A method, apparatus, equipment, medium, and product for acquiring basic data of hydrogeological models based on micro-motion signals.

Abstract

This application discloses a method, apparatus, equipment, medium, and product for acquiring basic data for hydrogeological models based on micromotion signals, relating to the interdisciplinary field of geophysical exploration and hydrogeological modeling. The method includes: acquiring high-frequency and low-frequency micromotion signals using a dual-instrument nested array; extracting the fundamental Rayleigh wave phase velocity dispersion curves from these two signals to obtain high-frequency dispersion segment data and low-frequency dispersion segment data; fusing the high-frequency and low-frequency dispersion segment data to obtain a broadband micromotion signal, which is then converted to obtain a broadband apparent S-wave velocity profile; finally, converting the high-frequency and low-frequency dispersion segment data to the apparent depth domain and overlaying them onto the apparent S-wave velocity profile for refined layering identification of hydrogeological structures. This application constructs a broadband dispersion curve with "high resolution in shallow areas + reliable data in deep areas," providing spatially continuous and high-precision stratigraphic structure information for hydrogeological model construction.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of geophysical exploration and hydrogeological modeling, and in particular to a method, apparatus, equipment, medium and product for acquiring basic data of hydrogeological models based on micro-motion signals. Background Technology

[0002] Traditional drilling is costly and produces discrete data, making it difficult to obtain continuous stratigraphic structures. Hydrogeological modeling requires as detailed and spatially continuous stratigraphic information as possible. While borehole data can provide accurate and detailed stratigraphic information, it is costly and provides only a single borehole view, failing to offer spatially continuous stratigraphic information. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, equipment, medium and product for acquiring basic data of hydrogeological models based on micro-motion signals, which solves the problems of high cost and discontinuous data in traditional methods.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] Firstly, this application provides a method for acquiring basic data for hydrogeological models based on micro-motion signals, including:

[0006] Acquire wideband micro-motion signals; the wideband micro-motion signals include: high-frequency micro-motion signals and low-frequency micro-motion signals; the wideband micro-motion signals are acquired by a dual-instrument nested array;

[0007] Based on the high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data;

[0008] Based on the low-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain low-frequency dispersion data.

[0009] By using a weighted fusion method, the high-frequency dispersion band data and the low-frequency dispersion band data are fused to obtain a wideband micro-motion signal;

[0010] A broadband apparent S-wave velocity profile is obtained by converting the aforementioned broadband micro-motion signal.

[0011] Based on the apparent depth estimation formula, the high-frequency dispersion data and the low-frequency dispersion data are converted to the apparent depth domain and superimposed on the apparent S-wave velocity profile to perform refined layering identification of hydrogeological structures.

[0012] Optionally, based on the high-frequency micro-motion signal, the fundamental order Rayleigh wave phase velocity dispersion curve is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data, specifically including:

[0013] Data with a high signal-to-noise ratio (SNR) is obtained by filtering out data from the high-frequency micro-motion signal that is below a preset SNR threshold.

[0014] The spectrum of each frame of signal in the high signal-to-noise ratio data is normalized by amplitude and the phase information is preserved to obtain the whitened spectrum data.

[0015] Perform an inverse Fourier transform on the whitened spectral data to obtain an equalized time-domain high-frequency micro-motion signal;

[0016] Based on the equalized time-domain high-frequency micro-motion signal, the fundamental Rayleigh wave phase velocity dispersion curve is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data.

[0017] Optionally, the weighted fusion method is used to fuse the high-frequency dispersion data and the low-frequency dispersion data to obtain the calculation formula for the wideband micro-motion signal:

[0018] ;

[0019] in, The weighting function is set to a linear transition frequency of 0.5~1Hz. This is high-frequency dispersion data; This is low-frequency dispersion data.

[0020] Optionally, based on the high-frequency micro-motion signal, the fundamental order Rayleigh wave phase velocity dispersion curve is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data, specifically including:

[0021] The real part of the autocorrelation coefficient spectrum of the high-frequency micro-motion signal is fitted with a zero-order Bessel function to obtain the fitted Bessel curve; wherein, the zero-order Bessel function is: In the formula, Represents the zeroth-order Bessel function. For the spacing between stations, Represents phase velocity, For frequency;

[0022] Based on the zero and extreme points of the fitted Bézier curve, the corresponding inter-station spacing and frequency values ​​are obtained;

[0023] The Rayleigh wave phase velocity dispersion curve for each station pair is calculated based on the station spacing and frequency value to obtain high-frequency dispersion segment data.

