A geological deformation three-dimensional observation system and method

Abstract

The present application relates to the technical field of three-dimensional observation, and particularly relates to a geological deformation three-dimensional observation system and method, which comprises multi-dimensional data acquisition: synchronously acquiring data, and collecting ground temperature gradient field data and underground water flow velocity field data of a monitoring area; data space-time calibration processing: uniformly processing the collected data according to time and space bases to generate a multi-source displacement data set after space-time calibration; cold variable extraction: generating an environment coupling variable matrix based on the ground temperature gradient field data and the underground water flow velocity field data in the multi-dimensional data acquisition; three-dimensional deformation field modeling: generating an initial three-dimensional deformation field model; model dynamic correction: iteratively correcting the initial three-dimensional deformation field model to generate an optimized three-dimensional deformation field model; visualized early warning output: generating an early warning signal with a risk level identifier. The present application realizes high-precision three-dimensional deformation field modeling and early warning by constructing a multi-source heterogeneous data fusion system and combining a cold geological variable dynamic compensation mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional observation, in particular to a geological deformation three-dimensional observation system and method. BACKGROUND

[0002] The traditional geological deformation monitoring method mainly relies on a single data source, which is difficult to comprehensively reflect the three-dimensional deformation characteristics under complex geological environment. In addition, the existing method ignores the influence of the geothermal gradient field change and the underground water flow velocity field on the deformation of rock-soil mass, resulting in systematic deviation of the monitoring results in the area where the day-night temperature difference is significant or the underground water level fluctuates dramatically.

[0003] The existing technology also has obvious deficiencies in multi-source data fusion. The traditional method usually adopts a simple weighted average method, without establishing a strict time and space calibration mechanism, resulting in dimensional distortion when radar phase data and ground sensor physical displacement data are fused. In addition, the existing method adopts a fixed threshold warning, which cannot adapt to the dynamic changes of complex geological environment, resulting in high false alarm rate and missed alarm rate. In view of the above problems, there is an urgent need for a geological deformation three-dimensional observation method which can fuse multi-source data, consider environmental coupling effect and realize dynamic warning. SUMMARY

[0004] Based on the above purpose, the present application provides a geological deformation three-dimensional observation method.

[0005] A geological deformation three-dimensional observation method, comprising the following steps:

[0006] S1, multi-dimensional data acquisition: synchronously acquiring satellite synthetic aperture radar interferometric measurement data, ground GNSS displacement monitoring data, underground tiltmeter array data, and collecting geothermal gradient field data and underground water flow velocity field data of the monitoring area;

[0007] S2, data time and space calibration processing: performing time and space reference unification processing on the collected radar interference data, GNSS data and tiltmeter data, establishing a three-dimensional time and space calibration chain with the geocentric coordinate system as the reference, and generating a multi-source displacement data set after time and space calibration;

[0008] S3, cold parameter extraction: based on the geothermal gradient field data and the underground water flow velocity field data in the multi-dimensional data acquisition, calculating the rock-soil mass thermal expansion coefficient dynamic matrix and the seepage shear stress tensor, and generating an environmental coupling parameter matrix;

[0009] S4, three-dimensional deformation field modeling: inputting the multi-source displacement data set obtained in the data time and space calibration processing and the generated environmental coupling parameter matrix into a three-dimensional gridding model, and generating an initial three-dimensional deformation field model through a displacement-temperature-seepage coupling algorithm;

[0010] S5, model dynamic correction: based on the real-time updated tiltmeter data in multi-dimensional data acquisition, a deformation gradient verification matrix is constructed to iteratively correct the initial three-dimensional deformation field model, and an optimized three-dimensional deformation field model is generated;

[0011] S6, visual warning output: comparing the generated optimized three-dimensional deformation field model with the preset geological safety threshold, a three-dimensional deformation thermal map with risk level identification and a warning signal are generated.

[0012] Further, the S1 comprises:

[0013] S11, deploy a multi-source sensor network;

[0014] S12, synchronous data acquisition;

[0015] S13, data encapsulation preprocessing: adding uniform space-time labels to each source data.

