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

Abstract

The invention relates to the technical field of earthquake monitoring, and discloses a construction method and device for a high-dynamic-range earthquake data acquisition circuit, and the method comprises the steps: analyzing a signal inferior factor in high-dynamic-range earthquake data, and calculating a factor energy value corresponding to the signal inferior factor; performing variable gain processing on the high-dynamic-range seismic data to obtain target seismic data, analyzing geologic feature parameters corresponding to a data acquisition area, extracting seismic time history data and seismic multi-component data from the target seismic data, querying a data sampling mechanism and data acquisition equipment corresponding to the target seismic data, and acquiring the seismic data according to the data sampling mechanism and the data acquisition equipment. Determining the number of data channels corresponding to the target seismic data, and calculating the data storage capacity corresponding to the target seismic data; and analyzing the circuit response efficiency corresponding to the data acquisition equipment, and constructing a data acquisition circuit corresponding to the data acquisition equipment. The invention mainly aims to solve the problem of low construction accuracy of a high-dynamic-range seismic data acquisition circuit.

Description

Technical Field

[0001] The invention relates to a construction method and device of a high dynamic range earthquake data acquisition circuit, belonging to the technical field of earthquake monitoring. Background Art

[0002] Seismic data acquisition is crucial for earthquake research and monitoring. High dynamic range seismic data contains a variety of signal information 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 seismic data acquisition circuits are usually based on traditional circuit design concepts. The specific processing process is: sensor selection, based on the frequency range and intensity of seismic waves, select high-sensitivity, wide-band sensors, such as seismic detectors with excellent low-frequency characteristics; signal conditioning circuits, design multi-stage amplification circuits to increase the amplitude of weak signals, and use high-precision filtering circuits to filter out noise and interference frequencies; analog-to-digital conversion modules, select high-resolution, high-speed ADCs to ensure accurate conversion of analog signals with a wide dynamic range; control and data processing, use FPGAs or high-performance microcontrollers to achieve precise control of the acquisition process and real-time processing of data. However, this method increases the cost of circuit construction and does not consider the various characteristics of seismic signals, which in turn leads to reduced accuracy in the construction of high dynamic range seismic data acquisition circuits. Summary of the invention

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

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

[0006] Acquire high dynamic range seismic data to be analyzed and a data acquisition area, extract transform domain features corresponding to the high dynamic range seismic data, analyze signal poor quality factors in the high dynamic range seismic data based on the transform domain features, and calculate factor energy values ​​corresponding to the signal poor quality factors;

[0007] Performing variable gain processing on the high dynamic range seismic data to obtain target seismic data, analyzing geological characteristic parameters corresponding to the data acquisition area, extracting seismic time history data and seismic multi-component data from the target seismic data, and combining the seismic time history data and the seismic multi-component data to analyze the source mechanism attributes corresponding to the data acquisition area;

[0008] Querying the data sampling mechanism and data acquisition device corresponding to the target seismic data, determining the number of data channels corresponding to the target seismic data based on the data sampling device, and calculating the data storage capacity corresponding to the target seismic data in combination with the number of data channels and the data sampling mechanism;

[0009] Collect the device operation log corresponding to the data acquisition device, analyze the circuit response performance corresponding to the data acquisition device in combination with the device operation log, 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.

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

[0011] Performing Fourier transform processing on the high dynamic range seismic data to obtain a seismic spectrum diagram;

[0012] Identifying the peak characteristics of the spectrum in the seismic spectrum diagram, and calculating the effective bandwidth of the spectrum in the seismic spectrum diagram;

[0013] Performing wavelet transform processing on the high dynamic range seismic data to obtain transformed seismic data;

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

[0015] The spectrum peak feature, the spectrum effective bandwidth and the instantaneous frequency feature are combined to generate transform domain features corresponding to the high dynamic range seismic data.

[0016] Optionally, analyzing the signal poor quality factor in the high dynamic range seismic data based on the transform domain feature includes:

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

[0018] Based on the feature semantics, performing feature classification processing on the transform domain features to obtain classified transform domain features;

[0019] Performing principal component analysis on the classification transformation domain features to obtain characteristic principal components;

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

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

[0022] Performing time-varying gain processing on the high dynamic range seismic data to obtain time-varying gain seismic data;

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

[0024] Based on the data complexity, performing adaptive gain processing on the time-varying gain seismic data to obtain adaptive gain seismic data;

[0025] The adaptive gain seismic data is subjected to smoothing gain processing to obtain target seismic data.

[0026] Optionally, the analyzing the geological characteristic parameters corresponding to the data acquisition area includes:

[0027] Collecting regional exploration data corresponding to the data collection area, and filtering the regional exploration data to obtain filtered exploration data;

[0028] Performing data integration processing on the filtered exploration data to obtain integrated exploration data;

[0029] Performing data correction processing on the integrated exploration data to obtain corrected exploration data;

[0030] The geological characteristic parameters corresponding to the data acquisition area are analyzed in combination with the preset geological characteristic indicators and the corrected exploration data.

[0031] Optionally, 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] Performing wave field identification processing on the seismic multi-component data to obtain seismic shear waves and seismic longitudinal waves;

[0033] In combination with the earthquake time history data, respectively calculating the time difference between the arrival of the earthquake shear wave and the earthquake longitudinal wave at different observation points to obtain the shear wave time difference and the longitudinal wave time difference;

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

[0035] Analyze the earthquake source position and earthquake source depth corresponding to the data acquisition area by combining the shear wave time difference, the longitudinal wave time difference, the shear wave velocity and the longitudinal wave velocity;

[0036] Combining the focal position and the focal depth, constructing a regional focal sphere corresponding to the data acquisition area;

[0037] The focal mechanism solution corresponding to the focal sphere of the region is calculated, and the focal mechanism properties corresponding to the data acquisition region are analyzed according to the focal mechanism solution.

