Method, device and equipment for identifying earth parameters of vibroseis

Abstract

The invention relates to the technical field of production and manufacturing, and discloses a vibroseis earth parameter identification method, device and equipment, and the method comprises the steps: obtaining a function model built based on the system parameters of a vibroseis system; on the basis of the function model, parameter vectors needing to be identified are determined through an index window function and by means of least square; solving process parameters of parameter vectors on the basis of the collected acceleration of the heavy hammer and the acceleration of the flat plate, and determining the parameter vector at the current moment; and based on the parameter vector at the current moment, identifying the earth damping coefficient and the earth elastic coefficient. According to the scheme of the invention, on the basis of the function model, the exponential window function is introduced, and the forgetting factor adapting to the time variation is added for the least square, so that the process parameters for solving through the acceleration of the heavy hammer and the acceleration of the flat plate can accurately reflect the system condition at the current moment, thereby reflecting the time variation of the data, and improving the accuracy of the calculation. And a guarantee is provided for the accuracy of subsequent data identification.

Description

Technical Field

[0001] This invention relates to the field of controlled seismic source technology, specifically to a method, apparatus, and equipment for identifying geodetic parameters of a controlled seismic source. Background Technology

[0002] In petroleum geological exploration, precise control of the vibration output behavior of a controllable seismic source requires utilizing the ground damping coefficient and ground elastic coefficient, which are coupled between the source and the ground. The physical process of this coupling between the source and the ground is complex, being time-varying and nonlinear, making it difficult to obtain model parameters through theoretical analysis. While there are relatively accurate models using mechanistic methods to establish the variation of ground damping and elastic coefficients with vibration frequency, these models are only applicable to simple frequency-sweeping vibration modes. Furthermore, the ground environment is diverse; for example, the ground type where the source is located may be concrete, sandy, or swampy. Vibration frequencies also exhibit time-varying diversity; for example, in a linear frequency sweep mode, the vibration frequency changes linearly with time, while in a logarithmic frequency sweep mode, the vibration frequency changes logarithmically with time. Simultaneously, the source's operating mode may change; for example, the source may operate in pseudo-random or pulsed vibration modes, in which case the vibration frequency lacks a clear relationship with time, thus limiting the application of accurate mechanistic models.

[0003] Due to the various reasons mentioned above, the earth damping coefficient and the earth equivalent elastic coefficient change over time, and there is no explicit function to describe this relationship. Therefore, in order to improve the vibration effect of the controllable source and enhance the system's adaptability, it is necessary to identify and correct the damping and elastic coefficients of the controllable source coupled with the earth in real time.

[0004] Given a known model structure, parameter identification determines model parameters by collecting data. The most commonly used method for constructing a controlled-source-earth coupling model is the least squares method, which can be used to identify the parameters of this model. Recursive least squares is an easy-to-understand and master identification method, and its implementation is relatively simple. In most cases, recursive least squares can provide parameter identification results with accurate statistical properties. However, in parameter identification, recursive least squares is an algorithm with an infinite memory length. For controlled-source-earth coupling systems, the amount of old data increases during the recursive calculation, leading to the recursive results failing to accurately reflect the characteristics of new data.

[0005] Therefore, developing a time-varying scheme with good identification capabilities is an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the present invention provides a method, apparatus and equipment for identifying controllable source geodetic parameters to solve the technical problems in related technologies that cannot adapt to time-varying conditions and cannot accurately identify the geodetic damping coefficient and geodetic elastic coefficient.

[0007] In a first aspect, the present invention provides a method for identifying geodetic parameters of a controllable source, the method comprising: acquiring a function model established based on system parameters of a controllable source system; determining the parameter vector to be identified based on the function model by using an exponential window function and least squares; solving for the process parameters of the parameter vector based on the collected weight acceleration and plate acceleration to determine the parameter vector at the current moment; and identifying the geodetic damping coefficient and geodetic elastic coefficient based on the parameter vector at the current moment.

[0008] In conjunction with the first aspect, in one possible implementation of the first aspect, the parameter vector to be identified is determined by using an exponential window function and least squares, including: converting the function model into a difference equation using bilinear discretization; determining the least squares iterative formula based on the difference equation and least squares; and determining the parameter vector to be identified by introducing an exponential window function based on the least squares iterative formula.

