Methods, devices and systems for inverting frequency response parameters of seismic signal acquisition equipment

Abstract

This invention discloses a method, apparatus, and system for inverting frequency response parameters of seismic signal acquisition equipment, relating to the field of geophysical exploration technology. The method is executed by a computer device within the seismic signal acquisition equipment frequency response parameter inversion system. After receiving seismic wavefield response signals synchronously acquired by the target node equipment and the standard node equipment, preprocessing and transfer function estimation are performed sequentially to obtain the relative transfer function of the equipment. Then, a nonlinear optimization algorithm is used to optimize the model parameters of the mathematical model of the frequency response of the target node equipment to obtain the optimal model parameters that minimize the objective function. Finally, the optimal model parameters are imported into the mathematical model to obtain the inversion calibration results of the frequency response parameters of the target node equipment. This method is applicable not only to on-site calibration and periodic performance evaluation of equipment but also to centralized management of large-scale node equipment, providing strong technical support for seismic data quality control.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration technology, specifically relating to a method, apparatus, and system for inverting frequency response parameters of seismic signal acquisition equipment. Background Technology

[0002] Seismic exploration is a crucial geophysical exploration technique for acquiring information on underground geological structures and oil and gas reservoirs. Nodal seismic acquisition equipment, with its advantages of flexible deployment, low cost, and ease of large-scale deployment, has been widely used in the field of seismic exploration. The core function of seismic acquisition equipment is to convert ground vibration signals into electrical signals and record them in digital form. The accuracy of this conversion process directly depends on the frequency response characteristics of the equipment, and its parameters (including amplitude frequency response and phase frequency response) are the foundation for subsequent seismic data processing, imaging, and interpretation.

[0003] In practical applications, due to factors such as manufacturing tolerances, temperature variations, mechanical stress, and long-term aging, the frequency response characteristics of nodal seismic acquisition equipment often deviate from the design values, and may even exhibit nonlinear distortion. This deviation directly affects the amplitude fidelity and phase consistency of seismic data, thereby impacting the accuracy of seismic imaging and the reliability of geological interpretation. Therefore, to ensure seismic data quality, it is crucial to periodically calibrate the frequency response parameters of nodal seismic acquisition equipment.

[0004] Currently, the acquisition of frequency response parameters for seismic acquisition equipment mainly includes detector calibration methods and equipment self-testing methods. The detector calibration method, conducted in a laboratory environment, uses a detector tester to measure key parameters such as the frequency response, sensitivity, and damping coefficient of the detectors in the seismic acquisition equipment to verify whether they meet design requirements or operating standards. This method has advantages such as high calibration accuracy, controllable environment, and good repeatability, and is currently the most authoritative calibration method. The equipment self-testing method aims to allow the seismic acquisition equipment to automatically execute a series of preset check procedures to evaluate the key performance indicators of the local equipment. This method can reduce reliance on laboratory calibration procedures to some extent and can be implemented under field conditions, improving maintenance efficiency.

[0005] However, the aforementioned detector calibration method requires sending the equipment back to the laboratory for calibration, which is cumbersome, costly, and inefficient. This makes real-time calibration impossible on-site, failing to meet the efficient management needs of large-scale node equipment and unable to reflect performance changes in actual field environments. While the aforementioned equipment self-testing method offers advantages such as on-site implementation, reduced equipment handling costs, and improved maintenance efficiency, the lack of high-precision reference signals and standard excitation sources still results in deficiencies in accuracy and reliability, making it difficult to accurately estimate frequency response parameters.

[0006] Traditional direct measurement methods (such as the aforementioned detector calibration methods and equipment self-testing methods) are insufficient in terms of efficiency, cost, and applicability to meet the current needs of large-scale node equipment management and real-time field monitoring. Furthermore, the output signal of seismic acquisition equipment is essentially a convolution of the input seismic signal and the equipment's own frequency response characteristics. Therefore, how to extract the equipment's frequency response parameters from the actual acquired seismic signals to provide a feasible path for efficient and accurate equipment testing is a topic that urgently needs to be studied by those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a method, apparatus, system, computer equipment, computer-readable storage medium, and computer program product for inverting frequency response parameters of seismic signal acquisition equipment, in order to solve the problem that existing frequency response parameter detection schemes for seismic acquisition equipment are difficult to meet the current needs of large-scale node equipment management and real-time field detection in terms of efficiency, cost, and applicability.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, a method for inverting frequency response parameters of a seismic signal acquisition device is provided, which is executed by a computer device in a seismic signal acquisition device frequency response parameter inversion system. The seismic signal acquisition device frequency response parameter inversion system further includes a target node device and a standard node device that is deployed side by side and closely adjacent to the target node device in a field site. The target node device refers to the seismic signal acquisition device whose frequency response parameters are to be inverted and calibrated. The standard node device refers to another seismic signal acquisition device whose frequency response parameters have been calibrated in a laboratory. The target node device and the standard node device are respectively communicatively connected to the computer device.

[0010] The method for inverting the frequency response parameters of the seismic signal acquisition equipment includes:

[0011] Receive seismic wavefield response signals synchronously acquired by the target node device and the standard node device when using naturally / artificially excited background seismic signals as excitation sources;

[0012] The seismic wavefield response signal is preprocessed to obtain the corresponding preprocessed signal;

[0013] Based on the preprocessed signals of the target node device and the standard node device, the transfer function of the target node device relative to the standard node device is estimated.

[0014] A mathematical model of the frequency response function of the target node device is established, wherein the mathematical model is used to describe the dynamic response characteristics of the seismic detector in the target node device to the input vibration signal;

[0015] The model parameters of the mathematical model are optimized using a nonlinear optimization algorithm to obtain the optimal model parameters that minimize the objective function. The objective function is determined as follows: first, the theoretical frequency domain amplitude response of the mathematical model is calculated, and then the mean square error between the theoretical frequency domain amplitude response and the actual frequency domain amplitude response of the transfer function is used as the objective function.

[0016] The optimal model parameters are imported into the mathematical model to obtain the inversion calibration results of the frequency response parameters of the target node device.