[0038] Optionally, respectively calculating the propagation velocities corresponding to the seismic shear wave and the seismic longitudinal wave to obtain the shear wave velocity and the longitudinal wave velocity includes:

[0039] Detecting the propagation carriers corresponding to the seismic shear wave and the seismic longitudinal wave, and querying the carrier density and carrier elastic modulus corresponding to the propagation carrier;

[0040] Combining the carrier density and the carrier elastic modulus, the propagation velocities corresponding to the seismic shear wave and the seismic longitudinal wave are calculated respectively by the following formulas to obtain the shear wave velocity and the longitudinal wave velocity, including:

[0041]

[0042] Among them, G represents the shear 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, the calculating the data storage capacity corresponding to the target seismic data in combination with the number of data channels and the data sampling mechanism includes:

[0044] Determining the sampling rate and sampling accuracy corresponding to the target seismic data according to the data sampling mechanism;

[0045] Identify an acquisition timestamp corresponding to the target seismic data, and calculate a 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 period and the number of data channels, the data storage capacity corresponding to the target seismic data can be calculated by the following formula:

[0047] H=F*T*M*φ;

[0048] Among them, 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, analyzing the circuit response performance corresponding to the data acquisition device in combination with the device operation log includes:

[0050] Identify the operation index and its corresponding index operation information in the equipment operation log;

[0051] Analyzing the indicator attributes corresponding to the operation indicator, extracting the circuit response indicator from the operation indicator based on the indicator attributes, and calculating the indicator weight corresponding to the circuit response indicator;

[0052] The circuit response index includes a circuit response time index, a signal accuracy index and a circuit stability index;

[0053] Based on the indicator operation information, calculating the indicator parameter value corresponding to the circuit response indicator;

[0054] In combination with the index parameter value, the index weight and the circuit response index, the index performance value corresponding to the data acquisition device can be calculated by the following formula:

[0055]

[0056] Among them, Q represents the index performance value corresponding to the circuit response index, α1, α2, and α3 represent the index weights corresponding to the circuit response time index, signal accuracy index, and circuit stability index, respectively. Indicates the average response time corresponding to the circuit response time indicator in the indicator parameter value, represents the preset maximum average response time, σ N Indicates the standard deviation of the response time corresponding to the circuit response time indicator in the indicator parameter value, It represents the average error of the indicator parameter value in terms of the intensity of the signal accuracy indicator. It represents the relative frequency error of the indicator parameter value with respect to the signal accuracy indicator. Indicates the mean value of the output signal strength corresponding to the circuit stability index in the index parameter value, ΔS max Indicates the signal strength range corresponding to the circuit stability index in the index parameter value;

[0057] Based on the indicator performance value, the circuit response performance corresponding to 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] An energy value calculation module is used to obtain high dynamic range seismic data and data acquisition area to be analyzed, extract transform domain features corresponding to the high dynamic range seismic data, analyze signal poor quality factors in the high dynamic range seismic data based on the transform domain features, and calculate the factor energy value corresponding to the signal poor quality factor;

[0060] an attribute analysis module, for performing variable gain processing on the high dynamic range seismic data to obtain target seismic data, analyzing geological characteristic parameters corresponding to the data acquisition area, extracting seismic time history data and seismic multi-component data from the target seismic data, and combining the seismic time history data and the seismic multi-component data to analyze the source mechanism attributes corresponding to the data acquisition area;

[0061] A storage capacity calculation module, used to query the data sampling mechanism and data acquisition device corresponding to the target seismic data, determine the number of data channels corresponding to the target seismic data based on the data sampling device, and calculate the data storage capacity corresponding to the target seismic data in combination with the number of data channels and the data sampling mechanism;

[0062] A circuit construction module is used to collect the device operation log corresponding to the data acquisition device, analyze the circuit response performance corresponding to the data acquisition device in combination with the device operation log, 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 with the problems described in the background technology, the present invention can more comprehensively and deeply understand the characteristics of seismic signals, including signal frequency composition, energy distribution, hidden information, etc., by extracting the transform domain features corresponding to the high dynamic range seismic data, thereby providing a more accurate and effective basis for subsequent seismic data analysis. The present invention can adaptively adjust the signal strength by performing variable gain processing on the high dynamic range seismic data, improve the recognizability of weak but valuable signals, and balance the energy differences of signals in different frequency bands or regions, so that the output signal is more reasonable in amplitude, which is convenient for subsequent analysis and processing. The present invention queries the data sampling mechanism and data acquisition device corresponding to the target seismic data, and determines the number of data channels corresponding to the target seismic data based on the data sampling device, so as to more accurately process seismic data and lay a foundation for the subsequent calculation of the data storage capacity corresponding to the target seismic data. The present invention analyzes the circuit response efficiency corresponding to the data acquisition device in combination with the device operation log, so as to understand the performance of the circuit in actual operation, and timely discover potential problems of the circuit, which provides a basis for improving the accuracy of the construction of the data acquisition circuit corresponding to the subsequent device circuit. Therefore, the present invention proposes a method and device for constructing a high dynamic range seismic data acquisition circuit, thereby improving the construction accuracy of the high dynamic range seismic data acquisition circuit. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic flow chart of a method for constructing a high dynamic range seismic data acquisition circuit provided by an embodiment of the present invention;

[0065] Figure 2 A functional module diagram of a construction device for implementing the high dynamic range seismic data acquisition circuit provided in one embodiment of the present invention.

[0066] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0068] The embodiment of the present application provides a method for constructing a high dynamic range seismic data acquisition circuit. The execution subject of the method for constructing the high dynamic range seismic data acquisition circuit includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for constructing the high dynamic range seismic data acquisition circuit can be executed by software or hardware installed in 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, etc.

