Precise monitoring method and system for mountain terrain seismic ground motion amplification effect based on space-air-ground integrated perception

Abstract

The application discloses a mountainous terrain seismic vibration amplification effect precision monitoring method and system based on space-air-ground integrated perception. The method comprises the following steps: performing space-based macroscopic screening by using synthetic aperture radar interferometry technology, and demarcating a key monitoring target area; performing air-based fine modeling by using unmanned aerial vehicle ground simulation flight, and generating a three-dimensional geological digital twin; running a measuring point optimization algorithm based on the three-dimensional geological digital twin, and outputting an optimal measuring point layout scheme; starting an artificial seismic source to emit a sweep frequency signal and synchronously recording a measuring point vibration time history; reconstructing a pure seismic vibration response signal according to the prior coding characteristics of the artificial source signal by using an adaptive variational mode decomposition algorithm; calculating the spectral ratio of the measuring point relative to a bedrock reference point, and generating a seismic vibration amplification coefficient cloud map. The application also provides a system for realizing the above method. The application realizes multi-scale collaborative monitoring from macroscopic screening to microscopic precise point layout, has the advantages of active excitation, intelligent point layout and strong noise suppression capacity, and provides an intuitive decision basis for mountainous area anti-seismic fortification.

Description

Technical Field

[0001] This invention belongs to the field of earthquake engineering and geotechnical engineering monitoring technology, specifically involving a method and system for monitoring ground motion response in complex mountainous terrain, inverting mountain dynamic effects based on artificial source signals, and optimizing the layout of measuring points. Background Technology

[0002] Mountainous terrain exhibits a significant amplification effect on seismic waves, such as the mountaintop amplification effect and the ridge focusing effect, which is a major reason for the exacerbation of earthquake disasters in mountainous areas. The spatial distribution of the seismic amplification effect is closely related to factors such as topographic geometry, the mechanical properties of soil and rock, and the incident direction of seismic waves. Accurately obtaining the topographic amplification effect is of great significance for seismic design of engineering projects in mountainous areas, geological hazard risk assessment, and site selection of major projects. Against the backdrop of frequent global earthquakes, mountainous regions often exhibit a significant seismic amplification effect due to their complex geological structure and topographic undulations. However, existing studies are mostly based on idealized topographic models or local observation data, and there is still a significant lack of systematic quantitative analysis of the seismic amplification effect in real mountainous terrain, which limits the accuracy of engineering seismic standards and hazard risk assessment. Current seismic design codes mainly rely on the results of macroscopic seismic damage surveys and two-dimensional seismic response analyses of different topographic conditions and soil and rock compositions to modify topographic effects, lacking dynamic and regional quantitative support, and are difficult to adapt to the diverse needs of complex mountainous environments.

[0003] The inventors of this application discovered the following common technical problems in existing technologies. First, the deployment of monitoring points is often arbitrary. Traditional monitoring methods rely on engineering experience for point deployment, lacking quantitative guidance on key influencing factors such as terrain irregularities and geological structural differences. This results in either insufficient deployment at key dynamic response singularities, failing to comprehensively capture the spatial distribution of terrain amplification effects, or excessive redundancy in non-critical areas, leading to both resource waste and monitoring blind spots. Second, the monitoring methods are passively limited. Existing methods heavily rely on natural earthquakes or aftershocks as excitation sources. These sources are scarce, and their occurrence time and location are uncontrollable, preventing researchers from actively acquiring dynamic response parameters of specific terrain under different directions and frequencies of excitation. This significantly limits the initiative and systematic nature of terrain amplification effect research, hindering routine "earthquake checkups" of key areas. Third, signal processing capabilities are insufficient. Mountainous environments are complex, with strong background noise from wind-induced vibrations, human activities, and ground tremors. This noise highly overlaps with weak seismic signals in both the time and frequency domains, making it difficult to effectively separate using conventional methods such as Fourier filtering and wavelet thresholding. This results in a low signal-to-noise ratio for the extracted signals, leading to significant distortion in the amplification factors calculated based on these signals, thus failing to provide a reliable basis for seismic design in engineering projects. Therefore, there is an urgent need for a comprehensive monitoring system and method capable of scientifically strategically placing monitoring points, actively exciting signals, and suppressing strong noise. Summary of the Invention

[0004] This application provides a method and system for accurate monitoring of the amplification effect of mountain terrain ground motion based on integrated air-space-ground sensing. The purpose is to solve the technical problems in the prior art, such as blind deployment of measuring points, passive reliance on natural earthquakes, and distortion of amplification factor calculation due to low signal-to-noise ratio.

[0005] To solve the above-mentioned technical problems, the technical solution proposed in this application is as follows:

[0006] This invention provides a method for accurately monitoring the seismic amplification effect of mountain terrain based on integrated air-space-ground sensing, comprising the following steps: Step S1, Airborne macroscopic screening: using synthetic aperture radar interferometry to process satellite images, generating a surface deformation rate map, and delineating key monitoring target areas; Step S2, Terrain digitization and pre-analysis: launching a UAV to perform terrain-mimicking flight over the key monitoring target areas, generating a high-precision three-dimensional terrain model; Step S3, Intelligent optimization of measuring points: based on the three-dimensional terrain model, running a measuring point optimization algorithm to output the optimal measuring point layout scheme; Step S4, Artificial source excitation and signal acquisition: launching an artificial source to transmit a frequency sweep signal, and simultaneously recording the vibration time history of the measuring points; Step S5, Adaptive filtering and signal reconstruction: using an adaptive variational mode decomposition algorithm, reconstructing a pure seismic response signal based on the prior coding characteristics of the artificial source signal; Step S6, Amplification factor calculation and visualization: calculating the spectral ratio of the measuring point relative to the bedrock reference point, and generating a seismic amplification factor cloud map.

