A method for simultaneous measurement and correlation analysis of surface displacement and seepage parameters

Abstract

This invention belongs to the field of surface displacement measurement technology, specifically disclosing a method for synchronous measurement and correlation analysis of surface displacement and seepage parameters. By deploying an initial measurement network with equal spacing on the area to be measured within the engineering construction zone, and then constructing a curvature-distance curve based on the measured displacement data and the distance to the baseline after construction begins, the spatial gradient change characteristics of surface displacement are effectively characterized. This allows for the partitioning of the area to be measured and the optimization of the network layout within each partition, realizing the transformation of the measurement network from static pre-setting to dynamic response optimization. After the monitoring network is optimized, spatiotemporal correlation analysis is conducted by synchronously collecting surface displacement data and adjacent groundwater level data at each measurement point. This enables accurate identification of the coordinated response characteristics from water level drop to increased surface displacement, improving the physical interpretability of surface displacement risk assessment, and thus enhancing the accuracy and pertinence of risk warning.

Description

Technical Field

[0001] This invention belongs to the field of surface displacement measurement technology, and specifically discloses a method for synchronous measurement and correlation analysis of surface displacement and seepage parameters. Background Technology

[0002] With the continuous growth in demand for infrastructure construction, the scale of engineering projects is constantly expanding. During construction, disturbance of the strata can easily trigger seepage, which in turn exacerbates the loss of soil particles and changes the effective stress distribution, leading to uneven deformation of the strata. In severe cases, this can cause secondary disasters such as ground subsidence. Therefore, measuring and providing early warning of surface displacement caused by construction is a key technical aspect of ensuring project safety.

[0003] However, existing measurement methods typically rely on prior geological models to spatially simulate surface displacement changes in order to delineate displacement influence zones and guide the layout of measurement points. While such methods can provide some spatial guidance during the design phase, they are essentially based on static assumptions and theoretical predictions, making it difficult to adapt to the actual deformation response characteristics during construction. Without fully considering the feedback from measured data, they can easily lead to an imbalance in the allocation of measurement resources: for example, insufficient measurement density in high-change areas and redundant point placement in low-change areas, resulting in low monitoring efficiency.

[0004] Secondly, in terms of data analysis and risk identification, the focus is mainly on the absolute value or rate of change of displacement. This single criterion cannot effectively distinguish the causes of displacement changes. The surface displacement caused by construction is a multi-factor coupled process. Relying solely on the displacement data itself, it is difficult to identify the displacement anomaly caused by the specific factor of water seepage from the complex deformation field, resulting in unclear early warning direction. Summary of the Invention

[0005] To solve the above-mentioned technical problems, or at least partially solve them, the present invention provides a method for synchronous measurement and correlation analysis of surface displacement and seepage parameters.

[0006] The objective of this invention can be achieved through the following technical solution: a method for synchronous measurement and correlation analysis of surface displacement and seepage parameters, comprising the following steps: S1, delineating the area to be measured along a preset baseline, and establishing an initial measurement network at equal intervals within the area to be measured.

[0007] S2. After construction begins, collect elevation data at each measurement point and compare it with the initial elevation to obtain surface displacement data. Based on the surface displacement data, perform curve fitting and differential processing to generate a curvature-distance curve.

[0008] S3. Based on the curvature characteristics of the curvature-distance curve, the area to be measured is divided into high-variance, medium-variance, and low-variance zones, and the initial measurement network is optimized in each zone.

[0009] S4. In the optimized measurement network, surface displacement data and adjacent groundwater level data of each measurement point are collected synchronously to construct a displacement-water level multi-source dataset.

[0010] S5. Use displacement-water level multi-source datasets to perform spatiotemporal correlation analysis to identify collaborative anomaly measurement points.

[0011] S6. Spatial clustering of collaborative abnormal measurement points generates anomaly clusters, and the spatial distribution range of the anomaly clusters is used as the seepage deformation risk zone, triggering an early warning.