[0069] Embodiment 1:

[0070] Reference Figure 1 FIG. 1 is a flow chart of a method for constructing a high dynamic range seismic data acquisition circuit according to an embodiment of the present invention. In this embodiment, the method for constructing a high dynamic range seismic data acquisition circuit includes:

[0071] S1. Acquire high dynamic range seismic data and data acquisition area to be analyzed, extract transform domain features corresponding to the high dynamic range seismic data, analyze signal poor quality factors in the high dynamic range seismic data based on the transform domain features, and calculate factor energy values ​​corresponding to the signal poor quality factors.

[0072] By extracting the transform domain features corresponding to the high dynamic range seismic data, the present invention can more comprehensively and deeply understand the characteristics of seismic signals, including signal frequency composition, energy distribution, hidden information, etc., thereby providing a more accurate and effective basis for subsequent seismic data analysis. It should be explained that the high dynamic range seismic data is obtained during the seismic monitoring process and contains a series of data ranging from extremely weak background noise signals to strong seismic wave signals. These data have the characteristics of a wide dynamic range, and their amplitudes span 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, it covers rich seismic information, including seismic waves of different frequency components, from low-frequency surface waves to high-frequency body waves. These frequency components are intertwined in time and space. At the same time, these data also reflect the various changes caused by the influence of underground geological structures on seismic waves during propagation, such as changes in signal characteristics caused by reflection, refraction, scattering and other phenomena. The data acquisition area is the data acquisition area corresponding to the high dynamic range seismic data, and the transform domain feature is a collection of features of the high dynamic range seismic data including time domain, frequency domain and possible other transform domains (such as time-frequency domain, fractional Fourier domain, etc.).

[0073] In detail, the extracting the transform domain features corresponding to the high dynamic range seismic data includes:

[0074] Performing Fourier transform processing on the high dynamic range seismic data to obtain a seismic spectrum diagram;

[0075] Identifying the peak characteristics of the spectrum in the seismic spectrum diagram, and calculating the effective bandwidth of the spectrum in the seismic spectrum diagram;

[0076] Performing wavelet transform processing on the high dynamic range seismic data to obtain transformed seismic data;

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

[0078] The spectrum peak feature, the spectrum effective bandwidth and the instantaneous frequency feature are combined to generate transform domain features corresponding to the high dynamic range seismic data.

[0079] It should be explained that the seismic spectrum diagram is the frequency domain representation of the high dynamic range seismic data after Fourier transform, the spectrum peak feature is the frequency characteristic of the amplitude reaching the local maximum in the seismic spectrum diagram, the spectrum effective bandwidth is the frequency range containing the main energy in the seismic spectrum diagram, the transformed seismic data is the data with time-frequency characteristics obtained after the high dynamic range seismic data is transformed by wavelet transform, and the instantaneous frequency feature is the characteristic of the transformed seismic data that reflects the change of signal frequency over time.

[0080] Furthermore, the Fourier transform processing of the high dynamic range seismic data can be achieved by using a discrete Fourier transform (DFT) formula or a computing tool such as a fast Fourier transform (FFT) algorithm; the identification of the spectrum peak feature in the seismic spectrum diagram can be achieved by an algorithm that finds the local maximum value of the spectrum amplitude, such as a peak detection algorithm; the calculation of the effective bandwidth of the spectrum in the seismic spectrum diagram can be achieved by setting an energy threshold and determining the upper and lower limits of the frequency range according to the energy distribution; the wavelet transform processing of the high dynamic range seismic data can be achieved by selecting a suitable wavelet basis function and utilizing a convolution operation combined with a transformation algorithm of scale and translation parameters; the extraction of the instantaneous frequency features in the transformed seismic data can be achieved by a calculation method of taking the phase derivative of the wavelet coefficients.

[0081] The present invention analyzes the signal poor quality factor in the high dynamic range seismic data based on the transform domain characteristics, and can effectively identify the interference, noise or signal distortion parts existing in the high dynamic range seismic data, thereby improving the analysis accuracy of the signal poor quality factor. The factor energy value corresponding to the signal poor quality factor is calculated, and a quantitative representation of the signal poor quality factor can be obtained, which can more intuitively display the signal quality problem. It should be explained that the signal poor quality factor is the noise component in the high dynamic range seismic data. Furthermore, the calculation of the factor energy value corresponding to the signal poor quality factor can be achieved by the frequency domain integration method.

[0082] Specifically, the signal quality factor in the high dynamic range seismic data is analyzed based on the transform domain characteristics, including:

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

[0084] Based on the feature semantics, performing feature classification processing on the transform domain features to obtain classified transform domain features;

[0085] Performing principal component analysis on the classification transformation domain features to obtain characteristic principal components;

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

[0087] It should be explained that the feature identifier is the symbol or name corresponding to the transform domain feature for distinguishing and marking different features; the feature semantics is the explanation of the transform domain feature in physical meaning and actual application scenarios; the classified transform domain feature is the result of classifying the transform domain feature according to the feature semantics; the feature principal component is the main linear combination component of the classified transform domain feature obtained by principal component analysis.

[0088] Furthermore, the identification of feature identifiers corresponding to the transform domain features can be achieved through an analytical transformation 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 similarities of the physical meaning, data representation form and other similarities of the feature semantics to obtain classified transform domain features; the principal component analysis of the classified transform domain features can be achieved through the principal component analysis method; the signal inferiority 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 of 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 analyze the source mechanism attributes corresponding to the data acquisition area by combining the seismic time history data and the seismic multi-component data.

[0090] The present invention can adaptively adjust the signal strength by performing variable gain processing on the high dynamic range seismic data, thereby improving the recognizability of weak but valuable signals, and balancing the energy differences of signals in different frequency bands or regions, so that the output signal is more reasonable in amplitude, which is convenient for subsequent analysis and processing. It should be explained that the target seismic data is the data after the quality of the high dynamic range seismic data has been improved.

