Slope uncertainty modeling method based on micro-motion analysis and related equipment

Abstract

This application provides a slope uncertainty modeling method and related equipment based on micromotion analysis. The slope uncertainty modeling method includes: acquiring micromotion signals of the slope collected by a detector array; obtaining a shear wave velocity profile of the slope based on the micromotion signals; the shear wave velocity profile is used to reflect the number of strata, the shear wave velocity of each stratum, and the thickness; fitting the autocorrelation function of the strength parameters of each layer and the corresponding correlation length based on the shear wave velocity profile; the correlation length is the horizontal correlation length; and constructing a random field model of slope strength parameters based on the autocorrelation function and the correlation length to achieve slope uncertainty modeling. This method can accurately obtain the parameters required for constructing the random field, especially the horizontal correlation length, thus improving the accuracy of the final obtained slope parameter random field model.

Description

Technical Field

[0001] This application relates to the field of slope engineering technology, and in particular to a slope uncertainty modeling method and related equipment based on micro-motion analysis. Background Technology

[0002] Slope soil and rock masses exhibit significant spatial variability, meaning that physical and mechanical parameters vary at different locations within the same stratum. Extensive engineering experience shows that if this spatial variability is ignored and calculations are performed based on deterministic slope models, the predicted slope results often deviate significantly from the actual situation, affecting the accuracy of slope stability analysis. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a slope uncertainty modeling method based on micro-motion analysis, including: Acquire the micro-motion signals of the slope collected by the detector array; The shear wave velocity profile of the slope is obtained based on the micro-motion signal; the shear wave velocity profile is used to reflect the number of strata, the shear wave velocity of each stratum, and the thickness of each stratum. Based on the shear wave velocity profile, shear wave velocity samples are obtained, the autocorrelation function values ​​of the shear wave velocity samples are calculated, and the autocorrelation function of the strength parameters of each stratum on the slope and the corresponding correlation length are fitted; the correlation length is the correlation length in the horizontal direction. Based on the autocorrelation function and the correlation length, a random field model of slope parameters is constructed to realize slope uncertainty modeling.

[0004] In some embodiments, the step of obtaining shear wave velocity samples based on the shear wave velocity profile, calculating the autocorrelation function values ​​of the shear wave velocity samples, and fitting the autocorrelation function of the strength parameters of each stratum of the slope and the corresponding correlation length includes: The transverse wave velocity in the same horizontal direction detected by the detector is decomposed into a combination of mean and random fluctuation components. Based on the random fluctuation component, the sample autocorrelation function value of the transverse wave velocity in the same horizontal direction detected by the detector is obtained; By fitting the autocorrelation function values ​​of the shear wave velocity samples using multiple autocorrelation function models, the autocorrelation function of the strength parameters of each stratum on the slope and the corresponding correlation length are obtained.

[0005] In some embodiments, fitting the autocorrelation function values ​​of the shear wave velocity samples using multiple autocorrelation function models to obtain the autocorrelation function of the soil strength parameters of each layer of the slope and the corresponding correlation length includes: By fitting the autocorrelation function values ​​of the shear wave velocity samples using multiple autocorrelation function models, multiple corresponding correlation lengths and multiple corresponding determination coefficients of the strength parameters of each stratum on the slope are obtained. The autocorrelation function and its corresponding correlation length are determined based on the plurality of corresponding determination coefficients; The plurality of autocorrelation function models include at least two of the following: single exponential autocorrelation function, squared exponential autocorrelation function, cosine exponential autocorrelation function, and second-order Markov autocorrelation function.

[0006] In some embodiments, determining the autocorrelation function and the corresponding correlation length based on the plurality of corresponding determination coefficients includes: determining the target autocorrelation function with the largest determination coefficient as the autocorrelation function.

[0007] In some embodiments, obtaining the shear wave velocity profile of the slope based on the micro-motion signal includes: The spatial autocorrelation method is used to extract the observed dispersion curve based on the micro-motion signal; The substitution function algorithm is used to invert the shear wave velocity profile of the slope.

[0008] In some embodiments, the detector array includes a center detector and peripheral detectors; the micro-motion signal includes a center micro-motion signal detected by the center detector and a peripheral micro-motion signal detected by the peripheral detectors; The extraction of the observation dispersion curve based on the micro-motion signal includes: representing the central micro-motion signal and the surrounding micro-motion signal as time- and position-dependent stationary random processes respectively; calculating the spatial autocorrelation coefficient to obtain the relationship between frequency and observed phase velocity in the dispersion curve; and converting the frequency to obtain the observation dispersion curve.

