Hydrogeological model basic data acquisition method, device and equipment based on micro-motion signal, medium and product

Abstract

The invention discloses a hydrogeological model basic data acquisition method, device and equipment based on micro-motion signals, a medium and a product, and relates to the technical field of crossing of geophysical exploration and hydrogeological modelling, the method comprises the following steps: acquiring high-frequency micro-motion signals and low-frequency micro-motion signals, which are acquired by a double-instrument nested array; respectively extracting a base order Rayleigh wave phase velocity dispersion curve according to the two signals to obtain high-frequency dispersion section data and low-frequency dispersion section data; performing data fusion on the high-frequency dispersion data and the low-frequency dispersion data to obtain a wide-frequency-band micro-motion signal, and converting the wide-frequency-band micro-motion signal to obtain a wide-frequency-band S-wave velocity profile map; and finally, converting the high-frequency dispersion section data and the low-frequency dispersion section data to an apparent depth domain, superposing the data on an apparent S-wave velocity profile map, and carrying out refined layered recognition on the hydrogeological structure. According to the method, a broadband dispersion curve with high resolution of a shallow part and reliable deep part is constructed, and high-precision stratigraphic structure information with continuous space is provided for construction of a hydrogeological model.

Description

Technical Field

[0001] This application relates to the cross - technical field of geophysical exploration and hydrogeological modeling, and particularly relates to a method, device, equipment, medium and product for obtaining basic data of a hydrogeological model based on micro - motion signals. Background Art

[0002] Traditional drilling is costly and the data is discrete, making it difficult to obtain a continuous stratigraphic structure. The construction of a hydrogeological model requires as detailed and spatially continuous stratigraphic structure information as possible. Borehole data can provide accurate and detailed stratigraphic structure information, but it is costly and only provides a one - hole view, unable to provide spatially continuous stratigraphic structure information. Summary of the Invention

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

[0004] To achieve the above purpose, this application provides the following solutions: In the first aspect, this application provides a method for obtaining basic data of a hydrogeological model based on micro - motion signals, including: Obtain 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 obtained by a dual - instrument nested array; According to the high - frequency micro - motion signals, based on the spatial autocorrelation method, extract the fundamental Rayleigh wave phase velocity dispersion curve to obtain high - frequency dispersion section data; According to the low - frequency micro - motion signals, based on the spatial autocorrelation method, extract the fundamental Rayleigh wave phase velocity dispersion curve to obtain low - frequency section dispersion data; Use a weighted fusion method to fuse the high - frequency dispersion section data and the low - frequency section dispersion data to obtain broadband micro - motion signals; Use the broadband micro - motion signals to convert to a broadband apparent S - wave velocity profile; Based on the apparent depth estimation formula, convert the high - frequency dispersion section data and the low - frequency section dispersion data to the apparent depth domain and superimpose them on the apparent S - wave velocity profile for refined stratification identification of the hydrogeological structure.

[0005] Optionally, according to the high - frequency micro - motion signals, based on the spatial autocorrelation method, extract the fundamental Rayleigh wave phase velocity dispersion curve to obtain high - frequency dispersion section data, specifically including: Filter out the data with a signal - to - noise ratio lower than a preset signal - to - noise ratio threshold in the high - frequency micro - motion signals to obtain high - signal - to - noise ratio data; Perform amplitude normalization on the spectrum of each frame of signal in the high - signal - to - noise ratio data and retain the phase information to obtain whitened spectrum data; Perform an inverse Fourier transform on the whitened spectral data to obtain an equalized time-domain high-frequency microseismic signal; Based on the equalized time-domain high-frequency microseismic signal, extract the fundamental Rayleigh wave phase velocity dispersion curve using the spatial autocorrelation method to obtain high-frequency dispersion section data.

[0006] Optionally, the calculation formula for using the weighted fusion method to fuse the high-frequency dispersion section data and the low-frequency dispersion data to obtain a wide-band microseismic signal is: ; where, is the weight function, taking a linear transition from 0.5 to 1 Hz; is the high-frequency dispersion section data; is the low-frequency dispersion data.

