at the same spatial sampling point at different times in the time-space domain.

[0008] Based on the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence, a first tensor and a second tensor are determined. The elements in the first tensor are the difference sequences of the second autocorrelation sequence and the first autocorrelation sequence at the same spatial sampling point in the spatial domain, and the elements in the second tensor are the sum sequences of the second cross-correlation sequence and the third cross-correlation sequence at the same spatial sampling point in the spatial domain.

[0009] The target calibration operator vector is obtained by iteratively solving the characteristic equation of the calibration operator constructed based on the first tensor and the second tensor.

[0010] The spatiotemporal land detection data are calibrated based on the target calibration operator vector.

[0011] On the other hand, a data calibration device for land and water inspections is provided, the device comprising:

[0012] The first determining module is used to determine a first cross-correlation sequence, a second cross-correlation sequence, a third cross-correlation sequence, a first autocorrelation sequence, and a second autocorrelation sequence based on spatiotemporal water and land sampling data. Elements in the first cross-correlation sequence represent the correlation between water and land sampling data at the same spatial sampling point in the spatial domain. Elements in the second cross-correlation sequence represent the correlation between water and land sampling data at the same spatial sampling point in the time-space domain. Elements in the third cross-correlation sequence represent the correlation between water and land sampling data after removing direct waves at the same spatial sampling point in the time-space domain. Elements in the first autocorrelation sequence represent the correlation between land sampling data at the same spatial sampling point at different times in the time-space domain. Elements in the second autocorrelation sequence represent the correlation between land sampling data after removing direct waves at the same spatial sampling point at different times in the time-space domain.

[0013] The second determining module is used to determine a first tensor and a second tensor based on the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence. The elements in the first tensor are the difference sequences of the second autocorrelation sequence and the first autocorrelation sequence at the same spatial sampling point in the spatial domain, and the elements in the second tensor are the sum sequences of the second cross-correlation sequence and the third cross-correlation sequence at the same spatial sampling point in the spatial domain.

[0014] The third determining module is used to obtain the target calibration operator vector by iteratively solving the characteristic equation of the calibration operator constructed based on the first tensor and the second tensor.

[0015] The calibration module is used to calibrate the spatiotemporal land detection data based on the target calibration operator vector.

[0016] In some embodiments, the first determining module includes:

[0017] The first determining unit is used to determine, based on the spatiotemporal domain water detection data and the spatiotemporal domain land detection data, first spatiotemporal domain data, second spatiotemporal domain data, third spatiotemporal domain data, and fourth spatiotemporal domain data, wherein the first spatiotemporal domain data is direct wave water detection data in the time-space domain, the second spatiotemporal domain data is water detection data after removing direct waves in the time-space domain, the third spatiotemporal domain data is direct wave land detection data in the time-space domain, and the fourth spatiotemporal domain data is land detection data after removing direct waves in the time-space domain.

[0018] The second determining unit is configured to determine the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence based on the first spatiotemporal domain data, the second spatiotemporal domain data, the third spatiotemporal domain data, and the fourth spatiotemporal domain data.

[0019] In some embodiments, the second determining unit includes:

[0020] The first determining subunit is used to determine the first frequency spatial domain data and the second frequency spatial domain data based on the first spatiotemporal domain data and the third spatiotemporal domain data. The first frequency spatial domain data is direct wave water detection data in the frequency-spatial domain, and the second frequency spatial domain data is direct wave land detection data in the frequency-spatial domain.

[0021] The second determining subunit is used to determine the first cross-correlation sequence based on the first frequency spatial domain data and the second frequency spatial domain data;

[0022] The third determining subunit is used to determine the first autocorrelation sequence, the second autocorrelation sequence, the second cross-correlation sequence, and the third cross-correlation sequence based on the first cross-correlation sequence, the spatiotemporal land detection data, the spatiotemporal water detection data, the second spatiotemporal data, and the fourth spatiotemporal data.

[0023] In some embodiments, the third determining subunit is configured to determine the first autocorrelation sequence based on the first cross-correlation sequence and the spatiotemporal land detection data; determine the second autocorrelation sequence based on the first cross-correlation sequence and the fourth spatiotemporal data; determine the second cross-correlation sequence based on the first cross-correlation sequence, the spatiotemporal water detection data and the spatiotemporal land detection data; and determine the third cross-correlation sequence based on the first cross-correlation sequence, the second spatiotemporal data and the fourth spatiotemporal data.

[0024] In some embodiments, the third determining module includes:

[0025] The acquisition unit is used to acquire the iteration number and the iteration residual vector of the current iteration for any iteration of the characteristic equation of the calibration operator.

[0026] The third determining unit is used to determine the iteration residual coefficients of the current iteration based on the iteration residual vector of the current iteration.

[0027] The fourth determining unit is used to determine the target calibration operator vector based on the number of iterations and the iteration residual coefficients of the current iteration.

[0028] In some embodiments, the fourth determining unit includes:

[0029] The fourth determining subunit is used to determine the iteration correction vector for the next iteration based on the iteration residual coefficient of the current iteration, the iteration residual coefficient of the previous iteration, the iteration residual vector of the current iteration, the intermediate transition iteration vector of the previous iteration, the first tensor, and the iteration correction vector of the current iteration when the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold.

[0030] The fifth determining subunit is used to determine the target calibration operator vector based on the iteration number of the next iteration, the iteration correction vector of the current iteration, and the iteration correction vector of the next iteration, when the iteration number of the next iteration and the iteration number threshold are equal.

[0031] In some embodiments, the apparatus further includes:

[0032] The fourth determining module is used to determine the target calibration operator vector based on the iteration correction vector of the current iteration and the iteration correction vector of the previous iteration, provided that the iteration residual coefficient of the current iteration is not greater than the iteration residual coefficient threshold.

[0033] In some embodiments, the apparatus further includes:

[0034] The fifth determining module is used to determine the iteration residual vector for the next iteration based on the iteration residual vector of the current iteration, the iteration residual coefficient of the current iteration, the iteration residual coefficient of the previous iteration, the intermediate transition iteration vector of the previous iteration, and the first tensor, and to perform the next iteration, when the iteration number of the next iteration is less than the iteration number threshold.

[0035] In some embodiments, the fourth determining subunit is configured to: determine an iteration residual ratio coefficient for the current iteration based on the iteration residual coefficient of the current iteration and the iteration residual coefficient of the previous iteration, when the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold; determine an intermediate transition iteration vector for the current iteration based on the iteration residual vector of the current iteration, the iteration residual ratio coefficient of the current iteration, and the intermediate transition iteration vector of the previous iteration; determine an intermediate transformation iteration vector for the current iteration based on the first tensor and the iteration residual vector of the current iteration; determine an iteration correction coefficient for the current iteration based on the intermediate transition iteration vector of the current iteration, the intermediate transformation iteration vector of the current iteration, and the iteration residual coefficient of the current iteration; and determine an iteration correction vector for the next iteration based on the iteration correction vector of the current iteration, the iteration correction coefficient of the current iteration, and the intermediate transition iteration vector of the current iteration.

[0036] In some embodiments, the apparatus further includes:

[0037] The sixth determining module is used to determine the calibrated uplink wave field data and the calibrated downlink wave field data based on the calibrated spatiotemporal land inspection data and the spatiotemporal water inspection data.

[0038] The plotting module is used to plot the calibrated spatiotemporal land detection data, the calibrated uplink wave field data, and the calibrated downlink wave field data.

[0039] On the other hand, a terminal is provided, the terminal including a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded and executed by the processor to implement the land and water inspection data calibration method in the embodiments of this application.

[0040] On the other hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, the at least one computer program being loaded and executed by a processor to implement the land and water inspection data calibration method as described in the embodiments of this application.

[0041] On the other hand, a computer program product is provided, including a computer program that is executed by a processor to implement the land and water inspection data calibration method provided in the embodiments of this application.