[0007] Furthermore, the measurement point optimization algorithm in step S3 specifically includes: extracting terrain feature factors from the three-dimensional geological digital twin, the terrain feature factors including the rate of change of terrain curvature, the degree of prominence and isolation, and the valley elevation difference ratio; using a clustering algorithm, combined with the terrain amplification effect sensitivity index, to divide the mountain surface into dynamic response similarity zones; introducing a multi-objective particle swarm optimization algorithm, with the minimum number of measurement points covering the maximum terrain change gradient and signal transmission stability as constraints, to solve for the optimal measurement point location.

[0008] Further, the adaptive filtering and signal reconstruction in step S5 specifically includes: receiving the original artificial source ground motion signal mixed with environmental noise; decomposing the original signal into multiple intrinsic mode components using adaptive variational mode decomposition; evaluating the noise level of each intrinsic mode component using singular value difference spectrum technology; identifying and selecting effective modes from the multiple intrinsic mode components based on the prior coding characteristics of the artificial source signal; and reconstructing the selected effective modes to obtain a clean ground motion response signal.

[0009] Furthermore, the number of modes in the adaptive variational mode decomposition is adaptively determined based on the spectral characteristics of the signal. Its objective function is to minimize the sum of the bandwidths of each mode, and the constraint condition is that the sum of all modes equals the original signal. The prior coding features include the dominant frequency range, frequency variation law, or correlation characteristics of the coding sequence.

[0010] Further, the calculation of the amplification factor in step S6 specifically includes: selecting a measuring point at the bedrock as a reference point and obtaining its pure seismic response signal; obtaining the pure seismic response signal of the target measuring point in the mountainous area; performing Fourier transform on the pure signals of the reference point and the target measuring point respectively, and calculating the spectral ratio of the target measuring point relative to the reference point; introducing an equivalent peak strain correction factor to correct the spectral ratio, and obtaining the predicted seismic amplification factor.

[0011] This invention also provides a precise monitoring system for the amplification effect of seismic motion in mountainous terrain based on integrated air-space-ground sensing, comprising: an air-based observation device for acquiring regional surface micro-deformation fields and identifying sensitive zones of topographic effects based on the surface micro-deformation fields; a space-based observation device for scanning the sensitive zones of topographic effects and generating a three-dimensional geological digital twin; and a ground-based sensing device including a smart measurement point optimization layout module, an artificial seismic source active excitation module, and a clutter filtering and signal enhancement module. The smart measurement point optimization layout module is used to extract topographic feature factors from the three-dimensional geological digital twin and output the optimal measurement point layout scheme through clustering and optimization algorithms; the artificial seismic source active excitation module is used to transmit scanning signals with specific coding sequences; and the clutter filtering and signal enhancement module is used to separate and reconstruct an effective seismic response signal from the received original signal based on the prior coding features of the scanning signal.

[0012] Furthermore, the space-based observation device acquires multi-temporal images using spaceborne synthetic aperture radar, processes the multi-temporal images using synthetic aperture radar interferometry, generates a surface deformation rate map, and identifies the terrain effect sensitive zone by analyzing the coherence changes of long-term series; the space-based observation device also provides real-time dynamic differential positioning services using the Global Navigation Satellite System.

[0013] Furthermore, the space-based observation device utilizes a drone equipped with LiDAR and an optical camera to perform high-precision scanning, generating digital surface models and digital orthophotos. Combined with geological drilling data, it constructs the three-dimensional geological digital twin containing topographic geometric features and physical and mechanical parameters of the soil and rock mass.

[0014] Furthermore, the intelligent measurement point optimization layout module extracts terrain feature factors from the three-dimensional geological digital twin, uses a clustering algorithm combined with the terrain amplification effect sensitivity index to divide the mountain surface into dynamic response similar zones, and introduces a multi-objective particle swarm optimization algorithm with the minimum number of measurement points covering the maximum terrain change gradient and signal transmission stability as constraints to output the optimal measurement point layout scheme.

[0015] Furthermore, the artificial seismic source active excitation module is deployed in the bedrock outcrop area or stable area at the foot of the mountain to transmit a broadband scanning signal with a specific coding sequence. The intensity of the scanning signal is controlled within a safe threshold. The clutter filtering and signal enhancement module inputs the received raw signal into the FPGA real-time preprocessing unit, and uses adaptive variational mode decomposition combined with singular value difference spectrum technology to decompose the signal into multiple intrinsic mode components. Based on the prior coding characteristics of the artificial source signal, environmental interference modes are automatically identified and eliminated, and effective modes related to the artificial source are retained for reconstruction.

[0016] Furthermore, the system also includes a real-time calculation and verification module for seismic amplification factor, which is used to select a measuring point at the foot of the mountain bedrock as a reference point, calculate the spectral ratio between the mountain measuring point and the reference point under the same artificial source excitation, and, in combination with the difference between the artificial source equivalent load and the natural seismic load, introduce an equivalent peak strain correction factor to output the predicted amplification factor.

