Construction method and device of high dynamic range seismic data acquisition circuit

Abstract

This invention relates to the field of earthquake monitoring technology, and discloses a method and apparatus for constructing a high dynamic range (HMR) seismic data acquisition circuit. The method includes: analyzing signal degradation factors in HMR seismic data and calculating the corresponding factor energy values; performing variable gain processing on the HMR seismic data to obtain target seismic data; analyzing geological characteristic parameters corresponding to the data acquisition area; extracting seismic time history data and multi-component seismic data from the target seismic data; querying the data sampling mechanism and data acquisition equipment corresponding to the target seismic data; determining the number of data channels corresponding to the target seismic data; calculating the data storage capacity corresponding to the target seismic data; analyzing the circuit response performance of the data acquisition equipment; and constructing the data acquisition circuit corresponding to the data acquisition equipment. The main objective of this invention is to solve the problem of low accuracy in the construction of HMR seismic data acquisition circuits.

Description

Technical Field

[0001] This invention relates to a method and apparatus for constructing a high dynamic range seismic data acquisition circuit, belonging to the field of seismic monitoring technology. Background Technology

[0002] Seismic data acquisition is crucial for earthquake research and monitoring. High dynamic range seismic data contains a wide range of signals, from weak to strong. As seismology continues to develop, the demand for accurate acquisition of high dynamic range seismic data is increasing.

[0003] Existing methods for constructing high dynamic range (HMR) seismic data acquisition circuits are typically based on traditional circuit design concepts. The specific process involves: sensor selection, choosing high-sensitivity, wide-bandwidth sensors based on the frequency range and intensity of seismic waves, such as seismic detectors with excellent low-frequency characteristics; signal conditioning circuitry, designing multi-stage amplification circuits to enhance the amplitude of weak signals, and employing high-precision filtering circuits to filter out noise and interference frequencies; analog-to-digital conversion (ADC) modules, using high-resolution, high-speed ADCs to ensure accurate conversion of wide dynamic range analog signals; and control and data processing, employing FPGAs or high-performance microcontrollers to achieve precise control of the acquisition process and real-time data processing. However, this method increases circuit construction costs and fails to consider the multifaceted characteristics of seismic signals, leading to reduced accuracy in constructing HMR seismic data acquisition circuits. Summary of the Invention

[0004] This invention provides a method and apparatus for constructing a high dynamic range seismic data acquisition circuit, the main purpose of which is to solve the problem of low accuracy in the construction of high dynamic range seismic data acquisition circuits.

[0005] To achieve the above objectives, the present invention provides a method for constructing a high dynamic range seismic data acquisition circuit, comprising:

[0006] Acquire the high dynamic range seismic data and data acquisition area to be analyzed, extract the transform domain features corresponding to the high dynamic range seismic data, analyze the signal inferiority factor in the high dynamic range seismic data based on the transform domain features, and calculate the factor energy value corresponding to the signal inferiority factor.

[0007] The high dynamic range seismic data is processed with variable gain to obtain target seismic data. The geological characteristic parameters corresponding to the data acquisition area are analyzed. Seismic time history data and seismic multi-component data are extracted from the target seismic data. The focal mechanism attributes corresponding to the data acquisition area are analyzed by combining the seismic time history data and the seismic multi-component data.

[0008] The data sampling mechanism and data acquisition equipment corresponding to the target seismic data are queried. Based on the data sampling equipment, the number of data channels corresponding to the target seismic data is determined. Combining the number of data channels and the data sampling mechanism, the data storage capacity corresponding to the target seismic data is calculated.

[0009] Collect the device operation logs corresponding to the data acquisition device, analyze the circuit response performance of the data acquisition device based on the device operation logs, and construct the data acquisition circuit corresponding to the data acquisition device based on the circuit response performance, the factor energy value, the source mechanism attribute and the data storage capacity.

[0010] Optionally, extracting the transform domain features corresponding to the high dynamic range seismic data includes:

[0011] The high dynamic range seismic data is subjected to Fourier transform processing to obtain the seismic spectrum.

[0012] Identify the spectral peak characteristics in the seismic spectrum and calculate the effective bandwidth of the seismic spectrum;

[0013] Wavelet transform processing is performed on the high dynamic range seismic data to obtain transformed seismic data;

[0014] Extract the instantaneous frequency features from the transformed seismic data;

[0015] By combining the spectral peak characteristics, the effective bandwidth of the spectrum, and the instantaneous frequency characteristics, the transform domain characteristics corresponding to the high dynamic range seismic data are generated.

[0016] Optionally, the step of analyzing the signal degradation factors in the high dynamic range seismic data based on the transform domain characteristics includes:

[0017] Identify the feature identifiers corresponding to the transform domain features, and analyze the feature semantics corresponding to the transform domain features based on the feature identifiers;

[0018] Based on the aforementioned semantic features, the transform domain features are subjected to feature classification processing to obtain classified transform domain features;

[0019] Principal component analysis was performed on the classification transformation domain features to obtain the feature principal components;

[0020] Based on the aforementioned principal components, signal quality factors in the high dynamic range seismic data are analyzed.

[0021] Optionally, the step of performing variable gain processing on the high dynamic range seismic data to obtain the target seismic data includes:

[0022] The high dynamic range seismic data is subjected to time-varying gain processing to obtain time-varying gain seismic data;

[0023] Calculate the data entropy corresponding to the time-varying gain seismic data, and determine the data complexity corresponding to the time-varying gain seismic data based on the data entropy;

[0024] Based on the data complexity, the time-varying gain seismic data is subjected to adaptive gain processing to obtain adaptive gain seismic data.

[0025] The adapted gain seismic data is subjected to smooth gain processing to obtain the target seismic data.

[0026] Optionally, the analysis of geological feature parameters corresponding to the data acquisition area includes:

[0027] Collect regional exploration data corresponding to the data acquisition area, and filter the regional exploration data to obtain filtered exploration data;

[0028] The filtered exploration data is then integrated to obtain integrated exploration data.

[0029] The integrated exploration data is then subjected to data correction processing to obtain corrected exploration data;

[0030] By combining the preset geological feature indicators and the corrected exploration data, the geological feature parameters corresponding to the data collection area are analyzed.

[0031] Optionally, the step of combining the earthquake time history data and the earthquake multi-component data to analyze the focal mechanism attributes corresponding to the data acquisition area includes:

[0032] Wavefield identification processing is performed on the multi-component seismic data to obtain seismic shear waves and seismic P-waves;

[0033] Based on the earthquake time history data, the arrival time differences of the earthquake shear wave and the earthquake p-wave at different observation points are calculated respectively, and the shear wave time difference and p-wave time difference are obtained.