[0042] This application provides a method for calibrating water and land survey data. By processing water and land survey data in both the spatiotemporal domains, multiple autocorrelation and cross-correlation sequences in the spatiotemporal domain are determined, thus preparing for calibration from water to land survey data. Then, a first and second tensor are determined based on these multiple sequences. Since solving the characteristic equation of the calibration operator constructed based on the first and second tensors involves matrix inversion, iterative solving can be used to obtain the target calibration operator vector, resulting in low computational cost, fast computation speed, and high computational accuracy. Finally, the land survey data in the spatiotemporal domain can be calibrated based on the target calibration operator vector. By simultaneously considering the temporal and spatial variations of water and land survey data, calibration accuracy is improved, enabling the separation of the upflow and downflow wave fields, eliminating interference from seawater tremors and virtual reflections, thereby improving exploration accuracy. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of the implementation environment of a method for calibrating land and water inspection data according to an embodiment of this application;

[0045] Figure 2 This is a flowchart of a method for calibrating land and water inspection data according to an embodiment of this application;

[0046] Figure 3 This is a comparative schematic diagram of the processing results of synthetic land and water inspection data provided in the embodiments of this application;

[0047] Figure 4 This is a comparative schematic diagram of the actual common receiving point gather data processing results provided in an embodiment of this application;

[0048] Figure 5 This is a comparative schematic diagram of the actual common artillery point road collection and land inspection data processing results provided in the embodiments of this application;

[0049] Figure 6 This is a comparative schematic diagram of the processing results of locally magnified land and water inspection data of an actual common shot point gather according to an embodiment of this application;

[0050] Figure 7 This is a block diagram of a water and land inspection data calibration device provided according to an embodiment of this application;

[0051] Figure 8 This is a block diagram of another land and water inspection data calibration device provided according to an embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0053] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "nth," nor are there any restrictions on quantity or execution order.

[0054] In this application, the term "at least one" means one or more, and "multiple" means two or more.

[0055] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the water and land inspection data involved in this application were obtained with full authorization.

[0056] The following is an explanation of the terms used in this application.

[0057] Direct wave: A seismic wave that travels directly from the source to the observation point in a homogeneous stratum.

[0058] Virtual reflection: a type of multiple reflection. It refers to the phenomenon where the energy propagates upwards from the point of explosion, encounters the bottom of a low-speed zone or the ground, reflects downwards, and finally reflects again from the lower reflection interface to the ground.

[0059] Deconvolution: Compresses the length of seismic reflection pulses, improves the resolution of reflected seismic records, and further estimates the reflection coefficient of subsurface reflection interfaces.

[0060] Common receiver gather: The gather of all the traces received by the same receiver point when fired from different shot points is called the common receiver gather of that receiver point.

[0061] Common firing point set: A collection of records from all firing points at the same firing point, i.e., a single record.

[0062] The land and water inspection data calibration method provided in this application can be executed by a computer device. In some embodiments, the computer device is a terminal or a server. See also Figure 1 The implementation environment includes terminal 101 and server 102.

[0063] Terminal 101 and server 102 can be connected directly or indirectly via wired or wireless communication, and this application does not impose any restrictions on this.

[0064] In some embodiments, terminal 101 has an application installed and running that supports data calibration.

[0065] In some embodiments, server 102 is an independent physical server, or it can be a server cluster or distributed system composed of multiple physical servers. Server 102 undertakes the main computing work, and terminal 101 undertakes the secondary computing work; or, server 102 undertakes the secondary computing work, and terminal 101 undertakes the main computing work; or, server 102 and terminal 101 cooperate in computing using a distributed computing architecture.

[0066] Figure 2This is a flowchart of a method for calibrating land and water inspection data according to an embodiment of this application, such as... Figure 2 As shown in the illustration, this application embodiment uses execution by a terminal as an example. The method includes the following steps:

[0067] 201. Based on the spatiotemporal water detection data and spatiotemporal land detection data, the terminal determines the first spatiotemporal domain data, the second spatiotemporal domain data, the third spatiotemporal domain data, and the fourth spatiotemporal domain data.

[0068] In this embodiment, due to the different recording mechanisms of underwater and land-based geophones, the seawater rumble and false reflection multiple wave interference recorded by the land-based geophone exhibit differences in polarity and amplitude characteristics compared to the seawater rumble and false reflection multiple wave interference recorded by the underwater geophone. The data recorded by the two types of geophones differ by a constant proportional to the seabed reflection coefficient; this constant is the calibration operator. Therefore, the terminal can determine multiple spatiotemporal domain data based on the spatiotemporal domain underwater and land-based geophone data to prepare for subsequent determination of the calibration operator. Specifically, the first spatiotemporal domain data is the direct wave underwater geophone data in the time-space domain; the second spatiotemporal domain data is the underwater geophone data after removing the direct wave in the time-space domain; the third spatiotemporal domain data is the direct wave land-based geophone data in the time-space domain; and the fourth spatiotemporal domain data is the land-based geophone data after removing the direct wave in the time-space domain.

[0069] In some embodiments, the terminal may determine the first spatiotemporal domain data using the following formula (1).

[0070] H d [i, j] = W d [i, j]H[i, j] (1)

[0071] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; H d [i, j] represents the first spatiotemporal domain data, i.e., the time-space domain direct wave water detection data; W d [i,j] represents the time window function used to extract direct waves from spatiotemporal water and land inspection data; H[i,j] represents the spatiotemporal water inspection data.

[0072] In some embodiments, the terminal may determine the second spatiotemporal domain data using the following formula (2).

[0073] H r [i, j] = H[i, j] - H d [i, j] (2)

[0074] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; H r [i, j] represents the second spatiotemporal domain data, i.e., the water detection data after removing direct waves from the time-space domain; H[i, j] represents the spatiotemporal domain water detection data; H d [i, j] represents the first spatiotemporal domain data, that is, the time-space domain direct wave water detection data.

[0075] In some embodiments, the terminal may determine the third spatiotemporal domain data using the following formula (3).

[0076] G d [i, j] = W d [i, j]G[i, j] (3)

[0077] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; G d [i, j] represents the third spatiotemporal domain data, i.e., the time-space domain direct wave ground detection data; W d [i, j] represents the time window function used to extract direct waves from spatiotemporal water and land inspection data; G[i, j] represents the spatiotemporal land inspection data.

[0078] In some embodiments, the terminal may determine the fourth spatiotemporal domain data using the following formula (4).

[0079] G r [i,j] = G[i,j] - G d [i, j] (4)

[0080] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; G r [i, j] represents the fourth spatiotemporal domain data, i.e., the land detection data after removing direct waves in the time-space domain; G[i, j] represents the spatiotemporal domain land detection data; G d [i, j] represents the third spatiotemporal domain data, that is, the time-space domain direct wave land detection data.

[0081] 202. Based on the first spatiotemporal domain data and the third spatiotemporal domain data, the terminal determines the first frequency spatial domain data and the second frequency spatial domain data. The first frequency spatial domain data is the direct wave water detection data in the frequency-spatial domain, and the second frequency spatial domain data is the direct wave land detection data in the frequency-spatial domain.

[0082] In this embodiment of the application, the terminal can obtain first frequency spatial domain data and second frequency spatial domain data by performing Fourier transform on the direct wave land and water detection data in the spatiotemporal domain along the time direction.

[0083] The first frequency spatial domain data is determined by the following formula (5).

[0084]

[0085] Where i represents the sequence number of the spatial sampling point in the time-space domain, i = 1, 2, 3, ..., NI, and NI represents the total number of spatial sampling points; m represents the sequence number of the frequency sampling point in the frequency-space domain, m = 1, 2, 3, ..., NM, and NM represents the total number of frequency sampling points; H f [i, m] represents the first frequency spatial domain data, i.e., the frequency-spatial domain direct wave water detection data; j represents the time-spatial domain time sampling point sequence number, j = 1, 2, 3, ..., NJ, where NJ represents the total number of time sampling points; H d [i, j] represents the first spatiotemporal domain data, i.e., the time-space domain direct wave water detection data; V NM The Fourier transform operator can be determined by formula (6).

[0086] V NM =e p2π / 2NM (6)

[0087] Where p represents the imaginary unit, and p 2 =-1.

[0088] The second frequency spatial domain data is determined by the following formula (7).

[0089]

[0090] Where i represents the sequence number of the spatial sampling point in the time-space domain, i = 1, 2, 3, ..., NI, and NI represents the total number of spatial sampling points; m represents the sequence number of the frequency sampling point in the frequency-space domain, m = 1, 2, 3, ..., NM, and NM represents the total number of frequency sampling points; G f [i, m] represents the second frequency spatial domain data, i.e., the frequency-spatial domain direct wave ground detection data; j represents the time-spatial domain time sampling point sequence number, j = 1, 2, 3…, NJ, where NJ represents the total number of time sampling points; G d [i, j] represents the third spatiotemporal domain data, i.e., the time-space domain direct wave ground detection data; VNM This represents the Fourier transform operator.

[0091] 203. The terminal determines a first cross-correlation sequence based on the first frequency spatial domain data and the second frequency spatial domain data. The elements in the first cross-correlation sequence are used to represent the degree of correlation between water and land sampling data at the same spatial sampling point in the spatial domain.