[0017] Compared with the prior art, the present invention achieves the following beneficial technical effects: This invention transforms the traditional passive earthquake monitoring model into an active detection model. By using controllable artificial sources, it allows for real-time "earthquake checks" of key areas, solving the problems of scarce and uncontrollable natural earthquake samples and significantly improving the initiative and flexibility of monitoring. The integrated air-space-ground sensing architecture realizes a complete chain from macroscopic screening to precise microscopic point deployment, greatly improving monitoring accuracy. Through deep learning and global optimization of terrain features using AI algorithms, it achieves optimal coverage of the spatial distribution of terrain amplification effects with a minimal number of sensors, avoiding missed detections or redundancy caused by manual point deployment. This significantly reduces system cost and construction difficulty while ensuring monitoring effectiveness. Employing advanced filtering technology based on adaptive variational mode decomposition and coded feature matching, it can accurately extract weak artificial source vibration signals in complex, high-background-noise environments in mountainous areas, improving the signal-to-noise ratio to over 20dB, ensuring the accuracy and reliability of subsequent amplification factor calculations. The final monitoring results are directly mapped onto the three-dimensional geological digital twin, forming an intuitive "magnification factor cloud map," which makes the spatial distribution of the terrain magnification effect clear at a glance, providing a direct and visualized decision-making basis for earthquake resistance design in mountainous areas, geological disaster risk assessment, and engineering site selection. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method for precise monitoring of seismic amplification effect in mountainous terrain based on integrated air-space-ground sensing, as provided in an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the overall architecture of a precision monitoring system for the amplification effect of mountain terrain ground motion based on integrated air-space-ground sensing, provided in an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the overall architecture of ground-based sensing provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] refer to Figure 1 In one embodiment of this application, the overall process of a method for precise monitoring of seismic amplification effects in mountainous terrain based on integrated air-space-ground sensing is first described in detail. This method includes six steps: airborne macroscopic screening, terrain digitization and pre-analysis, intelligent optimization of measuring point deployment, artificial seismic source excitation and signal acquisition, adaptive filtering and signal reconstruction, and amplification factor calculation and visualization. These steps are described in detail below: Step S1, Space-based macroscopic screening: Using synthetic aperture radar interferometry to process satellite images, generate a surface deformation rate map, and delineate key monitoring target areas; In the space-based macroscopic screening step, historical archived spaceborne synthetic aperture radar (SAR) satellite data for the target area is first retrieved, such as data from the European Space Agency's Sentinel-1 series satellites or China's Gaofen-3 satellite. Using SAR interferometry, these multi-temporal images are registered, interferometrically processed, de-flattened, filtered, and unwrapped to generate regional surface deformation rate maps. By analyzing long-term coherence variations, areas with sudden decreases in coherence or abnormal deformation rates are identified. These areas typically correspond to unstable surface cover, steep cliffs, loose deposits, or fault fracture zones with accumulated minor deformations—potential "topographically sensitive zones." This step rapidly narrows the macroscopic study area, which might have covered tens of square kilometers, to a "key monitoring target area" of several square kilometers. Simultaneously, the system connects to a global navigation satellite system, such as the BeiDou Navigation Satellite System, utilizing its real-time dynamic differential positioning service to provide centimeter-level coordinate references for the precise deployment of subsequent ground sensors, ensuring that sensors are accurately buried at their designed locations.

[0024] Step S2, Terrain digitization and pre-analysis: The UAV is launched to perform terrain-following flight over the key monitoring target area to generate a high-precision three-dimensional terrain model; After the initial airborne macroscopic screening, the terrain digitization and pre-analysis phase begins. Within the designated "key monitoring target area," drones are launched for terrain-following flight. Equipped with a lidar scanner and a high-resolution optical camera, the drones perform a full-coverage scan of the target mountain following a pre-set terrain-following flight path. The flight altitude is typically set between 100 and 300 meters above the ground, with a flight speed controlled between 5 and 10 meters per second, achieving a point cloud density of over 100 points per square meter. The laser pulses emitted by the lidar can penetrate part of the vegetation canopy, directly acquiring high-precision three-dimensional point cloud data of the ground. After point cloud filtering, classification, and interpolation, a digital surface model and digital orthophoto with centimeter-level accuracy are generated. The digital surface model accurately represents the geometry of the terrain surface, including key terrain feature factors such as slope, aspect, curvature, and elevation changes. Subsequently, combined with limited geological drilling data within the target area, physical and mechanical parameters of the soil and rock at different depths are obtained, such as density, elastic modulus, Poisson's ratio, damping ratio, and shear wave velocity. These soil and rock parameters are spatially matched and attribute-assigned with the digital surface model to ultimately construct a three-dimensional geological digital twin containing topographic geometric information and soil and rock physical and mechanical information. This digital twin serves as the data foundation for subsequent measurement point optimization and response analysis. Based on this model, potential "strong amplification areas" can be preliminarily identified, such as isolated ridges, prominent mountain peaks, steep cliff edges, and other locations with significant topographic geometric features.