[0091] In detail, the variable gain processing is performed on the high dynamic range seismic data to obtain target seismic data, including:

[0092] Performing time-varying gain processing on the high dynamic range seismic data to obtain time-varying gain seismic data;

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

[0094] Based on the data complexity, performing adaptive gain processing on the time-varying gain seismic data to obtain adaptive gain seismic data;

[0095] The adaptive gain seismic data is subjected to smoothing gain processing to obtain target seismic data.

[0096] It should be explained that the time-varying gain seismic data is the data obtained after the gain of the high dynamic range seismic data is adjusted according to the time change; the data entropy is a measure of the degree of data disorder or uncertainty corresponding to the time-varying gain seismic data; the data complexity represents a quantitative manifestation of the signal complexity corresponding to the time-varying gain seismic data; the adapted gain seismic data is the data obtained after the time-varying gain seismic data is targetedly adapted and adjusted in gain according to the data complexity.

[0097] Furthermore, the time-varying gain processing of the high dynamic range seismic data can be achieved by establishing a gain function according to the propagation time of the seismic wave, and by weighted adjustment of the seismic signal amplitude at different times according to time; the calculation of the data entropy corresponding to the time-varying gain seismic data can be obtained by discretizing the time-varying gain seismic data and then calculating it 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 interval, for example, a low entropy value corresponds to a low complexity, and a high entropy value corresponds to a 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, the gain can be appropriately increased for high complexity areas to highlight the details, and a moderate gain can be maintained for low complexity areas, and the time-varying gain seismic data can be adaptively gain processed to obtain adaptive gain seismic data; the smoothing gain processing of the adaptive gain seismic data can be achieved by smoothing the gain coefficient using a moving average method or a low-pass filtering algorithm.

[0098] By analyzing the geological characteristic parameters corresponding to the data acquisition area, the present invention can obtain indicators of various properties and characteristics of the quantitative geological body 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 characteristic parameters are the essential attribute descriptions corresponding to the data acquisition area.

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

[0100] Collecting regional exploration data corresponding to the data collection area, and filtering the regional exploration data to obtain filtered exploration data;

[0101] Performing data integration processing on the filtered exploration data to obtain integrated exploration data;

[0102] Performing data correction processing on the integrated exploration data to obtain corrected exploration data;

[0103] The geological characteristic parameters corresponding to the data acquisition area are analyzed in combination with the preset geological characteristic indicators and the corrected exploration data.

[0104] It should be explained that the regional exploration data are the original data obtained through various exploration means corresponding to the data collection area; the filtered exploration data are the data obtained after the regional exploration data are screened by removing errors, irrelevant data and other screening operations; the integrated exploration data are the data obtained after the filtered exploration data are merged and sorted out from different sources or types; the corrected exploration data are the data obtained after the integrated exploration data are corrected for deviations and inaccuracies in the data; the geological characteristic indicators are standard parameters or reference values ​​used to measure and describe the geological characteristics of the data collection area.

[0105] Furthermore, the collection of regional exploration data corresponding to the data acquisition area can be achieved by various geological exploration means such as arranging seismic exploration instruments in the area, drilling to obtain core samples, and conducting geological mapping; the filtering processing of the regional exploration data can be achieved by setting data quality standards and using algorithms to eliminate obvious errors, anomalies, and data irrelevant to the research objectives; the data integration processing of the filtered exploration data can be achieved by merging and sorting according to the data structure; the data correction processing of the integrated exploration data can be achieved by using professional geological data processing software to correct errors and inconsistencies in the data based on geological theory and experience; in combination with 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 are calculated using relevant formulas or algorithms in combination with the corrected exploration data, such as formation thickness = 1 / 2 (sound wave velocity × reflection time difference).

[0106] The present invention combines the earthquake time history data and the earthquake multi-component data to analyze the source mechanism attributes corresponding to the data acquisition area, and can obtain the characteristic parameters of the mechanical process of the earthquake occurring at the earthquake source in the data acquisition area.

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

[0108] Performing wave field identification processing on the seismic multi-component data to obtain seismic shear waves and seismic longitudinal waves;

[0109] In combination with the earthquake time history data, respectively calculating the time difference between the arrival of the earthquake shear wave and the earthquake longitudinal wave at different observation points to obtain the shear wave time difference and the longitudinal wave time difference;

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

[0111] Analyze the earthquake source position and earthquake source depth corresponding to the data acquisition area by combining the shear wave time difference, the longitudinal wave time difference, the shear wave velocity and the longitudinal wave velocity;

[0112] Combining the focal position and the focal depth, constructing a regional focal sphere corresponding to the data acquisition area;

[0113] The focal mechanism solution corresponding to the focal sphere of the region is calculated, and the focal mechanism properties corresponding to the data acquisition region are analyzed according to the focal mechanism solution.

[0114] It should be explained that the seismic shear wave and the seismic longitudinal wave are the constituent wave types of the seismic multi-component data; the shear wave time difference and the longitudinal wave time difference are the time intervals between the seismic shear wave and the seismic longitudinal wave arriving at different observation points; the shear wave velocity and the longitudinal wave velocity are the corresponding propagation velocities of the seismic shear wave and the seismic longitudinal wave; the focal position and the focal depth are the spatial position information of the earthquake source in the data acquisition area; the regional focal sphere is a virtual sphere corresponding to the data acquisition area, which is centered on the focal point and represents the initial motion direction of the seismic wave; 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 wave field recognition processing of the seismic multi-component data can be achieved by adopting advanced signal analysis algorithms and feature extraction methods based on physical models, such as using wavelet transform, principal component analysis and other means to distinguish different types of wave fields; combined with the seismic time history data, the time difference between the arrival of the seismic shear wave and the seismic longitudinal wave at different observation points can be calculated by an accurate wave arrival time picking algorithm, such as based on energy threshold, correlation analysis and other methods, to obtain the shear wave time difference and the longitudinal wave time difference; combined with the shear wave time difference, the longitudinal wave time difference, the shear wave velocity and the The longitudinal wave velocity can be analyzed by an inversion algorithm based on the hyperbola positioning principle, combined with optimization methods such as the least square method, to obtain the focal position and focal depth corresponding to the data acquisition area. Assuming that there are three seismic observation points A, B, and C in a plane area, the shear wave velocity is VS, the longitudinal wave velocity is VP, and the time difference between the longitudinal wave and the shear wave recorded at observation point A is ΔtA, ΔtB at point B, and ΔtC at point C. According to the hyperbola positioning principle, different hyperbola equations can be obtained with the observation point as the focus. For example, for observation points A and B, assuming the focal position coordinates are (x, y), then:

[0116] The least square method is used to optimize and solve these equations to find the (x, y) that minimizes the error. At the same time, the focal depth can be calculated by combining the relationship between wave velocity and time difference. In combination with the focal position and the focal depth, a regional focal sphere corresponding to the data acquisition area can be constructed with the focal as the center according to the initial motion direction of the seismic wave and the propagation theory, and the initial motion direction of the seismic wave is the starting polarity of the waveform. The focal mechanism solution corresponding to the regional focal sphere can be calculated by using the node solution method combined with the seismic wave radiation pattern theory. Based on the focal mechanism solution, the focal mechanism properties corresponding to the data acquisition area can be analyzed by combining the regional geological structure background and the stress field analysis method.

[0117] Further, as an optional embodiment of the present invention, respectively calculating the propagation velocities corresponding to the seismic shear wave and the seismic longitudinal wave to obtain the shear wave velocity and the longitudinal wave velocity includes:

[0118] Detecting the propagation carriers corresponding to the seismic shear wave and the seismic longitudinal wave, and querying the carrier density and carrier elastic modulus corresponding to the propagation carrier;

[0119] Combining the carrier density and the carrier elastic modulus, the propagation velocities corresponding to the seismic shear wave and the seismic longitudinal wave are calculated respectively by the following formulas to obtain the shear wave velocity and the longitudinal wave velocity, including:

[0120]

[0121] Among them, G represents the shear 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.

[0122] 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 respectively the physical properties of the propagation carrier that affect the propagation speed of the seismic wave; the carrier Poisson's ratio represents the elastic mechanical property of the ratio of the lateral strain to the longitudinal strain corresponding to the propagation carrier. Furthermore, the detection of the propagation carriers corresponding to the seismic shear wave and the seismic longitudinal wave can be achieved through geophysical exploration technology (such as seismic tomography); the query of the carrier density and carrier elastic modulus corresponding to the propagation carrier can be obtained through laboratory mechanical testing of core samples and reference to regional geological data databases; the carrier Poisson's ratio can be obtained by measuring the strain of rock samples through uniaxial or triaxial compression tests.

[0123] S3. Query the data sampling mechanism and data acquisition device corresponding to the target seismic data, determine the number of data channels corresponding to the target seismic data based on the data sampling device, and calculate the data storage capacity corresponding to the target seismic data in combination with the number of data channels and the data sampling mechanism.

[0124] The present invention queries the data sampling mechanism and data acquisition device corresponding to the target seismic data, and determines the number of data channels corresponding to the target seismic data based on the data sampling device, so as to process seismic data more accurately and lay a foundation for the subsequent calculation of the data storage capacity corresponding to the target seismic data. It should be explained that the data sampling mechanism is the sampling frequency, sampling time interval and other rules followed when acquiring data corresponding to the target seismic data; the data acquisition device is the instrument for collecting seismic signals corresponding to the target seismic data; the number of data channels is the number of independent data transmission paths corresponding to the target seismic data during the acquisition process. Furthermore, the query of the data sampling mechanism and data acquisition device 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 device. The technical manual of a high-precision seismic data acquisition instrument will list in detail the data channels that can be simultaneously acquired and processed under its hardware architecture. This is the most direct way to obtain the number of data channels.

[0125] The present invention calculates the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism, so as to understand the storage capacity requirements of the target seismic data, thereby facilitating improving the accuracy of subsequent construction of the data acquisition circuit corresponding to the target seismic data. It should be explained that the data storage capacity is the storage capacity corresponding to the target seismic data.

[0126] In detail, the calculating the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism includes:

[0127] Determining the sampling rate and sampling accuracy corresponding to the target seismic data according to the data sampling mechanism;

[0128] Identify an acquisition timestamp corresponding to the target seismic data, and calculate a data duration period corresponding to the target seismic data based on the acquisition timestamp;

[0129] Combining the sampling rate, the sampling accuracy, the data duration period and the number of data channels, the data storage capacity corresponding to the target seismic data can be calculated by the following formula:

[0130] H=F*T*M*φ;

[0131] Among them, 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.

[0132] It should be explained that the sampling rate and the sampling accuracy are respectively the number of samplings per unit time during the acquisition process corresponding to the target seismic data and the number of binary bits occupied by each sampled data; the acquisition timestamp is the identifier of the acquisition moment of the recorded data corresponding to the target seismic data; the data duration period is the time range from the start to the end of the acquisition corresponding to the target seismic data; further, according to the data sampling mechanism, the sampling rate and sampling accuracy corresponding to the target seismic data can be determined by checking the setting parameters of the data acquisition device; 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 period corresponding to the target seismic data can be obtained based on the end timestamp in the acquisition timestamp minus the start timestamp.

[0133] S4. Collect the device operation log corresponding to the data acquisition device, analyze the circuit response performance corresponding to the data acquisition device in combination with the device operation log, 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.

[0134] The present invention analyzes the circuit response efficiency corresponding to the data acquisition device in combination with the device operation log, thereby understanding the performance of the circuit in actual operation, and timely discovering potential problems in the circuit, providing a basis for improving the accuracy of the subsequent construction of the data acquisition circuit corresponding to the device circuit. It should be explained that the device operation log is a document that records various states, events, and parameter changes during the operation of the data acquisition device. The circuit response efficiency is the performance of the internal circuit corresponding to the data acquisition device. Furthermore, the collection of the device operation log corresponding to the data acquisition device can be read through the built-in storage module.