[0092] In this embodiment, the terminal can determine a first cross-correlation sequence based on first frequency spatial domain data and second frequency spatial domain data. This first cross-correlation sequence is used to weight the autocorrelation sequence and the cross-correlation sequence. Accordingly, the terminal determines a frequency-spatial normalized cross-correlation weighted sequence based on the first and second frequency spatial domain data. Then, the terminal sums the frequency-spatial normalized cross-correlation weighted sequence along the frequency direction to obtain the first cross-correlation sequence.

[0093] In some embodiments, the terminal can determine the frequency-space normalized cross-correlation weighted sequence using the following formula (8).

[0094]

[0095] Where i represents the sequence number of the spatial sampling point in the time-space domain, i = 1, 2, 3, ..., NI, and NI represents the total number of spatial sampling points; m represents the sequence number of the frequency sampling point in the frequency-space domain, m = 1, 2, 3, ..., NM, and NM represents the total number of frequency sampling points; X f [i, m] represents the frequency-space normalized cross-correlation weighted sequence; H f [i, m] represents the first frequency spatial domain data, which is also the frequency-spatial domain direct wave water detection data; This represents the frequency domain complex conjugate data of the first frequency spatial domain data; G f [i, m] represents the second frequency spatial domain data, that is, the frequency-spatial domain direct wave ground detection data; This represents the frequency domain complex conjugate data of the second frequency spatial domain data.

[0096] In some embodiments, the terminal may determine the first cross-correlation sequence using the following formula (9).

[0097]

[0098] Where i represents the sequence number of the spatial sampling point in the time-space domain, i = 1, 2, 3, ..., NI, and NI represents the total number of spatial sampling points; X[i] represents the first cross-correlation sequence, i.e., the spatially normalized correlation weighted sequence; m represents the sequence number of the frequency sampling point in the frequency-space domain, m = 1, 2, 3, ..., NM, and NM represents the total number of frequency sampling points; X f [i, m] represents a frequency-space normalized cross-correlation weighted sequence.

[0099] It should be noted that the terminal can determine the objective function based on spatiotemporal water and land inspection data. Then, the terminal minimizes the objective function by performing processes such as least squares to obtain multiple formulas in the following steps, thereby determining the target calibration operator vector and realizing the calibration of the land inspection data.

[0100] The calibrated spatiotemporal land inspection data are determined by the following equation (10).

[0101]

[0102] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; G c [i, j] represents the calibrated spatiotemporal domain land detection data; k represents the element sequence number in the target calibration operator vector, k = -NK, -NK+1, -NK+2, ..., NK, (2NK+1) represents the length of the target calibration operator vector; C z [k] represents the k-th element in the time-domain calibration operator sequence; G[i,j] represents the spatiotemporal domain land inspection data.

[0103] The calibration-processed up-wave field data is determined by the following equation (11).

[0104]

[0105] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; U[i, j] represents the calibrated upflow field data; H[i, j] represents the temporal-space domain water quality data; G c [i, j] represents the calibrated spatiotemporal domain land detection data.

[0106] The downlink wave field data after calibration is determined by the following equation (12).

[0107]

[0108] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; D[i, j] represents the downlink wavefield data after calibration processing; H[i, j] represents the temporal-space domain water detection data; G c[i, j] represents the calibrated spatiotemporal domain land detection data.

[0109] The downlink wave field data after removing the direct wave is determined by the following equation (13).

[0110]

[0111] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; D r [i, j] represents the downlink wavefield data after removing the direct wave; H r [i, j] represents the second spatiotemporal domain data, i.e., the water detection data after removing the direct wave in the time-space domain; k = -NK, -NK+1, -NK+2, ..., NK, (2NK+1) represents the length of the target calibration operator sequence; C z [k] represents the k-th element in the time-domain calibration operator sequence; G r [i, j] represents the fourth spatiotemporal domain data, which is the land detection data after removing the direct wave in the time-space domain.

[0112] Minimize the following objective function.

[0113]

[0114] in,

[0115] E[i,j]=|U[i,j]| 2 -|D r [i, j]| 2 (15)

[0116] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; E[i, j] represents the objective function; U[i, j] represents the calibrated up-wave field data; D r [i, j] represents the downlink wavefield data after removing the direct wave.

[0117] From equations (10), (11) and (13), we have

[0118]

[0119]

[0120] Substituting equations (16) and (17) into equation (15), we have

[0121]

[0122] Substituting equation (18) into equation (14), we have

[0123]

[0124] make

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] Substituting equations (20)-(25) into equation (19), we have

[0132]

[0133] Label both sides of equation (26) with the time-domain operator sequence C. z Differentiating [k], we have:

[0134]

[0135] Setting the right side of equation (27) to zero simplifies, we have

[0136]

[0137] make

[0138] c T =(C z [-NK],C z [-NK+1], C z [-NK+2],…,C z [NK]) (29)

[0139] Ψ T =(Ψ[-NK],Ψ[-NK+1],Ψ[-NK+2],…,Ψ[NK]) (30)

[0140]

[0141] Equation (28) simplifies to a matrix equation.

[0142] Φc =Ψ (32)

[0143] The solution is

[0144] c = Φ - 1 Ψ (33)

[0145] 204. Based on the first cross-correlation sequence, the spatiotemporal land detection data, the spatiotemporal water detection data, the second spatiotemporal data, and the fourth spatiotemporal data, the terminal determines the first autocorrelation sequence, the second autocorrelation sequence, the second cross-correlation sequence, and the third cross-correlation sequence.

[0146] In this embodiment, the elements in the second cross-correlation sequence represent the correlation between water and land inspection data at the same spatial sampling point in the time-space domain. The elements in the third cross-correlation sequence represent the correlation between water and land inspection data after removing direct waves at the same spatial sampling point in the time-space domain. The elements in the first autocorrelation sequence represent the correlation between land inspection data at the same spatial sampling point at different times in the time-space domain. The elements in the second autocorrelation sequence represent the correlation between land inspection data after removing direct waves at the same spatial sampling point at different times in the time-space domain. By determining multiple sequences, the terminal can determine the correlation between water and land inspection data in the time-space domain and the correlation between land inspection data at different times, preparing for the calibration of water and land inspection data.

[0147] In some embodiments, the terminal can determine multiple autocorrelation sequences and cross-correlation sequences based on a first cross-correlation sequence and spatiotemporal domain data. Accordingly, the terminal determines a first autocorrelation sequence based on the first cross-correlation sequence and spatiotemporal domain land detection data. Then, the terminal determines a second autocorrelation sequence based on the first cross-correlation sequence and fourth spatiotemporal domain data. Then, the terminal determines a second cross-correlation sequence based on the first cross-correlation sequence, spatiotemporal domain water detection data, and spatiotemporal domain land detection data. Finally, the terminal determines a third cross-correlation sequence based on the first cross-correlation sequence, the second spatiotemporal domain data, and the fourth spatiotemporal domain data.

[0148] In some embodiments, the terminal can determine the first autocorrelation sequence using the above formula (21).

[0149]

[0150] Where k represents the time sampling point sequence number of the first autocorrelation sequence, k = -NK, -NK+1, -NK+2, ..., NK, (2NK+1) represents the length of the first autocorrelation sequence; φ[k] represents the first autocorrelation sequence, which is also the weighted autocorrelation sequence of land inspection data; i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, NI represents the total number of spatial sampling points; j represents the time sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, NJ represents the total number of time sampling points; X[i] represents the first cross-correlation sequence, which is also the spatially normalized correlation weighted sequence; G[i, j] represents the land inspection data in the time-space domain.

[0151] In some embodiments, the terminal can determine the second autocorrelation sequence using the above formula (23).

[0152]

[0153] Where k represents the time sampling sequence number of the second autocorrelation sequence, k = -NK, -NK+1, -NK+2, ..., NK, and (2NK+1) represents the length of the second autocorrelation sequence; φ r [k] represents the second autocorrelation sequence, i.e., the weighted autocorrelation sequence of land-based detection data after removing the direct wave; i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3..., NJ, where NJ represents the total number of temporal sampling points; X[i] represents the first cross-correlation sequence, i.e., the spatially normalized correlation weighted sequence; G r [i, j] represents the fourth spatiotemporal domain data, which is the land detection data after removing the direct wave in the time-space domain.

[0154] In some embodiments, the terminal can determine the second cross-correlation sequence using the above formula (20).

[0155]

[0156] Where k represents the time sampling sequence number of the second cross-correlation sequence, k = -NK, -NK+1, -NK+2, ..., NK, and (2NK+1) represents the length of the second cross-correlation sequence; The first cross-correlation sequence is represented by X[i], which is the weighted cross-correlation sequence of water and land inspection data; i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the time-space domain time sampling point sequence number, j = 1, 2, 3, ..., NJ, where NJ represents the total number of time sampling points; H[i, j] represents the spatiotemporal domain water inspection data, which is the spatially normalized correlation weighted sequence; G[i, j] represents the spatiotemporal domain land inspection data.

