A method and device for monitoring terrain based on optical inner and outer three-dimensional imaging

Abstract

The present application relates to the field of measurement technology, in particular to a kind of based on optical inside and outside three-dimensional imaging topographic monitoring method and device, combined with external laser radar scanning and internal optical fiber sensing technology, realize the three-dimensional geological deformation monitoring of inside and outside integration.External monitoring part uses laser radar to carry out high-precision three-dimensional scanning to slope outer contour, obtains the information of slope external contour.Internal monitoring part measures the displacement change in the internal of slope rock mass by optical fiber displacement sensor, and combines with optical fiber microseismic sensor to collect microseismic data.Microseismic data is inverted rock mass velocity field by travel time tomography method based on physical information neural network, and is further converted into stress field.Finally, the inside and outside three-dimensional topographic monitoring model is constructed by comprehensively monitoring data, realizes the prediction to slope deformation and potential instability.The present application can obtain the displacement change and stress distribution of slope surface topography and internal rock mass in real time, improves the ability of slope topographic safety early warning and real-time monitoring.

Description

Technical Field

[0001] This invention relates to the field of measurement technology, and in particular to a method and device for terrain monitoring based on optical internal and external three-dimensional imaging. Background Technology

[0002] Topography, as a crucial component of the Earth's surface, including mountains, slopes, and mining areas, directly impacts the safety and development of various sectors such as engineering construction, ecological protection, and disaster prevention. Under the combined influence of natural evolution and human activities, topography is susceptible to gradual deformation caused by factors such as earthquakes, rainwater erosion, vegetation degradation, and engineering excavation. Failure to monitor and warn in a timely manner can easily trigger geological disasters such as landslides, mudslides, and ground subsidence. These disasters not only damage transportation routes and buildings, forcing the interruption of related production and daily life activities and causing huge economic losses, but also pose a serious threat to the lives of surrounding residents. In recent years, geological disasters caused by topographic instability have occurred frequently both domestically and internationally, causing irreparable damage to socio-economic development and the ecological environment. Topographic stability monitoring has become a core element of geological disaster prevention and control and engineering safety management.

[0003] Accurate prediction and forecasting of topographic instability disasters are prerequisites for achieving topographic management and stability control, and are also crucial for ensuring regional safe development. However, current topographic stability monitoring technologies still have significant shortcomings. Most monitoring schemes rely on single technical means, making it difficult to comprehensively and accurately reflect the overall stability of the terrain. For example, in existing technology, patent CN201510579347.0, "A Slope Rock Mass Monitoring System and Monitoring Method," uses electronic microseismic monitoring units to monitor vibration signals and collect data on the concentrated distribution areas of microseismic events in the slope rock mass. Simultaneously, it uses radar monitoring to emit electromagnetic waves to collect data on the external deformation and displacement of the entire slope rock mass. Ultimately, it achieves a combination of microseismic monitoring and radar monitoring, refining the monitoring area to achieve comprehensive and accurate monitoring of the internal and surface stability of the slope rock mass. However, this scheme still has significant shortcomings when applied to general terrain monitoring: on the one hand, electronic microseismic monitoring units are susceptible to electromagnetic interference from complex environments, resulting in poor data acquisition accuracy and stability, and making it difficult to achieve quantitative inversion of the stress field inside the terrain; on the other hand, its radar monitoring mainly targets the overall external deformation of specific slope structures, without forming a focused tracking mechanism for key changing areas in general terrain, without proposing the concept of imaging, and without establishing a deep coupling analysis model between internal and external monitoring data, making it impossible to achieve all-round imaging and real-time monitoring from the outer contour of the terrain to the internal stress field. The monitoring accuracy and early warning timeliness still cannot meet the monitoring needs of complex terrain scenarios.