[0025] Step S3, Intelligent Optimization of Measurement Point Layout: Based on the three-dimensional terrain model, run the measurement point optimization algorithm and output the optimal measurement point layout scheme; In the intelligent optimization and deployment step of measuring points, a measuring point optimization algorithm is run based on the aforementioned 3D geological digital twin. This algorithm first extracts topographic feature factors from the 3D geological digital twin, including the rate of change of topographic curvature, salient isolation, and valley elevation difference ratio. The rate of change of topographic curvature is obtained by calculating the first derivative of the surface curvature; the salient isolation is obtained by calculating the ratio of the target point to the average elevation of its neighborhood; and the valley elevation difference ratio is obtained by calculating the ratio of the elevation difference between the valley bottom and the ridges on both sides to the horizontal distance. Next, a clustering algorithm combined with a topographic amplification effect sensitivity index is used to divide the entire mountain surface into several dynamic response similarity zones. Points within each zone are expected to have similar dynamic response characteristics under seismic excitation. Based on this, a multi-objective particle swarm optimization algorithm is introduced for global optimization. This optimization algorithm uses the minimum number of measuring points covering the maximum topographic change gradient and signal transmission stability as its dual objective functions. The minimum number of measuring points covering the maximum terrain change gradient aims to capture areas of drastic terrain change, i.e., dynamic response singularities, with the fewest possible sensor locations. Signal transmission stability considers factors such as the communication distance between the sensor and the data acquisition station, and the attenuation of wireless signals by geological bodies. After iterative algorithm solving, a set of optimal measuring point layout schemes is output, including the latitude, longitude, and elevation information of each measuring point. This coordinate information will be sent to the mobile terminals of construction personnel to guide the precise installation of sensors such as three-component broadband seismometers, ensuring that the installation error of each sensor does not exceed 5 centimeters.

[0026] Step S4, Artificial source excitation and signal acquisition: Start the artificial source to transmit a frequency sweep signal and simultaneously record the vibration time history of the measuring point; In the artificial seismic source excitation and signal acquisition steps, a precise and controllable active seismic source, such as a servo-controlled drop-weighted seismic source or an electromagnetic seismic source, is deployed in a selected bedrock outcrop area or stable area at the foot of the mountain. The seismic source is activated during the time window with the lowest environmental noise, such as midnight to 4 AM. This seismic source has a wide-band scanning capability, capable of transmitting a scanning signal with an adjustable frequency range of 1Hz to 200Hz. To distinguish it from random noise in the environment, the signal transmitted by the seismic source uses a specific coded sequence, such as a pseudo-random sequence or a chirp sweep signal. The pseudo-random sequence has strong autocorrelation and weak cross-correlation, facilitating matched filtering in subsequent signal processing; the chirp sweep signal has wide bandwidth coverage and uniform energy distribution. The output intensity of the seismic source is precisely controlled within a safe threshold, for example, an equivalent energy between 50 N·m and 1000 N·m, ensuring that the generated elastic wave can effectively penetrate the mountain and be clearly received by sensors deployed at the summit and slope, without causing any damage to the mountain structure. The seismic source system is also equipped with a high-precision global navigation satellite system synchronization module, achieving microsecond-level time synchronization with the data acquisition systems at each measuring point. Simultaneously, all deployed measuring points synchronously initiate data acquisition, with a sampling rate set to 1000Hz, recording the complete vibration time history from the start of seismic source excitation to the end of energy decay; each record typically lasts 60 seconds.

[0027] Step S5, Adaptive Filtering and Signal Reconstruction: Using the adaptive variational mode decomposition algorithm, a clean seismic response signal is reconstructed based on the prior coding features of the artificial source signal. In the adaptive filtering and signal reconstruction steps, the raw vibration data collected from each measuring point are gathered at the data processing center. The processing center first standardizes and verifies the data format, removing obviously abnormal data segments. Then, it calls the core signal processing algorithm—Adaptive Variational Mode Decomposition (AMD). This algorithm adaptively decomposes non-stationary, nonlinear complex signals into several discrete eigenmode components with sparse characteristics. During the decomposition process, the number of modes is not fixed but adaptively determined based on the characteristics of the signal itself. Its core mathematical model is as follows: for the original signal, a constrained variational problem is solved. By introducing a quadratic penalty factor and Lagrange multipliers, the variational problem is transformed into an unconstrained problem, and the alternating direction multiplier method is used for iterative solution to obtain each eigenmode component. After obtaining a series of eigenmode components, singular value difference spectroscopy is introduced to further analyze the energy and identify noise components of each eigenmode component. Specifically, a Hankel matrix is ​​constructed for each intrinsic mode component (IMC), singular value decomposition (SVD) is performed, the difference values ​​of the singular value sequences are calculated, singular value abrupt change points are identified, and components after the abrupt change points are determined to be noise-dominant components. Most importantly, based on the prior coding characteristics of the artificial source signal, such as the dominant frequency range, frequency variation patterns, or correlation characteristics of pseudo-random sequences, the system automatically identifies one or several components with the highest correlation to the artificial source signal from all IMC components. These components represent the effective seismic response signal. Other components, such as those corresponding to wind vibration, human activity, or instrument self-noise, are automatically discarded. Finally, only the selected effective IMC components are reconstructed to obtain a clean response signal with an extremely high signal-to-noise ratio (SNR), with a target SNR exceeding 20 dB.