[0016] Further, the S2 comprises:

[0017] S21, radar interferometric data phase compensation: multi-physical field coupling correction is performed on the original radar interferometric phase;

[0018] S22, GNSS data coordinate conversion: converting GNSS geodetic coordinates into geocentric rectangular coordinate system displacement vectors;

[0019] S23, tiltmeter space-time interpolation: constructing a four-dimensional space-time interpolation function;

[0020] S24, space-time calibration chain construction: establishing a constraint equation set of uniform space-time reference;

[0021] S25, multi-source displacement data set generation: based on the obtained multi-source displacement data, output data structure.

[0022] Further, the S3 comprises:

[0023] S31, multi-source data preprocessing: space-time normalization processing is performed on the collected geothermal gradient field data and groundwater flow velocity field data;

[0024] S32, thermal expansion coefficient dynamic matrix calculation: constructing a temperature-dependent thermal expansion coefficient tensor;

[0025] S33, seepage shear stress tensor calculation: calculating seepage shear stress based on the modified Biot theory;

[0026] S34, environment coupling parameter matrix generation: establishing a thermal-seepage coupling matrix.

[0027] Further, the S4 comprises:

[0028] S41, three-dimensional grid model construction: the monitoring area is divided into hexahedral element grids, and grid node coordinates are defined;

[0029] S42, multi-field coupling constitutive equation establishment: extending the traditional elastic mechanics constitutive equation;

[0030] S43, multi-source data fusion weighting: establishing an observation data weight matrix;

[0031] S44, coupled stress field calculation: considering gravity, seepage fluid force and internal stress distribution of rock-soil mass, solving the coupled equilibrium equation;

[0032] S45, iterative solution and model generation: using the modified Newton-Raphson algorithm for iterative solution.

[0033] Further, the S5 comprises:

[0034] S51, gradient analysis of tiltmeter data: converting real-time tiltmeter measurement values into curvature tensors;

[0035] S52, model predicted gradient extraction: extracting displacement gradient tensors based on a three-dimensional deformation field model;

[0036] S53, residual weight matrix construction: defining a spatiotemporal weighted residual matrix;

[0037] S54, coupled stiffness matrix updating: in the model correction process, the updating of the stiffness matrix is based on the gradient information of the residual matrix, and the stiffness tensor in the constitutive equation is corrected;

[0038] S55, double feedback iteration: the double feedback iteration includes a curvature feedback loop and a phase consistency loop, which respectively corrects the displacement field from the micro curvature and the macro phase;

[0039] S56, convergence determination: the convergence of the iteration process is determined by the conditions of the displacement field change, the residual matrix norm and the phase consistency error.

[0040] Further, the S6 comprises:

[0041] S61, risk quantification and visualization mapping: extracting three-dimensional deformation field features, setting dynamic safety thresholds, constructing a risk level matrix, and rendering a three-dimensional heat map;

[0042] S62, early warning decision and signal generation: triggering a composite early warning logic based on the risk matrix.

[0043] Further, the S61 comprises:

[0044] S611, three-dimensional deformation field feature extraction: calculating multi-parameter risk indicators of each grid node;

[0045] S612, dynamic safety threshold setting: generating a spatially distributed safety threshold according to geological exploration data;

[0046] S613, risk level matrix calculation: constructing a three-dimensional risk quantification model;

[0047] S614, three-dimensional heat map rendering: using color-transparency-vector three-channel encoding technology to generate a visual result.

[0048] Further, the S62 multi-level early warning signal generation establishes a composite early warning trigger logic and outputs an early warning message.