[0004] In summary, current terrain monitoring technologies suffer from drawbacks such as difficulty in perceiving internal conditions, difficulty in correlating internal and external data, and difficulty in ensuring monitoring accuracy. There is an urgent need to develop a comprehensive system that deeply integrates external and internal monitoring to achieve all-round, high-precision real-time monitoring and imaging of terrain from its outer contour shape to its internal stress field distribution. This would provide reliable data support for early warning of terrain instability disasters, thereby effectively preventing geological disasters and ensuring the safety of regional production and life and the stability of the ecological environment. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention proposes a terrain monitoring method and device based on optical internal and external three-dimensional imaging. Through distributed monitoring by fiber optic microseismic and displacement sensors, PINN-based travel-time tomography, wave velocity-stress correlation model, vehicle-mounted lidar scanning, and spatial overlay matching of internal and external monitoring data, it achieves comprehensive and high-precision coordinated monitoring of the internal three-dimensional stress field, displacement state, and external deformation of the terrain, providing quantitative and reliable technical support for early warning of terrain instability disasters. The method includes the following steps:

[0006] S1: Build an internal data acquisition system and deploy fiber optic microseismic sensor arrays and fiber optic displacement sensor arrays within the monitoring area to simultaneously acquire microseismic acceleration and displacement signals within the terrain.

[0007] S2: The microseismic acceleration signal acquired by S1 is preprocessed, and the preprocessed microseismic signal is subjected to travel-time tomography based on PINN to obtain a three-dimensional wave velocity distribution model inside the monitoring area.

[0008] S3: Based on the pre-established correlation model between wave velocity and stress, the three-dimensional wave velocity distribution model obtained in S2 is substituted into the model to obtain the underground three-dimensional stress field distribution within the monitoring area.

[0009] S4: Perform noise reduction and data calibration on the displacement signal inside the terrain collected by S1 to obtain the displacement magnitude and displacement direction data inside the monitoring area.

[0010] S5: The vehicle-mounted lidar equipment equipped with a 1550nm narrow linewidth fiber laser is used to periodically scan the external surface of the monitoring area to obtain high-precision three-dimensional point cloud data at different time points.

[0011] S6: Perform registration and difference analysis on the three-dimensional point cloud data of multiple time nodes acquired in S5, identify and locate the external regions that have undergone significant deformation, focus on the region, and mark the region as the key external monitoring area.

[0012] S7: Based on the internal stress field distribution data obtained in S3 and the internal displacement data obtained in S4, perform terrain internal state evolution analysis and predict internal instability risk areas; perform spatial overlay matching and joint analysis with the predicted internal risk areas and the external key monitoring areas identified in S6 to achieve coordinated monitoring of terrain internal state and external morphology.

[0013] Furthermore, in order to better realize the present invention, the fiber optic microseismic sensor and fiber optic displacement sensor in S1 are arranged in a grid pattern along the geological fault zone or potential landslide area; multi-point distributed sensing is realized through optical fiber to monitor the displacement changes inside the terrain and microseismic events caused by rock fracture or friction in real time.

[0014] Furthermore, to better realize the present invention, the preprocessing of the microseismic acceleration signal in S2 includes: baseline correction of the microseismic acceleration signal to eliminate zero drift error, removal of environmental noise in the signal using a wavelet transform algorithm, and retention of effective microseismic signal frequency bands; the travel-time tomography of the preprocessed microseismic signal based on PINN includes constructing two parallel neural network architectures, and a travel-time calculation network. and wave speed inversion network By jointly optimizing, the synchronous inversion of seismic wavefield travel time simulation and underground velocity structure can be achieved.

[0015] Furthermore, in order to better realize the present invention, the PINN-based walk-time tomography method embeds the eikonal equation factorization into a neural network loss function, and combines automatic differentiation technology to achieve dual-drive optimization of physical constraints and data-driven approaches.

[0016] The eikonal equation is a nonlinear first-order hyperbolic partial differential equation, in the form of:

[0017]

[0018] in, From the source to any point The travel time or Euclidean distance. Is The speed defined above, and Represents the spatial differential operator;

[0019] right Factorize as follows:

[0020] in, ;

[0021] The factorization of the eikonal equation is expressed as:

[0022]

[0023] in, .