[0017] Based on the above-mentioned invention, a novel scheme is provided for inverting and calibrating the frequency response parameters of target node equipment in the field using standard node equipment and nonlinear optimization algorithms. This scheme is executed by a computer device in the seismic signal acquisition equipment frequency response parameter inversion system. After receiving seismic wavefield response signals synchronously acquired by the target node equipment and standard node equipment, preprocessing and transfer function estimation are performed sequentially to obtain the relative transfer function of the equipment. Then, a nonlinear optimization algorithm is used to optimize the model parameters of the mathematical model of the target node equipment's frequency response to obtain the optimal model parameters that minimize the objective function. Finally, the optimal model parameters are imported into the mathematical model to obtain the inversion calibration results of the target node equipment's frequency response parameters. This approach enables efficient and accurate estimation of node equipment frequency response parameters in a field environment by combining frequency domain power spectrum analysis, physical modeling, and nonlinear optimization algorithms. Therefore, it is applicable not only to field calibration and periodic performance evaluation of equipment but also to centralized management of large-scale node equipment, providing strong technical support for seismic data quality control and facilitating practical application and promotion.

[0018] In one possible design, the seismic wavefield response signal is preprocessed, including:

[0019] The linear or nonlinear trend terms in the seismic wavefield response signal are removed.

[0020] And / or, a low-pass filter, a band-pass filter, or an adaptive filter may be used to filter the seismic wavefield response signal;

[0021] And / or, the seismic wavefield response signal is sampled in a manner that reduces the sampling rate.

[0022] In one possible design, the transfer function of the target node device relative to the standard node device is estimated based on the preprocessed signals of the target node device and the standard node device, including:

[0023] The preprocessed signals of the target node device and the standard node device are respectively subjected to segmented windowing processing, and the processing results are respectively subjected to fast Fourier transform processing to obtain the signal frequency domain representation Y(f) of the target node device and the signal frequency domain representation X(f) of the standard node device, where f represents frequency;

[0024] Based on the signal frequency domain representation Y(f) of the target node device, the signal self-power spectrum P of the target node device is calculated. yy (f)=|Y(f)| 2 And based on the signal frequency domain representation X(f) of the standard node device, the signal self-power spectrum P of the standard node device is calculated. xx (f)=|X(f)| 2 ;

[0025] Based on the signal frequency domain representation Y(f) of the target node device and the signal frequency domain representation X(f) of the standard node device, the signal cross-power spectrum P of the target node device and the standard node device is calculated according to the following formula. xy (f):

[0026] P xy (f)=X * (f)×Y(f)

[0027] In the formula, X * (f) represents the conjugate complex number of the signal frequency domain representation X(f) of the standard node device;

[0028] According to the signal autopower spectrum P of the target node device yy (f) The signal self-power spectrum P of the standard node device xx (f) and the signal cross-power spectrum P between the target node device and the standard node device xy (f) The transfer function H of the target node device relative to the standard node device is estimated based on the following equation. x (f):

[0029]

[0030] In the formula, This indicates the frequency response characteristics of the target node device to the input signal.

[0031] In one possible design, when the seismic detector in the target node device is a moving-coil velocity detector, the mathematical model is expressed as follows:

[0032]

[0033] In the formula, f represents the frequency, θ represents the vector form of the model parameters to be optimized, and θ = [G, f n [,ζ], G represents sensitivity, f n The natural frequency is represented by ζ, the damping coefficient by ζ, and the imaginary unit by j. H model () represents the functional form of the mathematical model;

[0034] And / or, the objective function is expressed as follows:

[0035]

[0036] In the formula, N represents the total number of frequency sampling points, i represents a positive integer less than or equal to N, and f i Let represent the i-th sampling frequency point, M represent the total number of transfer functions obtained based on multiple time periods and / or multiple standard node devices, and m represent a positive integer less than or equal to M. Let represent the weight coefficients corresponding to the m-th transfer function, and have θ represents the vector form of the model parameters to be optimized, |H model (f i ,θ)| represents the theoretical frequency domain amplitude response output by the mathematical model. H represents the actual frequency domain amplitude response of the m-th transfer function. model () represents the functional form of the mathematical model. Let J represent the m-th transfer function, and J() represent the objective function.

[0037] In one possible design, the nonlinear optimization algorithm employs simulated annealing, genetic optimization, or particle swarm optimization with update rules related to inertia weights, learning factors, individual optimal positions, and global optimal positions.

[0038] Secondly, a frequency response parameter inversion device for seismic signal acquisition equipment is provided, which is arranged in the computer equipment of a seismic signal acquisition equipment frequency response parameter inversion system. The seismic signal acquisition equipment frequency response parameter inversion system further includes a target node device and a standard node device arranged side by side and closely adjacent to the target node device in a field site. The target node device refers to the seismic signal acquisition equipment to be inverted and calibrated for frequency response parameters. The standard node device refers to another seismic signal acquisition equipment whose frequency response parameters have been calibrated in a laboratory. The target node device and the standard node device are respectively communicatively connected to the computer equipment.

[0039] The frequency response parameter inversion device of the seismic signal acquisition equipment includes a seismic signal receiving unit, a signal preprocessing unit, a transfer function estimation unit, a mathematical model establishment unit, a model parameter optimization unit, and an optimal parameter import unit.

[0040] The seismic signal receiving unit is used to receive the seismic wavefield response signal synchronously acquired by the target node device and the standard node device when using the background seismic signal excited by natural / artificial means as the excitation source;

[0041] The signal preprocessing unit is communicatively connected to the seismic signal receiving unit and is used to preprocess the seismic wavefield response signal to obtain the corresponding preprocessed signal.

[0042] The transfer function estimation unit is communicatively connected to the signal preprocessing unit and is used to estimate the transfer function of the target node device relative to the standard node device based on the preprocessed signals of the target node device and the standard node device.

[0043] The mathematical model building unit is used to build a mathematical model of the frequency response function of the target node device, wherein the mathematical model is used to describe the dynamic response characteristics of the seismic detector in the target node device to the input vibration signal.

[0044] The model parameter optimization unit is communicatively connected to the transfer function estimation unit and the mathematical model establishment unit, respectively. It is used to perform optimization processing on the model parameters of the mathematical model using a nonlinear optimization algorithm to obtain the optimal model parameters that minimize the objective function. The objective function is determined as follows: first, the theoretical frequency domain amplitude response of the mathematical model output is calculated, and then the mean square error between the theoretical frequency domain amplitude response and the actual frequency domain amplitude response of the transfer function is used as the objective function.

