Land subsidence micro-motion monitoring system and method thereof

Abstract

The present application relates to the field of ground subsidence disaster early warning technology, in particular to a ground subsidence micro-motion monitoring system and method thereof; comprising a geomechanical model construction, micro-motion event interpretation, damage-stress coupling evolution, risk probability prediction module; the system obtains seismic moment by real-time interpretation of microseismic events; the core is based on seismic moment and historical cumulative damage factor, applying damage amplification effect, calculating the current cumulative damage factor, real-time shear stress and normal stress, and predicting time-varying instability risk index and instability probability; the present application assimilates discrete microseismic data into continuous damage accumulation and stress redistribution process in rock-soil body, realizing the change from passive recording to internal state quantitative early warning.

Description

Technical Field

[0001] This invention relates to the field of land subsidence disaster early warning technology, specifically to a land subsidence micro-motion monitoring system and method. Background Technology

[0002] In the fields of geotechnical engineering and ground settlement monitoring, tiny fractures within soil and rock masses generate massive, high-frequency microseismic events. Existing technologies mostly remain at the level of passively recording these events, lacking a physical mechanism that correlates discrete microseismic events with continuous damage accumulation and stress redistribution within the soil and rock mass. This lack of mechanism leads to two main drawbacks: first, it is impossible to quantify internal damage and stress states; second, it is difficult to distinguish between benign noise and key instability precursors from massive amounts of data, which can easily trigger warning storms and cause warnings to fail.

[0003] Therefore, how to establish an evolutionary model that can balance real-time performance and accuracy, assimilate high-frequency, discrete micro-event data into continuous internal state evolution in real time, and quantify and predict macro-instability risks accordingly, is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a ground subsidence micro-motion monitoring system and method. Specifically, the technical solution of this invention is as follows:

[0005] A ground subsidence micro-motion monitoring system, comprising:

[0006] The geomechanical model building module is used to build a three-dimensional geological mesh model and provide the initial stress field and initial mechanical parameters;

[0007] The micro-seismic event interpretation module is used to interpret micro-seismic events in real time and output seismic moments;

[0008] The damage-stress coupling evolution module is used to receive the seismic moment and, based on the historical cumulative damage factor maintained by the module, calculate the current cumulative damage factor, real-time shear stress, and real-time normal stress.

[0009] The risk probability prediction module is used to receive the current cumulative damage factor, the real-time shear stress, the real-time normal stress, and the initial mechanical parameters, and calculate the time-varying instability risk index and instability probability.

[0010] The damage-stress coupling evolution module is specifically used for:

[0011] Calculate the foundation damage degree based on the seismic moment;

[0012] Based on the baseline damage level and the historical cumulative damage factor, the damage amplification effect is applied to calculate the actual damage increment.

[0013] The historical cumulative damage factor is updated based on the actual damage increment to generate the current cumulative damage factor;

[0014] Based on the change in the current cumulative damage factor, a stress calculation step is performed to generate the real-time shear stress and the real-time normal stress.

[0015] Preferably, the risk probability prediction module is specifically used for:

[0016] Receive the current cumulative damage factor and the initial mechanical parameters;

[0017] Calculate the dynamic elastic modulus based on the current cumulative damage factor and the initial mechanical parameters;

[0018] Calculate the dynamic cohesion based on the current cumulative damage factor and the initial mechanical parameters;

[0019] Calculate the dynamic internal friction angle based on the current cumulative damage factor and the initial mechanical parameters.

[0020] Preferably, the risk probability prediction module is further specifically used for:

[0021] Based on the real-time shear stress, the real-time load is determined;

[0022] The real-time resistance is determined based on the real-time normal stress, the dynamic cohesion, and the dynamic internal friction angle.

[0023] The ratio of the real-time load to the real-time resistance is calculated to generate the time-varying instability risk index.

[0024] Preferably, the stress calculation step specifically includes:

[0025] Determine whether the current accumulated damage factor exceeds a preset global trigger threshold;

[0026] When the current cumulative damage factor does not exceed the global trigger threshold, a preset local stress transfer function is used to calculate the real-time shear stress and the real-time normal stress.

[0027] When the current cumulative damage factor exceeds the global trigger threshold, the geomechanical model construction module is triggered to perform a global recalculation to generate a global stress field;

[0028] The global stress field is used to calibrate and output the real-time shear stress and the real-time normal stress.

[0029] Preferably, when performing the global recalculation, the geomechanical model construction module is specifically used for:

[0030] The dynamic elastic modulus calculated by the risk probability prediction module is used as the updated model stiffness for global recalculation.

[0031] Preferably, the system further includes:

[0032] The macroscopic response monitoring module is used to monitor the macroscopic displacement of the soil and rock mass to generate measured displacement data;

[0033] The model calibration module is used for:

[0034] Based on the dynamic elastic modulus, the displacement is predicted by the model through the geomechanical model construction module.

[0035] Calculate the residual between the model-predicted displacement and the measured displacement data;

[0036] A data assimilation algorithm is used to iteratively adjust the model calibration parameters in the damage-stress coupling evolution module and the risk probability prediction module to minimize the residual.

[0037] Preferably, the model calibration parameters include:

[0038] The calibration coefficient, damage amplification coefficient, nonlinearity index, and material degradation index are related to the basic damage degree.

[0039] A method for monitoring ground subsidence micromotion includes:

[0040] The micro-seismic event interpretation module interprets micro-seismic events in real time and outputs seismic moments.

[0041] The seismic moment is received through the damage-stress coupling evolution module, and the current cumulative damage factor, real-time shear stress, and real-time normal stress are calculated based on the historical cumulative damage factor maintained by the module.

[0042] The risk probability prediction module receives the current cumulative damage factor, the real-time shear stress, the real-time normal stress, and the initial mechanical parameters provided by the geomechanical model construction module, and calculates the time-varying instability risk index and instability probability.