[0157] In some embodiments, the terminal can determine the third cross-correlation sequence using the above formula (22).

[0158]

[0159] Where k represents the time sampling sequence number of the third cross-correlation sequence, k = -NK, -NK+1, -NK+2, ..., NK, and (2NK+1) represents the length of the third cross-correlation sequence; The third cross-correlation sequence represents the weighted cross-correlation sequence of land and water data after removing the direct wave; i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the time-space domain time sampling point sequence number, j = 1, 2, 3, ..., NJ, where NJ represents the total number of time sampling points; X[i] represents the first cross-correlation sequence, which is the spatially normalized correlation weighted sequence; H r [i, j] represents the second spatiotemporal domain data, that is, the water detection data after removing direct waves from the time-space domain; G r [i, j] represents the fourth spatiotemporal domain data, which is the land detection data after removing the direct wave in the time-space domain.

[0160] 205. The terminal determines the first tensor and the second tensor based on the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence.

[0161] In this embodiment, the terminal can determine a first tensor and a second tensor based on multiple autocorrelation sequences and cross-correlation sequences. The elements of the first tensor are the differences between the second and first autocorrelation sequences at the same spatial sampling point in the spatial domain, and the elements of the second tensor are the sums of the second and third cross-correlation sequences at the same spatial sampling point in the spatial domain. Correspondingly, the terminal determines a weighted autocorrelation difference sequence of land-based inspection data based on the second and first autocorrelation sequences. Then, the terminal determines a weighted cross-correlation sum sequence of land-based and water-based inspection data based on the second and third cross-correlation sequences. Then, the terminal determines the first tensor based on the weighted autocorrelation difference sequence of land-based inspection data. Finally, the terminal determines the second tensor based on the weighted cross-correlation sum sequence of land-based and water-based inspection data.

[0162] In some embodiments, the terminal can determine the weighted autocorrelation difference sequence of land inspection data using the above formula (25).

[0163] Φ[k]=φ r [k]-φ[k] (25)

[0164] Where k represents the time sampling point sequence number of the autocorrelation sequence, k = -NK, -NK+1, -NK+2, ..., NK, (2NK+1) represents the length of the autocorrelation sequence; Φ[k] represents the weighted autocorrelation difference sequence of the land inspection data; φ r [k] represents the second autocorrelation sequence, which is the weighted autocorrelation sequence of the land inspection data after removing the direct wave; φ[k] represents the first autocorrelation sequence, which is the weighted autocorrelation sequence of the land inspection data.

[0165] In some embodiments, the terminal can determine the weighted cross-correlation and sequence of land and water inspection data using the above formula (24).

[0166]

[0167] Where k represents the time sampling point sequence number of the cross-correlation sequence, k = -NK, -NK+1, -NK+2, ..., NK, (2NK+1) represents the length of the cross-correlation sequence; Ψ[k] represents the weighted cross-correlation sequence of the land and water inspection data; This represents the third cross-correlation sequence, which is the weighted cross-correlation sequence of land and water detection data after removing the direct wave; This represents the second cross-correlation sequence, which is also the weighted cross-correlation sequence of land and water inspection data.

[0168] In some embodiments, the terminal may determine the first tensor using the above formula (31).

[0169]

[0170] Where Φ represents the first tensor, that is, the matrix tensor on the right side of the characteristic equation of the calibration operator; k represents the time sampling point sequence number of the autocorrelation sequence, k = -NK, -NK+1, -NK+2, ..., NK, (2NK+1) represents the length of the autocorrelation sequence; Φ[k] represents the weighted autocorrelation difference sequence of the land inspection data.

[0171] In some embodiments, the terminal can determine the second tensor using the above formula (30).

[0172] Ψ T =(Ψ[-NK],Ψ[-NK+1],Ψ[-NK+2],…,Ψ[NK]) (30)

[0173] Among them, Ψ T Ψ[k] represents the second tensor, which is the tensor vector on the right side of the characteristic equation of the calibration operator; T represents the vector transpose; k represents the time sampling point sequence number of the cross-correlation sequence, k = -NK, -NK+1, -NK+2, ..., NK, (2NK+1) represents the length of the cross-correlation sequence; Ψ[k] represents the weighted cross-correlation sequence of the land and water inspection data.

[0174] 206. The terminal constructs the characteristic equation of the calibration operator based on the first tensor and the second tensor.

[0175] In this embodiment, to effectively calibrate land inspection data, a short calibration operator sequence can be determined in the time domain. After the terminal constructs the characteristic equation of the calibration operator, the calibration operator sequence, i.e., the target calibration operator sequence, can be obtained by solving the characteristic equation of the calibration operator, and then the land inspection data can be calibrated based on the target calibration operator sequence.

[0176] In some embodiments, the terminal constructs the characteristic equation of the calibration operator based on a determined time-domain calibration operator sequence, a first tensor, and a second tensor.

[0177] In some embodiments, the terminal can determine the calibration operator tensor vector on the left side of the characteristic equation of the calibration operator using the above formula (29).

[0178] c T =(C z [-NK],C z [-NK+1], C z [-NK+2],…,C z [NK]) (29)

[0179] Among them, c T The calibration operator tensor vector on the left side of the characteristic equation of the calibration operator is represented by T, where T represents the vector transpose; k represents the sequence number of the elements in the calibration operator sequence, k = -NK, -NK+1, -NK+2, ..., NK, and (2NK+1) represents the length of the target calibration operator sequence; C z [k] represents the k-th element in the time-domain calibration operator sequence; Ψ[k] represents the weighted cross-correlation sequence of land and water inspection data.

[0180] In some embodiments, the terminal can construct the characteristic equation of the calibration operator using the above formula (32).

[0181] Φ c =Ψ (32)

[0182] Where Φ represents the first tensor, which is the matrix tensor on the right side of the characteristic equation of the calibration operator; c represents the transpose of the calibration operator tensor vector on the left side of the characteristic equation of the calibration operator; and Ψ represents the transpose of the second tensor, which is the transpose of the tensor vector on the right side of the characteristic equation of the calibration operator.

[0183] 207. For any iteration of the characteristic equation of the calibration operator, the terminal obtains the iteration number and the iteration residual vector of the current iteration.

[0184] In this embodiment, solving the characteristic equation of the calibration operator involves matrix inversion, which complicates the calculation. Therefore, the terminal can iteratively solve the characteristic equation of the calibration operator, making the calculation simpler and more accurate, thereby improving the calibration accuracy.

[0185] In some embodiments, the terminal can set initial values ​​for iteratively solving the characteristic equation of the calibration operator. Accordingly, the terminal can set initial iterative residual coefficients, initial intermediate iteration vectors, initial calibration operator vectors, and initial iteration counts, and calculate the initial residual vector. Then, the terminal sets iterative residual coefficient thresholds and iteration count thresholds, and determines the solution to the characteristic equation of the calibration operator by comparing the iterative residual coefficients and iteration counts obtained during the iteration process with the two thresholds.

[0186] The initial iteration residual coefficients are set using the following formula (34).

[0187] e -1 =1 (34)

[0188] Among them, e -1 This represents the initial iteration residual coefficient. In this embodiment, the initial iteration residual coefficient is 1 as an example.

[0189] The threshold for the iterative residual coefficient is set using the following formula (35).

[0190] e MIN =0.000001 (35)

[0191] Among them, e MIN This represents the threshold value of the iterative residual coefficient. The value of this iterative residual coefficient threshold can be a very small value. In this embodiment, 0.000001 is used as an example for illustration.

[0192] The initial intermediate transition iteration vector is set using the following formula (36).

[0193] P -1 =0 (36)

[0194] Among them, P -1 This represents the initial intermediate transition iteration vector. In this embodiment, the initial intermediate transition iteration vector is zero.

[0195] The initial calibration operator vector is set using the following formula (37).

[0196] c0=0 (37)

[0197] Where c0 represents the initial calibration operator vector, and this embodiment of the application uses the initial calibration operator vector as the zero vector as an example for illustration.

[0198] The initial residual vector is calculated using the following formula (38).

[0199] R0=Ψ-Φc0 (38)

[0200] Among them, R θ Ψ represents the initial residual vector; Ψ represents the transpose of the second tensor, which is the transpose of the tensor vector on the right side of the characteristic equation of the calibration operator; Φ represents the first tensor, which is the matrix tensor on the right side of the characteristic equation of the calibration operator; c0 represents the initial calibration operator vector.

[0201] The initial number of iterations is set using the following formula (39).