[0024] Optionally, the conversion formula for obtaining the broadband apparent S-wave velocity profile using the broadband micro-motion signal is as follows:

[0025] ;

[0026] in, VR It is phase velocity. t i For a period of time, The period is represented as i Phase velocity at time The period is represented as i Phase velocity at -1 t i-1 for i The period when -1 is reached.

[0027] Secondly, this application provides a device for acquiring basic data of hydrogeological models based on micro-motion signals, including: a dual-instrument nested array and a control module;

[0028] The dual-instrument nested array includes: a first group of seismic pickups and a second group of seismic pickups;

[0029] The first set of vibration sensors is used to acquire high-frequency micro-motion signals; the second set of vibration sensors is used to acquire low-frequency micro-motion signals.

[0030] The control module is used for:

[0031] Acquire wideband micro-motion signals; the wideband micro-motion signals include: high-frequency micro-motion signals and low-frequency micro-motion signals; the wideband micro-motion signals are acquired by a dual-instrument nested array;

[0032] Based on the high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data;

[0033] Based on the low-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain low-frequency dispersion data.

[0034] By using a weighted fusion method, the high-frequency dispersion band data and the low-frequency dispersion band data are fused to obtain a wideband micro-motion signal;

[0035] A broadband apparent S-wave velocity profile is obtained by converting the aforementioned broadband micro-motion signal.

[0036] Based on the apparent depth estimation formula, the high-frequency dispersion data and the low-frequency dispersion data are converted to the apparent depth domain and superimposed on the apparent S-wave velocity profile to perform refined layering identification of hydrogeological structures.

[0037] Optionally, the first group of vibration pickups is arranged with a radius of 10m / 20m / 40m, and the main frequency of the first group of vibration pickups is 2Hz, which is used to acquire high-frequency micro-motion signals of 2-30Hz.

[0038] The second set of vibration pickups is arranged with a radius of 80m / 160m / 320m. The main frequency of the second set of vibration pickups is 0.5Hz, which is used to acquire low-frequency micro-motion signals of 0.5-5Hz.

[0039] The measuring lines of both the first group of seismic sensors and the second group of seismic sensors are laid out perpendicular to the direction of the target probe.

[0040] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for acquiring basic data of hydrogeological models based on micro-motion signals as described above.

[0041] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for acquiring basic data of a hydrogeological model based on micro-motion signals as described above.

[0042] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method for acquiring basic data of a hydrogeological model based on micro-motion signals as described above.

[0043] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0044] This application provides a method, apparatus, equipment, medium, and product for acquiring basic data of a hydrogeological model based on micro-motion signals. The method includes: acquiring broadband micro-motion signals; the broadband micro-motion signals include high-frequency micro-motion signals and low-frequency micro-motion signals; the broadband micro-motion signals are acquired by a dual-instrument nested array; based on the high-frequency micro-motion signals, extracting the fundamental Rayleigh wave phase velocity dispersion curve using the spatial autocorrelation method to obtain high-frequency dispersion segment data; based on the low-frequency micro-motion signals, extracting the fundamental Rayleigh wave phase velocity dispersion curve using the spatial autocorrelation method to obtain low-frequency dispersion data; using a weighted fusion method to fuse the high-frequency dispersion segment data and the low-frequency dispersion data to obtain broadband micro-motion signals; using the broadband micro-motion signals to convert and obtain a broadband apparent S-wave velocity profile; based on the apparent depth estimation formula, converting the high-frequency dispersion segment data and the low-frequency dispersion data to the apparent depth domain and superimposing them on the apparent S-wave velocity profile to perform refined layered identification of hydrogeological structures. This application innovatively introduces a dual-instrument nested array to acquire broadband micro-motion signals and constructs a broadband dispersion curve with "high resolution in shallow areas and reliable data in deep areas". This provides spatially continuous and high-precision stratigraphic structure information for the construction of hydrogeological models. Combined with the characteristics of apparent depth dispersion curves, it realizes refined layer identification of hydrogeological structures and solves the problems of high cost and discontinuous data in traditional methods. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a method for acquiring basic data of a hydrogeological model based on micro-motion signals, provided as an embodiment of this application.