[0028] Step S6, Calculation and visualization of magnification factor: Calculate the spectral ratio of the measuring point relative to the bedrock reference point and generate a ground motion magnification factor cloud map; In the amplification factor calculation and visualization steps, the first step is to select a measuring point located at the foot of the mountain bedrock, least affected by the topographic amplification effect, as a reference point. The signal from this reference point represents the baseline for the input seismic motion. Then, the transfer function is solved using the standard spectral ratio method: Fourier transforms are performed on the pure response signals of the target measuring point and the bedrock reference point, respectively, to obtain their Fourier amplitude spectra, with a frequency resolution set to 0.1 Hz. The amplitude spectrum ratio between the target measuring point and the reference point in the frequency domain is calculated; this spectral ratio curve represents the topographic amplification factor as a function of frequency. The calculated amplification factor is used as an attribute value and assigned to the corresponding measuring point on the 3D geological digital twin. Based on this, the Kriging interpolation algorithm is used to perform spatial interpolation prediction on the entire mountain surface in areas without measuring points. A spherical model is used for the variogram model, with a search radius set to 50 meters, generating a seismic amplification factor cloud map of the entire mountain surface. Finally, combined with digital orthophotos, a comprehensive monitoring report is generated, including topography, measuring point locations, amplification factor cloud map, and assessment conclusions.

[0029] In one embodiment of this application, the measurement point optimization algorithm is further described in detail. The algorithm specifically includes the following steps: First, topographic feature factors are extracted from a three-dimensional geological digital twin. These factors include the rate of change of topographic curvature, the degree of prominence and isolation, and the valley elevation ratio. The rate of change of topographic curvature is obtained by calculating the first derivative of the surface curvature and is used to quantify the severity of topographic changes. The degree of prominence and isolation is obtained by calculating the ratio of the target point to the mean elevation of its neighborhood and is used to identify isolated ridges and protruding mountain peaks. The valley elevation ratio is obtained by calculating the ratio of the elevation difference between the valley bottom and the ridges on both sides to the horizontal distance and is used to quantify the influence of valley topography on the seismic wave focusing effect. Then, a clustering algorithm combined with a topographic amplification effect sensitivity index is used to divide the mountain surface into several dynamic response similarity zones. The topographic amplification effect sensitivity index is a pre-calibrated weighted coefficient based on the correlation between topographic feature factors and known historical data on amplification effects, used to quantify the contribution of different topographic features to seismic motion amplification. Clustering algorithms employ K-means clustering or density-based noise-based spatial clustering to group regions with similar sensitivity to terrain amplification effects into the same cluster. Finally, a multi-objective particle swarm optimization algorithm is introduced, using the constraints of minimizing the number of measuring points covering the maximum terrain change gradient and signal transmission stability to solve for the optimal measuring point locations. The constraint of minimizing the number of measuring points covering the maximum terrain change gradient is achieved by maximizing the sum of the gradient values ​​of terrain feature factors at each measuring point's location; the constraint of signal transmission stability is achieved by setting the maximum communication distance between the measuring point and the data acquisition station, and the minimum signal attenuation threshold. After iterative solving, the algorithm outputs a set of optimal measuring point layout schemes, including the latitude, longitude coordinates, and elevation information of each measuring point.

[0030] In one embodiment of this application, the adaptive filtering and signal reconstruction process is further described in detail. This process specifically includes: first, receiving the original artificial source ground motion signal mixed with environmental noise, the signal originating from three-component broadband seismometers deployed at various measuring points. Then, adaptive variational mode decomposition (ADD) is used to decompose the original signal into multiple intrinsic mode components. The number of modes in the adaptive variational mode decomposition is adaptively determined based on the spectral characteristics of the signal, with the objective function being to minimize the sum of the bandwidths of each mode, and the constraint condition being that the sum of all modes equals the original signal. Specifically, for the original signal, a constrained variational problem is solved. By introducing a quadratic penalty factor and Lagrange multipliers, the variational problem is transformed into an unconstrained problem, and the iterative solution using the alternating direction multiplier method is employed to obtain each intrinsic mode component. Next, singular value difference spectroscopy is used to evaluate the noise level of each intrinsic mode component. A Hankel matrix is ​​constructed for each intrinsic mode component, singular value decomposition is performed, the difference values ​​of the singular value sequence are calculated, singular value abrupt change points are identified, and the components after the abrupt change points are determined to be noise-dominant components. Then, based on the prior coding characteristics of the artificial source signal, effective modes are identified and selected from multiple intrinsic mode components. These prior coding characteristics include the dominant frequency range, frequency variation patterns, or correlation characteristics of the coded sequence. For example, if the artificial source transmits a Chirp signal with a linear frequency sweep from 5Hz to 100Hz, the system automatically locks the intrinsic mode components whose frequency variation patterns match this. If the artificial source transmits a pseudo-random sequence coded signal, the system calculates the correlation between each intrinsic mode component and the pseudo-random sequence, selecting the component with the highest correlation. Finally, the selected effective modes are reconstructed to obtain a clean seismic response signal.

[0031] In one embodiment of this application, the specific implementation of adaptive variational mode decomposition is further described. The number of modes in the adaptive variational mode decomposition is adaptively determined based on the spectral characteristics of the signal. Its objective function is to minimize the sum of the bandwidths of all modes, and the constraint condition is that the sum of all modes equals the original signal. Specifically, for the original signal... f ( t Solve the following constrained variational problem:

[0032] in, u k ( t ) represents the k-th intrinsic mode component, which is the time-domain signal to be solved; ω k The center frequency of the k-th intrinsic mode component is represented by K; K represents the total number of mode decompositions. t This represents taking the partial derivative with respect to time t; δ ( t ) represents the Dirac function;j Represents the imaginary unit; π Pi is a constant. This represents the convolution operation; represents the square of the L2 norm, used to measure the bandwidth of each mode; st represents the constraint condition, requiring the sum of all modes to equal the original signal.