[0045] The optimal parameter import unit is communicatively connected to the model parameter optimization unit and is used to import the optimal model parameters into the mathematical model to obtain the inversion calibration result of the frequency response parameters of the target node device.

[0046] Thirdly, the present invention provides a frequency response parameter inversion system for seismic signal acquisition equipment, comprising a computer device, a target node device, and a standard node device arranged side by side and closely adjacent to the target node device in a field site. The target node device refers to the seismic signal acquisition equipment for which frequency response parameters are to be inverted and calibrated, and the standard node device refers to another seismic signal acquisition equipment whose frequency response parameters have been calibrated in a laboratory. The target node device and the standard node device are respectively communicatively connected to the computer device.

[0047] The computer device is used to execute the frequency response parameter inversion method for seismic signal acquisition equipment as described in the first aspect or any possible design in the first aspect.

[0048] Fourthly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence for communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the frequency response parameter inversion method for a seismic signal acquisition device as described in the first aspect or any possible design in the first aspect.

[0049] Fifthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the frequency response parameter inversion method for a seismic signal acquisition device as described in the first aspect or any possible design within the first aspect.

[0050] In a sixth aspect, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a computer, implement the frequency response parameter inversion method for seismic signal acquisition equipment as described in the first aspect or any possible design in the first aspect.

[0051] The beneficial effects of the above scheme are:

[0052] (1) This invention creatively provides a new scheme for inverting and calibrating the frequency response parameters of target node equipment in the field based on standard node equipment and nonlinear optimization algorithm. The scheme is executed by the computer equipment in the frequency response parameter inversion system of seismic signal acquisition equipment. After receiving the seismic wave field response signals synchronously acquired by the target node equipment and standard node equipment, the system first performs preprocessing and transfer function estimation to obtain the relative transfer function of the equipment. Then, the nonlinear optimization algorithm is used to optimize the model parameters of the mathematical model of the frequency response of the target node equipment to obtain the optimal model parameters that minimize the objective function. Finally, the optimal model parameters are imported into the mathematical model to obtain the inversion calibration result of the frequency response parameters of the target node equipment. In this way, in the field environment, by combining frequency domain power spectrum analysis, physical model modeling and nonlinear optimization algorithm, the frequency response parameters of node equipment can be efficiently and accurately estimated. This scheme is applicable not only to the field calibration and periodic performance evaluation of equipment, but also to the centralized management of large-scale node equipment, providing strong technical support for seismic data quality control.

[0053] (2) By making full use of the known characteristics of the reference equipment and the consistency with the field environment, the frequency response parameters of the nodal seismic signal acquisition equipment can be calculated efficiently and accurately without the need for complex experimental equipment and high-cost calibration procedures. It has good robustness and inversion accuracy, and is suitable for field calibration of nodal equipment, periodic performance evaluation and centralized management of large-scale equipment in complex field environments, providing strong technical support for seismic data quality control.

[0054] (3) Compared with the traditional laboratory calibration method, this scheme can directly perform frequency response parameter inversion on the nodal seismic acquisition equipment in the field environment without sending the equipment back to the laboratory, which greatly improves the calibration efficiency, reduces the cost, and can truly reflect the performance changes of the equipment in the actual working environment.

[0055] (4) Compared with the equipment self-testing method, this scheme can achieve high-precision estimation of frequency response parameters by establishing a mathematical model of the frequency response function and using a nonlinear optimization algorithm for parameter inversion, thereby improving the quality and reliability of seismic data. At the same time, the particle swarm optimization algorithm used has the advantages of strong global search capability, fast convergence speed, insensitivity to initial values ​​and easy implementation, making this scheme easy to promote and apply in practice.

[0056] (5) By using the weighted average error of multiple measured transfer functions as the objective function, this scheme can also effectively reduce the impact of random errors and noise interference on the inversion results, and improve the stability and reliability of the inversion results.

[0057] (6) This solution is applicable to the on-site calibration of node equipment, periodic performance evaluation and centralized management of large-scale equipment in complex field environments. It has good adaptability and versatility, and provides strong technical support for seismic data quality control. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart illustrating the frequency response parameter inversion method for seismic signal acquisition equipment provided in this application embodiment.

[0060] Figure 2 This is an example diagram showing the target node device and standard node device being deployed side-by-side and closely adjacent to each other in a field, as provided in the embodiments of this application.

[0061] Figure 3 This is an example diagram illustrating the estimation of the transfer function of the device under test in Case 1, which is known in the embodiments of this application.

[0062] Figure 4 This is an example diagram illustrating the frequency response parameter inversion process in Case 1, provided as an embodiment of this application. Figure 4 Figure (a) shows an example of the optimization process of the objective function with the number of iterations. Figure 4 Figure (b) shows an example of the optimization process for the natural frequency as a function of the number of iterations. Figure 4 Figure (c) shows an example of the optimization process of the damping coefficient with the number of iterations. Figure 4 Figure (d) in the figure shows an example of the sensitivity optimization process with the number of iterations.

[0063] Figure 5 This is an example diagram comparing the frequency response curve obtained by frequency response parameter inversion and the relative transfer function estimate in Case 1, provided as an embodiment of this application.

[0064] Figure 6 This is an example diagram illustrating the frequency response parameter inversion process in Case 2, provided as an embodiment of this application. Figure 6 Figure (a) shows an example of the optimization process of the objective function with the number of iterations. Figure 6 Figure (b) shows an example of the optimization process for the natural frequency as a function of the number of iterations. Figure 6 Figure (c) shows an example of the optimization process of the damping coefficient with the number of iterations. Figure 6Figure (d) in the figure shows an example of the sensitivity optimization process with the number of iterations.

[0065] Figure 7 This is an example diagram comparing the frequency response curve obtained by frequency response parameter inversion in Case 2 with the amplitude-frequency response obtained by vibration table measurement, provided as an embodiment of this application.

[0066] Figure 8 A schematic diagram of the structure of the frequency response parameter inversion device for seismic signal acquisition equipment provided in this application embodiment.

[0067] Figure 9 A schematic diagram of the structure of the frequency response parameter inversion system of the seismic signal acquisition equipment provided in the embodiments of this application.