[0049] A geological deformation three-dimensional observation system for realizing the geological deformation three-dimensional observation method as described above, comprising the following modules:

[0050] Multi-dimensional data acquisition module: synchronously acquiring satellite synthetic aperture radar interferometric measurement data, ground GNSS displacement monitoring data, underground tiltmeter array data, and collecting ground temperature gradient field data and underground water flow velocity field data of the monitoring area;

[0051] Data space-time calibration module: performing space-time reference unification processing on the collected multi-source data, and establishing a three-dimensional space-time calibration chain with the geocentric coordinate system as the reference;

[0052] Cold parameter extraction module: based on the ground temperature gradient field and the underground water flow velocity field data, calculating the rock-soil thermal expansion coefficient dynamic matrix and the seepage shear stress tensor;

[0053] Three-dimensional deformation field modeling module: inputting the multi-source displacement data set and the environmental coupling parameter matrix into a three-dimensional gridding model, and generating an initial three-dimensional deformation field model through a displacement-temperature-seepage coupling algorithm;

[0054] Model dynamic correction module: constructing a deformation gradient verification matrix using real-time updated tiltmeter data, and iteratively correcting the initial three-dimensional deformation field model;

[0055] Visual early warning module: comparing the optimized three-dimensional deformation field model with the preset geological safety threshold, generating a three-dimensional deformation heat map with risk level identification and an early warning signal.

[0056] The beneficial effects of the present application are:

[0057] The present application realizes high-precision three-dimensional deformation field modeling and early warning by constructing a multi-source heterogeneous data fusion system combined with a cold geological parameter dynamic compensation mechanism. Firstly, the present application initiates a ground temperature-seepage-displacement multi-field coupling analysis model, which overcomes the technical difficulties of traditional methods that ignore the thermal expansion and seepage shear deformation mechanism of rock-soil bodies.

[0058] This invention establishes a three-dimensional grid dynamic compensation model based on less commonly used parameters, which improves the accuracy of surface displacement monitoring. It is particularly suitable for complex geological environments such as mining area subsidence and landslide monitoring. Through a dual-feedback dynamic correction algorithm, the reliability and stability of the model are significantly improved. At the same time, this invention constructs a multi-dimensional early warning index system, realizing advanced early warning of major geological disasters and extending the warning time. The visualization early warning module adopts color-transparency-vector three-channel encoding technology, which significantly improves the resolution of the three-dimensional risk field and greatly improves the efficiency of key information identification. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in this 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 only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0061] Figure 2 This is a system block diagram of an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0063] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0064] like Figure 1 As shown, a three-dimensional observation method for geological deformation includes the following steps:

[0065] S1, multi-dimensional data acquisition: synchronously acquire satellite synthetic aperture radar interferometry data, ground GNSS displacement monitoring data, underground tiltmeter array data, and collect ground temperature gradient field data and underground water flow velocity field data in the monitoring area;

[0066] S2, data space-time calibration processing: perform space-time reference unification processing on the collected radar interference data, GNSS data, and tiltmeter data, establish a three-dimensional space-time calibration chain with the geocentric coordinate system as the reference, and generate a multi-source displacement data set after space-time calibration;

[0067] S3, cold parameter extraction: based on the ground temperature gradient field data and the underground water flow velocity field data in the multi-dimensional data acquisition, calculate the rock-soil thermal expansion coefficient dynamic matrix and the seepage shear stress tensor, and generate an environmental coupling parameter matrix;

[0068] S4, three-dimensional deformation field modeling: input the multi-source displacement data set obtained in the data space-time calibration processing and the generated environmental coupling parameter matrix into the three-dimensional gridding model, and generate an initial three-dimensional deformation field model through displacement-temperature-seepage coupling algorithm;

[0069] S5, model dynamic correction: based on the real-time updated tiltmeter data in the multi-dimensional data acquisition, construct a deformation gradient verification matrix, iteratively correct the initial three-dimensional deformation field model, and generate an optimized three-dimensional deformation field model;

[0070] S6, visual warning output: compare the generated optimized three-dimensional deformation field model with the preset geological safety threshold, generate a three-dimensional deformation thermal map with risk level identification and a warning signal.