[0012] Combining all the above technical solutions, the positive effects of this invention are as follows: 1. This invention lays out an initial measurement network with equal spacing on the area to be measured in the engineering construction area, and then constructs a curvature-distance curve based on the measured displacement data and the distance to the baseline after the start of construction, effectively characterizing the spatial gradient change characteristics of the ground surface displacement. This allows the area to be measured to be divided into zones and the network layout to be optimized in each zone, realizing the transformation of the measurement network from static preset to dynamic response optimization, which is closer to the actual ground surface deformation morphology. It can improve measurement efficiency and reduce measurement costs while ensuring monitoring accuracy.

[0013] 2. After optimizing the measurement network, this invention synchronously collects surface displacement and adjacent groundwater level data from each measurement point, constructs a multi-source time-series dataset, and conducts spatiotemporal correlation analysis. This enables the accurate identification of the coordinated response characteristics from water level drop to increased surface displacement, effectively distinguishes displacement changes induced by seepage from other construction disturbances, improves the physical interpretability of surface displacement risk assessment, and thus enhances the accuracy and pertinence of risk warning.

[0014] 3. After identifying the abnormal surface displacement measurement points caused by seepage, this invention performs spatial cluster analysis on the abnormal points to form a cluster of abnormal points with spatial continuity, and delineates the seepage deformation risk zone accordingly. This can integrate discrete abnormal signals into regionalized risk units, which is conducive to improving the reliability of risk identification and providing spatial basis for risk prevention and control. Attached Figure Description

[0015] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0016] Figure 1 This is a diagram illustrating the implementation steps of the method of the present invention.

[0017] Figure 2This is a flowchart illustrating the process of dividing the area to be measured into high-variance, medium-variance, and low-variance zones based on the curvature characteristics of the curvature-distance curve in this invention.

[0018] Figure 3 This is a flowchart illustrating the operation of generating anomaly clusters by spatial clustering of collaborative anomaly measurement points in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] See Figure 1 As shown, the present invention proposes a method for synchronous measurement and correlation analysis of surface displacement and seepage parameters, including the following steps: S1, delineate the area to be measured along a preset baseline, and establish an initial measurement network at equal intervals within the area to be measured.

[0021] Considering that the changes in ground surface displacement caused by engineering construction have spatial limitations and are mainly concentrated in a certain range directly above the ground surface, delineating the area to be measured can define the geographical boundary of the measurement work, prevent the measurement points from spreading excessively to irrelevant areas, and improve the targeting of the network.

[0022] Based on this, the process of delineating the area to be measured is as follows: Extend a predetermined horizontal distance to both sides along a preset baseline. The baseline refers to the central axis of the engineering structure in space, which is usually the geometric center line in the design drawings that indicates the direction of engineering construction. It is the reference line for engineering construction surveying and layout, forming a rectangular monitoring area symmetrically distributed with the baseline as the center as the area to be measured.

[0023] Generally, the maximum value of surface displacement changes is located directly above the engineering structure, and its lateral influence range is approximately symmetrically distributed along the baseline. Based on this characteristic, the area to be measured is delineated by extending laterally to both sides of the baseline by a horizontal distance proportional to the engineering depth or span, thus ensuring that the lateral boundary of the area to be measured is outside the main displacement influence range, forming a measurement coverage area with sufficient safety redundancy.

[0024] Furthermore, the purpose of establishing an initial measurement network at equal intervals within the area to be measured is as follows: In the early stages of construction when measured displacement information is lacking, the equally spaced measurement points ensure that the measurement network provides uniform and complete coverage of the entire area to be measured, avoiding missing deformation areas due to sparse point placement, and improving the spatial representativeness of the data. At the same time, the initial measurement network serves as the basic framework for subsequent data acquisition, providing an initial data source for obtaining the measured surface displacement sequence during the construction process.

[0025] S2. After construction begins, collect elevation data at each measurement point and compare it with the initial elevation to obtain surface displacement data. The initial elevation refers to the original surface elevation value of each measurement point before construction begins. Based on the surface displacement data, perform curve fitting and differential processing to generate a curvature-distance curve.