[0034] Calculate the propagation velocities of the seismic shear wave and the seismic longitudinal wave respectively to obtain the shear wave velocity and the longitudinal wave velocity;

[0035] By combining the shear wave time difference, the longitudinal wave time difference, the shear wave velocity, and the longitudinal wave velocity, the source location and source depth corresponding to the data acquisition area are analyzed.

[0036] By combining the location and depth of the seismic source, a regional seismic source sphere corresponding to the data acquisition area is constructed;

[0037] Calculate the focal mechanism solution corresponding to the focal sphere in the region, and analyze the focal mechanism attributes corresponding to the data acquisition area based on the focal mechanism solution.

[0038] Optionally, the step of calculating the propagation velocities of the seismic shear wave and the seismic p-wave respectively to obtain the shear wave velocity and p-wave velocity includes:

[0039] Detect the propagation carriers corresponding to the seismic shear waves and the seismic longitudinal waves, and query the carrier density and carrier elastic modulus corresponding to the propagation carriers;

[0040] Combining the carrier density and the carrier elastic modulus, the propagation velocities of the seismic shear wave and the seismic p-wave are calculated using the following formulas to obtain the shear wave velocity and p-wave velocity, including:

[0041]

[0042] Where G represents the transverse wave velocity, D represents the longitudinal wave velocity, E represents the carrier elastic modulus, δ represents the carrier density, and β represents the carrier Poisson's ratio.

[0043] Optionally, calculating the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism includes:

[0044] Based on the data sampling mechanism, determine the sampling rate and sampling accuracy corresponding to the target seismic data;

[0045] Identify the acquisition timestamp corresponding to the target seismic data, and calculate the data duration period corresponding to the target seismic data based on the acquisition timestamp;

[0046] Combining the sampling rate, the sampling accuracy, the data duration, and the number of data channels, the data storage capacity corresponding to the target seismic data can be calculated using the following formula:

[0047] H = F * T * M * φ;

[0048] Where H represents the data storage capacity corresponding to the target seismic data, F represents the sampling rate, T represents the data duration period, M represents the number of data channels, and φ represents the sampling accuracy.

[0049] Optionally, the step of analyzing the circuit response performance of the data acquisition device in conjunction with the device operation log includes:

[0050] Identify the operating indicators and their corresponding operating information in the device's operating log;

[0051] Analyze the indicator attributes corresponding to the operation indicators, extract the circuit response indicators from the operation indicators based on the indicator attributes, and calculate the indicator weights corresponding to the circuit response indicators.

[0052] The circuit response metrics include circuit response time metrics, signal accuracy metrics, and circuit stability metrics;

[0053] Based on the aforementioned indicator operation information, the indicator parameter values ​​corresponding to the circuit response indicator are calculated.

[0054] Combining the indicator parameter values, the indicator weights, and the circuit response indicators, the performance value of the data acquisition device can be calculated using the following formula:

[0055]

[0056] Where Q represents the performance value corresponding to the circuit response index, and α1, α2, and α3 represent the index weights corresponding to the circuit response time index, signal accuracy index, and circuit stability index, respectively. This indicates the average response time among the index parameters related to circuit response time. σ represents the preset maximum average response time. N This represents the standard deviation of the response time among the index parameter values ​​related to circuit response time. This represents the average error in the strength of the indicator parameter values ​​related to signal accuracy. This represents the frequency relative error of the indicator parameter values ​​with respect to the accuracy of the signal. ΔS represents the average output signal strength corresponding to the circuit stability index among the index parameter values. max This indicates the signal strength range corresponding to the circuit stability index in the index parameter values;

[0057] Based on the aforementioned performance values, the circuit response performance of the data acquisition device is analyzed.

[0058] A device for constructing a high dynamic range seismic data acquisition circuit, characterized in that the device comprises:

[0059] The energy value calculation module is used to acquire the high dynamic range seismic data to be analyzed and the data acquisition area, extract the transform domain features corresponding to the high dynamic range seismic data, analyze the signal inferiority factor in the high dynamic range seismic data based on the transform domain features, and calculate the factor energy value corresponding to the signal inferiority factor.

[0060] The attribute analysis module is used to perform variable gain processing on the high dynamic range seismic data to obtain target seismic data, analyze the geological feature parameters corresponding to the data acquisition area, extract seismic time history data and seismic multi-component data from the target seismic data, and combine the seismic time history data and seismic multi-component data to analyze the focal mechanism attributes corresponding to the data acquisition area.

[0061] The storage capacity calculation module is used to query the data sampling mechanism and data acquisition equipment corresponding to the target seismic data, determine the number of data channels corresponding to the target seismic data based on the data sampling equipment, and calculate the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism.

[0062] The circuit construction module is used to collect the device operation logs corresponding to the data acquisition device, analyze the circuit response performance of the data acquisition device in combination with the device operation logs, and construct the data acquisition circuit corresponding to the data acquisition device in combination with the circuit response performance, the factor energy value, the source mechanism attribute and the data storage capacity.

[0063] Compared to the problems described in the background art, this invention, by extracting the transform domain features corresponding to the high dynamic range seismic data, can provide a more comprehensive and in-depth understanding of seismic signal characteristics, including signal frequency composition, energy distribution, and hidden information. This provides a more accurate and effective basis for subsequent seismic data analysis. By applying variable gain processing to the high dynamic range seismic data, this invention can adaptively adjust signal strength, improve the identifiability of weak but valuable signals, and balance the energy differences of signals in different frequency bands or regions, making the output signal more reasonable in amplitude, facilitating subsequent analysis and processing. This invention queries the data sampling mechanism and data acquisition equipment corresponding to the target seismic data, and based on the data sampling equipment, determines the number of data channels corresponding to the target seismic data, enabling more accurate processing of seismic data and laying the foundation for subsequent calculation of the data storage capacity corresponding to the target seismic data. By combining the equipment operation logs and analyzing the circuit response performance corresponding to the data acquisition equipment, this invention can understand the performance of the circuit in actual operation, promptly identify potential circuit problems, and provide a basis for improving the accuracy of the subsequent construction of the data acquisition circuit corresponding to the equipment circuit. Therefore, the present invention proposes a method and apparatus for constructing a high dynamic range seismic data acquisition circuit, thereby improving the accuracy of constructing a high dynamic range seismic data acquisition circuit. Attached Figure Description

[0064] Figure 1 A flowchart illustrating a method for constructing a high dynamic range seismic data acquisition circuit according to an embodiment of the present invention;

[0065] Figure 2 This is a functional block diagram of a device for constructing a high dynamic range seismic data acquisition circuit according to an embodiment of the present invention.

[0066] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0068] This application provides a method for constructing a high dynamic range (HMR) seismic data acquisition circuit. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for constructing the HMR seismic data acquisition circuit can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0069] Example 1:

[0070] Reference Figure 1 The diagram shown is a flowchart illustrating a method for constructing a high dynamic range (HMR) seismic data acquisition circuit according to an embodiment of the present invention. In this embodiment, the method for constructing the HMR seismic data acquisition circuit includes:

[0071] S1. Obtain the high dynamic range seismic data and data acquisition area to be analyzed, extract the transform domain features corresponding to the high dynamic range seismic data, analyze the signal inferiority factor in the high dynamic range seismic data based on the transform domain features, and calculate the factor energy value corresponding to the signal inferiority factor.