[0071] S1 includes:

[0072] S11, deploy a multi-source sensor network, the specific steps are:

[0073] (1) arrange GNSS monitoring stations (101) on the ground surface according to a 200*200m grid, each station is equipped with a dual-frequency receiver and set to a 10Hz sampling rate;

[0074] (2) arrange distributed optical fiber temperature sensors (102) in the vertical borehole in the monitoring area at an interval of 2m to form a ground temperature gradient monitoring array;

[0075] (3) install three-component tiltmeters (103) at depths of 15m, 30m, and 50m underground to form a three-dimensional underground deformation monitoring network;

[0076] S12, synchronous data acquisition, the specific steps are:

[0077] (1) synchronize the satellite SAR data receiving period with the GNSS data acquisition through GPS second pulse, and control the time alignment error to be represented as:

[0078] ;

[0079] wherein, is the satellite revisit period, when the time, the hour;

[0080] (2) The ground temperature gradient field data is calculated by using the vertical temperature change rate, which is expressed as:

[0081] ;

[0082] wherein, is the depth, is the distance between adjacent temperature measuring points, is the cement solidified body temperature correction coefficient, is the annual average temperature difference in the hole;

[0083] (3) The groundwater flow velocity field data is calculated by coupling Darcy's law and water level monitoring well data, which is expressed as:

[0084] ;

[0085] wherein, is the dynamic seepage correction term, is the permeability coefficient tensor, is the water level difference between adjacent monitoring wells, is the well spacing;

[0086] S13, data packaging preprocessing: add uniform space-time label to each source data, which is expressed as:

[0087] ;

[0088] ;

[0089] wherein, ECEF is the Earth-Centered Earth-Fixed coordinate system, is the GNSS positioning accuracy threshold.

[0090] S2 includes:

[0091] S21, radar interferometric data phase compensation: original radar interferometric phase is corrected by coupling multiple physical fields, which is expressed as:

[0092] ;

[0093] wherein, is the original interferometric phase (rad), is the radar wavelength (m), 0.056 m for C band, is the atmospheric path delay is the GNSS retrieved water vapor content (mm), is the thermal expansion path change is the surface material thermal expansion coefficient, is the S1 geothermal data, is the scatterer height (m), is the true deformation rate (m / year), is the radar incidence angle ;

[0094] S22, GNSS data coordinate conversion: convert GNSS geodetic coordinates to geocentric rectangular coordinate system displacement vector, denoted as:

[0095] ;

[0096] ;

[0097] where, are the latitude, longitude, and elevation (m), respectively, is the prime vertical circle radius of curvature , is the ellipsoid long semi-axis, 0.00669438 is the first eccentricity square, is the geocentric geodetic coordinate system displacement vector (m);

[0098] S23, tiltmeter space-time interpolation: construct a four-dimensional space-time interpolation function, denoted as:

[0099] ;

[0100] where, is the three-axis measurement value of the th tiltmeter (m / s2), is the horizontal interpolation grid spacing, is the time interpolation step,

[0101] is the cubic spline basis function;

[0102] S24, space-time calibration chain construction: establish a constraint equation set for unified space-time reference, denoted as:

[0103] ;

[0104] where, is the radar line-of-sight direction conversion matrix, is the satellite orbit velocity vector , is the spatial reference unified residual threshold value; ​

[0105] S25, multi-source displacement dataset generation: based on the obtained multi-source displacement data, output data structure, denoted as:

[0106] ;

[0107] wherein, is the GPS week-second timestamp (s), is the grid geocentric coordinate (m), is the calibration confidence index.