[0009] In conjunction with the first aspect, in one possible implementation of the first aspect, the process parameters of the parameter vector are solved based on the collected acceleration of the hammer and the acceleration of the plate to determine the parameter vector at the current moment, including: determining the first initial value of the parameter vector, the second initial value of the covariance matrix, the measurement vector, and the measurement transition matrix based on the collected acceleration of the hammer and the plate; and determining the parameter vector at the current moment based on the first initial value, the second initial value, the measurement vector, and the measurement transition matrix.

[0010] In conjunction with the first aspect, in one possible implementation of the first aspect, determining the parameter vector at the current moment based on the first initial value, the second initial value, the measurement vector, and the measurement transition matrix includes: determining the identification error corresponding to the current moment based on the first initial value, the measurement vector, and the measurement transition matrix; determining the forgetting factor corresponding to the exponential window function based on the identification error; determining the gain matrix based on the forgetting factor and the second initial value; and determining the parameter vector at the current moment based on the gain matrix and the forgetting factor.

[0011] In conjunction with the first aspect, in one possible implementation of the first aspect, the forgetting factor corresponding to the exponential window function is determined based on the identification error, including: determining the calculation formula of the forgetting factor based on the exponential window function; and determining the forgetting factor using the calculation formula based on a preset minimum value of the forgetting factor, the error gain, and the identification error.

[0012] In conjunction with the first aspect, one possible implementation of the first aspect further includes: determining the covariance at the current time using the parameter vector based on the forgetting factor and the second initial value; and updating the first initial value and the second initial value at the next time step based on the parameter vector at the current time step and the covariance at the current time step, respectively.

[0013] In conjunction with the first aspect, in one possible implementation of the first aspect, based on the acquired hammer acceleration and plate acceleration, the first initial value of the parameter vector, the second initial value of the covariance matrix, the measurement vector, and the measurement transition matrix are determined, including: based on the pre-acquired hammer acceleration and plate acceleration, using batch least squares, the first initial value of the parameter vector and the second initial value of the covariance matrix are determined; based on the real-time acquired hammer acceleration and plate acceleration and the values ​​corresponding to the previous several acquisition cycles, the measurement vector and the measurement transition matrix are determined.

[0014] In conjunction with the first aspect, in one possible implementation of the first aspect, identifying the earth damping coefficient and the earth elastic coefficient based on the parameter vector at the current moment includes: identifying the earth damping coefficient and the earth elastic coefficient based on the parameter vector at the current moment using the following expression:

[0015] in, S p Represents the area of ​​the flat plate. G v Indicates the earth damping coefficient. G s Represents the earth's elastic coefficient. M g Indicates equivalent earth mass. M p Indicates the quality of the flat plate. a This represents the equivalent earth damping coefficient. b This represents the equivalent earth elasticity coefficient. a 1. a 2 represents the parameter that needs to be identified, and T represents the discrete sampling interval.

[0016] Secondly, the present invention provides a device for identifying controllable source geodetic parameters, the device comprising: The function model building module is used to obtain the function model based on the system parameters of the controllable seismic source system. The parameter vector determination module is used to determine the parameter vector to be identified based on the function model, through an exponential window function and using least squares. The parameter vector solving module is used to solve the process parameters of the parameter vector based on the collected acceleration of the hammer and the plate, and to determine the parameter vector at the current moment. The system identification module is used to identify the earth damping coefficient and the earth elastic coefficient based on the parameter vector at the current time.

[0017] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for identifying controllable source geodetic parameters as described in the first aspect or any corresponding embodiment.