[0043] Preferably, the step of calculating the current cumulative damage factor, real-time shear stress, and real-time normal stress specifically includes:

[0044] Calculate the foundation damage degree based on the seismic moment;

[0045] Based on the baseline damage level and the historical cumulative damage factor, the damage amplification effect is applied to calculate the actual damage increment.

[0046] The historical cumulative damage factor is updated based on the actual damage increment to generate the current cumulative damage factor;

[0047] Perform a stress calculation step to generate the real-time shear stress and the real-time normal stress;

[0048] Specifically, the stress calculation step includes:

[0049] Determine whether the current accumulated damage factor exceeds a preset global trigger threshold;

[0050] When the current cumulative damage factor does not exceed the global trigger threshold, a preset local stress transfer function is used to calculate the real-time shear stress and the real-time normal stress.

[0051] When the current cumulative damage factor exceeds the global trigger threshold, the geomechanical model construction module is triggered to perform a global recalculation to generate a global stress field;

[0052] The global stress field is used to calibrate and output the real-time shear stress and the real-time normal stress.

[0053] Preferably, the step of calculating the time-varying instability risk index and the instability probability specifically includes:

[0054] Calculate the dynamic elastic modulus based on the current cumulative damage factor and the initial mechanical parameters;

[0055] Calculate the dynamic cohesion based on the current cumulative damage factor and the initial mechanical parameters;

[0056] Calculate the dynamic internal friction angle based on the current cumulative damage factor and the initial mechanical parameters;

[0057] Based on the real-time shear stress, the real-time load is determined;

[0058] The real-time resistance is determined based on the real-time normal stress, the dynamic cohesion, and the dynamic internal friction angle.

[0059] The ratio of the real-time load to the real-time resistance is calculated to generate the time-varying instability risk index.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. This invention establishes a set of physical evolution mechanisms that assimilate massive, discrete microseismic event data in real time into a continuous process of damage accumulation and stress redistribution within the soil and rock mass, thus solving the shortcomings of existing technologies that can only passively record and cannot quantify internal damage and stress states.

[0062] 2. This invention cleverly solves the core contradiction between the high real-time requirements of microseismic data and the time-consuming global calculation of precise stress field by adopting an innovative global-local hierarchical decoupled computational architecture and combining high-frequency local approximation calculation with low-frequency global precise calibration, thus ensuring the timeliness of system response and the long-term accuracy of results.

[0063] 3. This invention simulates the correlation of damage history by introducing a damage amplification effect, and calculates the time-varying instability risk index based on dynamic load and dynamic resistance. This index improves the traditional static safety factor and can dynamically quantify the consumption process of safety margin, thereby accurately distinguishing between benign noise and key instability precursors, and solving the problem of early warning storms.

[0064] 4. This invention introduces a feedback mechanism based on field-measured macroscopic displacement data. Through data assimilation algorithms, it automatically inverts and calibrates key parameters within the model, solving the problem of distorted laboratory calibration of model parameters and the difficulty in implementing them in the field. This greatly improves the engineering practical value and prediction accuracy of the system. Attached Figure Description

[0065] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0066] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0067] 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.

[0068] Example 1:

[0069] Please see Figure 1 A ground subsidence micro-motion monitoring system, comprising:

[0070] The geomechanical model building module is used to build a three-dimensional geological mesh model and provide the initial stress field and initial mechanical parameters;

[0071] The micro-seismic event interpretation module is used to interpret micro-seismic events in real time and output seismic moments;

[0072] The damage-stress coupling evolution module is used to receive seismic moments and, based on the historical cumulative damage factor maintained by the module, calculate the current cumulative damage factor, real-time shear stress, and real-time normal stress.

[0073] The risk probability prediction module is used to receive the current cumulative damage factor, real-time shear stress, real-time normal stress and initial mechanical parameters, and calculate the time-varying instability risk index and instability probability.

[0074] The damage-stress coupling evolution module is specifically used for:

[0075] Calculate the foundation damage degree based on the seismic moment;

[0076] Based on the basic damage degree and historical cumulative damage factor, the damage amplification effect is applied to calculate the actual damage increment.

[0077] Update the historical cumulative damage factor based on the actual damage increment to generate the current cumulative damage factor;

[0078] Based on the current changes in the cumulative damage factor, a stress calculation step is performed to generate real-time shear stress and real-time normal stress.

[0079] This embodiment provides a ground settlement micro-motion monitoring system. The system aims to extrapolate and quantify the hidden damage accumulation and stress redistribution process inside the soil and rock mass in real time from a massive flow of microseismic events, so as to predict the risk of structural instability.

[0080] In a preferred embodiment, the system includes:

[0081] Geomechanical Model Building Module (GMM):

[0082] The purpose of this module is to establish the initial static geomechanical framework of the entire monitoring area;

[0083] This module is used to construct three-dimensional geological mesh models, for example, by using finite element method (FEM) or discrete element method (DEM) technology, and inputting geological exploration data and physical and mechanical parameters of rock and soil to construct three-dimensional geological models.

[0084] This module provides the initial stress field for other dynamic modules of the system. Initial mechanical parameters, which refer to the baseline mechanical properties of the soil and rock mass in an undamaged state, serve as a benchmark for subsequent dynamic deterioration calculations. In this embodiment, these parameters specifically include the initial elastic modulus. Initial cohesion and initial internal friction angle ;

[0085] Micro-motion event interpretation module MEM:

[0086] The purpose of this module is to act as the real-time sensing unit of the system, capturing the precursor signals released by tiny fractures inside the rock and soil mass in real time.

[0087] This module employs standard sensor arrays such as geophones and seismic source location algorithms for real-time interpretation of microseismic events, and its output is the seismic moment. ;

[0088] Seismic Moment It refers to a physical quantity used to characterize the energy release scale of microseismic events. Its source is obtained by real-time interpretation by this module, and its function is to serve as an event input source that drives subsequent damage evolution.