[0108] S3 includes:

[0109] S31, multi-source data preprocessing: the collected geothermal gradient field data and groundwater flow velocity field data are subjected to spatio-temporal normalization processing, denoted as:

[0110] ;

[0111] wherein, is the historical mean and standard deviation of the geothermal gradient field , is the annual period temperature modulation factor (dimensionless), is the reference seepage velocity, is the main aquifer buried depth (m), is the seepage velocity decay length;

[0112] S32, dynamic matrix calculation of thermal expansion coefficient: a temperature-dependent thermal expansion coefficient tensor is constructed, denoted as:

[0113] ;

[0114] ;

[0115] wherein, is the reference thermal expansion coefficient, is the temperature sensitivity coefficient (dimensionless), is the reference temperature of the rock-soil mass, is the critical temperature change amount, is the unit tensor;

[0116] S33, seepage shear stress tensor calculation: based on the modified Biot theory, the seepage shear stress is calculated, denoted as:

[0117] ;

[0118] ;

[0119] wherein, is the pore water dynamic viscosity,​ is the coefficient of volume viscosity, is the flow velocity gradient tensor;

[0120] S34, environmental coupling parameter matrix generation: establish the heat-seepage coupling matrix, expressed as:

[0121] ;

[0122] wherein, is the tensor product operator, is the Hadamard product (element-by-element multiplication), is the Gaussian weight matrix, , is the spatial coordinate of the main fault / fracture zone.

[0123] S4 includes:

[0124] S41, three-dimensional grid model construction: divide the monitoring area into hexahedral element grid, define the grid node coordinates, expressed as:

[0125] ;

[0126] wherein, is the regional origin geocentric coordinate (m), is the grid step size, is the grid index number (n x, n y, n z) corresponding to the surface);

[0127] S42, multi-field coupling constitutive equation establishment: extend the traditional elastic mechanics constitutive equation, expressed as:

[0128] ;

[0129] ;

[0130] wherein, is the fourth-order stiffness tensor (GPa), the anisotropy parameter is taken from geological exploration data, is the obtained temperature-dependent thermal expansion coefficient tensor , is the shear modulus of rock mass, is the heat-seepage coupling coefficient (dimensionless), is the temperature gradient and hydraulic gradient vector ;

[0131] S43, multi-source data fusion weighting: establish the observation data weight matrix, expressed as:

[0132] ;

[0133] wherein, is the radar displacement measurement error, is the GNSS three-dimensional positioning error, is the inclinometer measurement error, is a three-dimensional identity matrix;

[0134] S44, coupled stress field calculation: considering the gravity, seepage fluid force and internal stress distribution of rock-soil mass, the coupled equilibrium equation is solved, which is expressed as:

[0135] ;

[0136] ;

[0137] wherein, is the density of rock-soil mass, is the acceleration of gravity, is the porosity, is the inertial resistance coefficient, is the density of groundwater;

[0138] S45, iterative solution and model generation: the modified Newton-Raphson algorithm is used for iterative solution, which is expressed as:

[0139] ;

[0140] ;

[0141] wherein, is the tangent stiffness matrix, is the elastic-plastic tangent modulus tensor, is the strain-displacement matrix, is the coupling stiffness term, and the convergence condition is:

[0142] m and N.

[0143] S5 includes:

[0144] S51, gradient analysis of inclinometer data: the real-time inclinometer measurement value is converted into the curvature tensor, which is expressed as:

[0145] ;

[0146] wherein, are the three-axis measurement values of the inclinometer respectively, the partial derivative is calculated by the difference between adjacent nodes, which is expressed as:

[0147] wherein, is the distance between adjacent nodes in the j direction;

[0148] S52, Model predicted gradient extraction: Based on the three-dimensional deformation field model, the displacement gradient tensor is extracted, represented as:

[0149] ;

[0150] ;

[0151] wherein, is the model displacement field vector (m), is the model strain tensor (dimensionless), is the model curvature tensor ;

[0152] S53, Residual weight matrix construction: Define the spatio-temporal weighted residual matrix, represented as:

[0153] ;

[0154] ;

[0155] wherein, is the distance to the nearest tiltmeter (m), is the current correction time, is the Hadamard product;

[0156] S54, Coupling stiffness matrix update: In the model correction process, the update of the stiffness matrix is based on the gradient information of the residual matrix, and the stiffness tensor in the constitutive equation is corrected, represented as:

[0157] ;

[0158] ;