[0068] Figure 10 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these embodiments without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0070] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0071] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0072] Example:

[0073] like Figure 1 As shown, the frequency response parameter inversion method for seismic signal acquisition equipment provided in the first aspect of this embodiment can be executed, but is not limited to, by a computer device with certain computing resources within the seismic signal acquisition equipment frequency response parameter inversion system. The seismic signal acquisition equipment frequency response parameter inversion system further includes, but is not limited to, a target node device and standard node devices deployed side-by-side and closely adjacent to the target node device in a field location. The target node device refers to the seismic signal acquisition equipment whose frequency response parameters are to be inverted and calibrated. The standard node device refers to another seismic signal acquisition device whose frequency response parameters have already been calibrated in a laboratory. The target node device and the standard node device are respectively communicatively connected to the computer device. Figure 9 As shown; specifically, the seismic signal acquisition equipment may be, but is not limited to, nodal seismographs or other types of seismic exploration instruments; the standard node device is a high-precision reference device with known frequency response characteristics deployed in the field, placed side-by-side and closely adjacent to the target node device (there is no specific requirement for the adjacent distance, but it needs to be as close as possible), ensuring that the device has good surface coupling effect (e.g., Figure 2 As shown, if the fifth gray triangle in the fifth row represents the target node device, then the standard node devices represented by black triangles can be arranged side-by-side in closely adjacent positions; the number of standard node devices can be more than one; furthermore, the target node device and the standard node device are preferably of the same model and have the same operating parameters (e.g., suitable sampling rate and gain parameters). Figure 1 As shown, the frequency response parameter inversion method of the seismic signal acquisition equipment may include, but is not limited to, the following steps S1 to S6.

[0074] S1. Receive the seismic wavefield response signal synchronously acquired by the target node device and the standard node device when using the background seismic signal excited by natural / artificial sources as the excitation source.

[0075] In step S1, the background seismic signal is specifically, but not limited to, environmental noise signals, microseismic activity signals, or controlled-source signals. Since the target node device and the standard node device acquire seismic wavefield response signals synchronously, a high degree of consistency between the signals received by the two devices can be ensured, providing a reliable data foundation for subsequent inversion. Furthermore, in addition to using naturally or artificially excited background seismic signals as excitation sources, other types of signal sources can be considered, such as different excitation methods of controlled-source signals or specific test signals, to enrich data acquisition methods and improve data quality.

[0076] S2. The seismic wave field response signal is preprocessed to obtain the corresponding preprocessed signal.

[0077] In step S2, the purpose of the preprocessing is to provide a data basis for subsequent analysis. Specifically, it includes, but is not limited to, steps such as detrending, filtering, and / or downsampling. In other words, the seismic wave field response signal is preprocessed, including, but not limited to, any one or any combination of the following processing methods (A) to (C).

[0078] (A) The linear or nonlinear trend terms in the seismic wavefield response signal are removed. The purpose of the aforementioned trend term removal is to eliminate the influence of instrument drift on the signal. The specific method of removal is existing technology, such as using a trend analysis algorithm to remove the long-term trend component in the seismic wavefield response signal; the trend analysis algorithm can be conventionally implemented using, but is not limited to, linear fitting or polynomial fitting methods, and the removed long-term trend term is the change term related to the independent variable (specifically time).

[0079] (B) The seismic wavefield response signal is filtered using a low-pass filter, a band-pass filter, or an adaptive filter. The purpose of the aforementioned filtering is to suppress high-frequency noise and power frequency interference. Furthermore, the specific method of the filtering is a prior art technique.

[0080] (C) The seismic wavefield response signal is sampled by reducing the sampling rate. By appropriately reducing the sampling rate (e.g., from 1000Hz to 100Hz), the signal bandwidth integrity can be maintained while reducing subsequent computational load. Furthermore, the specific method of sampling processing is also a prior art technique.

[0081] S3. Based on the preprocessed signals of the target node device and the standard node device, estimate the transfer function of the target node device relative to the standard node device.

[0082] In step S3, a suitable time window can be selected first to calculate the self-power spectrum of the node device and the cross-power spectrum between the devices, and the transfer function of the target node device relative to the standard node device can be estimated based on these power spectra. Specifically, the transfer function of the target node device relative to the standard node device can be estimated based on the preprocessed signals of the target node device and the standard node device, including but not limited to the following steps S31 to S34.

[0083] S31. The preprocessed signals of the target node device and the standard node device are respectively subjected to segmented windowing processing, and the processing results are respectively subjected to fast Fourier transform processing to obtain the signal frequency domain representation Y(f) of the target node device and the signal frequency domain representation X(f) of the standard node device, where f represents frequency.

[0084] In step S31, the segmented windowing process is a commonly used technique in digital signal processing, mainly used to analyze non-stationary signals (such as speech or audio signals). It reduces spectral leakage and boundary effects by dividing the signal into shorter segments and applying window functions. Furthermore, the Fast Fourier Transform process is also an existing technique and will not be elaborated upon here.

[0085] S32. Calculate the signal self-power spectrum P of the target node device based on the signal frequency domain representation Y(f). yy (f)=|Y(f)| 2 And based on the signal frequency domain representation X(f) of the standard node device, the signal self-power spectrum P of the standard node device is calculated. xx (f)=|X(f)| 2 .

[0086] In step S32, the signal self-power spectrum refers to the signal self-power spectrum, which is used to reflect the energy distribution of the corresponding signal at each frequency.

[0087] S33. Based on the signal frequency domain representation Y(f) of the target node device and the signal frequency domain representation X(f) of the standard node device, the signal cross-power spectrum P of the target node device and the standard node device is calculated according to the following formula. xy (f):

[0088] P xy (f)=X * (f)×Y(f)

[0089] In the formula, X * (f) represents the conjugate complex number of the signal frequency domain representation X(f) of the standard node device.

[0090] In step S33, the signal cross power spectrum refers to the cross power spectrum between signals, which is used to reflect the correlation between the two signals in the frequency domain.

[0091] S34. Based on the signal auto-power spectrum P of the target node device yy (f) The signal self-power spectrum P of the standard node device xx (f) and the signal cross-power spectrum P between the target node device and the standard node device xy(f) The transfer function H of the target node device relative to the standard node device is estimated based on the following equation. x (f):

[0092]

[0093] In the formula, This indicates the frequency response characteristics of the target node device to the input signal.