[0009] In some embodiments, the method of inverting the shear wave velocity profile of the slope using the substitution function algorithm includes: The inversion objective function is constructed by observing the phase velocity; Random initial test points are generated using a substitution function algorithm, and the objective function at the initial test points is evaluated. A surrogate model and an evaluation function are constructed, adaptive points are generated, the inverted objective function is evaluated at the adaptive points, and the surrogate model is updated until the surrogate model reaches the stopping criterion.

[0010] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the methods described in any of the preceding embodiments.

[0011] This application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to perform the methods described in any of the preceding embodiments.

[0012] This application also provides a computer program product, including computer program instructions, which, when run on a computer, cause the computer to perform the methods described in any of the preceding embodiments.

[0013] As can be seen from the above, the slope uncertainty modeling method and related equipment based on micro-motion analysis provided in this application acquires the micro-motion signals of the slope collected by a detector array; obtains the shear wave velocity profile of the slope based on the micro-motion signals; the shear wave velocity profile is used to reflect the number of strata, the shear wave velocity of each stratum, and the thickness of each stratum; based on the shear wave velocity profile, obtains shear wave velocity samples, calculates the autocorrelation function values ​​of the shear wave velocity samples, and fits the autocorrelation function of the strength parameters of each stratum of the slope and the corresponding correlation length; the correlation length is the horizontal correlation length; based on the autocorrelation function and the correlation length, constructs a slope parameter random field model to realize slope uncertainty modeling. This method can accurately obtain the parameters required for constructing the random field, especially the horizontal correlation length, thus improving the accuracy of the final obtained slope parameter random field model. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating the slope uncertainty modeling method based on micro-motion analysis, as described in an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of a detector array according to an embodiment of this application.

[0017] Figure 3 This is an exemplary structural diagram of a slope according to an embodiment of this application.

[0018] Figure 4 This is a flowchart illustrating how an autocorrelation function of intensity parameters and the corresponding correlation length are obtained based on the shear wave velocity profile, according to an embodiment of this application.

[0019] Figure 5 This is the observed dispersion curve of an embodiment of this application.

[0020] Figure 6 This is a schematic diagram of the inverted shear wave velocity profile according to an embodiment of this application.

[0021] Figure 7a This is a schematic diagram showing the fitting results of multiple autocorrelation function models in the first stratum according to an embodiment of this application.

[0022] Figure 7b This is a schematic diagram showing the fitting results of multiple autocorrelation function models in the second stratum according to an embodiment of this application.

[0023] Figure 8a This is a schematic diagram of a random field model for slope parameters according to an embodiment of this application.

[0024] Figure 8b This is a schematic diagram of another random field model for slope parameters according to an embodiment of this application.

[0025] Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0027] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "connected" or "linked" used in the embodiments of this application are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "up," "down," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0028] Random field models can account for the spatial variability of soil and rock parameters. To improve the rationality of modeling, existing studies generally attempt to obtain autocorrelation functions and correlation lengths by referencing values ​​of similar soil and rock masses in existing literature or by statistically analyzing strength parameters from field tests. However, referencing similar soil and rock masses cannot account for the characteristics of the site itself. Field tests mainly utilize borehole data, laboratory test data, and engineering experience information to statistically estimate random field characterization parameters such as variance, correlation function, and correlation distance. However, current technology mainly relies on point test data, such as borehole sampling or static cone penetration test data. The number of such test points is limited and their spatial distribution is discrete. Due to limitations such as site conditions, testing technology, and exploration costs, the number of strength parameters obtained is limited, making it difficult to obtain the complete parameters required to construct a random field model, especially the correlation length. The correlation length reflects the spatial correlation of physical and mechanical parameters at different locations and requires a large amount of evenly distributed sample data to obtain a reliable estimate.

[0029] Therefore, the relevant technologies suffer from the problem of not being able to accurately obtain the parameters required for random field modeling of slope strength parameters, making it difficult to meet the needs of accurately constructing slope uncertainty models.

[0030] Based on this, the embodiments of this application provide a slope uncertainty modeling method based on micro-motion analysis. By obtaining the shear wave velocity information based on micro-motion analysis, the autocorrelation function and correlation length are obtained to construct a parametric random field and realize slope uncertainty modeling. This method can solve the problem of not being able to accurately obtain the parameters required for slope random field modeling to a certain extent.