[0007] Optionally, based on the high-frequency microseismic signal, extract the fundamental Rayleigh wave phase velocity dispersion curve using the spatial autocorrelation method to obtain high-frequency dispersion section data, specifically including: Fit the real part of the autocorrelation coefficient spectrum of the high-frequency microseismic signal to a zero-order Bessel function to obtain a fitted Bessel curve; where, the zero-order Bessel function is: ; In the formula, represents the zero-order Bessel function, is the station pair spacing, represents the phase velocity, is the frequency; Based on the zero-value points and extreme value points of the fitted Bessel curve, obtain the corresponding station spacing and frequency values; Calculate the Rayleigh wave phase velocity dispersion curve for each station pair based on the station spacing and frequency values to obtain high-frequency dispersion section data.

[0008] Optionally, the conversion formula for converting the wide-band microseismic signal into a wide-band apparent S-wave velocity profile is: ; where, V R is the phase velocity, t i is the period, represents the phase velocity at a period of i , represents the phase velocity at a period of i - 1, t i-1 is i the period at - 1.

[0009] In a second aspect, the present application provides a device for acquiring basic data of a hydrogeological model based on microseismic signals, including: a dual-instrument nested array and a control module; The dual-instrument nested array includes: a first group of seismographs and a second group of seismographs; Among them, the first group of seismographs is used to acquire high-frequency microseismic signals; the second group of seismographs is used to acquire low-frequency microseismic signals; The control module is used for: Acquiring broadband microseismic signals; the broadband microseismic signals include: high-frequency microseismic signals and low-frequency microseismic signals; the broadband microseismic signals are acquired by the dual-instrument nested array; Based on the high-frequency microseismic signals, extracting the fundamental Rayleigh wave phase velocity dispersion curve by the spatial autocorrelation method to obtain high-frequency dispersion section data; Based on the low-frequency microseismic signals, extracting the fundamental Rayleigh wave phase velocity dispersion curve by the spatial autocorrelation method to obtain low-frequency section dispersion data; Using a weighted fusion method to perform data fusion on the high-frequency dispersion section data and the low-frequency section dispersion data to obtain broadband microseismic signals; Converting the broadband microseismic signals to obtain a broadband apparent S-wave velocity profile; Based on the apparent depth estimation formula, converting the high-frequency dispersion section data and the low-frequency section dispersion data to the apparent depth domain and superimposing them on the apparent S-wave velocity profile for refined stratification identification of the hydrogeological structure.

[0010] Optionally, the first group of seismographs is arranged at radii of 10m / 20m / 40m, and the main frequency of the first group of seismographs is 2Hz, which is used to acquire high-frequency microseismic signals of 2 - 30Hz; The second group of seismographs is arranged at radii of 80m / 160m / 320m, and the main frequency of the second group of seismographs is 0.5Hz, which is used to acquire low-frequency microseismic signals of 0.5 - 5Hz; The survey lines of the first group of seismographs and the second group of seismographs are both arranged perpendicular to the trend of the target detection body.

[0011] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for acquiring basic data of a hydrogeological model based on microseismic signals described in any one of the above.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for acquiring basic data of a hydrogeological model based on microseismic signals described in any one of the above.

[0013] In a fifth aspect, the present application provides a computer program product, including a computer program which, when executed by a processor, implements the method for acquiring basic data of a hydrogeological model based on microseismic signals described in any one of the above.

[0014] According to the specific embodiments provided by the present application, the following technical effects are disclosed: The present application provides a method, device, equipment, medium and product for acquiring basic data of a hydrogeological model based on microseismic signals. The method includes: acquiring broadband microseismic signals; the broadband microseismic signals include: high-frequency microseismic signals and low-frequency microseismic signals; the broadband microseismic signals are acquired by a double-instrument nested array; according to the high-frequency microseismic signals, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain high-frequency dispersion section data; according to the low-frequency microseismic signals, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain low-frequency section dispersion data; using a weighted fusion method to perform data fusion on the high-frequency dispersion section data and the low-frequency section dispersion data to obtain broadband microseismic signals; using the broadband microseismic signals to convert to a broadband apparent S-wave velocity profile; based on the apparent depth estimation formula, converting the high-frequency dispersion section data and the low-frequency section dispersion data to the apparent depth domain and superimposing them on the apparent S-wave velocity profile for refined stratification identification of the hydrogeological structure. The present application innovatively introduces a double-instrument nested array to acquire broadband microseismic signals, constructs a "high-resolution in the shallow part + reliable in the deep part" broadband dispersion curve, provides spatially continuous high-precision stratigraphic structure information for the construction of a hydrogeological model, and realizes refined stratification identification of the hydrogeological structure by combining the characteristics of the apparent depth dispersion curve, solving the problems of high cost and discontinuous data in traditional methods. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a schematic flow chart of a method for acquiring basic data of a hydrogeological model based on microseismic signals provided by an embodiment of the present application.