[0018] The technical solution of this invention has the following advantages: This invention provides a method, apparatus, and device for identifying geodetic parameters of a controllable source seismic source. The method establishes a controllable source system, uses system parameters to build a model function, and then introduces an exponential window function and uses least squares to determine the parameter vector to be identified. By collecting the accelerations of a weight and a plate, the process parameters are solved to determine the parameter vector at the current moment, thus identifying the geodetic damping coefficient and geoelastic coefficient at that moment. In this process, using the established controllable source system and the known system parameters in the model structure, a function model of the system is established. Based on this function model, derivations are made. By introducing an exponential window function, a time-varying forgetting factor is added to the least squares method, ensuring that the process parameters obtained from the collected weight and plate accelerations accurately reflect the data at the current moment, thus demonstrating the time-varying nature of the data and ensuring the accuracy of subsequent data identification. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a method for identifying controllable seismic source geodetic parameters according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a controllable source weight plate coupled with the earth according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the original acquisition and filtered acceleration of the weight and the plate provided according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the changes of genetic factors over time according to an embodiment of the present invention; Figure 5This is a schematic diagram showing the comparison of the identified earth damping coefficient before and after passing through a filter, according to an embodiment of the present invention. Figure 6 This is a schematic diagram comparing the identified earth elasticity coefficient before and after filtering according to an embodiment of the present invention; Figure 7 This is a structural block diagram of a controllable seismic source geodetic parameter identification device provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] According to an embodiment of the present invention, an embodiment of a method for identifying geodetic parameters of a controllable seismic source is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] This embodiment provides a method for identifying geodetic parameters of a controllable seismic source, such as... Figure 1 As shown, the method includes the following steps: S101. Obtain the function model established based on the system parameters of the controllable seismic source system.

[0024] Specifically, the established controllable seismic source system is as follows: Figure 2 As shown, where, RM Indicates a heavy hammer, BP Indicates flat plate, k Represents the earth's elastic coefficient, d Indicates the earth damping coefficient, Fo Indicates the hydraulic pressure acting on the weight and the plate. Ft This represents the ground reaction force. Since the model structure is known and the relationships between the parameters in the system are known, a continuous geodetic transfer function model coupling a controllable source to the earth is established, which is in the form of a second-order transfer function of the weight acceleration and the plate acceleration.

[0025] Specifically, the establishment and derivation of the function model are as described in the following formula: First, the equivalent differential equation model is expressed by formula (1): (1) in, G F Indicates the earth's forces. M g Indicates equivalent earth mass. M m Indicates the mass of the hammer. M p Indicates the quality of the flat plate. S p Represents the area of ​​the flat plate. G v Indicates the earth damping coefficient. G s Represents the earth's elastic coefficient. x m Indicates the displacement of the weight. x p Indicates the displacement of the plate. t Indicates time.

[0026] Secondly, formula (1) can be rewritten as a transfer function using formula (2): (2) Where s represents the Laplace operator.

[0027] Finally, since the independent displacements of the hammer and the plate cannot be measured, the measurable data are the accelerations of the hammer and the plate. Furthermore, since displacement and acceleration are in phase, and acceleration is the second derivative of displacement, the transfer functions of the hammer acceleration and the plate acceleration are the same as those of the hammer displacement and the plate displacement, and can be expressed by formula (3): (3) in, k 1 represents the equivalent mass ratio. a This represents the equivalent earth damping coefficient. b This represents the equivalent earth elasticity coefficient. A p Indicates the acceleration of the flat plate. A m This indicates the acceleration of the hammer.

[0028] Specifically, in formula (3), , , , k 1 is a constant value, when M g When it is zero, k 1 simplified to It should be understood that, since the model structure is known, the parameters involved in the above formulas, such as the mass of the counterweight, the mass of the plate, and the area of ​​the plate, are all system parameters and are known data.

[0029] S102. Based on the function model, the parameter vector to be identified is determined by using the least squares function through the exponential window function.

[0030] Specifically, based on the function model, the parameter vector to be identified is determined by using the least squares method through an exponential window function. This involves transforming the model function and combining it with the single-input, single-output coupling of the controllable source and the earth. The parameter vector to be identified is determined by the least squares method. An exponential window function is then introduced to add weights to the newly measured data and the original measured data. By using these added weights, the identification of the parameter vector focuses more on the newly measured data, thus clarifying the parameter vector to be identified. This is expressed as a recursive formula.

[0031] S103. Based on the collected acceleration of the hammer and the plate, solve for the process parameters of the parameter vector to determine the parameter vector at the current moment.

[0032] Specifically, the acceleration of the hammer and the plate are acquired using accelerometers. Based on the acquired accelerations of the hammer and the plate, the process parameters of the parameter vector are solved. The parameter vector at the current moment refers to the accelerations of the hammer and the plate acquired by the accelerometers. The parameter vector that needs to be identified is solved to determine the intermediate parameters in the recursive formula, and then the parameter vector at the current moment is solved using the determined intermediate parameters.