[0024] Furthermore, to better implement the present invention, the two parallel neural network architectures include:

[0025] Time-of-use computing network The system comprises an input layer, a hidden layer, and an output layer. The input layer connects to the hidden layer, and the hidden layer connects to the output layer. The input layer consists of 6 neurons, the hidden layer consists of 8 fully connected layers, each with 64 neurons, and the output layer consists of 1 neuron. The system uses spatial coordinates x and the source location x... s As input, output travel time field T(x);

[0026] Wave speed inversion network It includes an input layer, a hidden layer, and an output layer. The input layer is connected to the hidden layer, and the hidden layer is connected to the output layer. The input layer consists of 3 neurons, the hidden layer consists of 8 fully connected layers, each of which consists of 32 neurons, and the output layer consists of 1 neuron. It takes spatial coordinate x as input and outputs a velocity field v(x).

[0027] Furthermore, to better realize this invention, PINN-based walk-time tomography factorizes the eikonal equation and embeds it into a neural network loss function, thus enabling the neural network to... and Combining the outputs, the loss function constructed from the mean squared error norm is expressed as:

[0028]

[0029] in, For receiving location First Predicted seismic wave travel time for each epicenter.

[0030] Furthermore, in order to better realize the present invention, the earthquake source location x s The seismic source was located using the least squares method. A distance error was selected. For the first Only the true distance from the sensor to the earthquake source and measured distance The difference, that is

[0031]

[0032] right Find the partial derivatives for x, y, z, and t respectively, such that... By taking the minimum value and setting them all equal to zero, we obtain a set of normal equations. Then, we can solve them using Newton's iteration method to obtain the coordinates x, y, z of the earthquake source and the time t of the earthquake.

[0033] Furthermore, to better realize the present invention, there is a significant nonlinear correlation between rock mass stress and seismic wave propagation velocity in S3. A power function-form mathematical model can be established to describe the coupling relationship between them, i.e., the correlation model between wave velocity and stress:

[0034]

[0035] In the formula: These represent specific values ​​obtained through data fitting and parameter optimization, used to characterize and quantify the specific form and intensity of the power function relationship between rock mass stress and longitudinal wave propagation velocity;

[0036] In step S6, the ICP algorithm is used to register three-dimensional point cloud data at multiple time points; by comparing the point cloud coordinate change ΔP, the region with ΔP ≥ preset threshold ε is determined as the external region where significant deformation has occurred.

[0037] The spatial overlay matching in S7 is as follows: under a unified geographic coordinate system, the three-dimensional stress and displacement model of the predicted internal instability risk area is fused with the three-dimensional point cloud model of the identified external key monitoring area, so that the two are consistent in spatial position and vector direction, thereby predicting the internal stability of the terrain and the areas that may be deformed.

[0038] A terrain monitoring device based on optical internal and external three-dimensional imaging includes an external imaging monitoring module, an internal imaging monitoring module, an internal and external analysis module, a photovoltaic power supply system, and a 5G wireless data transmission module, characterized in that:

[0039] The external imaging monitoring module uses a vehicle-mounted lidar equipped with a 1550nm narrow linewidth fiber laser.

[0040] The internal imaging monitoring module includes a fiber optic micro-vibration submodule and a fiber optic grating displacement submodule.

[0041] The internal and external analysis modules are used to integrate the three-dimensional point cloud model of the external imaging monitoring module with the stress field and displacement model derived from the displacement data and microseismic data of the internal imaging monitoring module under a unified geographic coordinate system to form a complete three-dimensional monitoring model of the open-pit mine slope, enabling real-time monitoring and early warning.

[0042] The photovoltaic power supply system and the 5G wireless data transmission module are used to power the device and transmit data.

[0043] Furthermore, in order to better realize the present invention, the vehicle-mounted lidar of the external imaging monitoring module uses a 1550nm narrow linewidth fiber laser driven by the microprocessor main control unit as the light source to emit laser light, while modulating the switching frequency of the AOM acousto-optic modulator, and controlling the laser scanning head to receive the weak light signal reflected back, and finally sending it to the data acquisition circuit through the photoelectric conversion inside the scanning head to complete the collection of the original data of the slope point cloud.