[0047] Figure 2 is a schematic diagram of the frequency band micro-motion signal analysis process provided in an embodiment of this application.

[0048] Figure 3 This is a schematic diagram of a hydrogeological model basic data acquisition device based on micro-motion signals, provided as an embodiment of this application.

[0049] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] In this application, the basic data acquisition for hydrogeological models based on micromotion signals can be achieved by establishing profiles or area work to obtain stratigraphic information that is continuous in profile or in three-dimensional space. Furthermore, this application innovatively introduces a nested dual-instrument array to acquire broadband micromotion signals, and then improves the imaging quality of high-frequency (shallow) signals based on a two-stage artifact suppression method, thereby constructing a broadband dispersion curve that is "high-resolution for shallow areas + reliable for deep areas," thus providing spatially continuous, high-precision stratigraphic information for the construction of hydrogeological models.

[0052] This application primarily addresses the challenge of data sparsity in hydrogeological model construction. It provides a low-cost, high-resolution, and environmentally friendly data acquisition solution, based on frequency-band micro-motion signal processing and weighted fusion technology to obtain high-precision formation velocity information from broadband dispersion curves.

[0053] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] In one exemplary embodiment, such as Figure 1 As shown, a method for acquiring basic data for a hydrogeological model based on micromotion signals is provided, including the following steps S1 to S6. Wherein:

[0055] S1. Acquire wideband micro-motion signals; the wideband micro-motion signals include: high-frequency micro-motion signals and low-frequency micro-motion signals; the wideband micro-motion signals are acquired by a dual-instrument nested array.

[0056] In this embodiment, a dual-instrument nested array is first deployed to acquire wideband micro-motion signals.

[0057] The survey lines are laid out as perpendicularly as possible to the direction of the target probe, with a spacing of approximately 200m between survey points, totaling 30 points. A nested dual-instrument array is employed: seismographs with a dominant frequency of 2Hz are deployed with radii of 10m / 20m / 40m to primarily capture high-frequency micro-motion signals (2-30Hz); seismographs with a dominant frequency of 0.5Hz are deployed with radii of 80m / 160m / 320m to primarily identify low-frequency micro-motion signals (0.5-5Hz). In this embodiment, the target probe is a geological body, such as a geological body perpendicular to the strike of a fault, perpendicular to the long axis of a ground subsidence area, or perpendicular to the strike of strata.

[0058] Then, the broadband micro-motion signal is analyzed by frequency band, as shown in Figure 2. Figure 2(a) includes steps one and two, and Figure 2(b) includes steps three and four. For high-frequency micro-motion signals, a signal screening method based on the signal-to-noise ratio threshold is used to filter out low-quality data, and then a spectral equalization method based on spectral whitening is used to enhance signal coherence, achieving two-stage artifact suppression, as detailed below:

[0059] ① Signal framing and spectrum calculation: The original signal... Frame-based processing, with each frame having a length of T and an overlap rate ≥50%; Fourier transform is performed on each frame signal to obtain the spectrum. And calculate the power spectral density. .

[0060] ② Signal-to-noise ratio (SNR) quantization:

[0061] ;

[0062] In the formula: The power spectral density of the signal frequency band. This represents the power spectral density of the noise segment.

[0063] ③ Set a threshold to filter out low signal-to-noise ratio data that does not reach the threshold.

[0064] ④ Spectral amplitude normalization. Normalize the spectrum of each frame of the signal. Amplitude normalization is performed while preserving phase information:

[0065] ;

[0066] in It is a very small constant. To normalize the amplitude, it can be set to 1 or the global average amplitude.

[0067] ⑤ Time-domain signal reconstruction. Reconstruct the whitened spectrum. Perform an inverse Fourier transform to obtain an equalized time-domain signal.

[0068] S2. Based on the high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain the high-frequency dispersion segment data.

[0069] S3. Based on the low-frequency micro-motion signal, extract the phase velocity dispersion curve of the fundamental Rayleigh wave using the spatial autocorrelation method to obtain low-frequency dispersion data.