[0159] wherein, is the adaptive learning rate, is the Frobenius norm, is the projection operator to the allowable stiffness range of geological exploration, is the environmental coupling parameter matrix;

[0160] S55, Double feedback iteration: Double feedback iteration includes curvature feedback loop and phase consistency loop, which corrects the displacement field from two dimensions of micro curvature and macro phase, and the specific steps are:

[0161] (1) Curvature feedback loop, represented as:

[0162] ;

[0163] (2) Phase consistency loop, represented as:

[0164] ;

[0165] ;

[0166] wherein, is the phase-to-displacement conversion coefficient, is the data space-time calibrated radar interferometric phase, is the residual matrix, is the tangent stiffness matrix of the current iteration step;

[0167] S56, convergence determination: the convergence of the iteration process is determined by the conditions of the displacement field change, the residual matrix norm and the phase consistency error, which specifically includes:

[0168] (1) displacement field change: the 2-norm of the displacement field change of adjacent iteration steps is less than 0.5 mm, which is expressed as:

[0169] ;

[0170] (2) residual matrix norm: the Frobenius norm of the residual matrix is less than , which is expressed as:

[0171] ;

[0172] (3) phase consistency error: the difference between the calculated value and the observed value of the radar interferometric phase is less than 2 mm, which is expressed as:

[0173]

[0174] When the above three conditions are met at the same time, it is determined that the iteration converges, and the optimized three-dimensional deformation field model is output.

[0175] S6 includes:

[0176] S61, risk quantification and visualization mapping: extracting three-dimensional deformation field features, setting dynamic safety thresholds, constructing risk level matrix, rendering three-dimensional heat map;

[0177] S62, early warning decision and signal generation: triggering composite early warning logic based on risk matrix.

[0178] S61 includes:

[0179] S611, three-dimensional deformation field feature extraction: calculating the multi-parameter risk index of each grid node, which is expressed as:

[0180] ;

[0181] S612, Dynamic safety threshold setting: A spatially distributed safety threshold is generated based on geological exploration data, expressed as follows:

[0182] ;

[0183] in, It is the basic displacement threshold. It is the greatest depth of influence. Base rate threshold, It is the number of days the deformation lasts. , is the standard deviation of the coupling effect. To affect the number of faults;

[0184] S613, Risk Level Matrix Calculation: Construct a three-dimensional risk quantification model, represented as:

[0185] ;

[0186] ;

[0187] S614, 3D heatmap rendering: Employs color-transparency-vector three-channel encoding technology to generate visualization results, including:

[0188] (1) Hue mapping: The gradient from blue to yellow to red corresponds to an increase in risk level;

[0189] (2) Transparency control: dynamically adjusted based on calibration confidence, with low-confidence areas displayed semi-transparently;

[0190] (3) Vector superposition: The direction of the arrow indicates the displacement trend, and the length is logarithmically scaled to avoid visual overflow.

[0191] S62 multi-level early warning signal generation, establishing composite early warning triggering logic, and outputting early warning messages, represented as follows:

[0192] ;

[0193] in, For grid nodes Risk value The spatial gradient vector of the risk matrix. It is the gravitational acceleration vector. This indicates that the risk gradient direction is consistent with the direction of gravity, meaning the risk zone extends downwards. This represents the average value of the risk matrix within the current monitoring period. The rate of change of the average risk over time. It is the maximum value in the risk matrix. It is the sum of the risk values ​​of all grid nodes within the fault zone.

[0194] likeFigure 2 As shown in the figure, a geological deformation three-dimensional observation system for realizing the geological deformation three-dimensional observation method, characterized in that it comprises the following modules:

[0195] Multi-dimensional data acquisition module: synchronously acquire satellite synthetic aperture radar interferometry data, ground GNSS displacement monitoring data, underground tiltmeter array data, and collect ground temperature gradient field data and underground water flow velocity field data of the monitoring area;

[0196] Data space-time calibration module: perform space-time reference unification processing on the collected multi-source data, and establish a three-dimensional space-time calibration chain with the geocentric coordinate system as the reference;