[0044] The fiber optic microseismic submodule of the internal imaging monitoring module uses a second 1550nm narrow-linewidth fiber laser based on PZT modulation as the modulation source, and splits the light to multiple fiber optic interferometer microseismic sensors via a splitter. The fiber optic interferometer microseismic sensors then return the interference light signal to the photoelectric amplification and conversion circuit via a circulator, and then to the data acquisition circuit where the PGC algorithm demodulates and completes the analysis and processing of the microseismic signal.

[0045] The fiber grating displacement submodule of the internal imaging monitoring module uses a third 1550nm narrow-linewidth fiber laser tuned to the wavelength as a scanning light source, and performs wavelength scanning by tuning temperature or tuning current. Through the wavelength-tuned scanning principle, wavelength changes of multiple fiber grating displacement sensors after the splitter can be monitored, thereby achieving wavelength-resolution demodulation of minute displacements.

[0046] The beneficial effects of this invention are as follows:

[0047] (1) This invention combines vehicle-mounted lidar three-dimensional scanning, fiber optic microseismic sensors, fiber optic grating displacement sensors, and PINN travel-time tomography to achieve comprehensive stability monitoring of open-pit mine slopes or geological bodies. This method utilizes lidar to acquire high-precision point cloud data of the surface, accurately extracting subtle dynamic changes in characteristic terrain features, significantly reducing workload and costs. It is convenient, efficient in information acquisition, and highly refined. Simultaneously, by combining fiber optic grating displacement sensors and fiber optic microseismic sensors to monitor the internal displacement and microseismic signals of the rock mass in real time, and through PINN travel-time tomography, it achieves accurate inversion of the underground velocity field, revealing stress changes and failure processes within the slope.

[0048] (2) Compared with traditional methods, this invention uses PINN for travel-time tomography, which eliminates the need for mesh generation and employs two parallel neural network architectures, thereby improving the stability and accuracy of tomography. By fusing external topographic deformation information with internal stress field distribution, it achieves more comprehensive geological disaster early warning, providing early warning support for potential risks such as slope landslides and rock mass instability.

[0049] (3) The present invention adopts an integrated method based on a 1550nm narrow linewidth fiber laser as the core light source, which is of great significance for improving the measurement distance of lidar, increasing the sensitivity of fiber optic micro-vibration sensor and the accuracy of fiber optic grating displacement wavelength demodulation. Attached Figure Description

[0050] Figure 1 This invention relates to a PINN-based travel-time tomography network architecture for a three-dimensional terrain monitoring method based on optical internal and external imaging.

[0051] Figure 2 This is a structural diagram of the optical internal and external three-dimensional imaging terrain monitoring device of the present invention;

[0052] Figure 3 This is a system hardware schematic diagram of the optical internal and external three-dimensional imaging terrain monitoring device of the present invention;

[0053] Figure 4 This is a schematic diagram showing the installation positions of the fiber optic micro-vibration sensor and the fiber optic displacement sensor array according to an embodiment of the present invention;

[0054] Figure 5 This is a flowchart illustrating the terrain monitoring method based on optical internal and external three-dimensional imaging of the present invention.

[0055] Figure 6 This is a schematic diagram of the micro-vibration acceleration signal acquired by the fiber optic micro-vibration sensor according to an embodiment of the present invention;

[0056] Figure 7 This is a schematic diagram of three-dimensional imaging according to an embodiment of the present invention. Detailed Implementation

[0057] 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 a part of the embodiments of the present invention, and not all of them. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0058] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0059] Figures 1-7This invention provides a specific embodiment using the slope topography of an open-pit iron mine as an application scenario. The slope is a mixed rock-soil slope with a maximum height of 120m and an average slope of 35°. A known geological fault exists within the slope. The method and apparatus of this invention are then used to monitor this slope, as detailed below:

[0060] Figure 4 The illustration schematically shows the installation positions of fiber optic microseismic sensors and fiber optic displacement sensor arrays according to an embodiment of the optical internal and external three-dimensional imaging terrain monitoring method of this disclosure, wherein nine fiber optic microseismic sensors and four fiber optic displacement sensors are selected. The fiber optic microseismic sensors are arranged in a grid pattern along the potential landslide area in pre-drilled boreholes, with the borehole depth extending into stable rock layers below the potential slip surface. The fiber optic displacement sensors are deployed in steep areas and areas with soil fissures. The sensors are connected in series via optical cables and converged into a main optical cable, which is then led to an intrinsically safe fiber optic demodulator for mining on the slope.