[0094] Based on the above steps S2 to S3, multiple transfer functions can be obtained from all the seismic wavefield response signals collected by the target node device and the standard node device in multiple time periods and / or collected by the target node device and multiple standard node devices in the same time period. By introducing the actual amplitude response of these transfer functions, the subsequent target function has stronger robustness and statistical significance. In this way, compared with a single measurement, the average error of multiple sets of data can better reflect the true frequency response characteristics of the target node device, reducing the impact of random errors and noise interference on the inversion results.

[0095] S4. Establish a mathematical model of the frequency response function of the target node device, wherein the mathematical model is used to describe the dynamic response characteristics of the seismic detector in the target node device to the input vibration signal.

[0096] In step S4, a mathematical model of the frequency response function can be established based on the physical characteristics of the detector. Specifically, when the seismic detector in the target node device is a moving-coil velocity detector (which is currently the most widely used type of detector in seismic exploration), the mathematical model is expressed as follows:

[0097]

[0098] In the formula, f represents the frequency, θ represents the vector form of the model parameters to be optimized, and θ = [G, f n [,ζ], G represents sensitivity, f n The natural frequency is represented by ζ, the damping coefficient by ζ, and the imaginary unit by j. H model () denotes the functional form of the mathematical model. The aforementioned mathematical model is also a second-order system description model. Furthermore, for more complex frequency response characteristics, such as those with multiple resonance peaks or nonlinear responses, rational function models or zero-pole models can also be used for modeling.

[0099] S5. The model parameters of the mathematical model are optimized using a nonlinear optimization algorithm to obtain the optimal model parameters that minimize the objective function. The objective function is determined as follows: first, the theoretical frequency domain amplitude response of the mathematical model is calculated, and then the mean square error between the theoretical frequency domain amplitude response and the actual frequency domain amplitude response of the transfer function is used as the objective function.

[0100] In step S5, when the mathematical model adopts the above-described second-order system description model, the model parameters include sensitivity G and natural frequency f. n And damping coefficient ζ, etc. To efficiently search for the optimal solution in the parameter space, this embodiment employs a nonlinear optimization algorithm for parameter inversion; specifically, the nonlinear optimization algorithm may include, but is not limited to, simulated annealing optimization, genetic optimization, or particle swarm optimization, where the update rule is related to inertia weights, learning factors, individual optimal positions, and global optimal positions. The particle swarm optimization algorithm is a heuristic optimization algorithm based on swarm intelligence, possessing advantages such as strong global search capability, fast convergence speed, insensitivity to initial values, and ease of implementation, making it suitable for nonlinear and multi-parameter optimization problems.

[0101] In step S5, the principle of the particle swarm optimization algorithm is to simulate the behavior of flocks of birds or schools of fish searching for food in space, and to gradually approach the optimal solution in the solution space through information sharing and cooperative search among particles. In this embodiment, each particle represents a set of frequency response model parameters (e.g., sensitivity G, natural frequency f). n The position of a particle (including damping coefficient ζ, etc.) corresponds to a candidate solution in the parameter space, while its velocity represents the particle's search direction and step size in the parameter space. The particle swarm optimization algorithm updates the particle's motion state through two key mechanisms: recording the optimal position found by each particle during its own search (i.e., the individual optimal position) and recording the optimal positions found by all particles in the entire swarm so far (i.e., the global optimal position). By continuously updating the historical optimal information of individuals and the swarm, particles gradually move closer to the optimal solution. Assuming the particle swarm contains K particles, the search space is D-dimensional (corresponding to D parameters to be optimized), and the position of the k-th particle (k represents a positive integer less than or equal to K) at the t-th iteration (t represents a positive integer) is... and speed They are represented as follows:

[0102]

[0103] In the formula, d represents a positive integer less than or equal to D. This represents the parameter value of the d-th parameter to be optimized for the k-th particle in the t-th iteration. This represents the magnitude of the parameter change of the d-th parameter to be optimized for the k-th particle in the t-th iteration. Therefore, the velocity and position update formulas for the k-th particle in the (t+1)-th iteration are as follows:

[0104]

[0105] In the formula, ω represents the inertia weight, used to control the balance of particle search; c1 and c2 represent learning factors, used to control the influence of individual experience and group experience on particle updates, respectively, and are generally taken as c1 = c2 = 2; r1 and r2 are randomly generated numbers in the interval [0,1], used to introduce randomness and avoid getting trapped in local optima; pbest k denoted by , gbest represents the individual optimal position of the k-th particle, and gbest represents the global optimal position of the particle swarm.

[0106] In step S5, the detailed implementation steps of the particle swarm optimization algorithm may specifically include the following steps S51 to S56.

[0107] S51. Initialize the particle swarm: Randomly generate K particles in the parameter space, and initialize the position and velocity of each particle. The range is set according to the reasonable values ​​of the physical parameters.

[0108] S52. Calculate fitness: Substitute the model parameters corresponding to each particle into the frequency response function or the mathematical model, and calculate the objective function value between it and the measured transfer function, which is used as the fitness value.

[0109] S53. Update individual optimal position: If the current particle's fitness is better than its historical best value, then update the individual optimal position of the particle.

[0110] S54. Update Global Optimal Position: If the current particle's individual optimal fitness is better than the population optimal value, then update the global optimal position.

[0111] S55. Update velocity and position: Adjust the flight direction and step size of each particle according to the velocity and position update formulas above.

[0112] S56. Termination judgment: Repeat steps S52 to S55 until the termination condition is met, such as reaching the maximum number of iterations or the fitness change is less than the set threshold δ.