[0072] This invention, by extracting the transform domain features corresponding to the high dynamic range (HDR) seismic data, can provide a more comprehensive and in-depth understanding of seismic signal characteristics, including signal frequency composition, energy distribution, and hidden information. This provides a more accurate and effective basis for subsequent seismic data analysis. It should be noted that the HDR seismic data is acquired during earthquake monitoring and includes a range of data from extremely weak background noise signals to strong seismic wave signals. This data is characterized by a wide dynamic range, with amplitudes spanning multiple orders of magnitude. For example, weak signals may be at the microvolt level, while strong seismic wave signals may reach the volt level or even higher. The higher dynamic range (HRLL) seismic data encompasses a wealth of seismic information, including seismic waves of different frequencies, from low-frequency surface waves to high-frequency body waves. These frequency components are intertwined in time and space. Furthermore, this data reflects various changes in seismic waves caused by underground geological structures during propagation, such as signal characteristic changes resulting from reflection, refraction, and scattering. The data acquisition area is the geographical region corresponding to the high dynamic range seismic data. The transform domain features are a collection of features from the high dynamic range seismic data, including time domain, frequency domain, and possible other transform domain features (such as time-frequency domain, fractional Fourier domain, etc.).

[0073] Specifically, the extraction of transform domain features corresponding to the high dynamic range seismic data includes:

[0074] The high dynamic range seismic data is subjected to Fourier transform processing to obtain the seismic spectrum.

[0075] Identify the spectral peak characteristics in the seismic spectrum and calculate the effective bandwidth of the seismic spectrum;

[0076] Wavelet transform processing is performed on the high dynamic range seismic data to obtain transformed seismic data;

[0077] Extract the instantaneous frequency features from the transformed seismic data;

[0078] By combining the spectral peak characteristics, the effective bandwidth of the spectrum, and the instantaneous frequency characteristics, the transform domain characteristics corresponding to the high dynamic range seismic data are generated.

[0079] It should be explained that the earthquake spectrum is the frequency domain representation of the high dynamic range seismic data after Fourier transform; the spectral peak characteristic is the frequency characteristic of the earthquake spectrum where the amplitude reaches a local maximum; the effective bandwidth of the spectrum is the frequency range of the earthquake spectrum containing the main energy; the transformed seismic data is the time-frequency data obtained by wavelet transforming the high dynamic range seismic data; and the instantaneous frequency characteristic is the characteristic of the transformed seismic data reflecting the change of signal frequency over time.

[0080] Furthermore, the Fourier transform processing of the high dynamic range seismic data can be achieved using the Discrete Fourier Transform (DFT) formula or computational tools such as the Fast Fourier Transform (FFT) algorithm; the identification of spectral peak features in the seismic spectrogram can be achieved using algorithms that find local maxima of spectral amplitude, such as peak detection algorithms; the calculation of the effective bandwidth of the seismic spectrogram can be achieved by setting an energy threshold and determining the upper and lower limits of the frequency range based on the energy distribution; the wavelet transform processing of the high dynamic range seismic data can be achieved by selecting appropriate wavelet basis functions and using a transformation algorithm that combines convolution operations with scale and translation parameters; the extraction of instantaneous frequency features in the transformed seismic data can be achieved by calculating the phase derivative of the wavelet coefficients.

[0081] This invention analyzes signal degradation factors in high dynamic range seismic data based on the transform domain characteristics, effectively identifying interference, noise, or signal distortion in the high dynamic range seismic data, thereby improving the accuracy of signal degradation factor analysis. By calculating the factor energy value corresponding to the signal degradation factor, a quantitative representation of the signal degradation factor can be obtained, providing a more intuitive display of signal quality problems. It should be noted that the signal degradation factor is the noise component in the high dynamic range seismic data. Furthermore, the calculation of the factor energy value corresponding to the signal degradation factor can be achieved using the frequency domain integration method.

[0082] In detail, the analysis of signal degradation factors in the high dynamic range seismic data based on the transform domain characteristics includes:

[0083] Identify the feature identifiers corresponding to the transform domain features, and analyze the feature semantics corresponding to the transform domain features based on the feature identifiers;

[0084] Based on the aforementioned semantic features, the transform domain features are subjected to feature classification processing to obtain classified transform domain features;

[0085] Principal component analysis was performed on the classification transformation domain features to obtain the feature principal components;

[0086] Based on the aforementioned principal components, signal quality factors in the high dynamic range seismic data are analyzed.

[0087] It should be explained that the feature identifier is a symbol or name corresponding to the transform domain feature used to distinguish and mark different features; the feature semantics is the interpretation of the transform domain feature in physical meaning and practical application scenarios; the classification transform domain feature is the result of classifying the transform domain feature according to the feature semantics; and the feature principal component is the main linear combination component obtained by principal component analysis of the classification transform domain feature.

[0088] Furthermore, the identification of the feature identifiers corresponding to the transform domain features can be achieved through analysis of the transform method; based on the feature identifiers, the feature semantics corresponding to the transform domain features can be analyzed by combining seismological knowledge, physical principles, and experience in related fields; the transform domain features can be classified according to the similarity of the physical meaning, data representation, etc. of the feature semantics to obtain classified transform domain features; the principal component analysis of the classified transform domain features can be achieved by principal component analysis; the signal quality factors in the high dynamic range seismic data can be analyzed by comparing the feature principal components with the distribution range and threshold of the principal components in normal data.

[0089] S2. Perform variable gain processing on the high dynamic range seismic data to obtain target seismic data, analyze the geological characteristic parameters corresponding to the data acquisition area, extract seismic time history data and seismic multi-component data from the target seismic data, and combine the seismic time history data and the seismic multi-component data to analyze the focal mechanism attributes corresponding to the data acquisition area.

[0090] This invention enables adaptive adjustment of signal strength by performing variable gain processing on the high dynamic range seismic data, thereby improving the identifiability of weak but valuable signals and balancing the energy differences of signals in different frequency bands or regions. This makes the output signal more reasonable in amplitude, facilitating subsequent analysis and processing. It should be noted that the target seismic data is the high dynamic range seismic data after quality enhancement.

[0091] In detail, the process of performing variable gain processing on the high dynamic range seismic data to obtain the target seismic data includes:

[0092] The high dynamic range seismic data is subjected to time-varying gain processing to obtain time-varying gain seismic data;

[0093] Calculate the data entropy corresponding to the time-varying gain seismic data, and determine the data complexity corresponding to the time-varying gain seismic data based on the data entropy;

[0094] Based on the data complexity, the time-varying gain seismic data is subjected to adaptive gain processing to obtain adaptive gain seismic data.