[0070] In this embodiment, the dispersion curves of the high-frequency micro-motion signal after artifact suppression in steps ① to ⑤ and the unprocessed low-frequency micro-motion signal are extracted based on the spatial autocorrelation method. Since the process is the same for both, the micro-motion signal is used here to represent the high-frequency micro-motion signal or the low-frequency micro-motion signal.

[0071] When long-term observation is satisfied, the imaginary part of the autocorrelation coefficient spectrum of the micro-motion signal is zero, and the real part is approximately equal to the zeroth-order Bessel function of the first kind (through mathematical derivation, the final expression of the real part of the signal is similar to the zeroth-order Bessel function of the first kind). It's the Bessel function, where x is replaced with... This allows for an approximation of the two.

[0072] ;

[0073] In the formula, Represents the zeroth-order Bessel function. For the spacing between stations, Represents phase velocity, For frequency; when = hour, ,Right now It is a constant, at this time By analyzing the zero and extreme points of the fitted Bézier curve, the corresponding station spacing and frequency values ​​are obtained, and then the Rayleigh wave phase velocity dispersion curve of each station pair is calculated, thus obtaining the phase velocity dispersion curve. For frequency, The frequency value at a certain moment. This refers to when the frequency is Phase velocity at time It is a constant.

[0074] S4. Using a weighted fusion method, the high-frequency dispersion data and the low-frequency dispersion data are fused to obtain a wideband micro-motion signal.

[0075] In this embodiment, based on high-resolution high-frequency dispersion data and deep, reliable low-frequency dispersion data, a weighted fusion method is used to perform data fusion to obtain a wideband micro-motion signal.

[0076] The frequency of the micro-motion signal is inversely proportional to the detection depth. High-frequency signals reflect shallow structures but are highly susceptible to noise interference, while low-frequency signals reflect deep structures but have low resolution in shallow areas. By weighted averaging the overlapping frequency bands of high-quality high-frequency dispersion data extracted after artifact suppression and low-frequency dispersion data, a wideband patchwork curve can be constructed, achieving "high resolution in shallow areas + reliable detection in deep areas".

[0077] The calculation formula is:

[0078] ;

[0079] in, The weighting function is set to a linear transition frequency of 0.5~1Hz. This is high-frequency dispersion data; This is low-frequency dispersion data.

[0080] S5. Obtain a broadband apparent S-wave velocity profile using the broadband micro-motion signal conversion.

[0081] In this embodiment, based on wideband micro-motion signals c ( f By fitting empirical formulas, a broadband apparent S-wave velocity profile can be generated.

[0082] Apparent S-wave velocity can be directly derived from the dispersion curve of micro-motion signals (referring to broadband micro-motion signals), which can directly indicate the relative transformation characteristics of formation velocity structure and avoid the ambiguity of inversion. The calculation formula is as follows:

[0083] ;

[0084] in, V R It is phase velocity. t i For a period of time, i =1, 2, 3, ..., n, where n is an integer; The period is represented as iPhase velocity at time The period is represented as i Phase velocity at -1 t i-1 for i The period when -1 is reached.

[0085] S6. Based on the apparent depth estimation formula, the high-frequency dispersion data and the low-frequency dispersion data are converted to the apparent depth domain and superimposed on the apparent S-wave velocity profile to perform refined layering identification of hydrogeological structures.

[0086] In this embodiment, based on the apparent depth estimation formula, the dispersion data of each measuring point in the frequency domain is converted to the apparent depth domain and superimposed on the apparent S-wave velocity profile. The combination of the two enables refined layered identification of hydrogeological structures. For a profile map, three elements are needed: the horizontal axis, the vertical axis, and the attribute value. The apparent S-wave velocity is the attribute value, and the apparent depth is the vertical axis.

[0087] The apparent depth estimation formula is:

[0088] ;

[0089] Where H is the apparent depth, V R It is the Rayleigh wave phase velocity. It is a correction factor. In working areas where conditions permit, the depth should be corrected by comparing with known borehole data to improve the accuracy of depth interpretation.