[0135] In detail, analyzing the circuit response performance corresponding to the data acquisition device in combination with the device operation log includes:

[0136] Identify the operation index and its corresponding index operation information in the equipment operation log;

[0137] Analyzing the indicator attributes corresponding to the operation indicator, extracting the circuit response indicator from the operation indicator based on the indicator attributes, and calculating the indicator weight corresponding to the circuit response indicator;

[0138] The circuit response index includes a circuit response time index, a signal accuracy index and a circuit stability index;

[0139] Based on the indicator operation information, calculating the indicator parameter value corresponding to the circuit response indicator;

[0140] In combination with the index parameter value, the index weight and the circuit response index, the index performance value corresponding to the data acquisition device can be calculated by the following formula:

[0141]

[0142] Among them, Q represents the index performance value corresponding to the circuit response index, α1, α2, and α3 represent the index weights corresponding to the circuit response time index, signal accuracy index, and circuit stability index, respectively. Indicates the average response time corresponding to the circuit response time indicator in the indicator parameter value, represents the preset maximum average response time, σ N Indicates the standard deviation of the response time corresponding to the circuit response time indicator in the indicator parameter value, It represents the average error of the indicator parameter value in terms of the intensity of the signal accuracy indicator. It represents the relative frequency error of the indicator parameter value with respect to the signal accuracy indicator. Indicates the mean value of the output signal strength corresponding to the circuit stability index in the index parameter value, ΔS max Indicates the signal strength range corresponding to the circuit stability index in the index parameter value;

[0143] Based on the indicator performance value, the circuit response performance corresponding to the data acquisition device is analyzed.

[0144] It should be explained that the operating indicators are quantitative data in the equipment operation log that reflect the operating status of the equipment, the indicator operation information is the specific changes and related details of the operating indicators, the indicator attributes are the intrinsic properties and characteristics corresponding to the operating indicators, the circuit response indicators are indicators in the operating indicators that have a correlation with the circuit, the indicator weight represents the indicator importance corresponding to the circuit response indicator, the circuit response time indicator, the signal accuracy indicator and the circuit stability indicator are indicators for performance evaluation of the circuit response indicators, and the indicator parameter value is a set of parameters corresponding to the circuit response indicators, such as the mean, variance, standard deviation, etc.

[0145] Furthermore, the identification of the operating indicators and their corresponding indicator operating information in the equipment operation log can be achieved through a data mining algorithm, and the data mining algorithm includes a decision tree algorithm, such as C4.5 and CART algorithm; the analysis of the indicator attributes corresponding to the operating indicators can be achieved based on relevant field knowledge. If there is an operating indicator such as voltage, based on the relevant field knowledge of circuit principles, the indicator attribute of voltage stability is very critical. Stable voltage is conducive to accurate data collection. If the voltage fluctuates greatly, it may affect the circuit response. By judging the attribute of voltage stability, the indicators related to the circuit response can be better extracted; based on the indicator attributes, the circuit response indicator can be extracted from the operating indicators through a causal tracing method, such as starting from the indicator attributes that have a direct or indirect causal impact on the circuit response performance. For example, the indicator attribute of temperature may affect the performance of components in the circuit, thereby affecting the circuit response. If in the operation day If the temperature is found to be abnormally increased or decreased in the log, the circuit response related indicators that may be affected are traced, such as the resistance value, the charging and discharging time of the capacitor and other operating indicators, and these indicators that are causally affected by the temperature are extracted, because they are likely to be the key factors causing the circuit response change; the calculation of the indicator weight corresponding to the circuit response indicator can be achieved through the hierarchical analysis method; based on the indicator operation information, the indicator parameter value corresponding to the circuit response indicator can be calculated through the corresponding formula, such as the average function, the variance formula and other formulas; combined with the indicator operation information, the indicator performance value corresponding to the circuit response indicator can be calculated by using a specific calculation formula and a mathematical model, such as the response time related formula, and the time standard deviation corresponding to the indicator operation response time is calculated, and the time standard deviation is the indicator performance value corresponding to the response time indicator; the indicator performance value can be compared with the standard performance value, and the circuit response efficiency corresponding to the data acquisition device can be analyzed according to the comparison result.

[0146] The present invention constructs a data acquisition circuit corresponding to the data acquisition device by combining the circuit response efficiency, the factor energy value, the focal mechanism attribute and the data storage capacity, thereby improving the construction accuracy of the data acquisition device to improve the data quality of subsequent data acquisition. Furthermore, in combination with the circuit response efficiency, the factor energy value, the focal mechanism attribute and the data storage capacity, the circuit corresponding to the data acquisition device is optimized to obtain a data acquisition circuit. If the circuit response efficiency is poor, the amplifier in the circuit can use an operational amplifier with a higher bandwidth and a faster conversion rate, and 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. Stop frequency. If the source mechanism attribute is a strike-slip earthquake, the horizontal component of the seismic wave has strong energy and the propagation direction is mainly along a specific fault direction, then the circuit parameters of the horizontal signal acquisition channel can be optimized, such as improving the amplifier gain of the channel and the accuracy of the signal conditioning circuit to ensure that the strong horizontal seismic wave signal can be accurately collected and processed. According to the data storage capacity, the remaining storage capacity corresponding to the current storage device is judged, and a data compression algorithm can be configured in the data transmission circuit of the storage device. The transmission data is compressed using the data compression algorithm, such as a lossless compression algorithm. The above-mentioned optimization circuit is added to the data acquisition device to obtain a complete data acquisition circuit.