[0197] Cold parameter extraction module: based on the ground temperature gradient field and the underground water flow velocity field data, calculate the rock-soil thermal expansion coefficient dynamic matrix and the seepage shear stress tensor;

[0198] Three-dimensional deformation field modeling module: input the multi-source displacement data set and the environmental coupling parameter matrix into the three-dimensional gridding model, and generate an initial three-dimensional deformation field model through a displacement-temperature-seepage coupling algorithm;

[0199] Model dynamic correction module: use the real-time updated tiltmeter data to construct a deformation gradient verification matrix, and perform iterative correction on the initial three-dimensional deformation field model;

[0200] Visual warning module: compare the optimized three-dimensional deformation field model with the preset geological safety threshold, generate a three-dimensional deformation thermal map with risk level identification and a warning signal.

[0201] Those skilled in the art should understand that the above discussion of any embodiment is only exemplary and is not intended to suggest that the scope of the present application is limited to these examples; under the idea of the present application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the present application as described above. In order to be brief, they are not provided in details.

Claims

1. A method of three-dimensional observation of geological deformation, characterized in that, The method comprises the following steps: S1, multi-dimensional data acquisition: synchronously acquiring satellite synthetic aperture radar interferometry data, ground GNSS displacement monitoring data, and underground tiltmeter array data, and collecting ground temperature gradient field data and underground water flow velocity field data in a monitoring area; S2, data space-time calibration processing: performing space-time reference unification processing on the collected radar interference data, GNSS data, and tiltmeter data, establishing a three-dimensional space-time calibration chain with the geocentric coordinate system as the reference, and generating a multi-source displacement data set after space-time calibration; S3, cold parameter extraction: based on the ground temperature gradient field data and the underground water flow velocity field data in the multi-dimensional data acquisition, calculating a rock-soil thermal expansion coefficient dynamic matrix and a seepage shear stress tensor, and generating an environmental coupling parameter matrix; S4, three-dimensional deformation field modeling: inputting the multi-source displacement data set obtained in the data space-time calibration processing and the generated environmental coupling parameter matrix into a three-dimensional gridding model, and generating an initial three-dimensional deformation field model through a displacement-temperature-seepage coupling algorithm; specifically comprising: S41, three-dimensional grid model construction: the monitoring area is divided into hexahedral element grid, define the grid node coordinates; S42, multi-field coupling constitutive equation establishment: extending the traditional elastic mechanics constitutive equation, and representing it as: ； ； wherein, is the fourth order stiffness tensor, the anisotropy parameters are taken from geophysical data, is the temperature dependent thermal expansion coefficient tensor obtained, is the shear modulus of the rock mass, is the thermal-seepage coupling coefficient, is the temperature gradient and hydraulic gradient vector; S43, multi-source data fusion weighting: establishing an observation data weight matrix; S44, coupled stress field calculation: considering gravity, seepage fluid force, and rock-soil internal stress distribution, solving a coupled balance equation, and representing it as: ； ； wherein, is the density of the rock-soil mass, is the acceleration of gravity, is the porosity, is the inertial resistance coefficient, is the density of the groundwater, is the dynamic viscosity of the pore water; S45, iterative solution and model generation: iteratively solving by using a modified Newton-Raphson algorithm; S5, model dynamic correction: based on the real-time updated tiltmeter data in the multi-dimensional data acquisition, constructing a deformation gradient verification matrix, iteratively correcting the initial three-dimensional deformation field model, and generating an optimized three-dimensional deformation field model; S6, visualized early warning output: comparing the generated optimized three-dimensional deformation field model with a preset geological safety threshold, generating a three-dimensional deformation thermal map with a risk level identifier and a warning signal.

2. The method according to claim 1, wherein, The S1 comprises: S11, deploying a multi-source sensing network; S12, synchronous data acquisition; S13, data packaging preprocessing: adding a unified space-time label to each source data.