[0202] α0=-1 (39)

[0203] Wherein, α0 represents the initial iteration number. In this embodiment, the initial iteration number is -1 as an example for illustration.

[0204] Set the iteration threshold using the following formula (40).

[0205] α MAX =50 (40)

[0206] Where, α MAX This represents the iteration number threshold, which can be a large value. In this embodiment, 50 is used as an example for illustration.

[0207] In some embodiments, for any iteration of the characteristic equation of the calibration operator, the terminal can determine the solution of the characteristic equation of the calibration operator, i.e., the target calibration operator, through steps 207 to 214. Accordingly, in step 207, the terminal can obtain the iteration number and the iteration residual vector of the current iteration based on the iteration result of the previous iteration, and then execute step 208.

[0208] 208. The terminal determines the iteration residual coefficients for this iteration based on the iteration residual vector of this iteration.

[0209] In this embodiment of the application, the terminal can determine the iteration residual coefficients of the current iteration based on the iteration residual vector of the current iteration, and then execute step 209.

[0210] In some embodiments, the terminal can determine the iteration residual coefficient of the current iteration using the following formula (41).

[0211]

[0212] Where α represents the number of iterations in this iteration; e α This represents the iteration residual coefficient for this iteration; R represents the transpose of the iterative residual vector for this iteration, where T represents the vector transpose; α This represents the iteration residual vector for this iteration.

[0213] It should be noted that the terminal can perform different operations based on the relationship between the iteration residual coefficient and the iteration residual coefficient of the current iteration. Accordingly, if the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold, steps 209 to 213 are executed. If the iteration residual coefficient of the current iteration is not greater than the iteration residual coefficient threshold, step 215 is executed.

[0214] 209. If the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold, the terminal determines the iteration residual ratio coefficient of the current iteration based on the iteration residual coefficient of the current iteration and the iteration residual coefficient of the previous iteration.

[0215] In this embodiment of the application, the terminal can determine the iteration residual ratio coefficient of the current iteration by the following formula (42).

[0216]

[0217] Where α represents the number of iterations in this iteration; b α e represents the iteration residual ratio coefficient for this iteration; α e represents the iteration residual coefficient for this iteration; α-1 This represents the iteration residual coefficient of the previous iteration.

[0218] 210. The terminal determines the intermediate transition iteration vector for the current iteration based on the iteration residual vector of the current iteration, the iteration residual ratio coefficient of the current iteration, and the intermediate transition iteration vector of the previous iteration.

[0219] In this embodiment of the application, the terminal can determine the intermediate transition iteration vector of the current iteration by the following formula (43).

[0220] P α =R α +b α P α-1 (43)

[0221] Where α represents the number of iterations in this iteration; P α b represents the intermediate transition iteration vector for this iteration; α P represents the iteration residual ratio coefficient for this iteration; α-1 This represents the intermediate transition iteration vector from the previous iteration.

[0222] 211. The terminal determines the intermediate transformation iteration vector for this iteration based on the first tensor and the iteration residual vector of this iteration.

[0223] In this embodiment of the application, the terminal can determine the intermediate transformation iteration vector of the current iteration using the following formula (44).

[0224] Q α =ΦR α (44)

[0225] Where α represents the number of iterations in this iteration; Q α R represents the intermediate transformation iteration vector for this iteration; Φ represents the first tensor, which is the matrix tensor on the right side of the characteristic equation of the calibration operator; α This represents the iteration residual vector for this iteration.

[0226] 212. The terminal determines the iteration correction coefficient for this iteration based on the intermediate transition iteration vector, the intermediate transformation iteration vector, and the iteration residual coefficient for this iteration.

[0227] In this embodiment, the terminal can determine the iteration transition coefficients based on the intermediate transition iteration vector and the intermediate transformation iteration vector of the current iteration. Then, the terminal determines the iteration correction coefficients of the current iteration based on the iteration residual coefficients and the iteration transition coefficients of the current iteration.

[0228] In some embodiments, the terminal can determine the iterative transition conversion coefficients using the following formula (45).

[0229]

[0230] Where α represents the number of iterations in this iteration; d α Indicates the iterative transition coefficient; Q represents the transpose of the intermediate transitional iteration vector in this iteration, where T represents the vector transpose; α This represents the intermediate transformation iteration vector for this iteration.

[0231] In some embodiments, the terminal can determine the iteration correction coefficient for the current iteration using the following formula (46).

[0232]

[0233] Where α represents the number of iterations in this iteration; a α e represents the iteration correction coefficient for this iteration; α d represents the iteration residual coefficient of this iteration; α This represents the iterative transition coefficient.

[0234] 213. The terminal determines the iteration correction vector for the next iteration based on the iteration correction vector, the iteration correction coefficient, and the intermediate transition iteration vector of the current iteration.

[0235] In this embodiment, the terminal can obtain the iteration correction vector for the current iteration based on the iteration result of the previous iteration. Then, the terminal determines the iteration correction vector for the next iteration based on the iteration correction vector for the current iteration, the iteration correction coefficient for the current iteration, and the intermediate transition iteration vector for the current iteration.

[0236] The iteration correction vector for the next iteration is determined by the following formula (47).

[0237] c α+1 =c α +a α P α (47)

[0238] Where α represents the number of iterations in the current iteration; α+1 represents the number of iterations in the next iteration; c α a represents the iteration correction vector for this iteration; α P represents the iteration correction coefficient for this iteration; α This represents the intermediate transition iteration vector for this iteration.

[0239] It should be noted that the terminal can perform different operations based on the relationship between the next iteration number and the iteration number threshold. Accordingly, if the next iteration number and the iteration number threshold are equal, step 214 is executed. If the next iteration number is less than the iteration number threshold, step 216 is executed.

[0240] 214. When the number of iterations in the next iteration is equal to the iteration threshold, the terminal determines the target calibration operator vector based on the number of iterations in the next iteration, the iteration correction vector of the current iteration, and the iteration correction vector of the next iteration.

[0241] In this embodiment of the application, the terminal can determine the target calibration operator vector by the following formula (48).

[0242]

[0243] Among them, c best α represents the target calibration operator vector; α represents the iteration number of the current iteration; α+1 represents the iteration number of the next iteration; c α+1 c represents the iteration correction vector for the next iteration; α This represents the iteration correction vector for this iteration.

[0244] 215. If the residual coefficient of the first iteration is not greater than the threshold of the residual coefficient of the iteration, the terminal determines the target calibration operator vector based on the iteration correction vector of the current iteration and the iteration correction vector of the previous iteration.

[0245] In this embodiment, when the residual coefficient of the first iteration is not greater than the threshold of the residual coefficient, the terminal decrements the iteration number of the current iteration, thus obtaining the iteration number of the previous iteration. Then, based on the iteration result of the previous iteration, the terminal can obtain the iteration correction vector of the previous iteration. Finally, based on the iteration correction vector of the current iteration and the iteration correction vector of the previous iteration, the terminal determines the target calibration operator vector.

[0246] The number of iterations for this iteration is reduced by the following formula (49).

[0247] α=α-1 (49)

[0248] The target calibration operator vector is determined by the above formula (48), c α+1 c represents the iteration correction vector for this iteration; α This represents the iteration correction vector from the previous iteration.

[0249] 216. If the number of iterations in the next iteration is less than the iteration number threshold, the terminal determines the iteration residual vector for the next iteration based on the iteration residual vector of the current iteration, the iteration residual coefficient of the current iteration, the iteration residual coefficient of the previous iteration, the intermediate transition iteration vector of the previous iteration, and the first tensor, and executes step 208 to perform the next iteration.

[0250] In this embodiment, after the terminal determines the iteration residual vector for the next iteration, the current iteration ends. Then, the terminal can increment the iteration number of the current iteration to proceed to the next iteration, i.e., execute step 208. Since the iteration number has changed, the iteration residual vector of the next iteration will be used as the iteration residual vector of the current iteration in step 208, and the subsequent iteration process will be executed.

[0251] The iterative residual vector for the next iteration is determined by the following formula (50).

[0252] R α+1 =R α -a α Q α (50)

[0253] Where α represents the number of iterations in the current iteration; α+1 represents the number of iterations in the next iteration; R α+1 R represents the iterative residual vector for the next iteration; α a represents the iterative residual vector for this iteration; α Q represents the iteration correction coefficient for this iteration; α This represents the intermediate transformation iteration vector for this iteration.

[0254] The number of iterations for this iteration is incremented using the following formula (51).

[0255] α=α+1 (51)

[0256] 217. The terminal calibrates the spatiotemporal land detection data based on the target calibration operator vector.