[0095] The adapted gain seismic data is subjected to smooth gain processing to obtain the target seismic data.

[0096] It should be explained that the time-varying gain seismic data is the high dynamic range seismic data after adjusting the gain according to time changes; the data entropy is a measure of the disorder or uncertainty of the time-varying gain seismic data; the data complexity is a quantitative representation of the signal complexity corresponding to the time-varying gain seismic data; and the adapted gain seismic data is the time-varying gain seismic data after targeted gain adaptation adjustment based on the data complexity.

[0097] Furthermore, the time-varying gain processing of the high dynamic range seismic data can establish a gain function based on the seismic wave propagation time, and achieve weighted adjustment of the seismic signal amplitude at different times according to time. The data entropy corresponding to the time-varying gain seismic data can be calculated by discretizing the time-varying gain seismic data and then using the Shannon entropy formula. Based on the data entropy, the data complexity corresponding to the time-varying gain seismic data can be determined by mapping the data entropy value to a preset data complexity level range. For example, low entropy values ​​correspond to low complexity, and high entropy values ​​correspond to high complexity. Based on the data complexity, the gain coefficients of different frequency bands and time intervals can be adjusted according to the complexity level. For high complexity regions, the gain can be appropriately increased to highlight details, and for low complexity regions, a moderate gain can be maintained. The time-varying gain seismic data can be adapted by performing gain processing to obtain adapted gain seismic data. The smoothing gain processing of the adapted gain seismic data can be achieved by smoothing the gain coefficient using a moving average method or a low-pass filtering algorithm.

[0098] This invention analyzes the geological feature parameters corresponding to the data acquisition area to obtain indicators of various properties and characteristics of the quantitative geological bodies in the data acquisition area, thereby improving the accuracy of subsequent analysis of the focal mechanism attributes corresponding to the data acquisition area. It should be explained that the geological feature parameters are the essential attribute descriptions of the data acquisition area.

[0099] In detail, the analysis of the geological feature parameters corresponding to the data acquisition area includes:

[0100] Collect regional exploration data corresponding to the data acquisition area, and filter the regional exploration data to obtain filtered exploration data;

[0101] The filtered exploration data is then integrated to obtain integrated exploration data.

[0102] The integrated exploration data is then subjected to data correction processing to obtain corrected exploration data;

[0103] By combining the preset geological feature indicators and the corrected exploration data, the geological feature parameters corresponding to the data collection area are analyzed.

[0104] It should be explained that the regional exploration data refers to the raw data obtained through various exploration methods corresponding to the data collection area; the filtered exploration data refers to the data obtained after the regional exploration data has undergone screening operations such as removing errors and irrelevant data; the integrated exploration data refers to the data obtained after the filtered exploration data has undergone merging and sorting operations on data from different sources or types; the corrected exploration data refers to the data obtained after the integrated exploration data has undergone correction processing to correct deviations and inaccuracies in the data; and the geological feature indicators are standard parameters or reference values ​​used to measure and describe the geological features of the data collection area.

[0105] Furthermore, the collection of regional exploration data corresponding to the data acquisition area can be achieved through various geological exploration methods, such as deploying seismic exploration instruments in the area, conducting drilling to obtain core samples, and carrying out geological mapping. The filtering of the regional exploration data can be achieved by setting data quality standards and using algorithms to remove obvious errors, anomalies, and data irrelevant to the research objectives. The data integration of the filtered exploration data can be achieved by merging and organizing according to the data structure. The data correction of the integrated exploration data can be achieved by using professional geological data processing software, based on geological theories and experience, to correct errors and inconsistencies in the data. Combining preset geological characteristic indicators and the corrected exploration data, statistical analysis methods can be used to analyze the geological characteristic parameters corresponding to the data acquisition area. If the geological characteristic parameters need to be calculated, they can be calculated using relevant formulas or algorithms in conjunction with the corrected exploration data, such as stratum thickness = 1 / 2 (sound velocity × reflection time difference).

[0106] This invention combines the earthquake time history data and the earthquake multi-component data to analyze the focal mechanism attributes corresponding to the data acquisition area, thereby obtaining characteristic parameters of the mechanical process of earthquakes occurring at the earthquake source in the data acquisition area.

[0107] In detail, the analysis of the focal mechanism attributes corresponding to the data acquisition area, combining the earthquake time history data and the earthquake multi-component data, includes:

[0108] Wavefield identification processing is performed on the multi-component seismic data to obtain seismic shear waves and seismic P-waves;

[0109] Based on the earthquake time history data, the arrival time differences of the earthquake shear wave and the earthquake p-wave at different observation points are calculated respectively, and the shear wave time difference and p-wave time difference are obtained.

[0110] Calculate the propagation velocities of the seismic shear wave and the seismic longitudinal wave respectively to obtain the shear wave velocity and the longitudinal wave velocity;

[0111] By combining the shear wave time difference, the longitudinal wave time difference, the shear wave velocity, and the longitudinal wave velocity, the source location and source depth corresponding to the data acquisition area are analyzed.

[0112] By combining the location and depth of the seismic source, a regional seismic source sphere corresponding to the data acquisition area is constructed;

[0113] Calculate the focal mechanism solution corresponding to the focal sphere in the region, and analyze the focal mechanism attributes corresponding to the data acquisition area based on the focal mechanism solution.

[0114] It should be explained that the seismic shear wave and the seismic p-wave are the constituent waveforms of the multi-component seismic data; the shear wave time difference and the p-wave time difference are the time intervals between the arrival of the seismic shear wave and the seismic p-wave at different observation points; the shear wave velocity and the p-wave velocity are the corresponding propagation velocities of the seismic shear wave and the seismic p-wave; the focal location and the focal depth are the spatial location information of the seismic source in the data acquisition area; the regional focal sphere is a virtual sphere corresponding to the data acquisition area, centered on the focal point, representing the initial motion direction of the seismic waves; and the focal mechanism solution is the solution corresponding to the regional focal sphere that describes the fault movement mode and stress state at the focal point.