[0090] The interpolated apparent S-wave velocity profile shows relatively smooth changes, but the vertical stratification characteristics are not prominent enough. The dispersion curve in the frequency domain is converted into the depth domain according to the apparent depth estimation formula and superimposed on the apparent S-wave velocity profile. Based on the variation characteristics of its dispersion points, it can help to divide the shallow hydrostratigraphic structure.

[0091] This embodiment proposes a green and efficient method for acquiring micromotion signals, guided by hydrogeological model construction. A dual-instrument nested array is employed: seismographs with a dominant frequency of 2Hz are deployed in radii of 10m / 20m / 40m to primarily capture high-frequency micromotion signals (2-30Hz); seismographs with a dominant frequency of 0.5Hz are deployed in radii of 80m / 160m / 320m to identify low-frequency micromotion signals (0.5-5Hz). This embodiment acquires broadband micromotion signals by deploying a dual-instrument nested array. Micromotion signals are analyzed in frequency bands. For high-frequency micromotion signals, a signal screening method based on a signal-to-noise ratio threshold is used to filter out low-quality data, and then a spectral equalization method based on spectral whitening is used to enhance signal coherence, achieving two levels of artifact suppression. The processed signals are then processed using a weighted fusion algorithm to construct a broadband dispersion curve that is "high-resolution for shallow areas + reliable for deep areas". Finally, by combining the feature analysis of apparent depth dispersion curves, a refined layered identification of hydrogeological structures is achieved, thereby providing highly reliable basic data support for the construction of hydrogeological models and solving the technical problem of low accuracy in hydrogeological interface delineation using traditional methods.

[0092] It has the following technical effects:

[0093] 1) Provides spatially continuous basic data for the construction of hydrogeological models.

[0094] 2) Effective fusion of shallow (0-50m) and medium-deep (50-500m) data is achieved, improving shallow resolution by more than 30%. The inversion results match the borehole data by 90%, and the stratigraphic structure division error is less than 6% of the detection depth (borehole verification).

[0095] 3) Compared with the electromagnetic method, it is not affected by electromagnetic background interference or whether the strata are water-rich, and is suitable for complex urban environments with interference. It can efficiently support hydrogeological modeling and urban geological safety investigation.

[0096] 4) Compared with reflection seismic exploration, data acquisition efficiency is increased by more than 50%, cost is reduced by more than 60%, and it is green and environmentally friendly.

[0097] 5) It can be seamlessly integrated with other micro-motion signal dispersion imaging methods, such as the FK method.

[0098] Based on the same inventive concept, such as Figure 3 As shown ( Figure 3 a) is a schematic diagram of dual-instrument micro-motion signal acquisition; b) is a schematic diagram of single-instrument acquisition with non-uniform scale display; c) is a combination method of each seismic pickup pair when extracting dispersion curves. This application embodiment also provides a hydrogeological model basic data acquisition device based on micro-motion signals, including: a dual-instrument nested array and a control module.

[0099] The dual-instrument nested array includes: a first group of seismic pickups and a second group of seismic pickups.

[0100] The first set of vibration sensors is used to acquire high-frequency micro-motion signals; the second set of vibration sensors is used to acquire low-frequency micro-motion signals.

[0101] The control module is used for:

[0102] A wideband micro-motion signal is acquired; the wideband micro-motion signal includes: a high-frequency micro-motion signal and a low-frequency micro-motion signal; the wideband micro-motion signal is acquired by a dual-instrument nested array.

[0103] Based on the high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data.

[0104] Based on the low-frequency micro-motion signal, the fundamental Rayleigh wave phase velocity dispersion curve is extracted using the spatial autocorrelation method to obtain low-frequency dispersion data.

[0105] By using a weighted fusion method, the high-frequency dispersion data and the low-frequency dispersion data are fused to obtain a wideband micro-motion signal.

[0106] A broadband apparent S-wave velocity profile is obtained by converting the aforementioned broadband micro-motion signal.

[0107] Based on the apparent depth estimation formula, the high-frequency dispersion data and the low-frequency dispersion data are converted to the apparent depth domain and superimposed on the apparent S-wave velocity profile to perform refined layering identification of hydrogeological structures.

[0108] The first group of vibration pickups is arranged with a radius of 10m / 20m / 40m, and the main frequency of the first group of vibration pickups is 2Hz, which is used to acquire high-frequency micro-motion signals of 2-30Hz.