[0026] The surface displacement data of the measurement points obtained through S2 refers to the cumulative change in surface displacement of the measurement points since the start of construction, which truly reflects the actual development state of surface displacement under construction disturbance.

[0027] Since the actual measurement points are discretely distributed, while surface displacement is a continuous spatial process, in order to obtain continuous displacement field characteristics from discrete data, this invention uses a numerical fitting method to fit the displacement data of each measurement point into a continuous displacement-distance curve along a baseline.

[0028] In a specific implementation of the present invention, the curve fitting and differential processing based on surface displacement data to generate a curvature-distance curve includes the following: S21, using the surface displacement data of each measurement point as the vertical axis and the distance along the baseline as the horizontal axis, numerical fitting is used to fit the discrete measurement point data into a continuous displacement-distance curve.

[0029] Specifically, the distance of each measurement point along the baseline refers to the arc length or projected distance from the plane projection position of each measurement point on the ground surface to the starting point of the baseline along the direction of the baseline, reflecting the relative position of the measurement point in the longitudinal space of the engineering structure. For example, if a measurement point faces the design mileage of the section where the project is located at K3+150, where K3 represents the position of the line mileage at 3000 meters, then its distance along the baseline is 3150m.

[0030] S22. In actual engineering, the area to be measured is a two-dimensional strip-shaped area extending along the baseline. The measurement points are usually arranged in a grid array, so that the measurement points are not just arranged on a single line directly above the baseline, but multiple cross-sections are symmetrically arranged on both sides of the baseline to form multiple transverse measurement lines. Based on this, a displacement-distance curve is fitted on each transverse measurement line according to the surface displacement data of the measurement points and their distance along the baseline, generating multiple displacement-distance curves along the baseline.

[0031] S23. Given that the fitted displacement-distance curve is a planar curve, its first derivative represents the slope of the curve, and its second derivative represents the rate of change of the slope. The curvature of the curve, i.e., the quantitative index of its bending strength, can be mathematically calculated and characterized through the second derivative of the curve. Based on this, by calculating the second derivative of the fitted displacement-distance curve, a curvature-distance curve that directly reflects the spatial distribution of the degree of surface bending deformation can be obtained.

[0032] The curvature-distance curve generated above is essentially a spatial response function of the longitudinal curvature of the Earth's surface. It can effectively characterize the gradient distribution of displacement changes in the area to be measured, providing a quantitative basis for dividing measurement zones and optimizing network layout strategies.

[0033] S3. Based on the curvature characteristics of the curvature-distance curve, the area to be measured is divided into high-variance, medium-variance, and low-variance zones, and the initial measurement network is optimized in each zone.

[0034] Given that surface displacement caused by construction often exhibits a nonlinear distribution pattern with a large center and smaller sides in the longitudinal profile, it can be analogous to the flexural deformation of an elastic foundation beam under localized loads. According to structural mechanics theory, the curvature of a beam's deflection curve directly reflects its internal bending moment distribution and the bending stress state of the material. Applying this principle to surface deformation analysis, the curvature of the displacement curve similarly reveals the spatial distribution differences of bending stress on the surface soil. Sections with larger absolute curvature values ​​indicate steeper deformation gradients and more severe uneven deformation. Therefore, the curvature amplitude and variation characteristics of the curvature-distance curve can be used to quantitatively divide the risk areas of surface deformation.

[0035] See Figure 2 As shown, based on this, the area to be measured is divided into high variation zone, medium variation zone and low variation zone according to the curvature characteristics of the curvature-distance curve. See the following process: S31, capture the point on the curvature-distance curve where the absolute value of curvature reaches the global maximum value as the curvature peak point, and extract its local extreme point as the curvature inflection point by calculating the first derivative of curvature.

[0036] Understandably, the peak curvature point in the curvature-distance curve corresponds to the location where the surface bending is most intense; the curvature inflection point indicates the turning point where the surface deformation transitions from accelerated deformation to gradual deformation. This point marks the spatial boundary characteristics of the deformation influence range. Based on these geometric feature points, partitioning can divide the continuous area to be measured into spatial units of risk level.