[0089] Damage-Stress Coupled Evolution Module (DSEM):

[0090] This module is the core evolution module of this invention, and its purpose is to assimilate discrete microseismic events into continuous internal state evolution.

[0091] This module is used to receive seismic moments. And based on the historical cumulative damage factor maintained by this module Through a series of coupled calculations, the current cumulative damage factor is calculated. and real-time shear stress and real-time normal stress ;

[0092] Historical cumulative damage factors : refers to model unit i in t The accumulated damage state at time 1 is a dimensionless scalar from 0 to 1, which is derived from the previous iteration output of this module and serves as a historical benchmark for calculating the current damage increment.

[0093] Current cumulative damage factor : refers to the latest damage state of model unit i at time t, after assimilating the new event k. It is obtained by calculation by this module and its function is to characterize the current degree of material degradation and serve as the input of the risk probability prediction module.

[0094] Real-time shear stress and real-time normal stress This refers to the cumulative damage. The change leads to a change in material stiffness, which in turn triggers stress redistribution. The latest stress state of element i at time t is as follows:

[0095] In this embodiment, the damage-stress coupling evolution module is specifically used to execute the following internal logic steps:

[0096] Step a: Calculate the foundation damage degree based on the seismic moment;

[0097] The physical dimension N·m Mapped to dimensionless basic damage ;

[0098] This step introduces the basic damage calculation formula:

[0099]

[0100] in, The seismic moment is measured in N·m and originates from the micro-motion event interpretation module.

[0101] Mref is the reference seismic moment, with dimensions in N·m. In this embodiment, it is preferably a fixed constant, for example, Mref = 1.0 N·m, and its function is only to ensure ( ) dimensionless processing;

[0102] It is a calibration coefficient related to the basic damage degree, which is a dimensionless parameter and is used to match the brittleness of soil and rock in a specific site.

[0103] Step a-1: Damage event mapping;

[0104] Based on the epicenter location of event k output by the micro-motion event interpretation module, one or more three-dimensional geological mesh model elements i affected by the event are determined; in a preferred embodiment, the epicenter envelope or a preset influence radius can be used to measure the basic damage degree. Assign it to cell i where the epicenter is located; or in another embodiment, based on the distance from event k to cell i. Using spatial decay function right Weighting is applied to influence multiple neighboring cells i; for example, the spatial decay function. The function can be preferably a Gaussian function. ,in The calibration parameters characterize the range of influence and are used to determine the spatial distribution weight of damage energy;

[0105] Step b: Based on the baseline damage level and historical cumulative damage factor, apply the damage amplification effect to calculate the actual damage increment;

[0106] This step is one of the innovations of this invention; it solves the defect of existing models that ignore the correlation of damage history; its technical motivation lies in: existing historical damage This increases the brittleness of the rock and soil mass, thereby nonlinearly amplifying new events. This represents the actual damage caused;

[0107] This step introduces the damage amplification effect formula:

[0108]

[0109] in, The basic damage degree is a dimensionless parameter derived from the calculation results of step a.

[0110] The historical cumulative damage factor is a dimensionless parameter derived from the state of this module at the previous moment.

[0111] is the damage amplification factor, and is a dimensionless parameter;

[0112] It is a nonlinear exponent and a dimensionless parameter, which is preferred. ;

[0113] Step c: Update the historical cumulative damage factor based on the actual damage increment to generate the current cumulative damage factor;

[0114] The newly calculated damage increment is added to the historical state, and it is ensured that it does not exceed the physical limit of 1.0, which represents complete failure.

[0115] This step uses the damage accumulation update formula:

[0116]

[0117] Step d: Based on the current changes in the cumulative damage factor, perform a stress calculation step to generate real-time shear stress and real-time normal stress;

[0118] Accumulation of damage An increase in stiffness leads to a change in material stiffness, i.e., the dynamic elastic modulus. The purpose of this step is to calculate the stress redistribution caused by this change in stiffness in real time, thereby obtaining the updated value. and ;

[0119] Note: The specific implementation method of this step is further specified in detail.

[0120] Risk Probability Prediction Module (RPPM):

[0121] This module is the risk decision-making module of the present invention, and its purpose is to collect all current status information and predict future risks;

[0122] This module is used to receive the current cumulative damage factor. Real-time shear stress Real-time normal stress All data are from the DSEM module and the initial mechanical parameters. The time-varying instability risk index is calculated from the GMM module. and instability probability ;

[0123] Time-varying instability risk index :

[0124] Instability probability : refers to based on Field and parameter uncertainties, such as those calculated through Monte Carlo simulations, indicate the future monitoring area. Instability occurs within a time window, such as 24 hours. The probability of;

[0125] The system disclosed in this embodiment establishes a complete quantitative chain from micro-events to macro-states and then to future risks through the coordinated work of the above four modules, especially through the logical closed loop of damage amplification and stress calculation within the damage-stress coupling evolution module. It solves two major pain points in the existing technology: the inability to distinguish between benign noise and key precursors in storm warnings and the inability to quantify internal damage and stress states due to the lack of mechanisms. It achieves a leap from passive event recording to proactive risk prediction, greatly improving the accuracy and timeliness of ground subsidence disaster early warning.

[0126] Example 2:

[0127] The risk probability prediction module is specifically used for:

[0128] Receive the current cumulative damage factor and initial mechanical parameters;

[0129] Calculate the dynamic elastic modulus based on the current cumulative damage factor and initial mechanical parameters;

[0130] Calculate the dynamic cohesion based on the current cumulative damage factor and initial mechanical parameters;

[0131] Calculate the dynamic internal friction angle based on the current cumulative damage factor and initial mechanical parameters.

[0132] This embodiment, based on Embodiment 1, specifies the internal implementation of the risk probability prediction module; the purpose of this specification is to achieve a physical correlation between microscopic damage and macroscopic mechanical properties, i.e., quantification. How does the macroscopic strength of soil and rock mass deteriorate in real time?