[0031] refer to Figure 1 The slope uncertainty modeling method based on micro-motion analysis provided in this application embodiment may include: S100, acquire the micro-motion signal of the slope collected by the detector; S200, the shear wave velocity profile of the slope is obtained based on the micro-motion signal; the shear wave velocity profile is used to reflect the number of strata, the shear wave velocity of each stratum and the thickness of each stratum. S300, based on the shear wave velocity profile, obtain shear wave velocity samples, calculate the autocorrelation function value of the shear wave velocity samples, and fit the autocorrelation function of the strength parameters of each stratum of the slope and the corresponding correlation length; the correlation length is the correlation length in the horizontal direction; S400, Based on the autocorrelation function and the correlation length, construct a random field model of slope parameters to realize slope uncertainty modeling.

[0032] This application provides a slope uncertainty modeling method and related equipment based on micro-motion analysis. The method acquires micro-motion signals of the slope collected by a detector array; obtains a shear wave velocity profile of the slope based on the micro-motion signals; the shear wave velocity profile reflects the number of strata, the shear wave velocity of each stratum, and the thickness of each stratum; based on the shear wave velocity profile, obtains shear wave velocity samples, calculates the autocorrelation function values ​​of the shear wave velocity samples, and fits the autocorrelation function of the strength parameters of each stratum of the slope and the corresponding correlation length; the correlation length is the horizontal correlation length; based on the autocorrelation function and the correlation length, a slope parameter random field model is constructed to realize the construction of a slope uncertainty model. This method can accurately obtain the parameters required for constructing the random field, especially the horizontal correlation length, improving the accuracy of the final obtained slope parameter random field model.

[0033] In some embodiments, in step S100, the detector can be a seismic detector. Typically, a seismic detector needs to meet the frequency range requirements of the micro-motion signal. For example, the technical specifications of a seismic detector may be as follows: sensitivity 200 V / m / s, sampling rate 0.25-20 ms, and frequency range 0.2-150 Hz.

[0034] In some embodiments, the detector can be an array-type detector. Specifically, it can be a nested triangular array detector, see reference. Figure 2 As shown. Typically, arrays of different sizes can be set up according to the site conditions of the slope and the detection requirements, with the center point of the array as the measuring point and the remaining points as observation stations. The geophones on the slope can be set at the top of the slope. This allows for a dense deployment of geophones, facilitating the acquisition of a dense shear wave velocity profile.

[0035] In some embodiments, exemplarily, such as Figure 3 The diagram shows a slope model. The slope can be a two-layer structure, including a first stratum (i.e., stratum I) and a second stratum (i.e., stratum II). The stratum interface can be located at 27.5 meters. For example, the number of detectors in this nested triangular array can be 10, labeled S1 to S2. 10 S1 is the center station of the array, which can be a measurement point, and the surrounding stations are S2 to S... 10 It can be an observation station.

[0036] In some embodiments, step S200 may involve extracting the observed dispersion curve using the spatial autocorrelation method and inverting the shear wave velocity profile using the substitution function algorithm. Specifically, obtaining the shear wave velocity profile of the slope based on the micro-motion signal may include: S201: The spatial autocorrelation method is used to extract the observed dispersion curve. Specifically, the micro-motion signals of the central station and surrounding stations can be represented as time- and position-dependent stationary random processes; the spatial autocorrelation coefficient is calculated to obtain the relationship between the frequency on the horizontal axis and the phase velocity on the vertical axis in the dispersion curve; the frequency is then converted to obtain the dispersion curve.

[0037] Typically, the central station can be represented by a small oscillation signal (e.g., S1(0,0)) as a time- and location-dependent stationary random process. . Include surrounding sites (e.g., S) i ( r,θ The micro-motion signal can be represented as a time- and position-dependent stationary random process. .in, X This is a micro-motion signal. t For time. exp It is a natural exponential function. i It is the imaginary unit. w It is the angular frequency. φ The direction of wave propagation; ζ ( w , φ ) is a random process. r This represents the distance between the peripheral detectors and the center detector. θ It is the azimuth angle.

[0038] In some embodiments, the relationship between the horizontal axis (circular frequency) and the vertical axis (observed phase velocity) of the observed dispersion curve can be: .in, χ ( w , r ) represents the spatial autocorrelation coefficient. J 0( rw / c ( w )) as the basis rw / c ( w Let ) be the zeroth-order Bessel function of the first kind of variable; c ( w () represents the observed phase velocity.