[0113] In step S5, the objective function is a core indicator used to measure the difference between the frequency response of the model output and the actual estimated transfer function. To reduce the impact of random errors and noise interference on the inversion results, the objective function is preferably expressed as follows:

[0114]

[0115] In the formula, N represents the total number of frequency sampling points, i represents a positive integer less than or equal to N, and f i Let represent the i-th sampling frequency point, M represent the total number of transfer functions obtained based on multiple time periods and / or multiple standard node devices, and m represent a positive integer less than or equal to M. Let represent the weight coefficients corresponding to the m-th transfer function, and have θ represents the vector form of the model parameters to be optimized, |H model (f i ,θ)| represents the theoretical frequency domain amplitude response output by the mathematical model. H represents the actual frequency domain amplitude response of the m-th transfer function. model () represents the functional form of the mathematical model. Let J(θ) represent the m-th transfer function, and J(θ) represent the objective function. The aforementioned weighting coefficients can be used to emphasize data segments with high signal-to-noise ratio and / or strong coherence. The aforementioned J(θ) can reflect the overall error between the mathematical model and the measured response, such that if the optimal model parameters can be found... This allows the frequency response output by the mathematical model to approximate the actual observation results as closely as possible. Based on the above formula, the termination condition can be expressed as |J(θ) (t+1) )-J(θ (t) )|<δ, where θ (t+1) Let θ represent the model parameter vector at the (t+1)th iteration. (t) This represents the model parameter vector at the t-th iteration. Furthermore, the objective function can be appropriately adjusted based on the data characteristics and inversion requirements in practical applications. For example, phase response and other error metrics can be introduced, or the distribution of weight values ​​can be adjusted, to further improve the accuracy and reliability of the inversion results.

[0116] S6. Import the optimal model parameters into the mathematical model to obtain the inversion calibration results of the frequency response parameters of the target node device.

[0117] In step S6, the inversion calibration result of the frequency response parameters of the target node device is a complete frequency response function, which can be used for quality control and equipment response correction of subsequent seismic data acquisition.

[0118] Based on the aforementioned steps S1 to S6, this embodiment was further tested in the following two real-world scenarios to verify its feasibility and effectiveness in the frequency response parameter inversion of seismic signal acquisition equipment.

[0119] Case 1: Using a nodal seismic signal acquisition device with known frequency response characteristics (i.e., the standard nodal device) and a similar seismic signal acquisition device to be tested (i.e., the target nodal device), seismic wavefield response signals were synchronously and continuously acquired for 3 hours. Using the method of this embodiment, the signals acquired by the two devices were preprocessed, and the self-power spectrum and cross-power spectrum were calculated with each hour as a time window. Furthermore, an estimate of the transfer function of the device under test (e.g., ...) was obtained. Figure 3 As shown in the figure, the blue line represents the known frequency response curve. Based on the characteristics of the detector in the device under test (DUT), a second-order system description model was constructed, and the frequency response parameters of the DUT were retrieved using a particle swarm optimization (PSO) algorithm. The parameters of the PSO algorithm are set as follows: number of particles: 50; maximum number of iterations: 200; acceleration factors (i.e., learning factors c1 and c2) are both 2.0; inertia weight: 0.7; lower bounds for parameters such as natural frequency, damping coefficient, and sensitivity are all 0, and upper bounds are set to 10, 1, and 3, respectively. The root mean square error of the amplitude-frequency response within the frequency band from 0.5Hz to 50Hz is used as the objective function. Figure 4 The inversion optimization process is demonstrated, in which, Figure 4 Figures (a), (b), (c), and (d) show the changes in the objective function, natural frequency, damping, and sensitivity with the number of iterations, respectively. The final inversion results are as follows: natural frequency is 4.7485, damping coefficient is 0.7638, and sensitivity is 1.0175; the obtained frequency response curve (as shown in Figure 1) is... Figure 5 Comparison of the curves shown by the red line with the estimated transfer function curve ( Figure 5 As shown in the figure, the ideal inversion result verifies the effectiveness of the method in this embodiment. Furthermore, based on the obtained results, the device to be tested can be compared and analyzed with the nominal parameters to determine whether the device parameters meet the requirements.

[0120] Case 2: A shaking table test was conducted on a seismic detector with a natural frequency of 5 Hz. The purpose of the experiment was to verify the performance of the seismic detector through shaking table testing and to estimate its frequency response parameters (including natural frequency, damping coefficient, and sensitivity) using the method of this embodiment. The parameter settings of the particle swarm optimization algorithm were consistent with those in Case 1, with the lower bounds of parameters such as natural frequency, damping coefficient, and sensitivity all set to 0, and the upper bounds set to 10, 1, and 100, respectively. Figure 6 The optimization process of the particle swarm optimization algorithm is demonstrated, wherein, Figure 6 Figure (a) shows how the objective function changes with the number of iterations. Figure 6 Figures (b), (c), and (d) illustrate the optimization process for the natural frequency, damping coefficient, and sensitivity, respectively. A comparison of the final inversion results with the measured frequency response curves shows (e.g.) Figure 7As shown in the figure, the fitting effect is very ideal, which further verifies the feasibility and effectiveness of the method in this embodiment.

[0121] Therefore, based on the frequency response parameter inversion method for seismic signal acquisition equipment described in steps S1 to S6 above, a new scheme is provided for inverting and calibrating the frequency response parameters of target node equipment in the field based on standard node equipment and nonlinear optimization algorithms. This scheme is executed by the computer equipment in the seismic signal acquisition equipment frequency response parameter inversion system. After receiving the seismic wavefield response signals synchronously acquired by the target node equipment and standard node equipment, preprocessing and transfer function estimation are performed sequentially to obtain the relative transfer function of the equipment. Then, a nonlinear optimization algorithm is used to optimize the model parameters of the mathematical model of the frequency response of the target node equipment to obtain the optimal model parameters that minimize the objective function. Finally, the optimal model parameters are imported into the mathematical model to obtain the inversion calibration result of the frequency response parameters of the target node equipment. This allows for efficient and accurate estimation of the frequency response parameters of node equipment in a field environment by combining frequency domain power spectrum analysis, physical model modeling, and nonlinear optimization algorithms. This approach is applicable not only to field calibration and periodic performance evaluation of equipment but also to centralized management of large-scale node equipment, providing strong technical support for seismic data quality control and facilitating practical application and promotion.

[0122] like Figure 8 As shown, the second aspect of this embodiment provides a virtual device for implementing the frequency response parameter inversion method of the seismic signal acquisition equipment described in the first aspect. This device is arranged in the computer equipment of the seismic signal acquisition equipment frequency response parameter inversion system. The seismic signal acquisition equipment frequency response parameter inversion system further includes a target node device and standard node devices arranged side-by-side and closely adjacent to the target node device in a field location. The target node device refers to the seismic signal acquisition equipment whose frequency response parameters are to be inverted and calibrated. The standard node device refers to another seismic signal acquisition equipment whose frequency response parameters have been calibrated in a laboratory. The target node device and the standard node device are respectively communicatively connected to the computer equipment.