[0133] To further clarify, the risk probability prediction module is specifically used to perform the following steps:

[0134] Receive current cumulative damage factor From DSEM and initial mechanical parameters From GMM;

[0135] Calculate the dynamic elastic modulus based on the current cumulative damage factor and initial mechanical parameters;

[0136] Calculate the real-time stiffness after damage; this parameter is a key input for the calibration of the subsequent stress redistribution calculation model.

[0137] This step uses the dynamic elastic modulus reduction formula, which is derived from the standard continuous damage mechanics theory:

[0138]

[0139] in, The initial elastic modulus, in Pa, is derived from the geomechanical model construction module.

[0140] The current cumulative damage factor is a dimensionless parameter originating from the damage-stress coupling evolution module.

[0141] Calculate the dynamic cohesion based on the current cumulative damage factor and initial mechanical parameters;

[0142] Calculate the real-time cohesion after damage, which is a key component of shear strength;

[0143] This step uses the dynamic cohesion reduction formula:

[0144]

[0145] in, The initial cohesive force, with dimensions in Pa, originates from the geomechanical model construction module;

[0146] Cohesion is the material degradation index, which is a dimensionless parameter.

[0147] Calculate the dynamic internal friction angle based on the current cumulative damage factor and initial mechanical parameters;

[0148] Calculating the real-time internal friction angle after damage is another key component of shear strength; to ensure the rigor of the physical meaning, this invention preferably uses the coefficient of friction, which represents frictional capacity, as the basis for calculation. Reduce the amount;

[0149] This step uses the dynamic internal friction coefficient reduction formula:

[0150]

[0151] in, The initial internal friction angle, in degrees or radians, is derived from the geomechanical model construction module.

[0152] Friction angle is the material degradation index, and is a dimensionless parameter.

[0153] This embodiment uses the three reduction formulas described above to transform the abstract, microscopic current cumulative damage factor output by the damage-stress coupling evolution module into a single, more comprehensive value. This is explicitly and quantitatively transformed into macroscopic engineering mechanical parameters familiar to those skilled in the art, such as geotechnical engineers. The dynamic changes provide a solid, physically consistent input for subsequent calculations of real-time resistance and time-varying instability risk indices, and are a key link in realizing the logical leap from damage to risk.

[0154] Example 3:

[0155] The risk probability prediction module is also specifically used for:

[0156] Determine the real-time load based on the real-time shear stress;

[0157] The real-time resistance is determined based on the real-time normal stress, dynamic cohesion, and dynamic internal friction angle.

[0158] Calculate the ratio of real-time load to real-time resistance to generate a time-varying instability risk index.

[0159] Based on Example 2, this embodiment further specifies the internal implementation of the risk probability prediction module; the purpose of this specification is to combine dynamic loads and dynamic resistance to calculate decision indicators.

[0160] Based on the results of the previous step, the risk probability prediction module is also used to perform the following steps:

[0161] Determine the real-time load based on the real-time shear stress;

[0162] Determine the load force driving instability;

[0163] In this embodiment, the real-time load DrivingForce is defined as the real-time shear stress calculated by the damage-stress coupling evolution module. ;

[0164] The real-time resistance is determined based on the real-time normal stress, dynamic cohesion, and dynamic internal friction angle.

[0165] Determine the shear strength to resist instability;

[0166] Real-time resistance (ResistingForce) is based on the Mohr-Coulomb failure criterion and uses the dynamic parameters calculated in Example 2. and To calculate;

[0167] This step uses the formula for calculating the dynamic shear strength of real-time resistance:

[0168]

[0169] in, The dynamic cohesive force, with dimensions in Pa, is derived from the calculation results of Example 2.

[0170] The stress is real-time normal stress, with dimensions in Pa, and originates from the damage-stress coupling evolution module.

[0171] The dynamic internal friction coefficient is a dimensionless parameter derived from the calculation results of Example 2.

[0172] Calculate the ratio of real-time load to real-time resistance to generate a time-varying instability risk index;

[0173] To avoid instability in numerical calculations, this step is specifically used for:

[0174] Determine real-time resistance ;

[0175] Set a very small resistance threshold. This threshold is used to determine numerical stability; It is a positive number close to zero, preset to prevent division by zero errors in numerical calculations. For example, it can be based on the initial cohesion. Or it can be determined by the precision of machine calculations;

[0176] if The time-varying instability risk index will then be... Set to the preset maximum value or mark as invalid;

[0177] Otherwise (i.e.) ), perform ratio calculation:

[0178] Time-varying instability risk index =Real-time load / Real-time resistance= / = / ( + )

[0179] This embodiment defines a time-varying instability risk index. This core innovative parameter improves the static safety factor in geotechnical engineering. Resistance / load); the present invention The index is a dynamic early warning indicator that measures the load that increases in real time due to stress redistribution. Resistance that decreases in real time due to damage and deterioration Dynamic coupling was implemented; this allows the system to perfectly quantify the real-time consumption process of the safety margin, transforming a statically designed approach. The parameters were modified to be used for dynamic early warning. The index enables precise early warning, solving the problem of early warning storms; the risk probability prediction module is also specifically used to calculate the instability probability based on the time-varying instability risk index.

[0180] Receiver Time-Varying Instability Risk Index field;

[0181] Considering initial mechanical parameters The uncertainty is assumed to follow a pre-defined probability distribution;

[0182] Using the Monte Carlo simulation method, perform N iterations:

[0183] a. In each iteration, from A set of parameters is randomly selected from the probability distribution.

[0184] b. Use this set of sampling parameters and substitute them into the current... and According to the definition in this embodiment (Embodiment 3) Formula, recalculate ;

[0185] In the N iterations, Number of times greater than or equal to 1.0 ;

[0186] Calculate the probability of instability .