[0147] Compared with the problems described in the background technology, the present invention can more comprehensively and deeply understand the characteristics of seismic signals, including signal frequency composition, energy distribution, hidden information, etc., by extracting the transform domain features corresponding to the high dynamic range seismic data, thereby providing a more accurate and effective basis for subsequent seismic data analysis. The present invention can adaptively adjust the signal strength by performing variable gain processing on the high dynamic range seismic data, improve the recognizability of weak but valuable signals, and balance the energy differences of signals in different frequency bands or regions, so that the output signal is more reasonable in amplitude, which is convenient for subsequent analysis and processing. The present invention queries the data sampling mechanism and data acquisition device corresponding to the target seismic data, and determines the number of data channels corresponding to the target seismic data based on the data sampling device, so as to more accurately process seismic data and lay a foundation for the subsequent calculation of the data storage capacity corresponding to the target seismic data. The present invention analyzes the circuit response efficiency corresponding to the data acquisition device in combination with the device operation log, so as to understand the performance of the circuit in actual operation, and timely discover potential problems of the circuit, which provides a basis for improving the accuracy of the construction of the data acquisition circuit corresponding to the subsequent device circuit. Therefore, the present invention proposes a method for constructing a high dynamic range seismic data acquisition circuit to improve the construction accuracy of the high dynamic range seismic data acquisition circuit.

[0148] Embodiment 2:

[0149] like Figure 2 , is a functional module diagram of a construction device for implementing a high dynamic range seismic data acquisition circuit provided by an embodiment of the present invention.

[0150] The device 100 for constructing a high dynamic range seismic data acquisition circuit of the present invention can be installed in an electronic device. According to the functions to be implemented, the device 100 for constructing a high dynamic range seismic data acquisition circuit can 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 of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0151] In this embodiment, the functions of each module / unit are as follows:

[0152] The energy value calculation module 101 is used to obtain high dynamic range seismic data and data acquisition area to be analyzed, extract transform domain features corresponding to the high dynamic range seismic data, analyze the signal poor quality factor in the high dynamic range seismic data based on the transform domain features, and calculate the factor energy value corresponding to the signal poor quality factor;

[0153] 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 characteristic parameters corresponding to the data acquisition area, extract seismic time history data and seismic multi-component data from the target seismic data, and analyze the source mechanism attributes corresponding to the data acquisition area by combining the seismic time history data and the seismic multi-component data;

[0154] The storage capacity calculation module 103 is used to query the data sampling mechanism and data acquisition device corresponding to the target seismic data, determine the number of data channels corresponding to the target seismic data based on the data sampling device, and calculate the data storage capacity corresponding to the target seismic data in combination with the number of data channels and the data sampling mechanism;

[0155] The circuit construction module 104 is used to collect the device operation log corresponding to the data acquisition device, analyze the circuit response performance corresponding to the data acquisition device in combination with the device operation log, 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.

[0156] In detail, each module described in the device 100 for constructing a high dynamic range seismic data acquisition circuit in the embodiment of the present application is used in the same manner as described above. Figure 1 The same technical means are used as the method for constructing a high dynamic range seismic data acquisition circuit described in the specification and can produce the same technical effects, which will not be repeated here.

[0157] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for constructing a high dynamic range seismic data acquisition circuit, characterized in that: The method comprises: Acquire high dynamic range seismic data to be analyzed and a data acquisition area, extract transform domain features corresponding to the high dynamic range seismic data, analyze signal poor quality factors in the high dynamic range seismic data based on the transform domain features, and calculate factor energy values ​​corresponding to the signal poor quality factors; Performing variable gain processing on the high dynamic range seismic data to obtain target seismic data, analyzing geological characteristic parameters corresponding to the data acquisition area, extracting seismic time history data and seismic multi-component data from the target seismic data, and combining the seismic time history data and the seismic multi-component data to analyze the source mechanism attributes corresponding to the data acquisition area; Querying the data sampling mechanism and data acquisition device corresponding to the target seismic data, determining the number of data channels corresponding to the target seismic data based on the data sampling device, and calculating the data storage capacity corresponding to the target seismic data in combination with the number of data channels and the data sampling mechanism; Collect the device operation log corresponding to the data acquisition device, analyze the circuit response performance corresponding to the data acquisition device in combination with the device operation log, 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.

2. The method for constructing a high dynamic range seismic data acquisition circuit according to claim 1, characterized in that: The extracting the transform domain features corresponding to the high dynamic range seismic data comprises: Performing Fourier transform processing on the high dynamic range seismic data to obtain a seismic spectrum diagram; Identifying the peak characteristics of the spectrum in the seismic spectrum diagram, and calculating the effective bandwidth of the spectrum in the seismic spectrum diagram; Performing wavelet transform processing on the high dynamic range seismic data to obtain transformed seismic data; extracting instantaneous frequency features from the transformed seismic data; The spectrum peak feature, the spectrum effective bandwidth and the instantaneous frequency feature are combined to generate transform domain features corresponding to the high dynamic range seismic data.

3. The method for constructing a high dynamic range seismic data acquisition circuit according to claim 1, characterized in that: The step of analyzing the signal quality factor in the high dynamic range seismic data based on the transform domain characteristics comprises: Identify a feature identifier corresponding to the transform domain feature, and analyze feature semantics corresponding to the transform domain feature based on the feature identifier; Based on the feature semantics, performing feature classification processing on the transform domain features to obtain classified transform domain features; Performing principal component analysis on the classification transformation domain features to obtain characteristic principal components; Based on the characteristic principal components, signal poor quality factors in the high dynamic range seismic data are analyzed.

4. The method for constructing a high dynamic range seismic data acquisition circuit according to claim 1, characterized in that: The step of performing variable gain processing on the high dynamic range seismic data to obtain target seismic data includes: Performing time-varying gain processing on the high dynamic range seismic data to obtain time-varying gain seismic data; Calculating data entropy corresponding to the time-varying gain seismic data, and determining data complexity corresponding to the time-varying gain seismic data based on the data entropy; Based on the data complexity, performing adaptive gain processing on the time-varying gain seismic data to obtain adaptive gain seismic data; The adaptive gain seismic data is subjected to smoothing gain processing to obtain target seismic data.