[0115] Furthermore, the wavefield identification processing of the multi-component seismic data can be achieved by employing advanced signal analysis algorithms and physical model-based feature extraction methods, such as wavelet transform and principal component analysis, to distinguish different types of wavefields. Combined with the seismic time history data, precise wave arrival time picking algorithms, such as those based on energy thresholding and correlation analysis, can be used to calculate the arrival time differences of the seismic shear waves and the seismic p-waves at different observation points, thus obtaining the shear wave time difference and the p-wave time difference. Combining the shear wave time difference, the p-wave time difference, the shear wave velocity, and the... The P-wave velocity can be determined using an inversion algorithm based on the hyperbolic positioning principle, combined with optimization methods such as the least squares method, to analyze the source location and source depth corresponding to the data acquisition area. Assuming there are three seismic observation points A, B, and C within a planar area, and the known S-wave velocity is VS, the P-wave velocity is VP, and the time difference between the P-wave and S-wave recorded at observation point A is ΔtA, at point B it is ΔtB, and at point C it is ΔtC, according to the hyperbolic positioning principle, different hyperbolic equations can be obtained with the observation points as the foci. For example, for observation points A and B, let the source location coordinates be (x, y), then: The least squares method is used to optimize the solution of these equations, finding the (x, y) that minimizes the error. Simultaneously, the focal depth can be calculated by combining the relationship between wave velocity and time difference. Based on the focal location and depth, a regional focal sphere corresponding to the data acquisition area can be constructed, centered on the focal point and according to the initial motion direction and propagation theory of seismic waves, with the initial motion direction of the seismic waves representing the initial polarity of the waveform. The focal mechanism solution corresponding to the regional focal sphere can be calculated using the nodal surface solution method combined with the theory of seismic wave radiation patterns. Based on the focal mechanism solution, the focal mechanism attributes corresponding to the data acquisition area can be analyzed by combining regional geological tectonic background and stress field analysis methods.

[0116] Furthermore, as an optional embodiment of the present invention, the step of calculating the propagation velocities corresponding to the seismic shear wave and the seismic P-wave respectively to obtain the shear wave velocity and the P-wave velocity includes:

[0117] Detect the propagation carriers corresponding to the seismic shear waves and the seismic longitudinal waves, and query the carrier density and carrier elastic modulus corresponding to the propagation carriers;

[0118] Combining the carrier density and the carrier elastic modulus, the propagation velocities of the seismic shear wave and the seismic p-wave are calculated using the following formulas to obtain the shear wave velocity and p-wave velocity, including:

[0119]

[0120] Where G represents the transverse wave velocity, D represents the longitudinal wave velocity, E represents the carrier elastic modulus, δ represents the carrier density, and β represents the carrier Poisson's ratio.

[0121] It should be explained that the propagation carrier is the propagation medium corresponding to the seismic shear wave and the seismic longitudinal wave; the carrier density and the carrier elastic modulus are the physical properties of the propagation carrier that affect the propagation velocity of the seismic wave; the carrier Poisson's ratio represents the elastic mechanical property of the ratio of transverse strain to longitudinal strain of the propagation carrier. Furthermore, the detection of the propagation carrier corresponding to the seismic shear wave and the seismic longitudinal wave can be achieved through geophysical exploration techniques (such as seismic tomography); the carrier density and carrier elastic modulus can be obtained through mechanical testing of rock core samples in the laboratory and by referring to regional geological data databases; the carrier Poisson's ratio can be obtained by measuring the strain through uniaxial or triaxial compression tests on rock samples.

[0122] S3. Query the data sampling mechanism and data acquisition equipment corresponding to the target seismic data. Based on the data sampling equipment, determine the number of data channels corresponding to the target seismic data. Combine the number of data channels and the data sampling mechanism to calculate the data storage capacity corresponding to the target seismic data.

[0123] This invention queries the data sampling mechanism and data acquisition equipment corresponding to the target seismic data. Based on the data sampling equipment, it determines the number of data channels corresponding to the target seismic data, enabling more accurate processing of seismic data and laying the foundation for subsequent calculation of the data storage capacity corresponding to the target seismic data. It should be explained that the data sampling mechanism refers to the rules such as sampling frequency and sampling time interval followed when acquiring data corresponding to the target seismic data; the data acquisition equipment is the instrument used to collect seismic signals corresponding to the target seismic data; and the number of data channels is the number of independent data transmission paths during the acquisition process of the target seismic data. Furthermore, the query of the data sampling mechanism and data acquisition equipment corresponding to the target seismic data can be achieved through human-computer interaction. The number of data channels corresponding to the target seismic data can be determined through the equipment manual of the data sampling equipment. The technical manual of a high-precision seismic data acquisition instrument will list in detail the data channels that can be acquired and processed simultaneously under its hardware architecture; this is the most direct way to obtain the number of data channels.

[0124] This invention calculates the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism. This allows for an understanding of the storage capacity requirements of the target seismic data, thereby facilitating the improvement of the accuracy of the subsequent construction of the data acquisition circuit corresponding to the target seismic data. It should be noted that the data storage capacity refers to the storage capacity corresponding to the target seismic data.

[0125] In detail, the calculation of the data storage capacity corresponding to the target seismic data, combining the number of data channels and the data sampling mechanism, includes:

[0126] Based on the data sampling mechanism, determine the sampling rate and sampling accuracy corresponding to the target seismic data;

[0127] Identify the acquisition timestamp corresponding to the target seismic data, and calculate the data duration period corresponding to the target seismic data based on the acquisition timestamp;

[0128] Combining the sampling rate, the sampling accuracy, the data duration, and the number of data channels, the data storage capacity corresponding to the target seismic data can be calculated using the following formula:

[0129] H = F * T * M * φ;

[0130] Where H represents the data storage capacity corresponding to the target seismic data, F represents the sampling rate, T represents the data duration period, M represents the number of data channels, and φ represents the sampling accuracy.

[0131] It should be explained that the sampling rate and the sampling precision are respectively the number of samples taken per unit time during the acquisition process of the target seismic data and the number of binary bits occupied by each sampled data; the acquisition timestamp is the identifier of the time of acquisition of the recorded data corresponding to the target seismic data; the data duration is the time range from the start to the end of acquisition corresponding to the target seismic data; furthermore, according to the data sampling mechanism, the sampling rate and sampling precision corresponding to the target seismic data can be determined by checking the setting parameters of the data acquisition equipment; the acquisition timestamp corresponding to the target seismic data can be obtained by identifying the metadata information in the data file header, and the data duration corresponding to the target seismic data can be obtained by subtracting the start timestamp from the end timestamp in the acquisition timestamp.

[0132] S4. Collect the device operation logs corresponding to the data acquisition device, analyze the circuit response performance of the data acquisition device based on the device operation logs, and construct the data acquisition circuit corresponding to the data acquisition device based on the circuit response performance, the factor energy value, the source mechanism attribute and the data storage capacity.

[0133] This invention analyzes the circuit response performance of the data acquisition device by combining the device operation log, which allows for an understanding of the circuit's performance in actual operation and timely detection of potential circuit problems. This provides a basis for improving the accuracy of subsequent data acquisition circuit construction. It should be noted that the device operation log is a document recording various states, events, and parameter changes during the operation of the data acquisition device, and the circuit response performance refers to the performance of the internal circuits of the data acquisition device. Furthermore, the device operation log can be collected by reading it through the built-in storage module.