[0187] Example 4:

[0188] The specific steps for performing stress calculations include:

[0189] Determine whether the current accumulated damage factor exceeds the preset global trigger threshold;

[0190] When the current cumulative damage factor does not exceed the global trigger threshold, a preset local stress transfer function is used to calculate the real-time shear stress and real-time normal stress.

[0191] When the current cumulative damage factor exceeds the global trigger threshold, the geomechanical model building module is triggered to perform a global recalculation to generate a global stress field;

[0192] It employs a global stress field to calibrate and output real-time shear stress and real-time normal stress.

[0193] Based on Example 1, this embodiment specifies the stress calculation steps in the damage-stress coupling evolution module; the purpose of this specification is to resolve the core contradiction between real-time performance and calculation accuracy.

[0194] Microseismic events occur very frequently, every second or every minute, while precise global recalculation of the stress field, such as FEM / DEM, is very time-consuming, every hour or every day. If global recalculation is performed for every microseismic event, the system will lose its real-time performance; if only coarse calculations are performed, huge errors will accumulate.

[0195] This embodiment employs a global-local hierarchical decoupling evolutionary architecture to address this issue. The stress calculation steps specifically include:

[0196] Determine whether the global cumulative damage status exceeds the preset global trigger threshold. This judgment specifically includes:

[0197] Among all N model units, its The number of cells greater than the preset cell threshold ;

[0198] Calculate the global average damage factor ;

[0199] when Exceeding the preset quantity, or When this occurs, it is determined that the global trigger threshold has been exceeded;

[0200] Global trigger threshold This refers to a preset damage accumulation value; when the accumulated damage is small, its disturbance to the stress field is local; when the accumulated damage exceeds this threshold, it indicates that the structure may have undergone significant changes, and a global high-precision check must be performed; the method for determining this threshold can be predetermined through historical data analysis or numerical simulation.

[0201] When the current cumulative damage factor does not exceed the global trigger threshold, the local path uses a preset local stress transfer function to calculate the real-time shear stress and real-time normal stress.

[0202] This is a high-frequency, real-time approximate calculation path; the local stress transfer function is specifically used for:

[0203] Based on the current cumulative damage factor generated in step c And apply the dynamic elastic modulus reduction formula Calculate the dynamic elastic modulus Changes Computing unit Stiffness loss;

[0204] This stiffness loss is equivalent to a set of inverse virtual nodal forces. Specifically, the virtual node force Based on continuum mechanics and finite element theory, through Calculate, where, The strain-displacement matrix, For changes in dynamic elastic modulus Exported cells The change in stiffness matrix, For this unit in A dynamic field of response at all times;

[0205] Using pre-calculated local stress transfer coefficient Calculation by Caused neighboring units Stress increment The local stress transfer coefficient Let i be a Green's function or influence coefficient, whose physical meaning is the unit virtual nodal force at element i. Stress response induced at element j This coefficient can be pre-calculated and stored all at once during the geomechanical model building (GMM) stage by applying unit nodal forces to the initial model and calculating the global response field;

[0206] This stress increment is superimposed onto the historical stress field to generate real-time shear stress and real-time normal stress: ;

[0207] When the current cumulative damage factor exceeds the global trigger threshold, the global path triggers the geomechanical model building module to perform a global recalculation to generate a global stress field.

[0208] This is a low-frequency, high-precision calibration path; at this point, the Damage-Stress Coupled Evolution Module (DSEM) will trigger in reverse and call the Geomechanical Model Building Module (GMM), forcing it to perform a complete, high-precision FEM / DEM numerical simulation to obtain the most accurate global stress field under the current condition. ;

[0209] It employs a global stress field to calibrate and output real-time shear stress and real-time normal stress;

[0210] The system will generate a high-precision global stress field. The calculation results are fed back into the Damage-Stress Coupled Evolution (DSEM) module to calibrate and cover the stress field estimated by the local path in the DSEM. Then, the DSEM uses this calibrated stress field as a reference to continue the next round of local approximation calculations.

[0211] This embodiment employs a global-local layered decoupling architecture and a state-aware switching mechanism. The threshold judgment cleverly resolves the contradiction between real-time performance and accuracy. The system operates in a locally approximate high-frequency real-time mode when the threshold is not exceeded most of the time, ensuring an immediate response to micro-seismic events. At the same time, the low-frequency calibration path of global recalculation ensures that the cumulative error of the stress field is always controllable and periodically eliminated, thus guaranteeing the long-term reliability and real-time performance of the entire system's prediction results in terms of architecture.

[0212] Example 5:

[0213] The geomechanical model building module is specifically used during global recalculation for:

[0214] The dynamic elastic modulus calculated by the risk probability prediction module is used as the updated model stiffness for global recalculation.

[0215] Based on Example 4, this embodiment further specifies the implementation method of the geomechanical model construction module in the global path when performing global recalculation;

[0216] The Geomechanical Modeling Module (GMM) is specifically used during global recalculation for:

[0217] The dynamic elastic modulus calculated by the risk probability prediction module is used as the updated model stiffness for global recalculation;

[0218] This embodiment establishes a crucial state feedback closed loop; when the global path in Embodiment 4 is triggered, the GMM module, such as the FEM model, needs to know the current material stiffness to perform calculations; this embodiment explicitly states that the GMM should not use the initial elastic modulus at this time. Instead, it is necessary to use the risk probability prediction module RPPM based on the logic of Example 2. Dynamic elastic modulus calculated in real time ;

[0219] This embodiment ensures the physical authenticity of the high-precision calibration step of global recalculation; it forces the GMM to use a dynamic elastic modulus that reflects the current cumulative damage state. As the updated model stiffness, the result of the global recalculation It can accurately reflect the true stress distribution of soil and rock in a deteriorated state; this greatly improves the accuracy of calibration and ensures that the entire system can maintain high fidelity even when damage accumulates significantly during long-term operation.