[0109] The second set of vibration pickups is arranged with a radius of 80m / 160m / 320m. The main frequency of the second set of vibration pickups is 0.5Hz, which is used to acquire low-frequency micro-motion signals of 0.5-5Hz.

[0110] The measuring lines of both the first group of seismic sensors and the second group of seismic sensors are laid out perpendicular to the direction of the target probe.

[0111] This embodiment designs a dual-instrument nested array observation system guided by hydrogeological model construction to acquire broadband micromotion signals. Two-stage artifact suppression is applied to the high-frequency micromotion signals to construct a broadband dispersion pattern that is "high-resolution for shallow areas + reliable for deep areas." Combined with the characteristics of the apparent depth dispersion curve, this achieves refined layered identification of hydrogeological structures. This solves the problems of high cost and discontinuous data associated with traditional methods. It is applicable to fields such as hydrogeological model construction, urban geological safety investigation and prevention.

[0112] First, we designed a green and efficient micro-motion signal acquisition system.

[0113] Then, two-stage artifact suppression is performed on the high-frequency micro-motion signal.

[0114] Then, a broadband dispersion curve with "high resolution in shallow areas and reliable performance in deep areas" is constructed.

[0115] Finally, a refined, layered identification of hydrogeological structures was achieved.

[0116] Specifically, a signal screening method based on the signal-to-noise ratio threshold and a spectral equalization method based on spectral whitening are used to suppress artifacts and enhance the coherence of micro-motion signals; a weighted fusion algorithm is used to achieve effective splicing of high-frequency and low-frequency signals; and the combination of apparent depth domain dispersion curves and apparent S-wave velocity profiles enhances the identification of formation interfaces.

[0117] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a method for acquiring basic data for a hydrogeological model based on micro-motion signals.

[0118] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0119] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0120] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0121] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0122] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0123] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0124] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0125] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for acquiring basic data for hydrogeological models based on micro-motion signals, characterized in that, include: Acquire wideband micro-motion signals; The broadband micro-motion signal includes a high-frequency micro-motion signal and a low-frequency micro-motion signal; the broadband micro-motion signal is acquired by a dual-instrument nested array; the dual-instrument nested array includes a first group of seismic sensors and a second group of seismic sensors; wherein, the first group of seismic sensors is used to acquire the high-frequency micro-motion signal; the second group of seismic sensors is used to acquire the low-frequency micro-motion signal; the first group of seismic sensors is arranged with a radius of 10m / 20m / 40m, and the main frequency of the first group of seismic sensors is 2Hz, used to acquire high-frequency micro-motion signals of 2-30Hz; the second group of seismic sensors is arranged with a radius of 80m / 160m / 320m, and the main frequency of the second group of seismic sensors is 0.5Hz, used to acquire low-frequency micro-motion signals of 0.5-5Hz; the measuring lines of the first group of seismic sensors and the second group of seismic sensors are both arranged perpendicular to the direction of the target probe; Based on the high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data; Based on the low-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain low-frequency dispersion data. By using a weighted fusion method, the high-frequency dispersion band data and the low-frequency dispersion band data are fused to obtain a wideband micro-motion signal; A broadband apparent S-wave velocity profile is obtained by converting the aforementioned broadband micro-motion signal. Based on the apparent depth estimation formula, the high-frequency dispersion data and the low-frequency dispersion data are converted to the apparent depth domain and superimposed on the apparent S-wave velocity profile to perform refined layered identification of hydrogeological structures. Based on the aforementioned high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data, specifically including: Data with a high signal-to-noise ratio (SNR) is obtained by filtering out data from the high-frequency micro-motion signal that is below a preset SNR threshold. The spectrum of each frame of signal in the high signal-to-noise ratio data is normalized by amplitude and the phase information is preserved to obtain the whitened spectrum data. Perform an inverse Fourier transform on the whitened spectral data to obtain an equalized time-domain high-frequency micro-motion signal; Based on the equalized time-domain high-frequency micro-motion signal, the fundamental order Rayleigh wave phase velocity dispersion curve is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data. The weighted fusion method is used to fuse the high-frequency dispersion data and the low-frequency dispersion data to obtain the calculation formula for the wideband micro-motion signal: ； in, The weighting function is set to a linear transition frequency of 0.5~1Hz. This is high-frequency dispersion data; This is low-frequency dispersion data.