[0017] Figure 2 is a schematic flow chart of the analysis of microseismic signals in frequency bands provided by an embodiment of the present application.

[0018] Figure 3 It is a schematic diagram of a device for acquiring basic data of a hydrogeological model based on microseismic signals provided by an embodiment of the present application.

[0019] Figure 4 The structural schematic diagram of a computer device provided by an embodiment of the present application. Specific embodiments

[0020] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0021] In the present application, the acquisition of basic data for the hydrogeological model based on microtremor signals can obtain continuous stratigraphic structure information in the profile or three-dimensional space by arranging profiles or areal work. In addition, the present application innovatively introduces a double-instrument nested array to obtain broadband microtremor signals, and then improves the imaging quality of high-frequency band (shallow part) signals based on a two-stage artifact suppression method, thereby constructing a "high-resolution in the shallow part + reliable in the deep part" broadband dispersion curve, and further providing spatially continuous high-precision stratigraphic structure information for the construction of the hydrogeological model.

[0022] The present application is mainly used to solve the problem of data sparsity in the construction of hydrogeological models. It provides a low-cost, high-resolution, green and environment-friendly data acquisition scheme, and obtains high-precision formation velocity information of broadband dispersion curves based on band-separated microtremor signal processing and through weighted fusion technology.

[0023] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0024] In an exemplary embodiment, as Figure 1 shown, a method for obtaining basic data of a hydrogeological model based on microtremor signals is provided, including the following steps S1 to S6. Among them: S1. Obtain broadband microtremor signals; the broadband microtremor signals include: high-frequency microtremor signals and low-frequency microtremor signals; the broadband microtremor signals are obtained by a double-instrument nested array.

[0025] In this embodiment, first deploy a double-instrument nested array to obtain broadband microtremor signals.

[0026] The survey lines should be arranged as vertically as possible along the trend of the target detector, with a point spacing of about 200 m and a total of 30 points. A dual-instrument nested array is adopted: seismographs with a main frequency of 2 Hz are arranged at radii of 10 m / 20 m / 40 m to capture high-frequency microtremor signals (2 - 30 Hz) mainly; seismographs with a main frequency of 0.5 Hz are arranged at radii of 80 m / 160 m / 320 m to identify low-frequency microtremor signals (0.5 - 5 Hz) mainly. The target detector in this embodiment is a geological body for target detection, such as along the strike of a vertical fault, the long axis direction of a vertical ground settlement area, the strike of a vertical stratum, etc.

[0027] Then, the broadband microtremor signal is analyzed in different frequency bands. Refer to Figure 2, where Figure 2(a) includes Step 1 and Step 2, and Figure 2(b) includes Step 3 and Step 4. For high-frequency microtremor signals, low-quality data are filtered out by a signal screening method based on the signal-to-noise ratio threshold, and then the signal coherence is enhanced by a spectral equalization method based on spectral whitening to achieve two-stage artifact suppression, specifically as follows: ① Signal framing and spectrum calculation: The original signal is framed, with each frame having a length of T and an overlap rate ≥ 50%; the Fourier transform is performed on each frame of the signal to obtain the spectrum , and the power spectral density is calculated.

[0028] ② Signal-to-noise ratio (SNR) quantization: ; In the formula: is the power spectral density of the signal frequency band, is the power spectral density of the noise band.

[0029] ③ Set the threshold to filter out low-signal-to-noise data that do not reach the threshold.