[0123] The virtual device includes a seismic signal receiving unit, a signal preprocessing unit, a transfer function estimation unit, a mathematical model building unit, a model parameter optimization unit, and an optimal parameter import unit.

[0124] The seismic signal receiving unit is used to receive the seismic wavefield response signal synchronously acquired by the target node device and the standard node device when using the background seismic signal excited by natural / artificial means as the excitation source;

[0125] The signal preprocessing unit is communicatively connected to the seismic signal receiving unit and is used to preprocess the seismic wavefield response signal to obtain the corresponding preprocessed signal.

[0126] The transfer function estimation unit is communicatively connected to the signal preprocessing unit and is used to estimate the transfer function of the target node device relative to the standard node device based on the preprocessed signals of the target node device and the standard node device.

[0127] The mathematical model building unit is used to build a mathematical model of the frequency response function of the target node device, wherein the mathematical model is used to describe the dynamic response characteristics of the seismic detector in the target node device to the input vibration signal.

[0128] The model parameter optimization unit is communicatively connected to the transfer function estimation unit and the mathematical model establishment unit, respectively. It is used to perform optimization processing on the model parameters of the mathematical model using a nonlinear optimization algorithm to obtain the optimal model parameters that minimize the objective function. The objective function is determined as follows: first, the theoretical frequency domain amplitude response of the mathematical model output is calculated, and then the mean square error between the theoretical frequency domain amplitude response and the actual frequency domain amplitude response of the transfer function is used as the objective function.

[0129] The optimal parameter import unit is communicatively connected to the model parameter optimization unit and is used to import the optimal model parameters into the mathematical model to obtain the inversion calibration result of the frequency response parameters of the target node device.

[0130] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the frequency response parameter inversion method of the seismic signal acquisition equipment described in the first aspect, and will not be repeated here.

[0131] like Figure 9 As shown, the third aspect of this embodiment provides a physical system for implementing the frequency response parameter inversion method of the seismic signal acquisition device described in the first aspect, including a computer device, a target node device, and a standard node device that is deployed side by side and closely adjacent to the target node device in a field site. The target node device refers to the seismic signal acquisition device whose frequency response parameters are to be inverted and calibrated, and the standard node device refers to another seismic signal acquisition device whose frequency response parameters have been calibrated in a laboratory. The target node device and the standard node device are respectively communicatively connected to the computer device.

[0132] The computer device is used to execute the frequency response parameter inversion method for seismic signal acquisition equipment as described in the first aspect.

[0133] The working process, working details and technical effects of the aforementioned system provided in the third aspect of this embodiment can be found in the frequency response parameter inversion method of the seismic signal acquisition equipment described in the first aspect, and will not be repeated here.

[0134] like Figure 10 As shown, the fourth aspect of this embodiment provides a computer device for executing the frequency response parameter inversion method of the seismic signal acquisition device as described in the first aspect. The device includes a memory, a processor, and a transceiver connected in sequence. The memory stores a computer program, the transceiver sends and receives messages, and the processor reads the computer program to execute the frequency response parameter inversion method of the seismic signal acquisition device as described in the first aspect. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0135] The working process, working details and technical effects of the aforementioned computer equipment provided in the fourth aspect of this embodiment can be found in the frequency response parameter inversion method of the seismic signal acquisition equipment described in the first aspect, and will not be repeated here.

[0136] This fifth aspect of the embodiment provides a computer-readable storage medium storing instructions comprising the frequency response parameter inversion method for seismic signal acquisition equipment as described in the first aspect. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the frequency response parameter inversion method for seismic signal acquisition equipment as described in the first aspect. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0137] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fifth aspect of this embodiment can be found in the frequency response parameter inversion method of the seismic signal acquisition equipment as described in the first aspect, and will not be repeated here.

[0138] The sixth aspect of this embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements the frequency response parameter inversion method for seismic signal acquisition equipment as described in the first aspect. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0139] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for inverting frequency response parameters of a seismic signal acquisition device, characterized in that, The process is executed by a computer device in the frequency response parameter inversion system of the seismic signal acquisition equipment. The seismic signal acquisition equipment frequency response parameter inversion system also includes a target node device and a standard node device that is deployed side by side and closely adjacent to the target node device in the field. The target node device refers to the seismic signal acquisition equipment to be inverted and calibrated for frequency response parameters. The standard node device refers to another seismic signal acquisition equipment whose frequency response parameters have been calibrated in the laboratory. The target node device and the standard node device are respectively communicatively connected to the computer device. The method for inverting the frequency response parameters of the seismic signal acquisition equipment includes: Receive seismic wavefield response signals synchronously acquired by the target node device and the standard node device when using naturally / artificially excited background seismic signals as excitation sources; The seismic wavefield response signal is preprocessed to obtain the corresponding preprocessed signal; Based on the preprocessed signals of the target node device and the standard node device, the transfer function of the target node device relative to the standard node device is estimated, wherein the transfer function of the target node device relative to the standard node device is... It is estimated based on the following equation: In the formula, This indicates the frequency response characteristics of the target node device to the input signal. This represents the signal auto-power spectrum of the standard node device. This represents the signal cross-power spectrum between the target node device and the standard node device; A mathematical model of the frequency response function of the target node device is established, wherein the mathematical model is used to describe the dynamic response characteristics of the seismic detector in the target node device to the input vibration signal; A nonlinear optimization algorithm is used to optimize the model parameters of the mathematical model to obtain the optimal model parameters that minimize the objective function, wherein the objective function is expressed as follows: In the formula, Indicates the total number of frequency sampling points. Indicates less than or equal to positive integers, Indicates the first Each sampling frequency point This represents the total number of transfer functions derived from multiple time periods and / or multiple standard node devices. Indicates less than or equal to positive integers, Indicates the relationship with the first The weight coefficients corresponding to the transfer function, and have , Represents the vector form of the model parameters to be optimized. This represents the theoretical frequency domain amplitude response output by the mathematical model. Indicates the first The actual frequency domain amplitude response of the transfer function. The mathematical model is represented in functional form. Indicates the first The aforementioned transfer function Describe the objective function; The optimal model parameters are imported into the mathematical model to obtain the inversion calibration results of the frequency response parameters of the target node device.