2. The method for acquiring basic data of hydrogeological models based on micro-motion signals according to claim 1, characterized in that, Based on the aforementioned high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data, specifically including: The real part of the autocorrelation coefficient spectrum of the high-frequency micro-motion signal is fitted with a zero-order Bessel function to obtain the fitted Bessel curve; wherein, the zero-order Bessel function is: In the formula, Represents the zeroth-order Bessel function. For the spacing between stations, Represents phase velocity, For frequency; Based on the zero and extreme points of the fitted Bézier curve, the corresponding inter-station spacing and frequency values ​​are obtained; The Rayleigh wave phase velocity dispersion curve for each station pair is calculated based on the station spacing and frequency value to obtain high-frequency dispersion segment data.

3. The method for acquiring basic data of hydrogeological models based on micro-motion signals according to claim 1, characterized in that, The conversion formula for obtaining the broadband apparent S-wave velocity profile using the broadband micro-motion signal conversion is as follows: ； in, V R It is phase velocity. t i For a period of time, The period is represented as i Phase velocity at time The period is represented as i Phase velocity at -1 t i-1 for i The period when -1.

4. A device for acquiring basic data of a hydrogeological model based on micro-motion signals, characterized in that, include: Dual-instrument nested array and control module; The dual-instrument nested array includes: a first group of seismic pickups and a second group of seismic pickups; The first set of seismic sensors is used to acquire high-frequency micro-motion signals; the second set of seismic sensors is used to acquire low-frequency micro-motion signals. The first set of seismic sensors is deployed with radii of 10m / 20m / 40m, and its main frequency is 2Hz, used to acquire high-frequency micro-motion signals of 2-30Hz. The second set of seismic sensors is deployed with radii of 80m / 160m / 320m, and its main frequency is 0.5Hz, used to acquire low-frequency micro-motion signals of 0.5-5Hz. The measuring lines of both the first and second sets of seismic sensors are deployed perpendicular to the direction of the target probe. The control module is used for: Acquire wideband micro-motion signals; the wideband micro-motion signals include: high-frequency micro-motion signals and low-frequency micro-motion signals; the wideband micro-motion signals are acquired by a dual-instrument nested array; Based on the high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data; Based on the low-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain low-frequency dispersion data. By using a weighted fusion method, the high-frequency dispersion band data and the low-frequency dispersion band data are fused to obtain a wideband micro-motion signal; A broadband apparent S-wave velocity profile is obtained by converting the aforementioned broadband micro-motion signal. Based on the apparent depth estimation formula, the high-frequency dispersion data and the low-frequency dispersion data are converted to the apparent depth domain and superimposed on the apparent S-wave velocity profile to perform refined layered identification of hydrogeological structures. Based on the aforementioned high-frequency micro-motion signal, the phase velocity dispersion curve of the fundamental Rayleigh wave is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data, specifically including: Data with a high signal-to-noise ratio (SNR) is obtained by filtering out data from the high-frequency micro-motion signal that is below a preset SNR threshold. The spectrum of each frame of signal in the high signal-to-noise ratio data is normalized by amplitude and the phase information is preserved to obtain the whitened spectrum data. Perform an inverse Fourier transform on the whitened spectral data to obtain an equalized time-domain high-frequency micro-motion signal; Based on the equalized time-domain high-frequency micro-motion signal, the fundamental order Rayleigh wave phase velocity dispersion curve is extracted using the spatial autocorrelation method to obtain high-frequency dispersion segment data. The weighted fusion method is used to fuse the high-frequency dispersion data and the low-frequency dispersion data to obtain the calculation formula for the wideband micro-motion signal: ； in, The weighting function is set to a linear transition frequency of 0.5~1Hz. This is high-frequency dispersion data; This is low-frequency dispersion data.

5. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for acquiring basic data of a hydrogeological model based on micro-motion signals as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for acquiring basic data of hydrogeological models based on micro-motion signals as described in any one of claims 1-3.

7. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for acquiring basic data of hydrogeological models based on micro-motion signals as described in any one of claims 1-3.

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