[0039] In some embodiments, the angular frequency can be... w Convert to frequency f The observed phase velocity can be The relationship between angular frequency and frequency can be expressed as follows: w = 2πf As shown. After this conversion, the frequency can be obtained. f The observed dispersion curve is shown on the horizontal axis, as shown in the example below. Figure 5 As shown.

[0040] S202: The transverse wave velocity profile of the slope is inverted using the substitution function algorithm. Specifically, the inversion objective function can be constructed by observing the phase velocity. The inversion objective function is shown in the following equation. Where A is the inversion objective function. The observed phase velocity is extracted from the micro-motion signal recording in S201. This represents the theoretical phase velocity. f min This represents the lower limit of the effective frequency range. f max This represents the upper limit of the effective frequency range. m The number of frequencies within the effective frequency range.

[0041] Next, a set of random initial test points can be generated within the constraints using the substitution function algorithm. The objective function at the initial test points is evaluated, and a surrogate model is constructed. Subsequently, an evaluation function is constructed, and adaptive points are generated. The inverted objective function is evaluated at the adaptive points, and the surrogate model is updated until the stopping criterion is met. The surrogate model is... ;in, ; λ i The coefficients are given; the first term is the radial basis function (RBF), using a cubic kernel function. ( z ) = z 3 . p ( n () represents a linear term. The evaluation function can be... .in, α The weights are 0.3, 0.5, 0.8, and 0.95. s ( n ) as candidate test sites n The alternative model is used to calculate the value at that location. s min It is the minimum value calculated by the alternative model in the current set of candidate test points. s max This is the maximum value calculated by the alternative model in the current set of sampled test points. β ( n ) as candidate test sites n Minimum distance to the evaluated test site; β max The maximum distance from all candidate test points to the current sampling test point. β min This represents the minimum distance from all candidate test points to the current sampling test point. The resulting transverse wave velocity profiles obtained after inversion at each detection point for different detectors can be represented as follows: Figure 6 As shown.

[0042] In some embodiments, in step S300, such as Figure 4 As shown, based on the shear wave velocity profile, shear wave velocity samples are obtained, the autocorrelation function values ​​of the shear wave velocity samples are calculated, and the autocorrelation function of the strength parameters of each stratum on the slope and the corresponding correlation length are fitted, including: S301: Decompose the transverse wave velocity in the same horizontal direction detected by the detector into a combination of mean and random fluctuation components. S302: Calculate the sample autocorrelation function value of the horizontal shear wave velocity of each stratum based on the random fluctuation component; S303: By fitting the autocorrelation function values ​​of the shear wave velocity samples using multiple autocorrelation function models, the autocorrelation function of the strength parameters of each stratum on the slope and the corresponding correlation length are obtained.

[0043] Typically, the transverse wave velocity in the same horizontal direction obtained from micromotion analysis inversion can be... .in, Vs ( x ) represents the transverse wave velocity that varies in the horizontal direction. This represents the average transverse wave velocity. η ( x ) represents the random fluctuation component. Typically, the sample autocorrelation function value of shear wave velocity can be obtained from the random fluctuation components in the shear wave velocity profile. η ( x )calculate. .in, sam ( d j ) represents the sample autocorrelation function value of the shear wave velocity. M This represents the number of wave velocity data samples. j =1, 2, …, M . η ( x i () represents the transverse wave velocity at x i The fluctuation component at that location. η ( x i+j () represents the transverse wave velocity at x i+j The fluctuation component at that location. d j The distance between two measuring points is considered when only the horizontal parameter fluctuation is taken into account. d j This represents the horizontal distance between the two measuring points.

[0044] In some embodiments, fitting the autocorrelation function values ​​of the shear wave velocity samples using multiple autocorrelation function models to obtain the autocorrelation function of the strength parameters of each stratum of the slope and the corresponding correlation length may include: By fitting the autocorrelation function values ​​of the shear wave velocity samples using multiple autocorrelation function models, multiple corresponding correlation lengths and multiple corresponding determination coefficients of the strength parameters of each stratum on the slope are obtained. The autocorrelation function and the corresponding correlation length are determined based on the multiple corresponding determination coefficients.