[0134] In detail, the analysis of the circuit response performance of the data acquisition device in conjunction with the device operation log includes:

[0135] Identify the operating indicators and their corresponding operating information in the device's operating log;

[0136] Analyze the indicator attributes corresponding to the operation indicators, extract the circuit response indicators from the operation indicators based on the indicator attributes, and calculate the indicator weights corresponding to the circuit response indicators.

[0137] The circuit response metrics include circuit response time metrics, signal accuracy metrics, and circuit stability metrics;

[0138] Based on the aforementioned indicator operation information, the indicator parameter values ​​corresponding to the circuit response indicator are calculated.

[0139] Combining the indicator parameter values, the indicator weights, and the circuit response indicators, the performance value of the data acquisition device can be calculated using the following formula:

[0140]

[0141] Where Q represents the performance value corresponding to the circuit response index, and α1, α2, and α3 represent the index weights corresponding to the circuit response time index, signal accuracy index, and circuit stability index, respectively. This indicates the average response time among the index parameters related to circuit response time. σ represents the preset maximum average response time. N This represents the standard deviation of the response time among the index parameter values ​​related to circuit response time. This represents the average error in the strength of the indicator parameter values ​​related to signal accuracy. This represents the frequency relative error of the indicator parameter values ​​with respect to the accuracy of the signal. ΔS represents the average output signal strength corresponding to the circuit stability index among the index parameter values. max This indicates the signal strength range corresponding to the circuit stability index in the index parameter values;

[0142] Based on the aforementioned performance values, the circuit response performance of the data acquisition device is analyzed.

[0143] It should be explained that the operating indicators are quantitative data reflecting the operating status of the equipment in the equipment operation log; the indicator operating information is the specific changes and related details of the operating indicators; the indicator attributes are the inherent properties and characteristics corresponding to the operating indicators; the circuit response indicators are the operating indicators that are related to the circuit; the indicator weights represent the importance of the indicators corresponding to the circuit response indicators; the circuit response time indicators, the signal accuracy indicators, and the circuit stability indicators are the performance evaluation indicators of the circuit response indicators; and the indicator parameter values ​​are the sets of various parameters corresponding to the circuit response indicators, such as average, variance, and standard deviation.

[0144] Furthermore, the identification of operating indicators and their corresponding operating information in the equipment operation log can be achieved through data mining algorithms, including decision tree algorithms such as C4.5 and CART. The analysis of the indicator attributes corresponding to the operating indicators can be achieved based on relevant domain knowledge. For example, if voltage is an operating indicator, based on knowledge of circuit principles, voltage stability is a crucial indicator attribute. Stable voltage facilitates accurate data acquisition, while large voltage fluctuations may affect circuit response. By judging the voltage stability attribute, indicators related to circuit response can be better extracted. Based on these indicator attributes, circuit response indicators can be extracted from the operating indicators through causal relationship tracing. For instance, extraction can begin with indicator attributes that have a direct or indirect causal impact on circuit response performance. For example, temperature may affect the performance of components in the circuit, thereby affecting the circuit response. When abnormal temperature increases or decreases are detected, the system traces the potential impact on circuit response-related indicators, such as resistance values ​​and capacitor charging / discharging times. These temperature-dependent indicators are extracted as they are likely key factors causing changes in circuit response. The weights of these circuit response indicators can be calculated using the analytic hierarchy process (AHP). Based on the indicator operation information, the corresponding indicator parameter values ​​can be calculated using formulas such as the mean function and variance formula. Combining this information, specific calculation formulas and mathematical models can be used to calculate the performance values ​​of the circuit response indicators. For example, using response time-related formulas, the time standard deviation of the indicator's response time can be calculated, and this time standard deviation represents the performance value of the response time indicator. By comparing these performance values ​​with the standard performance values, the circuit response efficiency of the data acquisition equipment can be analyzed based on the comparison results.

[0145] This invention improves the accuracy of data acquisition device construction by combining the circuit response performance, the factor energy value, the source mechanism attributes, and the data storage capacity to construct the corresponding data acquisition circuit. This enhances the data quality of subsequent data acquisition. Furthermore, the circuit corresponding to the data acquisition device is optimized by combining the circuit response performance, the factor energy value, the source mechanism attributes, and the data storage capacity to obtain the data acquisition circuit. If the circuit response performance is poor, the amplifier in the circuit can be an operational amplifier with higher bandwidth and faster conversion rate. The cutoff value of the low-pass filter in the circuit is set according to the upper and lower limits of the factor energy value. If the source mechanism is a strike-slip earthquake, the horizontal component of the seismic wave has strong energy and its propagation direction is mainly along a specific fault direction. Therefore, the circuit parameters of the horizontal signal acquisition channel can be optimized, such as increasing the amplifier gain and signal conditioning circuit precision to ensure accurate acquisition and processing of strong horizontal seismic wave signals. Based on the data storage capacity, the remaining storage capacity of the current storage device is determined. A data compression algorithm can be configured in the data transmission circuit of the storage device to compress the transmitted data, such as using a lossless compression algorithm. The aforementioned optimized circuit is then added to the data acquisition device to obtain a complete data acquisition circuit.

[0146] Compared to the problems described in the background art, this invention, by extracting the transform domain features corresponding to the high dynamic range seismic data, can provide a more comprehensive and in-depth understanding of seismic signal characteristics, including signal frequency composition, energy distribution, and hidden information. This provides a more accurate and effective basis for subsequent seismic data analysis. By applying variable gain processing to the high dynamic range seismic data, this invention can adaptively adjust signal strength, improve the identifiability of weak but valuable signals, and balance the energy differences of signals in different frequency bands or regions, making the output signal more reasonable in amplitude, facilitating subsequent analysis and processing. This invention queries the data sampling mechanism and data acquisition equipment corresponding to the target seismic data, and based on the data sampling equipment, determines the number of data channels corresponding to the target seismic data, enabling more accurate processing of seismic data and laying the foundation for subsequent calculation of the data storage capacity corresponding to the target seismic data. By combining the equipment operation logs and analyzing the circuit response performance corresponding to the data acquisition equipment, this invention can understand the performance of the circuit in actual operation, promptly identify potential circuit problems, and provide a basis for improving the accuracy of the subsequent construction of the data acquisition circuit corresponding to the equipment circuit. Therefore, the present invention proposes a method for constructing a high dynamic range seismic data acquisition circuit, thereby improving the accuracy of constructing such a circuit.

[0147] Example 2:

[0148] like Figure 2 The diagram shown is a functional block diagram of a device for constructing a high dynamic range seismic data acquisition circuit according to an embodiment of the present invention.

[0149] The high dynamic range seismic data acquisition circuit construction device 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the high dynamic range seismic data acquisition circuit construction device 100 may include an energy value calculation module 101, an attribute analysis module 102, a storage capacity calculation module 103, and a circuit construction module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0150] In this embodiment, the functions of each module / unit are as follows:

[0151] The energy value calculation module 101 is used to acquire the high dynamic range seismic data to be analyzed and the data acquisition area, extract the transform domain features corresponding to the high dynamic range seismic data, analyze the signal inferiority factor in the high dynamic range seismic data based on the transform domain features, and calculate the factor energy value corresponding to the signal inferiority factor.