[0033] The objective of this variational problem is to minimize the sum of the bandwidths of all modes, and the constraints ensure the completeness of the decomposition. By introducing a quadratic penalty factor α and Lagrange multipliers λ(t), this constrained variational problem is transformed into an unconstrained problem, and the constructed augmented Lagrange function is:

[0034]

[0035] The augmented Lagrangian function is solved iteratively using the alternating direction multiplier method, through alternating updates. u k ( t ), ω k The number of modes K is adaptively determined based on the spectral characteristics of the signal, typically between 5 and 10; the quadratic penalty factor α controls the bandwidth of each mode, usually set to 2000 to 5000.

[0036] Prior coding features include the dominant frequency range, frequency variation patterns, or correlation characteristics of the coded sequence. The dominant frequency range refers to the range of the main frequency components of the artificial source signal, such as 5Hz to 100Hz; the frequency variation pattern refers to the frequency variation pattern of the artificial source signal over time, such as linear sweep, exponential sweep, or step frequency; the correlation characteristics of the coded sequence refer to the autocorrelation and cross-correlation functions of the specific coded sequence used by the artificial source signal, for example, pseudo-random sequences have sharp autocorrelation peaks and low cross-correlation characteristics.

[0037] In one embodiment of this application, the calculation process of the amplification factor is further described in detail. This process specifically includes: First, selecting a bedrock measuring point as a reference point and obtaining its pure seismic response signal. The bedrock measuring point is typically selected at a location in the bedrock outcrop area at the foot of a mountain, where the influence of topographic amplification is minimal; the signal from this measuring point represents the reference for the input seismic motion. Then, pure seismic response signals from target measuring points in the mountainous area are obtained; these signals are obtained through the aforementioned adaptive filtering and signal reconstruction methods. Next, Fourier transforms are performed on the pure signals from the reference point and the target measuring point respectively to obtain their respective Fourier amplitude spectra. The spectral ratio of the target measuring point relative to the reference point is calculated, i.e., the ratio of the amplitude spectrum of the target measuring point to the amplitude spectrum of the reference point; this spectral ratio curve is the curve of the topographic amplification factor changing with frequency. Finally, an equivalent peak strain correction factor is introduced to correct the spectral ratio, obtaining the predicted seismic amplification factor. The equivalent peak strain correction factor is obtained by comparing and analyzing the peak strain response of soil under artificial source excitation and natural seismic excitation, or by calibration through finite element numerical simulation. In the finite element simulation, the equivalent load from an artificial source and the typical natural seismic load are input respectively to calculate the peak strain at key points within the mountain. The ratio of the two is the correction factor. The final output prediction amplification factor is the spectral ratio multiplied by the correction factor.

[0038] refer to Figure 2 In one embodiment of this application, the overall architecture of a precise monitoring system for the amplification effect of seismic motion in mountainous terrain based on integrated air-space-ground sensing is described in detail. The system includes an airborne observation device, a spaceborne observation device, and a ground-based sensing device. The airborne observation device is used to acquire regional surface micro-deformation fields and identify sensitive zones for topographic effects based on these fields. Specifically, the airborne observation device uses spaceborne synthetic aperture radar to acquire multi-temporal images covering the study area, processes these images using synthetic aperture radar interferometry, generates a surface deformation rate map, and identifies sensitive zones for topographic effects by analyzing the coherence changes over long time series. The airborne observation device also utilizes the Global Navigation Satellite System to provide real-time dynamic differential positioning services, providing centimeter-level coordinate references for the precise deployment of subsequent ground sensors. The spaceborne observation device is used to scan the sensitive zones for topographic effects and generate a three-dimensional geological digital twin. Specifically, the space-based observation device utilizes drones equipped with LiDAR and optical cameras to perform high-precision scanning, generating digital surface models and digital orthophotos. Combined with geological drilling data, it constructs a three-dimensional geological digital twin containing topographic geometric features and physical and mechanical parameters of the soil and rock mass. For example... Figure 3 As shown, the ground-based sensing device includes a module for intelligent optimization of measuring point layout, a module for active excitation of artificial seismic sources, and a module for clutter filtering and signal enhancement.

[0039] In one embodiment of this application, the specific implementation of the space-based observation device is further described. The space-based observation device utilizes spaceborne synthetic aperture radar to acquire multi-temporal images, such as data from the European Space Agency's Sentinel-1 series satellites or China's Gaofen-3 satellite. Using synthetic aperture radar interferometry, these multi-temporal images are registered, interferometrically processed, de-flattened, filtered, and unwrapped to generate regional surface deformation rate maps. By analyzing the coherence changes over long time series, areas with sudden decreases in coherence or abnormal deformation rates are identified. These areas typically correspond to unstable surface cover, steep cliffs with accumulated minor deformation, loose deposits, or fault fracture zones—potential "topographically sensitive zones." The space-based observation device also utilizes global navigation satellite systems to provide real-time dynamic differential positioning services, such as the BeiDou Navigation Satellite System, to provide centimeter-level coordinate references for the precise deployment of subsequent ground sensors, ensuring that sensors can be accurately buried at their designed locations.