[0045] In some embodiments, the plurality of autocorrelation function models include at least two of the following: a single exponential autocorrelation function, a squared exponential autocorrelation function, a cosine exponential autocorrelation function, and a second-order Markov autocorrelation function. The one-dimensional form of the single exponential autocorrelation function can be... .in, ρ SNX ( τ ) is a single exponential autocorrelation function. τ It represents the one-dimensional distance between two points. b For the corresponding length. In two-dimensional form, τ / b for: .in,( x 1, y 1) is a point x coordinate,( x 2, y 2) is a point coordinate; l x and l y These are the correlation lengths in the horizontal and vertical directions, respectively. The squared exponential autocorrelation function is... .in, ρ SQX ( τ The quadratic exponential autocorrelation function is given by [formula missing]. The cosine exponential autocorrelation function is given by [formula missing]. .in, ρ CSX ( τ Let be the cosine exponential autocorrelation function. The second-order Markov autocorrelation function is... .in, ρ SMK ( τ ) is a second-order Markov autocorrelation function.

[0046] In some embodiments, determining the autocorrelation function and the corresponding correlation length based on the plurality of corresponding determination coefficients includes: determining the target autocorrelation function with the largest determination coefficient as the autocorrelation function.

[0047] For example, for those with Figure 3 The autocorrelation function model was fitted to the two strata of the slope with medium parameters. The fitting results are shown in Table 1 below. Figure 7a and Figure 7b As shown in Table 1, the fitting results include the correlation lengths and their respective coefficients of determination obtained from fitting four autocorrelation function models. Table 1 shows that for a double-layered slope, the optimal autocorrelation function for both stratum strength parameters is the squared exponential autocorrelation function. Therefore, the horizontal correlation lengths of the double-layered slope are determined to be 2.900 meters and 3.503 meters, respectively.

[0048] Table 1. Correlation lengths and corresponding coefficients of determination for each model.

[0049] The slope uncertainty modeling method based on micro-motion analysis in this application utilizes the characteristic that autocorrelation distance is an inherent property of soil and rock, and that the fitting values ​​of autocorrelation distance for different types of parameters are relatively similar. Based on the shear wave velocity profile of the slope, a relatively easy-to-obtain shear wave velocity sample of soil and rock is obtained, and the correlation length is calculated. This method can accurately obtain the parameters required to construct a random field, especially the horizontal correlation length.

[0050] In some embodiments, step S400, constructing a random field model of slope parameters based on the autocorrelation function and the correlation length, may include: The covariance function of the random field is calculated based on the fitted autocorrelation function and the correlation length. Extract the eigenvalues ​​and eigenfunctions of the covariance function of the random field, and generate a parametric random field; Based on the aforementioned parametric random field, a slope parametric random field model is constructed to realize the construction of a slope uncertainty model.

[0051] In some embodiments, the covariance function of the random field can be... .in, Let covariance function be used. σ 2 Represents the variance of a random field variable; For point x and x’ Distance; autocorrelation function ( τ ) is the autocorrelation function determined in step S300 above. x It is a spatial location point.

[0052] In some embodiments, a random field model of slope strength parameters can be constructed using the Karhunen-Loève expansion method: .in, F ( x ) indicates spatial location x The strength parameter value on; μ ( x ) is the mean function of the intensity parameters; ψ i Let it be a set of standard normal random variables; λ i and These are the covariance functions. eigenvalues ​​and eigenfunctions; N This represents the number of truncated terms.

[0053] For example, Figure 8a and Figure 8b Here are two examples of generated parametric two-dimensional random field models. l y The grid size is set to 1 m, and the number of truncation terms N is 1000 to ensure sufficient computational accuracy. The spatial discrete grid size is 0.5 × 0.5 m.

[0054] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0055] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the slope uncertainty modeling method based on micro-motion analysis described in any of the above embodiments.

[0057] Figure 9This illustration shows a more specific hardware structure diagram of an electronic device provided in an embodiment of this application. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0058] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0059] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0060] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0061] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0062] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0063] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0064] The electronic devices described in the above embodiments are used to implement the slope uncertainty modeling method based on micro-motion analysis in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0065] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the slope uncertainty modeling method based on micro-motion analysis as described in any of the above embodiments.

[0066] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0067] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the slope uncertainty modeling method based on micro-motion analysis as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0068] Based on the same inventive concept, corresponding to the slope uncertainty modeling method based on micro-motion analysis described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to execute the slope uncertainty modeling method based on micro-motion analysis. Corresponding to the execution entity for each step in each embodiment of the slope uncertainty modeling method based on micro-motion analysis, the processor executing the corresponding step can belong to the corresponding execution entity.