[0257] In this embodiment, the terminal obtains the target calibration operator vector for calibration by iteratively solving the characteristic equation of the calibration operator. Since the determination of the target calibration operator vector considers both the temporal and spatial variations of the land and water survey data, calibration based on the spatiotemporal land survey data using this target calibration operator vector improves calibration accuracy. This provides data for subsequent uplink and downlink wavefield separation, thereby enhancing exploration accuracy.

[0258] In some embodiments, the terminal can calibrate the spatiotemporal land detection data using the above formula (10).

[0259]

[0260] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; G c [i, j] represents the calibrated spatiotemporal domain land detection data; k represents the element sequence number in the target calibration operator vector, k = -NK, -NK+1, -NK+2, ..., NK, (2NK+1) represents the length of the target calibration operator vector; c best [k] represents the target calibration operator vector; G[i,j] represents the spatiotemporal land detection data.

[0261] 218. Based on the calibrated spatiotemporal land inspection data and spatiotemporal water inspection data, the terminal determines the calibrated uplink wave field data and the calibrated downlink wave field data.

[0262] In this embodiment, the terminal, based on the calibrated spatiotemporal land survey data, can accurately separate the uplink and downlink wavefields to eliminate interference from seawater vibration and virtual reflection multiples in the land and water survey data. This provides uplink and downlink wavefield data for subsequent joint deconvolution and mirror migration imaging processing, thereby improving exploration accuracy. Furthermore, by plotting the calibrated spatiotemporal land survey data, uplink wavefield data, and downlink wavefield data, the actual effectiveness of the land and water survey calibration method provided in this embodiment can be clearly observed.

[0263] In some embodiments, the terminal can determine the calibrated uplink field data using the above formula (11).

[0264]

[0265] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; U[i, j] represents the calibrated upflow field data; H[i, j] represents the temporal-space domain water quality data; G c [i, j] represents the calibrated spatiotemporal domain land detection data.

[0266] In some embodiments, the terminal can determine the downlink wave field data after calibration processing using the above formula (12).

[0267]

[0268] Where i represents the spatial sampling point sequence number in the time-space domain, i = 1, 2, 3, ..., NI, where NI represents the total number of spatial sampling points; j represents the temporal sampling point sequence number in the time-space domain, j = 1, 2, 3, ..., NJ, where NJ represents the total number of temporal sampling points; D[i, j] represents the downlink wavefield data after calibration processing; H[i, j] represents the temporal-space domain water detection data; G c [i, j] represents the calibrated spatiotemporal domain land detection data.

[0269] For example, Figure 3 This is a comparative schematic diagram of the processing results of synthetic land and water surveillance data provided according to an embodiment of this application. For example... Figure 3 As shown, Figure (a) is a schematic diagram of synthetic waterborne data, Figure (b) is a schematic diagram of synthetic landborne data, Figure (c) is a schematic diagram of uplink wavefield data separated by conventional methods, Figure (d) is a schematic diagram of downlink wavefield data separated by conventional methods, Figure (e) is a schematic diagram of uplink wavefield data separated by the method of this application, and Figure (f) is a schematic diagram of downlink wavefield data separated by the method of this application. Figure 3 As shown in Figures (c) and (e), the conventional method and the method of this application achieve comparable separation results when separating upgoing wave field data. Figure 3 As shown in Figures (d) and (f), Figure (d) contains a relatively strong upward wave field, while Figure (f) shows almost no remnant of the upward wave field. Therefore, after processing by the method of this application, the upward and downward wave fields can be completely separated, and almost no remnant of the upward wave field can be seen in the downward wave field.

[0270] For example, Figure 4This is a comparative schematic diagram of the actual common receiving point gather data processing results provided according to an embodiment of this application. For example... Figure 4 As shown, Figure (a) is a schematic diagram of actual common receiving point gather water sampling data, Figure (b) is a schematic diagram of actual common receiving point gather land sampling data, Figure (c) is a schematic diagram of uplink wave field data separated by conventional methods, Figure (d) is a schematic diagram of downlink wave field data separated by conventional methods, Figure (e) is a schematic diagram of uplink wave field data separated by the method of this application, and Figure (f) is a schematic diagram of downlink wave field data separated by the method of this application. Figure 4 As shown in Figures (c) and (e), the conventional method and the method of this application achieve comparable separation results in separating the upgoing wave field data; almost no false reflections from the seabed are visible (around 960 ms), indicating that the upgoing wave field is completely separated. Figure 4 As shown in Figures (d) and (f), when separating the downlink wave field using both the conventional method and the method of this application, especially between 400ms and 800ms, Figure (d) still contains a relatively strong uplink wave field, while Figure (f) shows a weaker uplink wave field with lower energy and a weaker residual uplink wave field. Therefore, after processing by the method of this application, complete separation of the uplink and downlink wave fields can be achieved, with a smaller residual uplink wave field in the downlink wave field.

[0271] For example, Figure 5 This is a comparative schematic diagram of the processing results of actual land and water inspection data from a common artillery point, provided in an embodiment of this application. For example... Figure 5 As shown, Figure (a) is a schematic diagram of actual common shot point gather water reconnaissance data, Figure (b) is a schematic diagram of actual common shot point gather land reconnaissance data, Figure (c) is a schematic diagram of uplink wave field data separated by conventional methods, Figure (d) is a schematic diagram of downlink wave field data separated by conventional methods, Figure (e) is a schematic diagram of uplink wave field data separated by the method of this application, and Figure (f) is a schematic diagram of downlink wave field data separated by the method of this application. Figure 5 As shown in Figures (c) and (e), the conventional method and the method of this application achieve comparable separation results when separating upriding wave field data. See also Figure 6 As shown, the uplink and downlink wave fields separated by the method of this application are cleaner in terms of local details than those separated by conventional methods.

[0272] For example, for Figure 5 By magnifying the local area, we can obtain Figure 6 . Figure 6 This is a comparative schematic diagram of the processing results of locally magnified land and water reconnaissance data from an actual common shot point gather, according to an embodiment of this application. The trace numbers are 51-261, and the times are 1350ms-2650ms. Figure 6As shown, Figure (a) is a schematic diagram of locally magnified water-based detection data from an actual common shot gather; Figure (b) is a schematic diagram of locally magnified land-based detection data from an actual common shot gather; Figure (c) is a schematic diagram of uplink wavefield data separated by conventional methods; Figure (d) is a schematic diagram of downlink wavefield data separated by conventional methods; Figure (e) is a schematic diagram of uplink wavefield data separated by the method of this application; and Figure (f) is a schematic diagram of downlink wavefield data separated by the method of this application. Figure 6 As shown in Figures (d) and (f), after local magnification, Figure (d) still contains a relatively strong refracted wave field, while Figure (f) shows almost no refracted wave field energy, indicating a weak residual refracted wave field energy. Therefore, after processing by the method of this application, the uplink and downlink wave fields can be completely separated, with a smaller residual uplink wave field in the downlink wave field (the refracted wave field in the land and water inspection data is treated as the uplink wave field and separated into the uplink wave field).

[0273] This application provides a method for calibrating water and land survey data. By processing water and land survey data in both the spatiotemporal domains, multiple autocorrelation and cross-correlation sequences in the spatiotemporal domain are determined, thus preparing for calibration from water to land survey data. Then, a first and second tensor are determined based on these multiple sequences. Since solving the characteristic equation of the calibration operator constructed based on the first and second tensors involves matrix inversion, iterative solving can be used to obtain the target calibration operator vector, resulting in low computational cost, fast computation speed, and high computational accuracy. Finally, the land survey data in the spatiotemporal domain can be calibrated based on the target calibration operator vector. By simultaneously considering the temporal and spatial variations of water and land survey data, calibration accuracy is improved, enabling the separation of the upflow and downflow wave fields, eliminating interference from seawater tremors and virtual reflections, thereby improving exploration accuracy.

[0274] Figure 7 This is a block diagram of a land and water inspection data calibration device according to an embodiment of this application. The device is used to perform the steps of the above-described method, see [link to relevant documentation]. Figure 7 The device includes:

[0275] The first determining module 701 is used to determine a first cross-correlation sequence, a second cross-correlation sequence, a third cross-correlation sequence, a first autocorrelation sequence, and a second autocorrelation sequence based on spatiotemporal water sampling data and spatiotemporal land sampling data. The elements in the first cross-correlation sequence are used to represent the degree of correlation between water sampling data and land sampling data at the same spatial sampling point in the spatial domain. The elements in the second cross-correlation sequence are used to represent the degree of correlation between water sampling data and land sampling data after removing direct waves in the time-space domain at the same spatial sampling point. The elements in the first autocorrelation sequence are used to represent the degree of correlation between land sampling data at different times at the same spatial sampling point in the time-space domain. The elements in the second autocorrelation sequence are used to represent the degree of correlation between land sampling data after removing direct waves at different times at the same spatial sampling point in the time-space domain.