[0152] The attribute analysis module 102 is used to perform variable gain processing on the high dynamic range seismic data to obtain target seismic data, analyze the geological feature parameters corresponding to the data acquisition area, extract seismic time history data and seismic multi-component data from the target seismic data, and combine the seismic time history data and seismic multi-component data to analyze the focal mechanism attributes corresponding to the data acquisition area.

[0153] The storage capacity calculation module 103 is used to query the data sampling mechanism and data acquisition equipment corresponding to the target seismic data, determine the number of data channels corresponding to the target seismic data based on the data sampling equipment, and calculate the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism.

[0154] The circuit construction module 104 is used to collect the device operation logs corresponding to the data acquisition device, analyze the circuit response performance of the data acquisition device in combination with the device operation logs, and construct the data acquisition circuit corresponding to the data acquisition device in combination with the circuit response performance, the factor energy value, the source mechanism attribute and the data storage capacity.

[0155] In detail, the modules described in the high dynamic range seismic data acquisition circuit construction device 100 in this application embodiment adopt the same characteristics as described above during use. Figure 1 The method used is the same as the one described above for constructing a high dynamic range seismic data acquisition circuit, and it can produce the same technical effect, so it will not be repeated here.

[0156] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing a high dynamic range seismic data acquisition circuit, characterized in that, The method includes: Acquire high dynamic range (HMR) seismic data and the data acquisition area to be analyzed, extract the transform domain features corresponding to the HMR seismic data, analyze the signal degradation factors in the HMR seismic data based on the transform domain features, and calculate the factor energy values ​​corresponding to the signal degradation factors. The extraction of the transform domain features corresponding to the HMR seismic data includes: The high dynamic range seismic data is subjected to Fourier transform processing to obtain the seismic spectrum. Identify the spectral peak characteristics in the seismic spectrum and calculate the effective bandwidth of the seismic spectrum; Wavelet transform processing is performed on the high dynamic range seismic data to obtain transformed seismic data; Extract the instantaneous frequency features from the transformed seismic data; By combining the spectral peak characteristics, the effective spectral bandwidth, and the instantaneous frequency characteristics, transform domain characteristics corresponding to the high dynamic range seismic data are generated; The step of analyzing the signal degradation factors in the high dynamic range seismic data based on the transform domain characteristics includes: Identify the feature identifiers corresponding to the transform domain features, and analyze the feature semantics corresponding to the transform domain features based on the feature identifiers; Based on the aforementioned semantic features, the transform domain features are subjected to feature classification processing to obtain classified transform domain features; Principal component analysis was performed on the classification transformation domain features to obtain the feature principal components; Based on the aforementioned principal components, the signal quality factors in the high dynamic range seismic data are analyzed. The high dynamic range seismic data is processed with variable gain to obtain target seismic data. The geological characteristic parameters corresponding to the data acquisition area are analyzed. Seismic time history data and seismic multi-component data are extracted from the target seismic data. Combining the seismic time history data and the seismic multi-component data with the focal location and focal depth, a regional focal sphere corresponding to the data acquisition area is constructed. The focal mechanism solution corresponding to the regional focal sphere is calculated. Based on the focal mechanism solution, the focal mechanism attributes corresponding to the data acquisition area are analyzed. The step of performing variable gain processing on the high dynamic range seismic data to obtain the target seismic data includes: The high dynamic range seismic data is subjected to time-varying gain processing to obtain time-varying gain seismic data; Calculate the data entropy corresponding to the time-varying gain seismic data, and determine the data complexity corresponding to the time-varying gain seismic data based on the data entropy; Based on the data complexity, the time-varying gain seismic data is subjected to adaptive gain processing to obtain adaptive gain seismic data. The adapted gain seismic data is subjected to smooth gain processing to obtain the target seismic data; The data sampling mechanism and data acquisition equipment corresponding to the target seismic data are queried. Based on the data acquisition equipment, the number of data channels corresponding to the target seismic data is determined. Combining the number of data channels and the data sampling mechanism, the data storage capacity corresponding to the target seismic data is calculated. Collect the device operation logs corresponding to the data acquisition device, analyze the circuit response performance of the data acquisition device based on the device operation logs, and construct the data acquisition circuit corresponding to the data acquisition device based on the circuit response performance, the factor energy value, the source mechanism attribute and the data storage capacity. The step of analyzing the circuit response performance of the data acquisition device in conjunction with the device operation log includes: Identify the operating indicators and their corresponding operating information in the device's operating log; Analyze the indicator attributes corresponding to the operation indicators, extract the circuit response indicators from the operation indicators based on the indicator attributes, and calculate the indicator weights corresponding to the circuit response indicators. The circuit response metrics include circuit response time metrics, signal accuracy metrics, and circuit stability metrics; Based on the aforementioned indicator operation information, the indicator parameter values ​​corresponding to the circuit response indicator are calculated. Combining the indicator parameter values, the indicator weights, and the circuit response indicators, the performance value of the data acquisition device is calculated using the following formula: ； Where Q represents the performance value corresponding to the circuit response index. , , These represent the weights of the circuit response time index, signal accuracy index, and circuit stability index, respectively. This indicates the average response time among the index parameters related to circuit response time. This indicates the preset maximum average response time. This represents the standard deviation of the response time among the index parameter values ​​related to circuit response time. This represents the average error in the strength of the indicator parameter values ​​related to signal accuracy. This represents the frequency relative error of the indicator parameter values ​​with respect to the accuracy of the signal. This represents the average output signal strength corresponding to the circuit stability index among the index parameter values. This indicates the signal strength range corresponding to the circuit stability index in the index parameter values; Based on the aforementioned performance values, the circuit response performance of the data acquisition device is analyzed.

2. The method for constructing a high dynamic range seismic data acquisition circuit as described in claim 1, characterized in that, The analysis of the geological feature parameters corresponding to the data acquisition area includes: Collect regional exploration data corresponding to the data acquisition area, and filter the regional exploration data to obtain filtered exploration data; The filtered exploration data is then integrated to obtain integrated exploration data. The integrated exploration data is then subjected to data correction processing to obtain corrected exploration data; By combining the preset geological feature indicators and the corrected exploration data, the geological feature parameters corresponding to the data collection area are analyzed.