5. The method for constructing a high dynamic range seismic data acquisition circuit according to claim 1, characterized in that: The analyzing of geological characteristic parameters corresponding to the data acquisition area includes: Collecting regional exploration data corresponding to the data collection area, and filtering the regional exploration data to obtain filtered exploration data; Performing data integration processing on the filtered exploration data to obtain integrated exploration data; Performing data correction processing on the integrated exploration data to obtain corrected exploration data; The geological characteristic parameters corresponding to the data acquisition area are analyzed in combination with the preset geological characteristic indicators and the corrected exploration data.

6. The method for constructing a high dynamic range seismic data acquisition circuit according to claim 1, characterized in that: The 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: Performing wave field identification processing on the seismic multi-component data to obtain seismic shear waves and seismic longitudinal waves; In combination with the earthquake time history data, respectively calculating the time difference between the arrival of the earthquake shear wave and the earthquake longitudinal wave at different observation points to obtain the shear wave time difference and the longitudinal wave time difference; Calculating the propagation speeds of the seismic shear wave and the seismic longitudinal wave respectively to obtain the shear wave velocity and the longitudinal wave velocity; Analyze the earthquake source position and earthquake source depth corresponding to the data acquisition area by combining the shear wave time difference, the longitudinal wave time difference, the shear wave velocity and the longitudinal wave velocity; Combining the focal position and the focal depth, constructing a regional focal sphere corresponding to the data acquisition area; The focal mechanism solution corresponding to the focal sphere of the region is calculated, and the focal mechanism properties corresponding to the data acquisition region are analyzed according to the focal mechanism solution.

7. The method for constructing a high dynamic range seismic data acquisition circuit according to claim 6, characterized in that: The method of respectively calculating the propagation velocities of the seismic shear wave and the seismic longitudinal wave to obtain the shear wave velocity and the longitudinal wave velocity comprises: Detecting the propagation carriers corresponding to the seismic shear wave and the seismic longitudinal wave, and querying the carrier density and carrier elastic modulus corresponding to the propagation carrier; Combining the carrier density and the carrier elastic modulus, the propagation velocities corresponding to the seismic shear wave and the seismic longitudinal wave are calculated respectively by the following formulas to obtain the shear wave velocity and the longitudinal wave velocity, including: Among them, G represents the shear 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.

8. The method for constructing a high dynamic range seismic data acquisition circuit according to claim 1, characterized in that: The calculating the data storage capacity corresponding to the target seismic data by combining the number of data channels and the data sampling mechanism comprises: Determining the sampling rate and sampling accuracy corresponding to the target seismic data according to the data sampling mechanism; Identify an acquisition timestamp corresponding to the target seismic data, and calculate a data duration period corresponding to the target seismic data based on the acquisition timestamp; Combining the sampling rate, the sampling accuracy, the data duration period and the number of data channels, the data storage capacity corresponding to the target seismic data can be calculated by the following formula: H=F*T*M*φ; Among them, 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.

9. The method for constructing a high dynamic range seismic data acquisition circuit according to claim 1, characterized in that: The analyzing the circuit response performance corresponding to the data acquisition device in combination with the device operation log includes: Identify the operation index and its corresponding index operation information in the equipment operation log; Analyzing the indicator attributes corresponding to the operation indicator, extracting the circuit response indicator from the operation indicator based on the indicator attributes, and calculating the indicator weight corresponding to the circuit response indicator; The circuit response index includes a circuit response time index, a signal accuracy index and a circuit stability index; Based on the indicator operation information, calculating the indicator parameter value corresponding to the circuit response indicator; In combination with the index parameter value, the index weight and the circuit response index, the index performance value corresponding to the data acquisition device can be calculated by the following formula: Among them, Q represents the index performance value corresponding to the circuit response index, α1, α2, and α3 represent the index weights corresponding to the circuit response time index, signal accuracy index, and circuit stability index, respectively. Indicates the average response time corresponding to the circuit response time indicator in the indicator parameter value, represents the preset maximum average response time, σ N Indicates the standard deviation of the response time corresponding to the circuit response time indicator in the indicator parameter value, It represents the average error of the indicator parameter value in terms of the intensity of the signal accuracy indicator. It represents the relative frequency error of the indicator parameter value with respect to the signal accuracy indicator. Indicates the mean value of the output signal strength corresponding to the circuit stability index in the index parameter value, ΔS max Indicates the signal strength range corresponding to the circuit stability index in the index parameter value; Based on the indicator performance value, the circuit response performance corresponding to the data acquisition device is analyzed.

10. A device for constructing a high dynamic range seismic data acquisition circuit, characterized in that: The device comprises: An energy value calculation module is used to obtain high dynamic range seismic data and data acquisition area to be analyzed, extract transform domain features corresponding to the high dynamic range seismic data, analyze signal poor quality factors in the high dynamic range seismic data based on the transform domain features, and calculate the factor energy value corresponding to the signal poor quality factor; an attribute analysis module, for performing variable gain processing on the high dynamic range seismic data to obtain target seismic data, analyzing geological characteristic parameters corresponding to the data acquisition area, extracting seismic time history data and seismic multi-component data from the target seismic data, and combining the seismic time history data and the seismic multi-component data to analyze the source mechanism attributes corresponding to the data acquisition area; A storage capacity calculation module, used to query the data sampling mechanism and data acquisition device corresponding to the target seismic data, determine the number of data channels corresponding to the target seismic data based on the data sampling device, and calculate the data storage capacity corresponding to the target seismic data in combination with the number of data channels and the data sampling mechanism; A circuit construction module is used to collect the device operation log corresponding to the data acquisition device, analyze the circuit response performance corresponding to the data acquisition device in combination with the device operation log, 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.

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