[0220] Example 6:

[0221] The system also includes:

[0222] The macroscopic response monitoring module is used to monitor the macroscopic displacement of the soil and rock mass to generate measured displacement data;

[0223] The model calibration module is used for:

[0224] Based on the dynamic elastic modulus, displacement is predicted by calculating the model through the geomechanical model construction module;

[0225] Calculate the residual between the displacement predicted by the model and the measured displacement data;

[0226] A data assimilation algorithm is used to iteratively adjust the model calibration parameters in the damage-stress coupling evolution module and the risk probability prediction module to minimize the residuals.

[0227] This embodiment adds an adaptive calibration mechanism to the system based on embodiment 2;

[0228] Examples 1 and 2 introduce a large number of custom model parameters, such as How to determine these parameters is crucial to whether this invention can be implemented in practical engineering. Traditional laboratory calibration methods are severely distorted due to problems such as scale effects and cannot be applied in the field.

[0229] To address this feasibility issue, the system also includes:

[0230] Macro-response monitoring module:

[0231] The purpose of this module is to provide macroscopic feedback from field measurements as a calibration benchmark for model inversion;

[0232] This module is used to monitor the macroscopic displacement of soil and rock masses, for example, using GPS, InSAR, total station, or high-precision strain gauges, to generate measured displacement data. ;

[0233] Model calibration module:

[0234] The purpose of this module is to automatically and iteratively adjust the model calibration parameters within the system through model inversion and data assimilation, so that its output matches the feedback from field measurements. Matching;

[0235] The model calibration module is used for:

[0236] Based on the dynamic elastic modulus, the model predicts displacement by constructing a geomechanical model.

[0237] This module calls the FEM model from the GMM module and uses the dynamic elastic modulus calculated by Example 2 of the RPPM module. As input, the macroscopic displacement predicted by the system under the current model parameters is calculated, denoted as... ;

[0238] b. Calculate the residual between the displacement predicted by the model and the measured displacement data;

[0239] calculate ;

[0240] c. A data assimilation algorithm is used to iteratively adjust the model calibration parameters in the damage-stress coupling evolution module and the risk probability prediction module to minimize the residuals.

[0241] Data assimilation algorithms, such as Kalman filtering, ensemble Kalman filtering (EnKF), or particle filtering, automatically execute this iterative process; it continuously fine-tunes the model calibration parameters (see definition) until the residual calculated in step b is reached. To reach a minimum, for example, less than a certain convergence threshold;

[0242] This embodiment introduces a macroscopic response monitoring module and a model calibration module, providing an adaptive calibration mechanism based on real-time on-site inversion; this solves the problem of all custom model parameters. This invention addresses the key feasibility challenge of determining parameters; it enables system parameters to be adaptively adjusted and optimized based on actual displacement data from the field, rather than relying on distorted laboratory calibrations. This ensures that the parameters of all core formulas in this invention are objective and traceable, greatly enhancing the engineering practical value and prediction accuracy of the entire system.

[0243] Example 7:

[0244] Model calibration parameters include:

[0245] The calibration coefficient, damage amplification coefficient, nonlinearity index, and material degradation index are related to the basic damage degree.

[0246] Based on Example 6, this embodiment further specifies the model calibration parameters adjusted by the model calibration module.

[0247] Model calibration parameters include:

[0248] Calibration coefficients related to basic damage degree:

[0249] Referring to the formula for calculating the basic damage degree in step a of Example 1. In and ;

[0250] Calibration of seismic moment To basic damage The mapping relationship;

[0251] Damage amplification factor:

[0252] In Example 1, the formula for damage amplification effect in step b is... In ;

[0253] Determine the sensitivity of historical damage to new damage;

[0254] Nonlinear exponent:

[0255] In Example 1, the damage amplification effect formula in step b... ;

[0256] The nonlinearity of the damage amplification effect is calibrated;

[0257] Material degradation index:

[0258] Referring to the dynamic cohesion reduction formula in Example 2 In and the formula for reducing the dynamic internal friction coefficient In ;

[0259] Calibrated cumulative damage Leading to macroeconomic intensity The rate of decrease; in a preferred embodiment, the global trigger threshold defined in Embodiment 4. It can also be regarded as a key model calibration parameter and included in the parameter list of this embodiment;

[0260] Calibrate the switching sensitivity of the global-local layered decoupling architecture;

[0261] At this time, The parameters should be the same as With all parameters together, the model calibration module defined in Example 6 uses a data assimilation algorithm for iterative optimization and calibration;

[0262] This embodiment clarifies the optimization objective of the data assimilation algorithm in Embodiment 6; by selecting these coefficients that have the greatest impact on the dynamic behavior of the model and are most critical in physical meaning. Defined as calibrable parameters, this ensures that the model calibration module can accurately calibrate all the core innovative formulas in this invention, such as damage mapping, damage amplification, and intensity reduction, thereby making the entire prediction model approximate the physical reality of the field to the greatest extent possible.

[0263] Example 8:

[0264] A method for monitoring ground subsidence micromotion includes:

[0265] The micro-seismic event interpretation module interprets micro-seismic events in real time and outputs seismic moments.

[0266] The damage-stress coupling evolution module receives the seismic moment and calculates the current cumulative damage factor, real-time shear stress, and real-time normal stress based on the historical cumulative damage factor maintained by the module.

[0267] The risk probability prediction module receives the current cumulative damage factor, real-time shear stress, real-time normal stress, and initial mechanical parameters provided by the geomechanical model construction module, and calculates the time-varying instability risk index and instability probability.

[0268] This embodiment provides a method for monitoring ground subsidence micro-motions, which is applied to a ground subsidence micro-motion monitoring system as described in any of Embodiments 1 to 7; this method is the core processing flow during system operation.

[0269] The methods include:

[0270] The micro-seismic event interpretation module interprets micro-seismic events in real time and outputs seismic moments.

[0271] This step corresponds to the function of the MEM module in Example 1.

[0272] The damage-stress coupling evolution module receives the seismic moment and calculates the current cumulative damage factor, real-time shear stress, and real-time normal stress based on the historical cumulative damage factor maintained by the module.