[0033] S104. Based on the parameter vector at the current moment, identify the earth damping coefficient and the earth elastic coefficient.

[0034] Specifically, identifying the earth damping coefficient and earth elastic coefficient based on the parameter vector at the current moment means determining the functional expressions of the earth damping coefficient and earth elastic coefficient through the model function, and determining the earth damping coefficient and earth elastic coefficient corresponding to the parameter vector at the current moment by substituting the parameter vector at the current moment into the functional expression.

[0035] This invention provides a method, apparatus, and device for identifying geodetic parameters of a controllable source seismic source. The method establishes a controllable source system, uses system parameters to build a model function, and then introduces an exponential window function and uses least squares to determine the parameter vector to be identified. By collecting the accelerations of a weight and a plate, the process parameters are solved to determine the parameter vector at the current moment, thus identifying the geodetic damping coefficient and geoelastic coefficient at that moment. In this process, using the established controllable source system and the known system parameters in the model structure, a function model of the system is established. Based on this function model, derivations are made. By introducing an exponential window function, a time-varying forgetting factor is added to the least squares method, ensuring that the process parameters obtained from the collected weight and plate accelerations accurately reflect the data at the current moment, thus demonstrating the time-varying nature of the data and ensuring the accuracy of subsequent data identification.

[0036] In one alternative implementation, based on a function model, the parameter vector to be identified is determined using an exponential window function and least squares, including: By using bilinear discretization, the function model is transformed into a difference equation; based on the difference equation, the least squares iterative formula is determined using least squares; based on the least squares iterative formula, the parameter vector to be identified is determined by introducing an exponential window function.

[0037] Specifically, the bilinear discrete expression is as follows: Where T represents the discrete sampling interval. The above formula (3) can be converted into a difference equation using the following formula: First, the continuous form transfer function of the above formula (3) is subjected to z-transformation using formula (4): (4) in, a 1. a 2 indicates the parameter that needs to be identified. a 1= bT 2 , a 2=2 aT .

[0038] Secondly, by deriving formula (4) from formulas (5)-(7), the formula containing... a 1. a Merge the related items in 2: (5)

[0039] (6)

[0040] (7) Then, using formula (8), the above formula (7) can be rewritten as a vector equation. z ( k )= h ( k ) θ + v ( k (in the form of) (8) in, z ( k ) represents the measurement vector. h ( k ) represents the measurement transition matrix, v ( k ) represents the noise vector. θ This represents the set of parameter vectors that need to be identified.

[0041] Specifically, since the controlled seismic source-earth coupling system is a single-input, single-output system, the difference equation of this system can be written as: z ( k )= h ( k ) θ + v ( k Formula (8) can be described by formula (9) as a difference equation conforming to a controllable source coupled with the earth: (9) Where u and y represent the sets of observed quantities, and the observed quantities are the acceleration of the weight and the acceleration of the plate.

[0042] Specifically, the least squares iterative formula is expressed by the following formula: When there are multiple difference equations at discrete time points (from discrete time point 1 to discrete time point m), the combination of the difference equations at discrete time points 1 to m in the above formula (9) can be expressed by matrix expression (10): (10) in, , , , .

[0043] in, Z m Represents the set of measurement vectors. H m Represents the set of measurement transition matrices.V m This represents the set of noise vectors.

[0044] The purpose of the least squares method is to find a set of parameters. θ Best estimate Make each measurement Z i With measurement estimation The difference Minimum, characterized as: The least squares estimate of the parameter vector set to be identified is obtained by differentiating this equation using the extreme value theorem, and is expressed by formula (11): (11) in, express θ The estimate.

[0045] Specifically, formula (11) is a batch least squares estimation, which requires a batch of data to start the calculation and cannot be calculated in real time. Therefore, it is necessary to use recursive least squares pairs for real-time calculation. When new data arrives, the recursive least squares pairs iteratively calculate the new parameter set in real time. θ The recursive least squares iterative formulas can be expressed by formulas (12)-(15): (12) (13) (14) (15) in, This represents the parameter set estimate from the previous time step. This represents the parameter set estimate for the next time step. z ( m +1) represents the measurement value at the next moment. This represents the prediction of the measurement at a later time, and the difference between the two. en ( m +1) represents the prediction error. K m+1 Let I represent the gain matrix and I represent the current coefficient.