3. The method for constructing a high dynamic range seismic data acquisition circuit as described in claim 1, characterized in that, The analysis of the focal mechanism attributes corresponding to the data acquisition area, combining the earthquake time history data and the earthquake multi-component data, includes: Wavefield identification processing is performed on the multi-component seismic data to obtain seismic shear waves and seismic P-waves; Based on the earthquake time history data, the arrival time differences of the earthquake shear wave and the earthquake p-wave at different observation points are calculated respectively, and the shear wave time difference and p-wave time difference are obtained. Calculate the propagation velocities of the seismic shear wave and the seismic longitudinal wave respectively to obtain the shear wave velocity and the longitudinal wave velocity; By combining the shear wave time difference, the longitudinal wave time difference, the shear wave velocity, and the longitudinal wave velocity, the source location and source depth corresponding to the data acquisition area are analyzed. By combining the location and depth of the seismic source, a regional seismic source sphere corresponding to the data acquisition area is constructed; Calculate the focal mechanism solution corresponding to the focal sphere in the region, and analyze the focal mechanism attributes corresponding to the data acquisition area based on the focal mechanism solution.

4. The method for constructing a high dynamic range seismic data acquisition circuit as described in claim 3, characterized in that, The step of calculating the propagation velocities of the seismic shear wave and the seismic longitudinal wave respectively, to obtain the shear wave velocity and the longitudinal wave velocity, includes: Detect the propagation carriers corresponding to the seismic shear waves and the seismic longitudinal waves, and query the carrier density and carrier elastic modulus corresponding to the propagation carriers; Combining the carrier density and the carrier elastic modulus, the propagation velocities of the seismic shear wave and the seismic p-wave are calculated using the following formulas to obtain the shear wave velocity and p-wave velocity, including: ； Where G represents the transverse wave velocity, D represents the longitudinal wave velocity, and E represents the elastic modulus of the carrier. Indicates carrier density, This represents the Poisson's ratio of the carrier.

5. The method for constructing a high dynamic range seismic data acquisition circuit as described in claim 1, characterized in that, The step of calculating the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism includes: Based on the data sampling mechanism, determine the sampling rate and sampling accuracy corresponding to the target seismic data; Identify the acquisition timestamp corresponding to the target seismic data, and calculate the data duration period corresponding to the target seismic data based on the acquisition timestamp; Combining the sampling rate, sampling accuracy, data duration, and number of data channels, the data storage capacity corresponding to the target seismic data is calculated using the following formula: ； Where H represents the data storage capacity corresponding to the target seismic data, F represents the sampling rate, T represents the data duration, and M represents the number of data channels. Indicates the sampling precision.

6. A device for constructing a high dynamic range seismic data acquisition circuit, characterized in that, The device includes: The energy value calculation module is used to acquire the high dynamic range seismic data and data acquisition area to be analyzed, extract the transform domain features corresponding to the high dynamic range seismic data, analyze the signal inferiority factors in the high dynamic range seismic data based on the transform domain features, and calculate the factor energy value corresponding to the signal inferiority factors. The extraction of the transform domain features corresponding to the high dynamic range seismic data includes: The high dynamic range seismic data is subjected to Fourier transform processing to obtain the seismic spectrum. Identify the spectral peak characteristics in the seismic spectrum and calculate the effective bandwidth of the seismic spectrum; Wavelet transform processing is performed on the high dynamic range seismic data to obtain transformed seismic data; Extract the instantaneous frequency features from the transformed seismic data; By combining the spectral peak characteristics, the effective spectral bandwidth, and the instantaneous frequency characteristics, transform domain characteristics corresponding to the high dynamic range seismic data are generated; The step of analyzing the signal degradation factors in the high dynamic range seismic data based on the transform domain characteristics includes: Identify the feature identifiers corresponding to the transform domain features, and analyze the feature semantics corresponding to the transform domain features based on the feature identifiers; Based on the aforementioned semantic features, the transform domain features are subjected to feature classification processing to obtain classified transform domain features; Principal component analysis was performed on the classification transformation domain features to obtain the feature principal components; Based on the aforementioned principal components, the signal quality factors in the high dynamic range seismic data are analyzed. The attribute analysis module is used to perform variable gain processing on the high dynamic range seismic data to obtain target seismic data, analyze the geological feature parameters corresponding to the data acquisition area, extract seismic time history data and seismic multi-component data from the target seismic data, combine the seismic time history data and seismic multi-component data, and combine the seismic time history data and seismic multi-component data with the focal location and focal depth to construct the regional focal sphere corresponding to the data acquisition area, calculate the focal mechanism solution corresponding to the regional focal sphere, and analyze the focal mechanism attributes corresponding to the data acquisition area based on the focal mechanism solution. The step of performing variable gain processing on the high dynamic range seismic data to obtain the target seismic data includes: The high dynamic range seismic data is subjected to time-varying gain processing to obtain time-varying gain seismic data; Calculate the data entropy corresponding to the time-varying gain seismic data, and determine the data complexity corresponding to the time-varying gain seismic data based on the data entropy; Based on the data complexity, the time-varying gain seismic data is subjected to adaptive gain processing to obtain adaptive gain seismic data. The adapted gain seismic data is subjected to smooth gain processing to obtain the target seismic data; The storage capacity calculation module is used to query the data sampling mechanism and data acquisition equipment corresponding to the target seismic data, determine the number of data channels corresponding to the target seismic data based on the data acquisition equipment, and calculate the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism. The circuit construction module is used to collect the device operation logs corresponding to the data acquisition device, analyze the circuit response performance of the data acquisition device in combination with the device operation logs, and construct the data acquisition circuit corresponding to the data acquisition device in combination with the circuit response performance, the factor energy value, the source mechanism attribute and the data storage capacity. The step of analyzing the circuit response performance of the data acquisition device in conjunction with the device operation log includes: Identify the operating indicators and their corresponding operating information in the device's operating log; Analyze the indicator attributes corresponding to the operation indicators, extract the circuit response indicators from the operation indicators based on the indicator attributes, and calculate the indicator weights corresponding to the circuit response indicators. The circuit response metrics include circuit response time metrics, signal accuracy metrics, and circuit stability metrics; Based on the aforementioned indicator operation information, the indicator parameter values ​​corresponding to the circuit response indicator are calculated. Combining the indicator parameter values, the indicator weights, and the circuit response indicators, the performance value of the data acquisition device is calculated using the following formula: ； Where Q represents the performance value corresponding to the circuit response index. , , These represent the weights of the circuit response time index, signal accuracy index, and circuit stability index, respectively. This indicates the average response time among the index parameters related to circuit response time. This indicates the preset maximum average response time. This represents the standard deviation of the response time among the index parameter values ​​related to circuit response time. This represents the average error in the strength of the indicator parameter values ​​related to signal accuracy. This represents the frequency relative error of the indicator parameter values ​​with respect to the accuracy of the signal. This represents the average output signal strength corresponding to the circuit stability index among the index parameter values. This indicates the signal strength range corresponding to the circuit stability index in the index parameter values; Based on the aforementioned performance values, the circuit response performance of the data acquisition device is analyzed.

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