2. The method for inverting frequency response parameters of seismic signal acquisition equipment according to claim 1, characterized in that, The preprocessing of the seismic wavefield response signal includes: The linear or nonlinear trend terms in the seismic wavefield response signal are removed. And / or, a low-pass filter, a band-pass filter, or an adaptive filter may be used to filter the seismic wavefield response signal; And / or, the seismic wavefield response signal is sampled in a manner that reduces the sampling rate.

3. The method for inverting frequency response parameters of seismic signal acquisition equipment according to claim 1, characterized in that, Based on the preprocessed signals of the target node device and the standard node device, the transfer function of the target node device relative to the standard node device is estimated, including: The preprocessed signals of the target node device and the standard node device are subjected to segmented windowing processing, and the processing results are subjected to Fast Fourier Transform to obtain the frequency domain representation of the signal of the target node device. and the signal frequency domain representation of the standard node device. ,in, Indicates frequency; According to the signal frequency domain representation of the target node device The signal power spectrum of the target node device is calculated. and the signal frequency domain representation based on the standard node device. The signal self-power spectrum of the standard node device is calculated. ; According to the signal frequency domain representation of the target node device and the signal frequency domain representation of the standard node device. The signal cross-power spectrum between the target node device and the standard node device is calculated according to the following formula. : In the formula, The signal frequency domain representation of the standard node device. The conjugate of complex numbers; Based on the signal power spectrum of the target node device The signal self-power spectrum of the standard node device and the signal cross-power spectrum between the target node device and the standard node device The transfer function of the target node device relative to the standard node device is estimated based on the following equation. : In the formula, This indicates the frequency response characteristics of the target node device to the input signal.

4. The method for inverting frequency response parameters of seismic signal acquisition equipment according to claim 1, characterized in that, When the seismic detector in the target node device is a moving-coil velocity detector, the mathematical model is expressed as follows: In the formula, Indicates frequency, Represents the vector form of the model parameters to be optimized, and has , Indicates sensitivity. Represents natural frequency. Indicates the damping coefficient. Represents the imaginary unit. This represents the functional form of the mathematical model.

5. The method for inverting frequency response parameters of seismic signal acquisition equipment according to claim 1, characterized in that, The nonlinear optimization algorithm employs simulated annealing, genetic optimization, or particle swarm optimization with update rules related to inertia weights, learning factors, individual optimal positions, and global optimal positions.

6. A frequency response parameter inversion device for seismic signal acquisition equipment, characterized in that, The computer equipment is arranged in the frequency response parameter inversion system of the seismic signal acquisition equipment. The frequency response parameter inversion system of the seismic signal acquisition equipment also includes a target node device and a standard node device arranged side by side and closely adjacent to the target node device in the field. The target node device refers to the seismic signal acquisition equipment to be inverted and calibrated for frequency response parameters. The standard node device refers to another seismic signal acquisition equipment whose frequency response parameters have been calibrated in the laboratory. The target node device and the standard node device are respectively communicatively connected to the computer equipment. The frequency response parameter inversion device of the seismic signal acquisition equipment includes a seismic signal receiving unit, a signal preprocessing unit, a transfer function estimation unit, a mathematical model establishment unit, a model parameter optimization unit, and an optimal parameter import unit. The seismic signal receiving unit is used to receive the seismic wavefield response signal synchronously acquired by the target node device and the standard node device when using the background seismic signal excited by natural / artificial means as the excitation source; The signal preprocessing unit is communicatively connected to the seismic signal receiving unit and is used to preprocess the seismic wavefield response signal to obtain the corresponding preprocessed signal. The transfer function estimation unit, communicatively connected to the signal preprocessing unit, is used to estimate the transfer function of the target node device relative to the standard node device based on the preprocessed signals of the target node device and the standard node device. It is estimated based on the following equation: In the formula, This indicates the frequency response characteristics of the target node device to the input signal. This represents the signal auto-power spectrum of the standard node device. This represents the signal cross-power spectrum between the target node device and the standard node device; The mathematical model building unit is used to build a mathematical model of the frequency response function of the target node device, wherein the mathematical model is used to describe the dynamic response characteristics of the seismic detector in the target node device to the input vibration signal. The model parameter optimization unit is communicatively connected to the transfer function estimation unit and the mathematical model establishment unit, respectively. It is used to perform optimization processing on the model parameters of the mathematical model using a nonlinear optimization algorithm to obtain the optimal model parameters that minimize the objective function. The objective function is expressed as follows: In the formula, Indicates the total number of frequency sampling points. Indicates less than or equal to positive integers, Indicates the first Each sampling frequency point This represents the total number of transfer functions derived from multiple time periods and / or multiple standard node devices. Indicates less than or equal to positive integers, Indicates the relationship with the first The weight coefficients corresponding to the transfer function, and have , Represents the vector form of the model parameters to be optimized. This represents the theoretical frequency domain amplitude response output by the mathematical model. Indicates the first The actual frequency domain amplitude response of the transfer function. The mathematical model is represented in functional form. Indicates the first The aforementioned transfer function Describe the objective function; The optimal parameter import unit is communicatively connected to the model parameter optimization unit and is used to import the optimal model parameters into the mathematical model to obtain the inversion calibration result of the frequency response parameters of the target node device.

7. A frequency response parameter inversion system for seismic signal acquisition equipment, characterized in that, It includes computer equipment, target node equipment, and standard node equipment deployed side by side and closely adjacent to the target node equipment in the field. The target node equipment refers to the seismic signal acquisition equipment for which frequency response parameters are to be inverted and calibrated, and the standard node equipment refers to another seismic signal acquisition equipment whose frequency response parameters have been calibrated in the laboratory. The target node equipment and the standard node equipment are respectively communicatively connected to the computer equipment. The computer device is used to execute the frequency response parameter inversion method for seismic signal acquisition equipment as described in any one of claims 1 to 5.

8. A computer device, characterized in that, The device includes a memory, a processor, and a transceiver connected in sequence for communication. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the frequency response parameter inversion method for seismic signal acquisition equipment as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that... The computer-readable storage medium stores instructions that, when executed on a computer, perform the frequency response parameter inversion method for seismic signal acquisition equipment as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the frequency response parameter inversion method for seismic signal acquisition equipment as described in any one of claims 1 to 5.

Images