[0046] It should be understood that the gain matrix is ​​a correction factor that integrates the parameter set estimation and prediction errors from the previous time step. The recursive least squares are based on the covariance matrix from the previous step. P m The gain matrix is ​​calculated from the measurement data at the next time step, and then... Calculate Simultaneously calculate the covariance matrix at the next time step. P m+1This is for use in the next iteration.

[0047] Specifically, for time-invariant systems, the recursive least squares method described above can obtain stable parameter set estimates. However, for time-varying systems like controlled-source geodetic coupling, data saturation can easily occur if new and old data are given equal importance, meaning new measurements are overwhelmed by older ones. To address this issue, an exponential window function is introduced. This function adds a forgetting factor to the acquired data; that is, based on the least squares iteration, the parameter vectors that need to be identified are determined by introducing the exponential window function. The exponential window function is expressed as follows: w ( k )= λ m-k ,when k When smaller, w ( k The weight is relatively small, when k When it is large, w ( k The weights are relatively large, so an exponential window function is introduced to increase the weight of new measurement data and decrease the weight of old measurement data, such as... Figure 4 As shown, this exemplifies the changes of genetic factors over time. The recursive formulas for the parameter vector after introducing the exponential window function are expressed by equations (16)-(19): (16) (17) (18) (19) in, λ It is a positive number less than 1, usually ranging from 0.9 to 0.99. When the parameter changes drastically, λ We can take a smaller value, which means that the old data has a lower weight and the new data has a higher weight, but... λ The smaller the value, the greater the noise interference and the larger the estimation error variance. Therefore, when the noise level is high, it should be... λ Take a larger value. This is to adaptively adjust based on the current identification error. λ The value of is expressed by formula (20) to represent the forgetting factor. λ : (20) in, λ min Represents the minimum forgetting factor. γ Indicates the error gain. floor This indicates the downward proof operator. en ( m+1) represents the identification error at the current moment, i.e., the prediction error.

[0048] In one optional implementation, based on the collected accelerations of the weight and the plate, the process parameters of the parameter vector are solved to determine the parameter vector at the current moment, including: Based on the collected accelerations of the hammer and the plate, the first initial value of the parameter vector, the second initial value of the covariance matrix, the measurement vector, and the measurement transition matrix are determined; based on the first initial value, the second initial value, the measurement vector, and the measurement transition matrix, the parameter vector at the current moment is determined.

[0049] Specifically, based on the collected acceleration of the hammer and the plate, the process parameters of the parameter vector are solved. The process of determining the parameter vector at the current moment is to solve the position parameters in the above formula using the collected acceleration of the hammer and the plate, that is, to realize the process parameter solution. The process parameters are determined by using the real-time collected acceleration of the hammer and the plate to determine the parameter vector at the current moment.

[0050] Specifically, the acquired hammer acceleration and plate acceleration include real-time hammer acceleration and plate acceleration acquired by an accelerometer, and pre-acquired hammer acceleration and plate acceleration. The real-time acquired hammer acceleration and plate acceleration are used to identify the parameter vector at the current moment, while the pre-acquired hammer acceleration and plate acceleration are used to determine the first and second initial values ​​through offline calculation, providing initial values ​​for intermediate data during parameter vector identification at the current moment. It should be understood that the hammer acceleration and plate acceleration acquired by the accelerometer need to be filtered. Typically, the hammer acceleration and plate acceleration of the controllable source are acquired in real-time during a sampling period T. A high-pass IIR (Infinite Impulse Response) filter is used to filter out low-frequency noise from the hammer acceleration and plate acceleration, and then the data is input into a low-pass FIR (Finite Impulse Response) filter to filter out high-frequency noise from the hammer acceleration and plate acceleration, such as... Figure 3 As shown, an exemplary comparison of the original acquisition and the filtered acceleration of the weight and the flat plate is presented, thereby avoiding the unavoidable acquisition noise in actual controlled source system applications.

[0051] In one optional implementation, the parameter vector at the current time is determined based on the first initial value, the second initial value, the measurement vector, and the measurement transition matrix, including: Based on the first initial value, the measurement vector, and the measurement transition matrix, determine the identification error corresponding to the current time step; based on the identification error, determine the forgetting factor corresponding to the exponential window function; based on the forgetting factor and the second initial value, determine the gain matrix; based on the gain matrix and the forgetting factor, determine the parameter vector at the current time step.