[0037] S32. Taking the peak curvature point as the center, extend along both sides of the baseline to the horizontal positions corresponding to the adjacent left and right curvature inflection points to form a continuous spatial interval along the baseline. This interval is determined to be a high-change zone. The curvature amplitude is significant, the longitudinal bending deformation of the ground surface is concentrated, and the uneven deformation characteristics are prominent. Therefore, it is determined to be a high-change zone and high-density measurement needs to be carried out.

[0038] S33. The area between adjacent curvature inflection points outside the high-variation zone is defined as the medium-variation zone. This zone is located in the transition zone between the main deformation zone and the far-field zone. Although the deformation gradient has decreased, it still has a certain rate of change and has the potential to develop further as construction progresses. Effective measurements need to be maintained to track the deformation expansion.

[0039] S34. In the curvature-distance curve, the area outside the outermost curvature inflection point is designated as a low-variation zone. In this zone, the curvature value approaches zero, the deformation space changes gently, the surface deformation tends to be uniform, and it is far from the current construction disturbance range, with a low risk of significant structural damage. In this zone, the measurement density can be appropriately reduced to optimize resource allocation.

[0040] As a further way to achieve the above scheme, the initial measurement network is optimized in each partition as follows: in the high-variable region, the spacing between measurement points is set to half of the measurement spacing in the initial measurement network, and a high-resolution measurement array is formed by doubling the measurement density.

[0041] In the intermediate change zone, the initial measurement point spacing is kept constant, and the original layout interval is used to ensure basic coverage and stable measurement of the transition deformation zone.

[0042] In regions of low variation, the spacing between measurement points is set to a multiple of the initial measurement spacing in the measurement network. In the example, the multiple is 2, halving the measurement density to implement a sparser layout, thereby reducing measurement costs and ensuring boundary integrity.

[0043] This invention utilizes the mathematical feature points of the curvature-distance curve to adaptively identify the spatial pattern of deformation response based on the differential geometric characteristics of surface deformation within the measurement area. Furthermore, a hierarchical network deployment strategy is constructed by combining the deformation measurement sensitivity requirements of each zone, achieving a gradient allocation of measurement resources in space. This ensures measurement accuracy and early warning capabilities in high-risk areas while avoiding resource redundancy in low-impact areas.

[0044] S4. In the optimized measurement network, surface displacement data and adjacent groundwater level data of each measurement point are collected synchronously to construct a displacement-water level multi-source dataset.

[0045] In the above scheme, the groundwater level data of the vicinity of each measurement point is collected by groundwater level observation wells installed on the ground surface. The specific collection process is as follows: First, observation wells are drilled near each measurement point, and then electronic water level gauges are lowered into the wells to the designed depth.

[0046] Finally, a time step consistent with the displacement measurement is set, such as once per hour, to synchronously acquire the surface displacement of each measurement point and the groundwater depth of the corresponding adjacent well.

[0047] The above-mentioned method of collecting groundwater level data while conducting surface displacement measurements at various measurement points is used to reveal the spatiotemporal coupling relationship between surface displacement changes and groundwater changes, providing a basis for synchronously aligned multi-source observation data for subsequent spatiotemporal correlation analysis.

[0048] S5. Use displacement-water level multi-source datasets to perform spatiotemporal correlation analysis to identify collaborative anomaly measurement points.

[0049] In the optional implementation of the above scheme, the spatiotemporal correlation analysis using the displacement-water level multi-source dataset is performed as follows: S51, the measurement points are arranged in an orderly manner according to the mileage along the baseline direction in each zone.

[0050] The above-mentioned arrangement of measurement points along the baseline aims to establish a longitudinally continuous topological structure between measurement points, restore the true relative positions of measurement points on the engineering baseline, and provide a spatial reference for the spatial evolution of surface deformation.

[0051] S52. During the observation period, the surface displacement sequence and the adjacent groundwater level depth sequence of each measurement point are extracted simultaneously.