[0061] Figure 5 The schematic diagram illustrates a flowchart of a topographic monitoring method based on optical internal and external three-dimensional imaging according to some embodiments of the present disclosure. After the system is powered on, the control and acquisition system modules simultaneously activate narrow-linewidth lasers with different functions, including a narrow-linewidth laser for vehicle-mounted radar, a PZT-modulated narrow-linewidth laser (fiber optic microseismic sensor), and a wavelength-tuned narrow-linewidth laser (fiber grating displacement). The vehicle-mounted lidar equipment periodically scans the external surface of the monitoring area to acquire high-precision three-dimensional point cloud data at different time points; the fiber optic microseismic sensor acquires microseismic acceleration signals generated by internal rock fractures at a sampling rate of not less than 100,000.

[0062] Figure 6 The illustration schematically depicts microseismic acceleration signals acquired by a fiber optic microseismic sensor based on an optical internal and external three-dimensional imaging terrain monitoring device according to some embodiments of this disclosure; a fiber optic displacement sensor monitors the absolute displacement at different depths within the borehole in real time. All data is transmitted in real time to the internal and external analysis modules of the mine data center via the mine's industrial ring network.

[0063] After receiving the microseismic signal, the internal and external analysis modules first perform baseline correction to eliminate sensor zero drift. Subsequently, a wavelet transform algorithm is used to filter out high-frequency environmental noise generated by mining blasting and heavy equipment operation, retaining the effective microseismic signal frequency band of 10Hz-500Hz.

[0064] For the selected valid microseismic events, the least squares method was used for source location. Then, the source location, sensor coordinates, and corresponding seismic wave travel times were used as training data and input into the constructed PINN network. During training, the factorized Eikonal equation was embedded as a physical constraint into the loss function, and the partial derivatives were calculated using automatic differentiation techniques to jointly optimize the two networks. After training, the wave velocity inversion network directly outputs the three-dimensional wave velocity field inside the mine slope.

[0065] Based on laboratory test results of core samples from the mining area, a wave velocity-stress relationship model applicable to the local lithology has been pre-established. Substituting the obtained wave velocity field model into this formula, a three-dimensional stress field distribution map inside the slope can be obtained, which can visually display the stress concentration area.

[0066] The signal from the fiber optic displacement sensor is demodulated to analyze the magnitude and direction of the minute displacements, generating displacement vector fields at different depths inside the slope.

[0067] ICP registration was performed between the point cloud data scanned this week and the data from last week to eliminate errors caused by different scanning perspectives. Voxelized difference calculations were then performed on the registered point clouds from both periods to generate a difference point cloud model. A change threshold was set, and regions with point cloud coordinate changes greater than the threshold were automatically identified as significantly deformed regions and outlined in the 3D point cloud model.

[0068] Figure 7 The illustration shows a three-dimensional imaging diagram of the topographic monitoring method based on optical internal and external three-dimensional imaging according to an embodiment of the present disclosure. Under a unified mining area coordinate system, the internal and external analysis module superimposes and fuses the three-dimensional stress and displacement model of the internally predicted risk area with the three-dimensional point cloud of the externally identified deformation area, showing the external significant deformation area, the three-dimensional stress field and the direction of internal topographic displacement change.

[0069] The system analyzes various signals to identify the locations of abnormal signals and feeds this information back to the control and acquisition system module. This allows for adjustment of the laser 3D scanning frequency to focus on polling the abnormal areas and providing timely warnings of changes. The system also records the event time and status signals at the abnormal locations and provides them to the host computer for display and querying.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Any other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention, as long as they do not depart from the spirit and scope of the technical solutions of the present invention, should be covered within the scope of the claims of the present invention.