[0052] Specifically, based on the first initial value, the measurement vector, and the measurement transfer matrix, the process of determining the identification error at the current moment is as follows: by substituting the determined first initial value, the measurement vector, and the measurement transfer matrix into the above formula (16), the identification error at the current moment is determined. en ( m +1). It should be understood that the determination of the first initial value, the measurement vector, and the measurement transition matrix are detailed in the following description and will not be repeated here.

[0053] Specifically, based on the identification error, determining the forgetting factor corresponding to the exponential window function refers to using the exponential window function ( w ( k )= λ m-k The formula for calculating the forgetting factor is determined, and the forgetting factor is determined by using the set minimum value of the forgetting factor, error gain and identification error.

[0054] Specifically, the process of determining the gain matrix based on the forgetting factor and the second initial value is the process of determining the forgetting factor. λ and covariance matrix P m After obtaining the second initial value, the measurement transition matrix is ​​used. h ( k The gain matrix is ​​determined using the formula (18) above. K m+1 It should be understood that the determination of the second initial value is detailed in the following description and will not be repeated here.

[0055] In one alternative implementation, the forgetting factor corresponding to the exponential window function is determined based on the identification error, including: Based on the exponential window function, the calculation formula for the forgetting factor is determined; based on the preset minimum value of the forgetting factor, error gain, and identification error, the forgetting factor is determined using the calculation formula.

[0056] Specifically, based on the exponential window function, the calculation formula for the forgetting factor is the above formula (20). Based on the preset minimum forgetting factor, error gain, and identification error, the calculation formula is used to determine the forgetting factor, which means using the set minimum forgetting factor. λ min and error gain γ After determining the identification error at the current moment en (m Based on +1), the corresponding forgetting factor is determined using the above formula (20). λ This results in lower weights for older data and higher weights for newer data, while avoiding interference from noise.

[0057] In one alternative implementation, the method further includes: Based on the forgetting factor and the second initial value, the covariance matrix at the current time is determined using the parameter vector; based on the parameter vector at the current time and the covariance matrix at the current time, the first initial value and the second initial value at the next time are updated respectively.

[0058] Specifically, determining the covariance matrix at the current time based on the forgetting factor and the second initial value, using the parameter vector, means determining the forgetting factor... λ and the second initial value P m Substitute into the above formula (19) to determine the covariance matrix at the next time step. P m+1 .

[0059] Specifically, updating the first and second initial values ​​based on the parameter vector and covariance matrix at the current time means that after determining the parameter vector and covariance matrix at the current time, recursively applying least squares based on the previous covariance matrix... P m The gain matrix is ​​calculated from the measurement data at the next time step, and then... Calculate Simultaneously calculate the covariance matrix at the next time step. P m+1 This initial value is used for the next iteration. That is, when calculating the parameter vector corresponding to the current time step in the next iteration, the first initial value corresponds to the parameter vector at the current time step, and the second initial value corresponds to the covariance matrix at the current time step.

[0060] In one optional implementation, based on the collected accelerations of the hammer and the plate, determining a first initial value for the parameter vector, a second initial value for the covariance matrix, a measurement vector, and a measurement transfer matrix includes: Based on the pre-acquired hammer acceleration and plate acceleration, the first initial value of the parameter vector and the second initial value of the covariance matrix are determined using batch least squares; based on the real-time acquired hammer acceleration and plate acceleration and the values ​​corresponding to the previous several acquisition cycles, the measurement vector and measurement transfer matrix are determined.

[0061] Specifically, formulas (21) and (22) represent the first initial value of the parameter vector and the second initial value of the covariance matrix, respectively: (twenty one) (twenty two) in, This represents the first initial value of the parameter vector. P 0 represents the second initial value of the covariance matrix.

[0062] Specifically, determining the measurement vector and measurement transfer matrix based on the real-time collected accelerations of the hammer and the plate, and by using the sampling period, refers to using the sampling period... T Real-time collected hammer acceleration Am ( k ) and flat plate acceleration Ap ( k After IIR and FIR filtering, the filtered acceleration of the hammer is obtained. Am + ( k ) and flat plate acceleration Ap + ( k ), and so on, thus utilizing the cycle. T Measurement value after the first filtering Am + ( k -1) Ap + ( k -1), the measured values ​​after the first two filters Am + ( k -2) Ap + ( k -2), substitute into the above formula (8) to determine the measurement vector respectively. z ( k ) and measurement transition matrix h ( k ).