[0030] ④ Spectrum amplitude normalization. The spectrum of each frame of the signal is subjected to amplitude normalization, and the phase information is retained: ; where is a very small constant, is the normalized amplitude, which can be set to 1 or the global average amplitude.

[0031] ⑤ Time-domain signal reconstruction. The inverse Fourier transform is performed on the whitened spectrum to obtain the equalized time-domain signal.

[0032] S2. According to the high-frequency microtremor signal, based on the spatial autocorrelation method, the fundamental Rayleigh wave phase velocity dispersion curve is extracted to obtain high-frequency dispersion segment data.

[0033] S3. Based on the low-frequency microtremor signal, extract the fundamental Rayleigh wave phase velocity dispersion curve using the spatial autocorrelation method to obtain the dispersion data in the low-frequency band.

[0034] In this embodiment, for the high-frequency microtremor signal after artifact suppression in steps ① to ⑤ and the untreated low-frequency microtremor signal, based on the spatial autocorrelation method, the dispersion curves are respectively extracted. Since the processes are the same for both, the microtremor signal is used here to represent either the high-frequency microtremor signal or the low-frequency microtremor signal.

[0035] When long-term observation is satisfied, the imaginary part of the autocorrelation coefficient spectrum of the microtremor signal is zero, and the real part is approximately the zero-order Bessel function of the first kind (through mathematical formula derivation, finally the real part expression of the signal is similar to the zero-order Bessel function of the first kind. That is the Bessel function. Replace x with to achieve the approximation between the two).

[0036] ; In the formula, represents the zero-order Bessel function, is the station pair spacing, represents the phase velocity, is the frequency; when = at this time, , that is is a constant. At this time, . By analyzing and fitting the zero-value points and extreme value points of the Bessel curve, the corresponding station spacing and frequency values are obtained, and then the Rayleigh wave phase velocity dispersion curve for each station pair is calculated, thereby obtaining the dispersion curve of the phase velocity. is the frequency, is the frequency value at a certain moment, means when the frequency is the phase velocity, is a constant.

[0037] S4. Use the weighted fusion method to perform data fusion on the high-frequency dispersion data and the low-frequency dispersion data to obtain a wide-band microtremor signal.

[0038] In this embodiment, based on the high-resolution high-frequency dispersion data and the deep and reliable low-frequency dispersion data, data fusion is performed through the weighted fusion method to obtain a wide-band microtremor signal.

[0039] The frequency of the micro - motion signal is inversely proportional to the detection depth. High - frequency signals reflect shallow structures but are highly interfered by noise, while low - frequency signals reflect deep structures but have low shallow - part resolution. By performing weighted averaging on the overlapping frequency bands of the high - quality high - frequency dispersion - segment data and low - frequency - segment dispersion data extracted after artifact suppression processing, a broadband stacking curve can be constructed to achieve the detection of "high resolution in the shallow part + reliable in the deep part".

[0040] The calculation formula is as follows: ; where, is the weight function, taking a linear transition from 0.5 to 1 Hz; is the high - frequency - segment dispersion data; is the low - frequency - segment dispersion data.

[0041] S5. Use the broadband micro - motion signal to convert and obtain a broadband apparent S - wave velocity profile.

[0042] In this embodiment, based on the broadband micro - motion signal c ( f ) and through empirical formula fitting, a broadband apparent S - wave velocity profile can be generated.

[0043] The apparent S - wave velocity directly obtained from the dispersion curve of the micro - motion signal (here, the micro - motion signal refers to the broadband micro - motion signal) can directly indicate the relative change characteristics of the formation velocity structure, avoiding the multi - solution problem of inversion. The calculation formula is as follows: ; where, V R is the phase velocity, t i is the period, i = 1, 2, 3,....n, is the statistical frequency point, and n is an integer; represents the phase velocity at the period of i , represents the phase velocity at the period of i - 1, t i-1 is i - 1 period.

[0044] S6. Based on the apparent depth estimation formula, convert the high - frequency dispersion - segment data and the low - frequency - segment dispersion data into the apparent depth domain, and superimpose them on the apparent S - wave velocity profile for refined stratification identification of the hydro - geological structure.