[0040] In one embodiment of this application, the specific implementation of the space-based observation device is further described. The space-based observation device utilizes a drone equipped with a LiDAR and an optical camera for high-precision scanning. The drone, whether rotary-wing or fixed-wing, performs a full-coverage scan of the target mountain according to a preset terrain-following flight path. The flight altitude is typically set between 100 and 300 meters above the ground, and the flight speed is controlled between 5 and 10 meters per second, achieving a point cloud density of over 100 points per square meter. The laser pulses emitted by the LiDAR can penetrate part of the vegetation canopy, directly acquiring high-precision three-dimensional point cloud data of the ground. After point cloud filtering, classification, and interpolation processing, a digital surface model and digital orthophoto with centimeter-level accuracy are generated. Combined with geological drilling data, the physical and mechanical parameters of soil and rock masses at different depths are obtained, including density, elastic modulus, Poisson's ratio, damping ratio, and shear wave velocity. These soil and rock parameters are spatially matched and attribute-assigned with the digital surface model, ultimately constructing a three-dimensional geological digital twin containing terrain geometric features and soil and rock physical and mechanical parameters.

[0041] In one embodiment of this application, the specific implementation of the intelligent measurement point optimization layout module is further described. The module first extracts topographic feature factors from a 3D geological digital twin, including topographic curvature change rate, salient isolation degree, and valley elevation ratio. Then, a clustering algorithm combined with a topographic amplification effect sensitivity index is used to divide the mountain surface into dynamic response similarity zones. The topographic amplification effect sensitivity index is a pre-calibrated weight coefficient based on the correlation between topographic feature factors and known amplification effect historical data. The clustering algorithm uses K-means clustering or a density-based noise-applied spatial clustering algorithm. Finally, a multi-objective particle swarm optimization algorithm is introduced, with constraints of minimizing the number of measurement points covering the maximum topographic change gradient and signal transmission stability, to output the optimal measurement point layout scheme. The constraint of minimizing the number of measurement points covering the maximum topographic change gradient is achieved by maximizing the sum of the gradient values ​​of the topographic feature factors at each measurement point location; the constraint of signal transmission stability is achieved by setting the maximum communication distance between the measurement points and the data acquisition station, and the minimum signal attenuation threshold.

[0042] In one embodiment of this application, the specific implementation of the artificial seismic source active excitation module and the clutter filtering and signal enhancement module is further described. The artificial seismic source active excitation module is deployed in the bedrock outcrop area or stable area at the foot of the mountain, and adopts a servo-controlled drop hammer seismic source, electromagnetic seismic source, or piezoelectric seismic source. The system can transmit a wideband scanning signal with a specific coded sequence. The wideband frequency range is 1Hz to 200Hz, and the specific coded sequence is a pseudo-random sequence, a Chirp sweep sequence, or a binary coded sequence. The intensity of the scanning signal is controlled within a safe threshold, which is an equivalent energy of 50N·m to 1000N·m. The clutter filtering and signal enhancement module inputs the received raw signal into the FPGA real-time preprocessing unit for amplification, filtering, and analog-to-digital conversion, with the sampling rate set to 500Hz to 2000Hz. Then, adaptive variational mode decomposition combined with singular value difference spectroscopy is used to decompose the signal into multiple intrinsic mode components. Based on the prior coding characteristics of artificial source signals, including the dominant frequency range, frequency variation law, or correlation characteristics of coding sequences, environmental interference modes are automatically identified and eliminated, while effective modes related to artificial sources are retained for reconstruction to obtain a pure seismic response signal.

[0043] In one embodiment of this application, the specific implementation of the real-time calculation and verification module for seismic amplification factor is further described. This module selects a measuring point at the foot of the mountain bedrock as a reference point, which is located in the bedrock outcrop area and is least affected by the topographic amplification effect. The module calculates the spectral ratio between the mountain measuring point and the reference point under the same artificial source excitation, i.e., performing Fourier transforms on the pure response signals of the target measuring point and the reference point respectively to obtain the Fourier amplitude spectrum, and calculating the ratio of the amplitude spectrum of the target measuring point to that of the reference point. Since the load generated by the artificial source differs from the natural seismic load in energy, frequency components, and duration, this module introduces an equivalent peak strain correction factor to correct the spectral ratio. The equivalent peak strain correction factor is obtained by comparing and analyzing the peak strain response of the soil under artificial source excitation and natural seismic excitation, or by calibration through finite element numerical simulation. Finally, a predicted amplification factor is output, which is the spectral ratio multiplied by the correction factor. The calculated amplification factor is assigned to the corresponding measuring point on the three-dimensional geological digital twin, and a seismic amplification factor cloud map of the entire mountain surface is generated through Kriging interpolation.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for precise monitoring of seismic motion amplification effect in mountainous terrain based on integrated air-space-ground sensing, characterized in that, Includes the following steps: Step S1, Space-based macroscopic screening: Using synthetic aperture radar interferometry to process satellite images, generate a surface deformation rate map, and delineate key monitoring target areas; Step S2, Terrain digitization and pre-analysis: The UAV is launched to perform terrain-following flight over the key monitoring target area to generate a high-precision three-dimensional terrain model; Step S3, Intelligent Optimization of Measurement Point Layout: Based on the three-dimensional terrain model, run the measurement point optimization algorithm and output the optimal measurement point layout scheme; Step S4, Artificial source excitation and signal acquisition: Start the artificial source to transmit a frequency sweep signal and simultaneously record the vibration time history of the measuring point; Step S5, Adaptive Filtering and Signal Reconstruction: Using the adaptive variational mode decomposition algorithm, a clean seismic response signal is reconstructed based on the prior coding features of the artificial source signal. Step S6, Calculation and visualization of magnification factor: Calculate the spectral ratio of the measuring point relative to the bedrock reference point and generate a ground motion magnification factor cloud map.