[0063] In one alternative implementation, identifying the earth damping coefficient and earth elastic coefficient based on the parameter vector at the current moment includes: Based on the parameter vector at the current moment, the earth damping coefficient and earth elastic coefficient are identified using the following expression.

[0064] Specifically, the expressions for the earth damping coefficient and the earth elastic coefficient are expressed by formula (23): (twenty three) in, .

[0065] In one alternative implementation, the method further includes: The identified earth damping coefficient and earth elastic coefficient are subjected to low-pass filtering, and the filtered earth damping coefficient and earth elastic coefficient are then subjected to interval limiting.

[0066] Specifically, such as Figure 5 , Figure 6 As shown, low-pass filtering of the identified earth damping coefficient and earth elastic coefficient refers to inputting the determined earth damping coefficient and earth elastic coefficient into the FIR filter to determine the filtered earth damping coefficient and earth elastic coefficient, thereby preventing drastic parameter changes. When the collected weight acceleration and plate acceleration data fluctuate significantly, the identification results will be greatly affected. Both ordinary recursive least squares and recursive least squares with a forgetting factor have weak noise suppression capabilities for the input collected data. Drastic parameter changes are essentially due to an excessive number of high-frequency components. To prevent drastic changes in the identified earth damping coefficient and earth elastic coefficient from affecting subsequent control effects, a low-pass filter can be used to eliminate high-frequency components and retain low-frequency components that change slowly, achieving the goal of gradual parameter changes.

[0067] Specifically, limiting the range of the filtered earth damping coefficient and earth elastic coefficient refers to limiting the range of these two coefficients to keep them within a set reasonable range. Because the collected weight acceleration and plate acceleration data may contain abnormal data, the identification results may exceed the reasonable range, such as negative values ​​or extreme values. To prevent these abnormal data from propagating to subsequent modules and causing more serious consequences, it is necessary to set a minimum value G for the earth damping coefficient and earth elastic coefficient. vmin G smin and maximum value G vmax G smax This means setting a reasonable range to limit the range of the earth damping coefficient and the earth elastic coefficient. When the range is greater than the maximum value, the earth damping coefficient and the earth elastic coefficient take the set maximum value. When the range is less than the minimum value, the earth damping coefficient and the earth elastic coefficient take the set minimum value.

[0068] This embodiment also provides a device for identifying controllable source geodetic parameters. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0069] This embodiment provides a device for identifying controllable source geodetic parameters, such as... Figure 7 As shown, it includes: The function model establishment module 201 is used to obtain a function model based on the system parameters of the controllable seismic source system. For details, please refer to the description of step S101 in the above embodiments, which will not be repeated here.

[0070] The parameter vector determination module 202 is used to determine the parameter vector to be identified based on the function model, through an exponential window function and using least squares. For details, please refer to the description of step S102 in the above embodiments, which will not be repeated here.

[0071] The parameter vector solving module 203 is used to solve for the process parameters of the parameter vector based on the collected acceleration of the hammer and the plate, and to determine the parameter vector at the current moment. For details, please refer to the relevant description of step S103 in the above embodiments, which will not be repeated here.

[0072] The system identification module 204 is used to identify the earth damping coefficient and the earth elastic coefficient based on the parameter vector at the current time. For details, please refer to the description of step S104 in the above embodiments, which will not be repeated here.

[0073] In this embodiment, the controllable source geodetic parameter identification device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0074] This invention also provides a computer device having the above-described features. Figure 7 The device shown is for identifying geodetic parameters of a controllable seismic source. Please refer to [link / reference]. Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8 As shown, the computer device includes one or more processors 301, memory 302, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take processor 301 as an example.

[0075] Processor 301 may be a central processing unit, a network processor, or a combination thereof. Processor 301 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0076] The memory 302 stores instructions executable by at least one processor 301 to cause the at least one processor 301 to perform the method shown in the above embodiments.

[0077] Memory 302 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, memory 302 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 302 may optionally include memory remotely located relative to processor 301, and this remote memory may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0078] The memory 302 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 302 may also include combinations of the above types of memory. The computer device also includes a communication interface 303 for communicating with other devices or communication networks.