[0045] In this embodiment, based on the apparent depth estimation formula, the dispersion data in the frequency domain of each measuring point is converted into the apparent depth domain and superimposed on the apparent S-wave velocity profile. By combining the two, refined stratification identification of the hydrogeological structure is achieved. For a cross-section, three elements are required: the abscissa, the ordinate, and the attribute value. The apparent S-wave velocity is the attribute value, and the apparent depth is the ordinate.

[0046] The apparent depth estimation formula is: ; where H is the apparent depth, V R is the Rayleigh wave phase velocity, is the correction coefficient. In the working area with conditions, it should be compared with the known borehole data for depth correction to improve the accuracy of depth interpretation.

[0047] The interpolated apparent S-wave velocity profile changes relatively smoothly, and 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. According to the change characteristics of its dispersion points, it can assist in the division of shallow hydrogeological strata structures.

[0048] This embodiment proposes a green and efficient microtremor signal acquisition method guided by the construction of a hydrogeological model. A dual-instrument nested array is adopted: seismographs with a main frequency of 2 Hz are arranged at radii of 10 m / 20 m / 40 m to capture high-frequency microtremor signals (2 - 30 Hz) with emphasis; seismographs with a main frequency of 0.5 Hz are arranged at radii of 80 m / 160 m / 320 m to identify low-frequency microtremor signals (0.5 - 5 Hz). In this embodiment, by deploying a dual-instrument nested array, broadband microtremor signals are acquired. The microtremor signals are analyzed in frequency bands. For high-frequency microtremor signals, low-quality data is filtered out by the signal screening method based on the signal-to-noise ratio threshold, and then the signal coherence is enhanced by the spectral equalization method based on spectral whitening to achieve two-level artifact suppression. After processing, the weighted fusion algorithm is adopted to construct a broadband dispersion curve of "high resolution in the shallow part + reliable in the deep part". Finally, combined with the characteristic analysis of the apparent depth dispersion curve, refined stratification identification of the hydrogeological structure is achieved, thereby providing high-reliability basic data support for the construction of the hydrogeological model and solving the technical problem of low accuracy in the division of hydrogeological interfaces by traditional methods.

[0049] It has the following technical effects: 1) Provide spatially continuous basic data for the construction of the hydrogeological model.

[0050] 2) Achieve effective fusion of shallow (0 - 50 m) and medium-deep (50 - 500 m) data, with the resolution of the shallow layer increased by more than 30%. The coincidence degree between the inversion result and the borehole reaches 90%, and the error in the division of the stratum structure is less than 6% of the detection depth (verified by boreholes).

[0051] 3) Compared with the electromagnetic method, it is not affected by electromagnetic background interference and is not affected by whether the formation is water-rich. It is applicable to complex urban interference environments and can efficiently support hydrogeological modeling and urban geological safety surveys.

[0052] 4) Compared with the reflection seismic exploration method, the data acquisition efficiency is increased by more than 50%, the cost is reduced by more than 60%, and it is green and environmentally friendly.

[0053] 5) It can be seamlessly integrated with other microtremor signal dispersion imaging methods, such as the F-K method, etc.

[0054] Based on the same inventive concept, as Figure 3 shown ( Figure 3 in a) is a schematic diagram of the acquisition of microtremor signals by a dual-instrument; b) is a non-uniform scale display schematic diagram of single-instrument acquisition; c) is the combination mode of each seismometer pair when extracting the dispersion curve), the embodiment of the present application also provides a device for obtaining basic data of a hydrogeological model based on microtremor signals, including: a dual-instrument nested array and a control module.

[0055] The dual-instrument nested array includes: a first group of seismometers and a second group of seismometers.

[0056] Among them, the first group of seismometers is used to obtain high-frequency microtremor signals; the second group of seismometers is used to obtain low-frequency microtremor signals.

[0057] The control module is used for: Obtaining broadband microtremor signals; the broadband microtremor signals include: high-frequency microtremor signals and low-frequency microtremor signals; the broadband microtremor signals are obtained by the dual-instrument nested array.

[0058] According to the high-frequency microtremor signals, based on the spatial autocorrelation method, extracting the phase velocity dispersion curve of the fundamental Rayleigh wave to obtain high-frequency dispersion section data.