3. The method according to claim 2, wherein, The S2 comprises: S21, radar interference data phase compensation: performing multi-physical field coupling correction on the original radar interference phase; S22, GNSS data coordinate conversion: converting GNSS geodetic coordinates into geocentric rectangular coordinate system displacement vectors; S23, tiltmeter space-time interpolation: constructing a four-dimensional space-time interpolation function; S24, space-time calibration chain construction: establishing a constraint equation group of unified space-time reference; S25, multi-source displacement data set generation: based on the obtained multi-source displacement data, outputting a data structure.

4. The method according to claim 3, wherein, The S3 comprises: S31, multi-source data preprocessing: performing space-time normalization processing on the collected ground temperature gradient field data and underground water flow velocity field data; S32, thermal expansion coefficient dynamic matrix calculation: constructing a temperature-related thermal expansion coefficient tensor; S33, seepage shear stress tensor calculation: calculating seepage shear stress based on a modified Biot theory; S34, environmental coupling parameter matrix generation: establishing a thermal-seepage coupling action matrix.

5. The method of claim 1, wherein, The S5 comprises: S51, tiltmeter data gradient analysis: converting real-time tiltmeter measurement values into a curvature tensor; S52, model predicted gradient extraction: based on the three-dimensional deformation field model, the displacement gradient tensor is extracted; S53, residual weight matrix construction: define the spatiotemporal weighted residual matrix; S54, coupling stiffness matrix update: in the model updating process, the update of the stiffness matrix is based on the gradient information of the residual matrix, and the stiffness tensor in the constitutive equation is corrected; S55, double feedback iteration: double feedback iteration includes curvature feedback loop and phase consistency loop, which respectively corrects the displacement field from micro curvature and macro phase two dimensions; S56, convergence determination: the convergence of the iteration process is determined by the conditions of displacement field change, residual matrix norm and phase consistency error.

6. The method according to claim 5, wherein, The S6 comprises: S61, risk quantification and visualization mapping: extracting three-dimensional deformation field features, setting dynamic safety threshold, constructing risk level matrix, and rendering three-dimensional thermal map; S62, early warning decision and signal generation: based on the risk matrix, the composite early warning logic is triggered.

7. The method according to claim 6, wherein, The S61 comprises: S611, three-dimensional deformation field feature extraction: calculating multi-parameter risk indicators of each grid node; S612, dynamic safety threshold setting: generating spatially distributed safety threshold according to geological exploration data; S613, risk level matrix calculation: constructing three-dimensional risk quantification model; S614, three-dimensional thermal map rendering: using color-transparency-vector three-channel coding to generate visual results.

8. The method according to claim 7, wherein, The S62 multi-level early warning signal generation establishes a composite early warning trigger logic and outputs early warning messages.

9. A geological deformation three-dimensional observation system for implementing a geological deformation three-dimensional observation method according to any one of claims 1 to 8, characterized by, It comprises the following modules: Multidimensional data acquisition module: synchronously acquiring satellite synthetic aperture radar interferometric data, ground GNSS displacement monitoring data, underground tiltmeter array data, and collecting ground temperature gradient field data and underground water flow velocity field data in the monitoring area; Data space-time calibration module: performing space-time reference unification processing on the collected multi-source data, and establishing a three-dimensional space-time calibration chain with the geocentric coordinate system as the reference; Cold parameter extraction module: based on the ground temperature gradient field and underground water flow velocity field data, calculating the dynamic matrix of rock-soil thermal expansion coefficient and the seepage shear stress tensor; Three-dimensional deformation field modeling module: inputting the multi-source displacement data set and the environmental coupling parameter matrix into the three-dimensional gridding model, and generating the initial three-dimensional deformation field model through displacement-temperature-seepage coupling algorithm; Model dynamic correction module: using the real-time updated tiltmeter data to construct the deformation gradient verification matrix, and iteratively correcting the initial three-dimensional deformation field model; Visual early warning module: comparing the optimized three-dimensional deformation field model with the preset geological safety threshold, generating a three-dimensional deformation thermal map with risk level identification and early warning signal.

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