[0079] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0080] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for identifying geodetic parameters of a controllable seismic source, characterized in that, The method includes: Obtain the function model established based on the system parameters of the controllable seismic source system; Based on the aforementioned function model, the parameter vector to be identified is determined using the least squares method through the exponential window function. Based on the collected acceleration of the hammer and the plate, the process parameters of the parameter vector are solved to determine the parameter vector at the current moment. Based on the parameter vector at the current moment, the earth damping coefficient and the earth elastic coefficient are identified.

2. The method according to claim 1, characterized in that, Based on the function model, the parameter vector to be identified is determined using the least squares method through an exponential window function, including: The function model is transformed into a difference equation using bilinear discretization. Based on the difference equation, the least squares iterative formula is determined using least squares. Based on the aforementioned least squares iterative formula, the parameter vector that needs to be identified is determined by introducing an exponential window function.

3. The method according to claim 1, characterized in that, The process of solving for the parameter vector based on the collected accelerations of the hammer and the plate to determine the parameter vector at the current moment includes: Based on the collected acceleration of the hammer and the plate, the first initial value of the parameter vector, the second initial value of the covariance matrix, the measurement vector, and the measurement transfer matrix are determined. Based on the first initial value, the second initial value, the measurement vector, and the measurement transition matrix, the parameter vector at the current moment is determined.

4. The method according to claim 3, characterized in that, Determining the parameter vector at the current moment based on the first initial value, the second initial value, the measurement vector, and the measurement transition matrix includes: Based on the first initial value, the measurement vector, and the measurement transition matrix, determine the identification error corresponding to the current moment; Based on the identification error, determine the forgetting factor corresponding to the exponential window function; Based on the forgetting factor and the second initial value, determine the gain matrix; The parameter vector at the current time is determined based on the gain matrix and the forgetting factor.

5. The method according to claim 4, characterized in that, The step of determining the forgetting factor corresponding to the exponential window function based on the identification error includes: The calculation formula for the forgetting factor is determined based on the exponential window function; Based on the preset minimum forgetting factor, error gain, and identification error, the forgetting factor is determined using the calculation formula.

6. The method according to claim 4, characterized in that, The method further includes: Based on the forgetting factor and the second initial value, the covariance matrix at the current time is determined using the parameter vector; Based on the parameter vector and the covariance matrix at the current time, update the first initial value and the second initial value at the next time step, respectively.

7. The method according to claim 3, characterized in that, The determination of the first initial value of the parameter vector, the second initial value of the covariance matrix, the measurement vector, and the measurement transfer matrix based on the collected acceleration of the hammer and the plate includes: Based on the pre-acquired acceleration of the hammer and the plate, the first initial value of the parameter vector and the second initial value of the covariance matrix are determined using batch least squares. Based on the real-time acquisition of the acceleration of the hammer and the plate, as well as the values ​​corresponding to the previous acquisition cycles, the measurement vector and the measurement transfer matrix are determined.

8. The method according to claim 1, characterized in that, The process of identifying the earth damping coefficient and the earth elastic coefficient based on the parameter vector at the current moment includes: Based on the parameter vector at the current moment, the earth damping coefficient and the earth elastic coefficient are identified using the following expression: in, S p Represents the area of ​​the flat plate. G v Indicates the earth damping coefficient. G s Represents the earth's elastic coefficient. M g Indicates equivalent earth mass. M p Indicates the quality of the flat plate. a This represents the equivalent earth damping coefficient. b This represents the equivalent earth elasticity coefficient. a 1. a 2 represents the parameter that needs to be identified, and T represents the discrete sampling interval.

9. A device for identifying controllable source geodetic parameters, characterized in that, The device includes: The function model building module is used to obtain the function model based on the system parameters of the controllable seismic source system. The parameter vector determination module is used to determine the parameter vector to be identified based on the function model, through an exponential window function and using least squares. The parameter vector solving module is used to solve the process parameters of the parameter vector based on the collected acceleration of the hammer and the plate, and to determine the parameter vector at the current moment. The system identification module is used to identify the earth damping coefficient and the earth elastic coefficient based on the parameter vector at the current time.

10. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for identifying controllable source geodetic parameters according to any one of claims 1 to 8.

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