[0059] According to the low-frequency microtremor signals, based on the spatial autocorrelation method, extracting the phase velocity dispersion curve of the fundamental Rayleigh wave to obtain low-frequency dispersion data.

[0060] Using a weighted fusion method, fusing the high-frequency dispersion section data and the low-frequency dispersion data to obtain broadband microtremor signals.

[0061] Using the broadband microtremor signals to convert to a broadband apparent S-wave velocity profile.

[0062] Based on the apparent depth estimation formula, converting the high-frequency dispersion section data and the low-frequency dispersion data to the apparent depth domain and superimposing them on the apparent S-wave velocity profile for refined stratification identification of the hydrogeological structure.

[0063] Among them, the first group of seismographs is arranged according to radii of 10m / 20m / 40m, and the main frequency of the first group of seismographs is 2Hz, which is used to obtain high-frequency microtremor signals of 2 - 30Hz.

[0064] The second group of seismographs is arranged according to radii of 80m / 160m / 320m, and the main frequency of the second group of seismographs is 0.5Hz, which is used to obtain low-frequency microtremor signals of 0.5 - 5Hz.

[0065] The survey lines of the first group of seismographs and the second group of seismographs are both arranged perpendicular to the strike of the target detection body.

[0066] In this embodiment, a dual-instrument nested array observation system is designed with the construction of a hydrogeological model as the guide to obtain broadband microtremor signals. Two-level artifact suppression is performed on the high-frequency microtremor signals, a broadband dispersion of "high resolution in the shallow part + reliable in the deep part" is constructed, and refined stratification identification of the hydrogeological structure is realized by combining the characteristics of the apparent depth dispersion curve. It solves the problems of high cost and discontinuous data in traditional methods. It is applicable to fields such as hydrogeological model construction, urban geological safety investigation and prevention.

[0067] First, a green and efficient microtremor signal acquisition system is designed.

[0068] Then, two-level artifact suppression is performed on the high-frequency microtremor signals.

[0069] Next, a broadband dispersion curve of "high resolution in the shallow part + reliable in the deep part" is constructed.

[0070] Finally, refined stratification identification of the hydrogeological structure is realized.

[0071] Among them, an artifact suppression method based on the signal-to-noise ratio threshold and a spectral equalization method of spectral whitening are used to suppress artifacts and enhance the coherence of microtremor signals; a weighted fusion algorithm is used to effectively splice high-frequency signals and low-frequency signals; the apparent depth domain dispersion curve is combined with the apparent S-wave velocity section to enhance the identification of formation interfaces.

[0072] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for obtaining basic data of a hydrogeological model based on micro-motion signals.

[0073] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0074] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps in the above method embodiments are implemented.

[0075] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0076] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0077] 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 for analysis, stored data, displayed data, 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 relevant data need to comply with relevant regulations.

[0078] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAMs), magnetoresistive random access memories (MRAMs), ferroelectric random access memories (FRAMs), phase change memories (PCMs), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0079] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchains, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0080] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

[0081] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for obtaining basic data of a hydrogeological model based on micro motion signals, characterized in that, Including: Obtaining broadband microtremor signals; The broadband microtremor signals include: high-frequency microtremor signals and low-frequency microtremor signals; the broadband microtremor signals are obtained by a dual-instrument nested array; According to the high-frequency microtremor signals, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain high-frequency dispersion section data; According to the low-frequency microtremor signals, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain low-frequency section dispersion data; Using a weighted fusion method to perform data fusion on the high-frequency dispersion section data and the low-frequency section dispersion data to obtain broadband microtremor signals; Using the broadband microtremor signals to convert to a broadband apparent S-wave velocity profile; Based on the apparent depth estimation formula, converting the high-frequency dispersion section data and the low-frequency section dispersion data to the apparent depth domain and superimposing them on the apparent S-wave velocity profile for refined stratification identification of the hydrogeological structure.