[0273] This step corresponds to the function of the DSEM module in Example 1.

[0274] The risk probability prediction module receives the current cumulative damage factor, real-time shear stress, real-time normal stress, and initial mechanical parameters provided by the geomechanical model construction module, and calculates the time-varying instability risk index and instability probability.

[0275] This step corresponds to the function of the RPPM module in Example 1.

[0276] The method provided in this embodiment constitutes a complete data processing and prediction closed loop through the above three core steps. It realizes the real-time transformation of high-frequency, discrete microseismic event data streams into quantitative and continuous damage and stress states, and outputs time-varying instability risk index and instability probability with clear physical meaning and decision-making value, thereby providing an effective physical mechanism-based prediction and early warning means for ground subsidence disasters.

[0277] Example 9:

[0278] The steps for calculating the current cumulative damage factor, real-time shear stress, and real-time normal stress include:

[0279] Calculate the foundation damage degree based on the seismic moment;

[0280] Based on the basic damage degree and historical cumulative damage factor, the damage amplification effect is applied to calculate the actual damage increment.

[0281] Update the historical cumulative damage factor based on the actual damage increment to generate the current cumulative damage factor;

[0282] Perform stress calculation steps to generate real-time shear stress and real-time normal stress;

[0283] The stress calculation steps specifically include:

[0284] Determine whether the current accumulated damage factor exceeds the preset global trigger threshold;

[0285] When the current cumulative damage factor does not exceed the global trigger threshold, a preset local stress transfer function is used to calculate the real-time shear stress and real-time normal stress.

[0286] When the current cumulative damage factor exceeds the global trigger threshold, the geomechanical model building module is triggered to perform a global recalculation to generate a global stress field;

[0287] It employs a global stress field to calibrate and output real-time shear stress and real-time normal stress.

[0288] Based on Example 8, this embodiment further specifies the steps of step 2, namely, calculating the current cumulative damage factor, real-time shear stress, and real-time normal stress.

[0289] The steps for calculating the current cumulative damage factor, real-time shear stress, and real-time normal stress include:

[0290] Calculate the foundation damage degree based on the seismic moment;

[0291] This step corresponds to step a in DSEM in Example 1, and adopts... formula

[0292] Based on the basic damage degree and historical cumulative damage factor, the damage amplification effect is applied to calculate the actual damage increment.

[0293] This step corresponds to step b of DSEM in Example 1, and adopts... formula

[0294] Update the historical cumulative damage factor based on the actual damage increment to generate the current cumulative damage factor;

[0295] This step corresponds to step c in DSEM in Example 1, and adopts... Update formula

[0296] Perform stress calculation steps to generate real-time shear stress and real-time normal stress;

[0297] Furthermore, the stress calculation step specifically includes:

[0298] Determine whether the global cumulative damage status exceeds the preset global trigger threshold;

[0299] When the current cumulative damage factor does not exceed the global trigger threshold, a preset local stress transfer function is used to calculate the real-time shear stress and real-time normal stress.

[0300] When the current cumulative damage factor exceeds the global trigger threshold, the geomechanical model building module is triggered to perform a global recalculation to generate a global stress field;

[0301] It employs a global stress field to calibrate and output real-time shear stress and real-time normal stress;

[0302] The method steps disclosed in this embodiment correspond completely to the system module functions in Embodiments 1 and 4; it specifies in detail the complete operation process of the evolution engine DSEM; by combining the two core method steps of damage amplification and global-local decoupling, this method simultaneously realizes nonlinear simulation of damage history correlation at the operational level, as well as balance between real-time performance and high accuracy, ensuring the accuracy and timeliness of the current cumulative damage factor and real-time stress output by the method.

[0303] Example 10:

[0304] The steps for calculating the time-varying instability risk index and instability probability specifically include:

[0305] Calculate the dynamic elastic modulus based on the current cumulative damage factor and initial mechanical parameters;

[0306] Calculate the dynamic cohesion based on the current cumulative damage factor and initial mechanical parameters;

[0307] Calculate the dynamic internal friction angle based on the current cumulative damage factor and initial mechanical parameters;

[0308] Determine the real-time load based on the real-time shear stress;

[0309] The real-time resistance is determined based on the real-time normal stress, dynamic cohesion, and dynamic internal friction angle.

[0310] Calculate the ratio of real-time load to real-time resistance to generate a time-varying instability risk index.

[0311] Based on Example 8, this embodiment further specifies the steps in step 3, namely, calculating the time-varying instability risk index and instability probability.

[0312] The steps for calculating the time-varying instability risk index and instability probability specifically include:

[0313] Calculate the dynamic elastic modulus based on the current cumulative damage factor and initial mechanical parameters;

[0314] This step corresponds to the one in Example 2. formula

[0315] Calculate the dynamic cohesion based on the current cumulative damage factor and initial mechanical parameters;

[0316] This step corresponds to the one in Example 2. formula

[0317] Calculate the dynamic internal friction angle based on the current cumulative damage factor and initial mechanical parameters;

[0318] This step corresponds to the one in Example 2. formula

[0319] Determine the real-time load based on the real-time shear stress;

[0320] This step corresponds to the one in Example 3.

[0321] The real-time resistance is determined based on the real-time normal stress, dynamic cohesion, and dynamic internal friction angle.

[0322] This step corresponds to the one in Example 3. formula

[0323] Calculate the ratio of real-time load to real-time resistance to generate a time-varying instability risk index;

[0324] This step corresponds to the one in Example 3. formula

[0325] The method steps disclosed in this embodiment completely correspond to the system module functions in Embodiments 2 and 3; it details the complete operation process of the decision-making center RPPM; by performing dynamic intensity reduction and risk index aggregation, this method realizes the transformation of abstract damage state into quantified macroscopic intensity at the operational level, and further dynamically couples real-time load with real-time resistance to calculate a single, clear... The early warning indicators provide a complete and executable algorithmic process for achieving accurate early warning.