[0276] The second determining module 702 is used to determine a first tensor and a second tensor based on the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence. The elements in the first tensor are the difference sequences of the second autocorrelation sequence and the first autocorrelation sequence at the same spatial sampling point in the spatial domain, and the elements in the second tensor are the sum sequences of the second cross-correlation sequence and the third cross-correlation sequence at the same spatial sampling point in the spatial domain.

[0277] The third determining module 703 is used to obtain the target calibration operator vector by iteratively solving the characteristic equation of the calibration operator constructed based on the first tensor and the second tensor.

[0278] The calibration module 704 is used to calibrate spatiotemporal land inspection data based on the target calibration operator vector.

[0279] In some embodiments, Figure 8 This is a block diagram of another land and water inspection data calibration device provided according to an embodiment of this application.

[0280] In some embodiments, see Figure 8 As shown, the first determining module 701 includes:

[0281] The first determining unit 801 is used to determine first spatiotemporal domain data, second spatiotemporal domain data, third spatiotemporal domain data, and fourth spatiotemporal domain data based on spatiotemporal domain water detection data and spatiotemporal domain land detection data. The first spatiotemporal domain data is direct wave water detection data in the time-space domain, the second spatiotemporal domain data is water detection data after removing direct waves in the time-space domain, the third spatiotemporal domain data is direct wave land detection data in the time-space domain, and the fourth spatiotemporal domain data is land detection data after removing direct waves in the time-space domain.

[0282] The second determining unit 802 is used to determine a first cross-correlation sequence, a second cross-correlation sequence, a third cross-correlation sequence, a first autocorrelation sequence, and a second autocorrelation sequence based on the first spatiotemporal domain data, the second spatiotemporal domain data, the third spatiotemporal domain data, and the fourth spatiotemporal domain data.

[0283] In some embodiments, see Figure 8 As shown, the second determining unit 802 includes:

[0284] The first determining subunit 8021 is used to determine the first frequency spatial domain data and the second frequency spatial domain data based on the first spatiotemporal domain data and the third spatiotemporal domain data. The first frequency spatial domain data is direct wave water detection data in the frequency-space domain, and the second frequency spatial domain data is direct wave land detection data in the frequency-space domain.

[0285] The second determining subunit 8022 is used to determine the first cross-correlation sequence based on the first frequency spatial domain data and the second frequency spatial domain data;

[0286] The third determining subunit 8023 is used to determine the first autocorrelation sequence, the second autocorrelation sequence, the second cross-correlation sequence, and the third cross-correlation sequence based on the first cross-correlation sequence, the spatiotemporal land detection data, the spatiotemporal water detection data, the second spatiotemporal data, and the fourth spatiotemporal data.

[0287] In some embodiments, the third determining subunit 8023 is configured to determine a first autocorrelation sequence based on a first cross-correlation sequence and spatiotemporal land detection data; determine a second autocorrelation sequence based on the first cross-correlation sequence and fourth spatiotemporal data; determine a second cross-correlation sequence based on the first cross-correlation sequence, spatiotemporal water detection data, and spatiotemporal land detection data; and determine a third cross-correlation sequence based on the first cross-correlation sequence, the second spatiotemporal data, and the fourth spatiotemporal data.

[0288] In some embodiments, see Figure 8 As shown, the third determining module 703 includes:

[0289] The acquisition unit 803 is used to acquire the iteration number and the iteration residual vector of the current iteration for any iteration of the characteristic equation of the calibration operator.

[0290] The third determining unit 804 is used to determine the iteration residual coefficients of the current iteration based on the iteration residual vector of the current iteration.

[0291] The fourth determining unit 805 is used to determine the target calibration operator vector based on the number of iterations and the iteration residual coefficients of the current iteration.

[0292] In some embodiments, see Figure 8 As shown, the fourth determining unit 805 includes:

[0293] The fourth determining subunit 8051 is used to determine the iteration correction vector for the next iteration based on the iteration residual coefficient of the current iteration, the iteration residual coefficient of the previous iteration, the iteration residual vector of the current iteration, the intermediate transition iteration vector of the previous iteration, the first tensor, and the iteration correction vector of the current iteration when the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold.

[0294] The fifth determining subunit 8052 is used to determine the target calibration operator vector based on the iteration number of the next iteration, the iteration correction vector of the current iteration, and the iteration correction vector of the next iteration, when the iteration number of the next iteration and the iteration number threshold are equal.

[0295] In some embodiments, see Figure 8 As shown, the device also includes:

[0296] The fourth determination module 705 is used to determine the target calibration operator vector based on the iteration correction vector of the current iteration and the iteration correction vector of the previous iteration, provided that the iteration residual coefficient of the current iteration is not greater than the iteration residual coefficient threshold.

[0297] In some embodiments, see Figure 8 As shown, the device also includes:

[0298] The fifth determining module 706 is used to determine the iteration residual vector for the next iteration based on the iteration residual vector of the current iteration, the iteration residual coefficient of the current iteration, the iteration residual coefficient of the previous iteration, the intermediate transition iteration vector of the previous iteration, and the first tensor when the number of iterations in the next iteration is less than the iteration number threshold, and then proceed with the next iteration.

[0299] In some embodiments, the fourth determining subunit 8051 is used to determine the iteration residual ratio coefficient of the current iteration based on the iteration residual coefficient of the current iteration and the iteration residual coefficient of the previous iteration when the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold; determine the intermediate transition iteration vector of the current iteration based on the iteration residual vector of the current iteration, the iteration residual ratio coefficient of the current iteration, and the intermediate transition iteration vector of the previous iteration; determine the intermediate transformation iteration vector of the current iteration based on the first tensor and the iteration residual vector of the current iteration; determine the iteration correction coefficient of the current iteration based on the intermediate transition iteration vector, the intermediate transformation iteration vector of the current iteration, and the iteration residual coefficient of the current iteration; and determine the iteration correction vector of the next iteration based on the iteration correction vector, the iteration correction coefficient of the current iteration, and the intermediate transition iteration vector of the current iteration.

[0300] In some embodiments, see Figure 8 As shown, the device also includes:

[0301] The sixth determination module 707 is used to determine the calibrated uplink wave field data and the calibrated downlink wave field data based on the calibrated spatiotemporal land inspection data and spatiotemporal water inspection data.

[0302] The plotting module 708 is used to plot the calibrated spatiotemporal land detection data, the calibrated uplink wave field data, and the calibrated downlink wave field data.

[0303] This application provides a calibration device for both water and land survey data. By processing water and land survey data in both the spatiotemporal domains, multiple autocorrelation and cross-correlation sequences in the spatiotemporal domain are determined, thus preparing for calibration from water survey data to land survey data. Then, a first and second tensor are determined based on these multiple sequences. Since solving the characteristic equation of the calibration operator constructed based on the first and second tensors involves matrix inversion, iterative solving can be used to obtain the target calibration operator vector, resulting in low computational cost, fast computation speed, and high computational accuracy. Finally, the land survey data in the spatiotemporal domain can be calibrated based on the target calibration operator vector. By simultaneously considering the temporal and spatial variations of water and land survey data, calibration accuracy is improved, enabling the separation of the upflow and downflow wave fields, eliminating interference from seawater hum and virtual reflection multiple waves, thereby improving exploration accuracy.

[0304] It should be noted that the above-described land and water inspection data calibration device, when running the application program, is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the land and water inspection data calibration device and the land and water inspection data calibration method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0305] This application also provides a computer-readable storage medium storing at least one computer program. This computer program is loaded and executed by a terminal's processor to implement the operations performed by the terminal in the land and water inspection data calibration method described above. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0306] This application also provides a computer program product or computer program, which includes computer program code stored in a computer-readable storage medium. The terminal's processor reads the computer program code from the computer-readable storage medium and executes the computer program code, causing the terminal to perform the land and water inspection data calibration method provided in the various optional implementations described above.