2. The method for obtaining basic data of a hydrogeological model based on micro motion signals according to claim 1, characterized in that, According to the high-frequency microtremor signals, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain high-frequency dispersion section data, specifically including: Filtering out the data with a signal-to-noise ratio lower than a preset threshold in the high-frequency microtremor signals to obtain high-signal-to-noise data; Performing amplitude normalization on the spectrum of each frame of signal in the high-signal-to-noise data and retaining the phase information to obtain whitened spectrum data; Performing inverse Fourier transform on the whitened spectrum data to obtain an equalized time-domain high-frequency microtremor signal; According to the equalized time-domain high-frequency microtremor signal, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain high-frequency dispersion section data.

3. The method for obtaining basic data of a hydrogeological model based on micro motion signals according to claim 1, wherein The calculation formula for using the weighted fusion method to perform data fusion on the high-frequency dispersion section data and the low-frequency section dispersion data to obtain broadband microtremor signals is: ； Among them, is the weight function, taking a linear transition from 0.5 to 1 Hz; is the dispersion data in the high-frequency band; is the dispersion data in the low-frequency band.

4. The method for obtaining basic data of a hydrogeological model based on micro motion signals according to claim 1, wherein According to the high-frequency microtremor signals, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain high-frequency dispersion section data, specifically including: Fitting the real part of the autocorrelation coefficient spectrum of the high-frequency micro-motion signal to a zero-order Bessel function to obtain a fitted Bessel curve; where the zero-order Bessel function is: ; in the formula, represents the zero-order Bessel function, is the station pair spacing, represents the phase velocity, is the frequency; Obtaining the corresponding station spacing and frequency values according to the zero-value points and extreme-value points of the fitted Bessel curve; Calculating the Rayleigh wave phase velocity dispersion curve for each pair of stations according to the station spacing and frequency values to obtain high-frequency dispersion section data.

5. The method for obtaining basic data of a hydrogeological model based on micro motion signals according to claim 1, characterized in that, The conversion formula for using the broadband microtremor signals to convert to a broadband apparent S-wave velocity profile is: ； in, V R is the phase velocity, t i For the cycle, Indicates the period is i The phase velocity at Indicates the period is i Phase velocity at -1, t i-1 for i -1 hour period.

6. A device for obtaining basic data of a hydrogeological model based on micro-motion signals, characterized in that, Including: A dual-instrument nested array and a control module; The dual-instrument nested array includes: a first group of seismographs and a second group of seismographs; Among them, the first group of seismographs is used to obtain high-frequency microtremor signals; the second group of seismographs is used to obtain low-frequency microtremor signals; The control module is used for: Obtaining broadband microtremor signals; the broadband microtremor signals include: high-frequency microtremor signals and low-frequency microtremor signals; the broadband microtremor signals are obtained by a dual-instrument nested array; According to the high-frequency microtremor signals, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain high-frequency dispersion section data; According to the low-frequency microtremor signals, based on the spatial autocorrelation method, extracting the fundamental Rayleigh wave phase velocity dispersion curve to obtain low-frequency section dispersion data; Using a weighted fusion method to perform data fusion on the high-frequency dispersion section data and the low-frequency section dispersion data to obtain broadband microtremor signals; The wide-band microseismic signal is utilized to obtain a wide-band apparent S-wave velocity profile; Based on the apparent depth estimation formula, the high-frequency dispersion data and the low-frequency dispersion data are converted into the apparent depth domain and superimposed on the apparent S-wave velocity profile for refined stratification identification of the hydrogeological structure.

7. The device for obtaining basic data of a hydrogeological model based on micro-motion signals according to claim 6, characterized in that, The first group of seismographs is arranged at radii of 10m / 20m / 40m, and the main frequency of the first group of seismographs is 2Hz, which is used to acquire high-frequency microseismic signals of 2 - 30Hz; The second group of seismographs is arranged at radii of 80m / 160m / 320m, and the main frequency of the second group of seismographs is 0.5Hz, which is used to acquire low-frequency microseismic signals of 0.5 - 5Hz; The survey lines of the first group of seismographs and the second group of seismographs are both arranged perpendicular to the strike of the target detection body.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for obtaining basic data of a hydrogeological model based on microseismic signals according to any one of claims 1 - 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for obtaining basic data of a hydrogeological model based on microseismic signals according to any one of claims 1 - 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for obtaining basic data of a hydrogeological model based on microseismic signals according to any one of claims 1 - 5.

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