Claims

1. A method for terrain monitoring based on optical internal and external three-dimensional imaging, characterized in that, Includes the following steps: S1: Build an internal data acquisition system and deploy fiber optic microseismic sensor arrays and fiber optic displacement sensor arrays within the monitoring area to simultaneously acquire microseismic acceleration and displacement signals within the terrain. S2: The microseismic acceleration signal acquired by S1 is preprocessed, and the preprocessed microseismic signal is subjected to travel-time tomography based on PINN to obtain a three-dimensional wave velocity distribution model inside the monitoring area. S3: Based on the pre-established correlation model between wave velocity and stress, the three-dimensional wave velocity distribution model obtained in S2 is substituted into the model to obtain the underground three-dimensional stress field distribution within the monitoring area. S4: Perform noise reduction and data calibration on the displacement signal inside the terrain collected by S1 to obtain the displacement magnitude and displacement direction data inside the monitoring area. S5: The vehicle-mounted lidar equipment equipped with a 1550nm narrow linewidth fiber laser is used to periodically scan the external surface of the monitoring area to obtain high-precision three-dimensional point cloud data at different time points. S6: Perform registration and difference analysis on the three-dimensional point cloud data of multiple time nodes acquired in S5, identify and locate the external regions that have undergone significant deformation, focus on the region, and mark the region as the key external monitoring area. S7: Based on the internal stress field distribution data obtained in S3 and the internal displacement data obtained in S4, perform terrain internal state evolution analysis and predict internal instability risk areas; perform spatial overlay matching and joint analysis with the predicted internal risk areas and the external key monitoring areas identified in S6 to achieve coordinated monitoring of terrain internal state and external morphology.

2. The method for terrain monitoring based on optical internal and external three-dimensional imaging according to claim 1, characterized in that: The arrangement of the fiber optic microseismic sensors and fiber optic displacement sensors in S1 includes: a grid-like distribution along geological fault zones or potential landslide areas; multi-point distributed sensing through optical fibers to monitor in real time the displacement changes within the terrain and microseismic events caused by rock fractures or friction.

3. The method for terrain monitoring based on optical internal and external three-dimensional imaging according to claim 1, characterized in that: The preprocessing of the microseismic acceleration signal in S2 includes: baseline correction of the microseismic acceleration signal to eliminate zero drift error, removal of environmental noise from the signal using a wavelet transform algorithm, and preservation of effective microseismic signal frequency bands; the travel-time tomography of the preprocessed microseismic signal based on PINN includes constructing two parallel neural network architectures, and a travel-time calculation network. and wave speed inversion network By jointly optimizing, the synchronous inversion of seismic wavefield travel time simulation and underground velocity structure can be achieved.

4. The method for terrain monitoring based on optical internal and external three-dimensional imaging according to claim 3, characterized in that: The PINN-based walk-time tomography method factorizes the eikonal equation and embeds it into a neural network loss function, combining automatic differentiation technology to achieve dual-drive optimization driven by physical constraints and data. The eikonal equation is a nonlinear first-order hyperbolic partial differential equation, in the form of: in, From the source point to any point The travel time or Euclidean distance. Is The speed defined above, and Represents the spatial differential operator; right Factorize as follows: in, ; The factorization of the eikonal equation is expressed as: in, .

5. The method for terrain monitoring based on optical internal and external three-dimensional imaging according to claim 3, characterized in that: The two parallel neural network architectures include: Time-of-use computing network The system comprises an input layer, a hidden layer, and an output layer. The input layer connects to the hidden layer, and the hidden layer connects to the output layer. The input layer consists of 6 neurons, the hidden layer consists of 8 fully connected layers, each with 64 neurons, and the output layer consists of 1 neuron. The system uses spatial coordinates x and the source location x... s As input, output travel time field T(x); Wave speed inversion network It includes an input layer, a hidden layer, and an output layer. The input layer is connected to the hidden layer, and the hidden layer is connected to the output layer. The input layer consists of 3 neurons, the hidden layer consists of 8 fully connected layers, each of which consists of 32 neurons, and the output layer consists of 1 neuron. It takes spatial coordinate x as input and outputs a velocity field v(x).