2. The method according to claim 1, characterized in that, The measurement point optimization algorithm described in step S3 specifically includes: Topographic feature factors are extracted from a three-dimensional geological digital twin, including the rate of change of topographic curvature, the degree of prominence and isolation, and the ratio of valley elevation differences. Clustering algorithms, combined with the terrain amplification effect sensitivity index, are used to divide the mountain surface into dynamic response similarity zones; A multi-objective particle swarm optimization algorithm is introduced to solve for the optimal measurement point location under the constraints of minimizing the number of measurement points covering the maximum terrain change gradient and signal transmission stability.

3. The method according to claim 1, characterized in that, The adaptive filtering and signal reconstruction described in step S5 specifically include: Receive raw artificial source ground motion signals mixed with environmental noise; The original signal is decomposed into multiple intrinsic mode components using adaptive variational mode decomposition. The noise level of each intrinsic mode component is evaluated using singular value difference spectroscopy. Based on the prior coding characteristics of artificial source signals, effective modes are identified and screened from multiple intrinsic mode components; The selected effective modes are reconstructed to obtain a pure seismic response signal.

4. The method according to claim 3, characterized in that, The number of modes in the adaptive variational mode decomposition is adaptively determined based on the spectral characteristics of the signal. Its objective function is to minimize the sum of the bandwidths of each mode, and the constraint condition is that the sum of all modes equals the original signal. The prior coding features include the dominant frequency range, frequency variation law, or correlation characteristics of the coding sequence.

5. The method according to claim 1, characterized in that, The calculation of the magnification factor in step S6 specifically includes: A measuring point at the bedrock was selected as a reference point to obtain its pure seismic response signal; Acquire clean seismic response signals from target measurement points in mountainous areas; Perform Fourier transforms on the clean signals of the reference point and the target measurement point respectively, and calculate the spectral ratio of the target measurement point relative to the reference point; An equivalent peak strain correction factor is introduced to correct the spectral ratio, thereby obtaining the predicted seismic amplification factor.

6. A system for implementing the precise monitoring method for the amplification effect of mountain terrain seismic motion based on integrated air-space-ground sensing as described in claim 1, characterized in that, include: An airborne observation device is used to acquire regional surface microdeformation fields and identify topographic effect sensitive zones based on the surface microdeformation fields. The space-based observation device is used to scan the terrain effect sensitive zone and generate a three-dimensional geological digital twin. Ground-based sensing device, including: The intelligent measurement point layout module is used to extract terrain feature factors from the three-dimensional geological digital twin and output the optimal measurement point layout scheme through clustering and optimization algorithms. The artificial seismic source active excitation module is used to transmit scanning signals with a specific coded sequence; The clutter filtering and signal enhancement module is used to separate and reconstruct an effective vibration response signal from the received original signal based on the prior coding characteristics of the scan signal.

7. The system according to claim 6, characterized in that, The space-based observation device acquires multi-temporal images using spaceborne synthetic aperture radar, processes the multi-temporal images using synthetic aperture radar interferometry, generates a surface deformation rate map, and identifies the terrain effect sensitive zone by analyzing the coherence changes of long-term series; the space-based observation device also provides real-time dynamic differential positioning services using the Global Navigation Satellite System.

8. The system according to claim 6, characterized in that, The space-based observation device uses a drone equipped with LiDAR and an optical camera to perform high-precision scanning, generating digital surface models and digital orthophotos. Combined with geological drilling data, it constructs the three-dimensional geological digital twin, which includes topographic geometric features and physical and mechanical parameters of the soil and rock mass.

9. The system according to claim 6, characterized in that, The intelligent measurement point optimization layout module extracts terrain feature factors from the three-dimensional geological digital twin, uses a clustering algorithm combined with the terrain amplification effect sensitivity index to divide the mountain surface into dynamic response similar zones, and introduces a multi-objective particle swarm optimization algorithm with the minimum number of measurement points covering the maximum terrain change gradient and signal transmission stability as constraints to output the optimal measurement point layout scheme.

10. The system according to claim 6, characterized in that, The artificial seismic source active excitation module is deployed in the bedrock outcrop area or stable area at the foot of the mountain to transmit a broadband scanning signal with a specific coding sequence. The intensity of the scanning signal is controlled within a safe threshold. The clutter filtering and signal enhancement module inputs the received raw signal into the FPGA real-time preprocessing unit, and uses adaptive variational mode decomposition combined with singular value difference spectrum technology to decompose the signal into multiple intrinsic mode components. Based on the prior coding characteristics of the artificial source signal, environmental interference modes are automatically identified and eliminated, and effective modes related to the artificial source are retained for reconstruction.

11. The system according to claim 6, characterized in that, It also includes a real-time calculation and verification module for seismic amplification factor, which is used to select a measuring point at the foot of the mountain bedrock as a reference point, calculate the spectral ratio between the mountain measuring point and the reference point under the same artificial source excitation, and combine the difference between the artificial source equivalent load and the natural seismic load to introduce an equivalent peak strain correction factor and output the predicted amplification factor.

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