[0307] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0308] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for calibrating land and water inspection data, characterized in that, The method includes: Based on spatiotemporal water and land inspection data, a first cross-correlation sequence, a second cross-correlation sequence, a third cross-correlation sequence, a first autocorrelation sequence, and a second autocorrelation sequence are determined. Elements in the first cross-correlation sequence represent the correlation between water and land inspection data at the same spatial sampling point in the spatial domain. Elements in the second cross-correlation sequence represent the correlation between water and land inspection data at the same spatial sampling point in the time-space domain. Elements in the third cross-correlation sequence represent the correlation between water and land inspection data after removing direct waves at the same spatial sampling point in the time-space domain. Elements in the first autocorrelation sequence represent the correlation between land inspection data at the same spatial sampling point at different times in the time-space domain. Elements in the second autocorrelation sequence represent the correlation between land inspection data after removing direct waves at the same spatial sampling point at different times in the time-space domain. Based on the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence, a first tensor and a second tensor are determined. The elements in the first tensor are the difference sequences of the second autocorrelation sequence and the first autocorrelation sequence at the same spatial sampling point in the spatial domain, and the elements in the second tensor are the sum sequences of the second cross-correlation sequence and the third cross-correlation sequence at the same spatial sampling point in the spatial domain. The target calibration operator vector is obtained by iteratively solving the characteristic equation of the calibration operator constructed based on the first tensor and the second tensor. The spatiotemporal land detection data are calibrated based on the target calibration operator vector.

2. The method according to claim 1, characterized in that, The determination of the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence based on spatiotemporal water and land data includes: Based on the spatiotemporal water detection data and the spatiotemporal land detection data, first spatiotemporal data, second spatiotemporal data, third spatiotemporal data, and fourth spatiotemporal data are determined. The first spatiotemporal data is direct wave water detection data in the time-space domain, the second spatiotemporal data is water detection data after removing direct waves in the time-space domain, the third spatiotemporal data is direct wave land detection data in the time-space domain, and the fourth spatiotemporal data is land detection data after removing direct waves in the time-space domain. Based on the first spatiotemporal domain data, the second spatiotemporal domain data, the third spatiotemporal domain data, and the fourth spatiotemporal domain data, the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence are determined.

3. The method according to claim 2, characterized in that, The step of determining the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence based on the first spatiotemporal domain data, the second spatiotemporal domain data, the third spatiotemporal domain data, and the fourth spatiotemporal domain data includes: Based on the first spatiotemporal domain data and the third spatiotemporal domain data, first frequency spatial domain data and second frequency spatial domain data are determined. The first frequency spatial domain data is direct wave water detection data in the frequency-spatial domain, and the second frequency spatial domain data is direct wave land detection data in the frequency-spatial domain. Based on the first frequency spatial domain data and the second frequency spatial domain data, the first cross-correlation sequence is determined; Based on the first cross-correlation sequence, the spatiotemporal land detection data, the spatiotemporal water detection data, the second spatiotemporal data, and the fourth spatiotemporal data, the first autocorrelation sequence, the second autocorrelation sequence, the second cross-correlation sequence, and the third cross-correlation sequence are determined.

4. The method according to claim 3, characterized in that, The step of determining the first autocorrelation sequence, the second autocorrelation sequence, the second cross-correlation sequence, and the third cross-correlation sequence based on the first cross-correlation sequence, the spatiotemporal land detection data, the spatiotemporal water detection data, the second spatiotemporal data, and the fourth spatiotemporal data includes: Based on the first cross-correlation sequence and the spatiotemporal land detection data, the first autocorrelation sequence is determined; Based on the first cross-correlation sequence and the fourth spatiotemporal domain data, the second autocorrelation sequence is determined; The second cross-correlation sequence is determined based on the first cross-correlation sequence, the spatiotemporal water detection data, and the spatiotemporal land detection data; The third cross-correlation sequence is determined based on the first cross-correlation sequence, the second spatiotemporal domain data, and the fourth spatiotemporal domain data.

5. The method according to claim 1, characterized in that, The step of iteratively solving the characteristic equation of the calibration operator constructed based on the first tensor and the second tensor to obtain the target calibration operator vector includes: For any iteration of the characteristic equation of the calibration operator, obtain the iteration number and the iteration residual vector of the current iteration; Based on the iteration residual vector of this iteration, determine the iteration residual coefficients of this iteration; The target calibration operator vector is determined based on the number of iterations and the iteration residual coefficients of the current iteration.

6. The method according to claim 5, characterized in that, The step of determining the target calibration operator vector based on the number of iterations and the iteration residual coefficients of the current iteration includes: If the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold, the iteration correction vector for the next iteration is determined based on the iteration residual coefficient of the current iteration, the iteration residual coefficient of the previous iteration, the iteration residual vector of the current iteration, the intermediate transition iteration vector of the previous iteration, the first tensor, and the iteration correction vector of the current iteration. If the number of iterations in the next iteration is equal to the iteration threshold, the target calibration operator vector is determined based on the number of iterations in the next iteration, the iteration correction vector of the current iteration, and the iteration correction vector of the next iteration.

7. The method according to claim 6, characterized in that, The method further includes: If the iteration residual coefficient of the current iteration is not greater than the iteration residual coefficient threshold, the target calibration operator vector is determined based on the iteration correction vector of the current iteration and the iteration correction vector of the previous iteration.

8. The method according to claim 6, characterized in that, The method further includes: If the number of iterations in the next iteration is less than the iteration number threshold, the iteration residual vector for the next iteration is determined based on the iteration residual vector of the current iteration, the iteration residual coefficient of the current iteration, the iteration residual coefficient of the previous iteration, the intermediate transition iteration vector of the previous iteration, and the first tensor, and the next iteration is performed.

9. The method according to claim 6, characterized in that, When the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold, the iteration correction vector for the next iteration is determined based on the iteration residual coefficient of the current iteration, the iteration residual coefficient of the previous iteration, the iteration residual vector of the current iteration, the intermediate transition iteration vector of the previous iteration, the first tensor, and the iteration correction vector of the current iteration, including: If the iteration residual coefficient of the current iteration is greater than the iteration residual coefficient threshold, the iteration residual ratio coefficient of the current iteration is determined based on the iteration residual coefficient of the current iteration and the iteration residual coefficient of the previous iteration. Based on the iteration residual vector of the current iteration, the iteration residual ratio coefficient of the current iteration, and the intermediate transition iteration vector of the previous iteration, the intermediate transition iteration vector of the current iteration is determined. Based on the first tensor and the iteration residual vector of the current iteration, determine the intermediate transformation iteration vector of the current iteration; Based on the intermediate transition iteration vector, the intermediate transformation iteration vector, and the iteration residual coefficient of the current iteration, the iteration correction coefficient of the current iteration is determined. Based on the iteration correction vector of the current iteration, the iteration correction coefficient of the current iteration, and the intermediate transition iteration vector of the current iteration, the iteration correction vector for the next iteration is determined.

10. The method according to claim 1, characterized in that, The method further includes: Based on the calibrated spatiotemporal land inspection data and the spatiotemporal water inspection data, the calibrated uplink wave field data and the calibrated downlink wave field data are determined. The calibrated spatiotemporal land detection data, the calibrated uplink wave field data, and the calibrated downlink wave field data are plotted.

11. A device for calibrating water and land inspection data, characterized in that, The device includes: The first determining module is used to determine a first cross-correlation sequence, a second cross-correlation sequence, a third cross-correlation sequence, a first autocorrelation sequence, and a second autocorrelation sequence based on spatiotemporal water and land sampling data. Elements in the first cross-correlation sequence represent the correlation between water and land sampling data at the same spatial sampling point in the spatial domain. Elements in the second cross-correlation sequence represent the correlation between water and land sampling data at the same spatial sampling point in the time-space domain. Elements in the third cross-correlation sequence represent the correlation between water and land sampling data after removing direct waves at the same spatial sampling point in the time-space domain. Elements in the first autocorrelation sequence represent the correlation between land sampling data at the same spatial sampling point at different times in the time-space domain. Elements in the second autocorrelation sequence represent the correlation between land sampling data after removing direct waves at the same spatial sampling point at different times in the time-space domain. The second determining module is used to determine a first tensor and a second tensor based on the first cross-correlation sequence, the second cross-correlation sequence, the third cross-correlation sequence, the first autocorrelation sequence, and the second autocorrelation sequence. The elements in the first tensor are the difference sequences of the second autocorrelation sequence and the first autocorrelation sequence at the same spatial sampling point in the spatial domain, and the elements in the second tensor are the sum sequences of the second cross-correlation sequence and the third cross-correlation sequence at the same spatial sampling point in the spatial domain. The third determining module is used to obtain the target calibration operator vector by iteratively solving the characteristic equation of the calibration operator constructed based on the first tensor and the second tensor. The calibration module is used to calibrate the spatiotemporal land detection data based on the target calibration operator vector.

12. A terminal, characterized in that, The terminal includes a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed as the aquatic and land inspection data calibration method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store at least one computer program for executing the land and water inspection data calibration method according to any one of claims 1 to 10.

14. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for calibrating land and water inspection data as described in any one of claims 1 to 10.

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