[0326] 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. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A microseismic monitoring system for ground subsidence, characterized in that, include: The geomechanical model building module is used to build a three-dimensional geological mesh model and provide the initial stress field and initial mechanical parameters; The micro-seismic event interpretation module is used to interpret micro-seismic events in real time and output seismic moments; The damage-stress coupling evolution module is used to receive the seismic moment and, based on the historical cumulative damage factor maintained by the module, calculate the current cumulative damage factor, real-time shear stress, and real-time normal stress. The risk probability prediction module is used to receive the current cumulative damage factor, the real-time shear stress, the real-time normal stress, and the initial mechanical parameters, and calculate the time-varying instability risk index and instability probability. The damage-stress coupling evolution module is specifically used for: Calculate the foundation damage degree based on the seismic moment; Based on the baseline damage level and the historical cumulative damage factor, the damage amplification effect is applied to calculate the actual damage increment. The historical cumulative damage factor is updated based on the actual damage increment to generate the current cumulative damage factor; Based on the change in the current cumulative damage factor, a stress calculation step is performed to generate the real-time shear stress and the real-time normal stress.

2. The ground settlement micro-motion monitoring system according to claim 1, characterized in that, The risk probability prediction module is specifically used for: Receive the current cumulative damage factor and the initial mechanical parameters; Calculate the dynamic elastic modulus based on the current cumulative damage factor and the initial mechanical parameters; Calculate the dynamic cohesion based on the current cumulative damage factor and the initial mechanical parameters; Calculate the dynamic internal friction angle based on the current cumulative damage factor and the initial mechanical parameters.

3. The land subsidence micro-motion monitoring system according to claim 2, wherein, The risk probability prediction module is also specifically used for: Based on the real-time shear stress, the real-time load is determined; The real-time resistance is determined based on the real-time normal stress, the dynamic cohesion, and the dynamic internal friction angle. The ratio of the real-time load to the real-time resistance is calculated to generate the time-varying instability risk index.

4. The land subsidence micro-motion monitoring system of claim 2, wherein, The stress calculation step specifically includes: Determine whether the current accumulated damage factor exceeds a preset global trigger threshold; When the current cumulative damage factor does not exceed the global trigger threshold, a preset local stress transfer function is used to calculate the real-time shear stress and the real-time normal stress. When the current cumulative damage factor exceeds the global trigger threshold, the geomechanical model construction module is triggered to perform a global recalculation to generate a global stress field; The global stress field is used to calibrate and output the real-time shear stress and the real-time normal stress.

5. The land subsidence micro-motion monitoring system according to claim 4, wherein, When performing the global recalculation, the geomechanical model construction module is specifically used for: The dynamic elastic modulus calculated by the risk probability prediction module is used as the updated model stiffness for global recalculation.

6. The land subsidence micro-motion monitoring system of claim 2, wherein, The system also includes: The macroscopic response monitoring module is used to monitor the macroscopic displacement of the soil and rock mass to generate measured displacement data; The model calibration module is used for: Based on the dynamic elastic modulus, the displacement is predicted by the model through the geomechanical model construction module. Calculate the residual between the model-predicted displacement and the measured displacement data; A data assimilation algorithm is used to iteratively adjust the model calibration parameters in the damage-stress coupling evolution module and the risk probability prediction module to minimize the residual.

7. The ground settlement micro-monitoring system according to claim 6, characterized in that, The model calibration parameters include: The calibration coefficient, damage amplification coefficient, nonlinearity index, and material degradation index are related to the basic damage degree.

8. A method of monitoring ground settlement microseisms, characterized by, The method, applied to a ground subsidence micro-motion monitoring system as described in any one of claims 1 to 7, comprises: The micro-seismic event interpretation module interprets micro-seismic events in real time and outputs seismic moments. The seismic moment is received through the damage-stress coupling evolution module, and the current cumulative damage factor, real-time shear stress, and real-time normal stress are calculated based on the historical cumulative damage factor maintained by the module. The risk probability prediction module receives the current cumulative damage factor, the real-time shear stress, the real-time normal stress, and the initial mechanical parameters provided by the geomechanical model construction module, and calculates the time-varying instability risk index and instability probability.

9. A method for monitoring ground subsidence micro-motion according to claim 8, characterized in that, The steps for calculating the current cumulative damage factor, real-time shear stress, and real-time normal stress specifically include: Calculate the foundation damage degree based on the seismic moment; Based on the baseline damage level and the historical cumulative damage factor, the damage amplification effect is applied to calculate the actual damage increment. The historical cumulative damage factor is updated based on the actual damage increment to generate the current cumulative damage factor; Perform a stress calculation step to generate the real-time shear stress and the real-time normal stress; Specifically, the stress calculation step includes: Determine whether the current accumulated damage factor exceeds a preset global trigger threshold; When the current cumulative damage factor does not exceed the global trigger threshold, a preset local stress transfer function is used to calculate the real-time shear stress and the real-time normal stress. When the current cumulative damage factor exceeds the global trigger threshold, the geomechanical model construction module is triggered to perform a global recalculation to generate a global stress field; The global stress field is used to calibrate and output the real-time shear stress and the real-time normal stress.

10. A method for monitoring ground subsidence micro-motion according to claim 8, characterized in that, The steps for calculating the time-varying instability risk index and instability probability specifically include: Calculate the dynamic elastic modulus based on the current cumulative damage factor and the initial mechanical parameters; Calculate the dynamic cohesion based on the current cumulative damage factor and the initial mechanical parameters; Calculate the dynamic internal friction angle based on the current cumulative damage factor and the initial mechanical parameters; Based on the real-time shear stress, the real-time load is determined; The real-time resistance is determined based on the real-time normal stress, the dynamic cohesion, and the dynamic internal friction angle. The ratio of the real-time load to the real-time resistance is calculated to generate the time-varying instability risk index.

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