[0069] The computer program product of the above embodiments is used to enable the computer and / or the processor to execute the slope uncertainty modeling method based on micro-motion analysis as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0070] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0071] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description.

[0072] This disclosure is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A slope uncertainty modeling method based on micro-motion analysis, characterized in that, include: Acquire the micro-motion signals of the slope collected by the detector array; The shear wave velocity profile of the slope is obtained based on the micro-motion signal; The shear wave velocity profile is used to reflect the number of formations, the shear wave velocity of each formation, and its thickness. Based on the shear wave velocity profile, shear wave velocity samples are obtained, the autocorrelation function values ​​of the shear wave velocity samples are calculated, and the autocorrelation function of the strength parameters of each stratum on the slope and the corresponding correlation length are fitted; the correlation length is the correlation length in the horizontal direction. Based on the autocorrelation function and the correlation length, a random field model of slope strength parameters is constructed to realize slope uncertainty modeling.

2. The slope uncertainty modeling method based on micro-motion analysis according to claim 1, characterized in that, Based on the shear wave velocity profile, shear wave velocity samples are obtained, and the autocorrelation function values ​​of the shear wave velocity samples are calculated. The autocorrelation function of the strength parameters of each stratum of the slope and the corresponding correlation length are then fitted, including: The horizontal shear wave velocity of the same stratum obtained by micromotion analysis is decomposed into a combination of mean and random fluctuation components. Based on the random fluctuation components, calculate the sample autocorrelation function value of the horizontal shear wave velocity of each stratum; By fitting the autocorrelation function values ​​of the shear wave velocity samples using multiple autocorrelation function models, the autocorrelation function of the strength parameters of each stratum on the slope and the corresponding correlation length are obtained.

3. The slope uncertainty modeling method based on micro-motion analysis according to claim 2, characterized in that, The process of fitting the autocorrelation function values ​​of the shear wave samples using multiple autocorrelation function models to obtain the autocorrelation function of the soil strength parameters of each layer of the slope and the corresponding correlation length includes: By fitting the autocorrelation function values ​​of the shear wave velocity samples using multiple autocorrelation function models, multiple corresponding correlation lengths and multiple corresponding determination coefficients of the strength parameters of each stratum on the slope are obtained. The autocorrelation function and its corresponding correlation length are determined based on the plurality of corresponding determination coefficients; The plurality of autocorrelation function models include at least two of the following: single exponential autocorrelation function, squared exponential autocorrelation function, cosine exponential autocorrelation function, and second-order Markov autocorrelation function.

4. The slope uncertainty modeling method based on micro-motion analysis according to claim 3, characterized in that, The step of determining the autocorrelation function and the corresponding correlation length based on the plurality of corresponding determination coefficients includes: determining the target autocorrelation function with the largest determination coefficient as the autocorrelation function.

5. The slope uncertainty modeling method based on micro-motion analysis according to claim 1, characterized in that, The process of obtaining the shear wave velocity profile of the slope based on the micro-motion signal includes: The spatial autocorrelation method is used to extract the observed dispersion curve based on the micro-motion signal; The substitution function algorithm is used to invert the shear wave velocity profile of the slope.

6. The slope uncertainty modeling method based on micro-motion analysis according to claim 5, characterized in that, The detector array includes a central detector and peripheral detectors; the micro-motion signal includes a central micro-motion signal detected by the central detector and a peripheral micro-motion signal detected by the peripheral detectors; The extraction of the observation dispersion curve based on the micro-motion signal includes: representing the central micro-motion signal and the surrounding micro-motion signal as time- and position-dependent stationary random processes respectively; calculating the spatial autocorrelation coefficient to obtain the relationship between frequency and observed phase velocity in the dispersion curve; and converting the frequency to obtain the observation dispersion curve.

7. The slope uncertainty modeling method based on micro-motion analysis according to claim 5, characterized in that, The method for inverting the shear wave velocity profile of the slope using the substitution function algorithm includes: The inversion objective function is constructed by observing the phase velocity; Random initial test points are generated using a substitution function algorithm, and the objective function at the initial test points is evaluated. A surrogate model and an evaluation function are constructed, adaptive points are generated, the inverted objective function is evaluated at the adaptive points, and the surrogate model is updated until the surrogate model reaches the stopping criterion.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as claimed in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.

10. A computer program product comprising computer program instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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