6. The method for terrain monitoring based on optical internal and external three-dimensional imaging according to claim 4, characterized in that: PINN-based walk-time tomography factorizes the eikonal equation and embeds it into a neural network loss function, thus enabling the neural network to... and Combining the outputs, the loss function constructed from the mean squared error norm is expressed as: in, For receiving location First Predicted seismic wave travel time for each epicenter.

7. The method for terrain monitoring based on optical internal and external three-dimensional imaging according to claim 5, characterized in that: The location of the earthquake source x s Earthquake source location was determined using the least squares method. Selecting distance error For the first Only the true distance from the sensor to the earthquake source and measured distance The difference, that is right Find the partial derivatives for x, y, z, and t respectively, such that... By taking the minimum value and setting them all equal to zero, we obtain a set of normal equations. Then, we can solve them using Newton's iteration method to obtain the coordinates x, y, z of the earthquake source and the time t of the earthquake.

8. The method for terrain monitoring based on optical internal and external three-dimensional imaging according to claim 1, characterized in that: There is a significant nonlinear correlation between rock mass stress and seismic wave propagation velocity in S3. A power-law mathematical model can be established to describe their coupling relationship, i.e., a correlation model between wave velocity and stress: In the formula: These represent specific values ​​obtained through data fitting and parameter optimization, used to characterize and quantify the specific form and intensity of the power function relationship between rock mass stress and longitudinal wave propagation velocity; In step S6, the ICP algorithm is used to register three-dimensional point cloud data at multiple time points; by comparing the point cloud coordinate change ΔP, the region with ΔP ≥ preset threshold ε is determined as the external region where significant deformation has occurred. The spatial overlay matching in S7 is as follows: under a unified geographic coordinate system, the three-dimensional stress and displacement model of the predicted internal instability risk area is fused with the three-dimensional point cloud model of the identified external key monitoring area, so that the two are consistent in spatial position and vector direction, thereby predicting the internal stability of the terrain and the areas that may be deformed.

9. An optical internal and external three-dimensional imaging terrain monitoring device according to claim 1, comprising an external imaging monitoring module, an internal imaging monitoring module, an internal and external analysis module, a photovoltaic power supply system, and a 5G wireless data transmission module, characterized in that: The external imaging monitoring module uses a vehicle-mounted lidar equipped with a 1550nm narrow linewidth fiber laser. The internal imaging monitoring module includes a fiber optic micro-vibration submodule and a fiber optic grating displacement submodule. The internal and external analysis modules are used to integrate the three-dimensional point cloud model of the external imaging monitoring module with the stress field and displacement model derived from the displacement data and microseismic data of the internal imaging monitoring module under a unified geographic coordinate system to form a complete three-dimensional monitoring model of the open-pit mine slope, enabling real-time monitoring and early warning. The photovoltaic power supply system and the 5G wireless data transmission module are used to power the device and transmit data.

10. The optical internal and external three-dimensional imaging terrain monitoring device according to claim 9, characterized in that: The vehicle-mounted lidar of the external monitoring module uses a microprocessor main control unit to drive the first 1550nm narrow linewidth fiber laser as the light source to emit laser light. At the same time, it modulates the switching frequency of the AOM acousto-optic modulator and controls the laser scanning head to receive the weak light signal reflected back. Finally, the photoelectric conversion inside the scanning head is sent to the data acquisition circuit to complete the collection of the original data of the slope point cloud. The fiber optic micro-vibration submodule of the internal monitoring module uses a second 1550nm narrow linewidth fiber laser based on PZT modulation as the modulation source, and splits the light to multiple fiber optic interferometer micro-vibration sensors through a splitter. The fiber optic interferometer micro-vibration sensor returns the interference light signal to the circulator, which then sends it to the photoelectric amplifier and conversion circuit, and finally to the data acquisition circuit where the PGC algorithm demodulates and completes the analysis and processing of the micro-vibration signal. The fiber grating displacement submodule of the internal monitoring module uses a third 1550nm narrow linewidth fiber laser with wavelength tuning as the scanning light source, and performs wavelength scanning by tuning temperature or tuning current. By using the wavelength tuning scanning principle, wavelength changes can be monitored in multiple fiber optic displacement sensors after passing through a splitter, thereby achieving wavelength-resolution demodulation of minute displacements.

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