[0052] S53. Divide the time series into consecutive adjacent time pairs, and calculate the change in surface displacement and groundwater level for each adjacent time pair. When the change in groundwater level is negative and the change in displacement is positive for an adjacent time pair, it indicates that the groundwater level has dropped and the surface deformation has intensified during that period. It can be determined that there is a potential water-settlement coupling effect during that period, which is consistent with the coordinated response characteristics of the drop in water level to the intensification of deformation. This response mode is distinguishable from deformation caused by construction disturbance or soil rheology alone, and excludes deformation interference dominated by non-seepage factors. Then, the later time in the adjacent time pair is recorded as the coordinated mutation time point, which is used to characterize deformation mutation events that may be driven by groundwater seepage.

[0053] In the innovative implementation of the above scheme, considering that the decline in groundwater level is a leading factor in deformation development, its impact on surface deformation is not completely synchronous and may have a certain hydraulic lag effect. This means that the adjacent time pair corresponding to the decline in groundwater level is not the same as the adjacent time pair corresponding to the intensification of deformation. Therefore, a time lag step can be set, such as 1 to 3 sampling intervals. When the time interval between the adjacent time pair corresponding to the negative change in groundwater level and the adjacent time pair corresponding to the positive change in deformation meets the time lag step, the end time in the later adjacent time pair can also be recorded as the co-mutation time point. This significantly improves the ability to identify the risk of slow-change seepage-induced subsidence.

[0054] S54. Calculate the percentage of time points with coordinated mutations at each measurement point during the entire observation period, and compare it with the percentage limit. If the percentage limit is reached, the measurement point is recorded as a time-coordinated abnormal measurement point.

[0055] Considering that a single coordinated mutation event may be caused by short-term environmental disturbances or measurement noise, and is random and non-continuous, it is difficult to characterize stable seepage deformation coupling behavior. Therefore, judging based solely on a single mutation is prone to misjudgment. By analyzing the proportion of coordinated mutation time points occurring at each measurement point during the observation period, only when the proportion reaches or exceeds the limit is it determined that the measurement point has a continuous, high-frequency water seepage deformation coordinated response.

[0056] The aforementioned percentage limit can be set to a time series percentage exceeding 50%. When the percentage of time points with coordinated mutations at a certain measurement point reaches or exceeds this threshold, it indicates that the point exhibits a synchronous response characteristic of groundwater level decline and surface deformation intensification for more than half of the observation period. This reflects a highly frequent and continuous coupling relationship between seepage and deformation, indicating significant systematic abnormal behavior rather than occasional disturbances.

[0057] S55. Define adjacent measurement point pairs in the ordered sequence of measurement points. If both adjacent measurement point pairs are identified as time-coordinated anomalous measurement points, calculate the time difference between the occurrence of the dominant coordinated mutation event between the later measurement point and the earlier measurement point in the adjacent measurement point pair.

[0058] It is important to understand that the master-controlled coordinated mutation event reflects the representative moment of the water seepage deformation coordinated behavior that occurs most frequently and has the most concentrated response at a certain time point during the observation period.

[0059] In the innovative implementation of the above scheme, the process of determining the main co-progressive mutation event is as follows: the observation period is divided into time windows of equal length.

[0060] For the identified time-coordinated anomaly measurement points, all coordinated mutation time points identified during the observation period are mapped to the corresponding time windows.

[0061] Count the number of co-mutation time points that occur within each window.

[0062] Extract the time window with the highest frequency of co-mutation time points, and take the co-mutation time point at the center of the window as the controlling co-mutation event.

[0063] When adjacent measurement point pairs are both determined to be time-coordinated abnormal measurement points, given that engineering construction has a clear characteristic of advancing along the baseline direction, the engineering excavation disturbance and the accompanying seepage field evolution usually exhibit orderly transmission characteristics along the baseline direction.

[0064] In this context, identifying only single-point anomalies is insufficient to reveal the spatial expansion mechanism of risks. Further spatiotemporal correlation analysis is needed. By extracting the occurrence times of the controlling events at each point and calculating the time difference between them, it can be determined whether the anomaly response exhibits an orderly spatiotemporal evolution pattern of forward or backward movement along the baseline. If the controlling event at a later point occurs immediately after the one at the earlier point, and the time difference is small, it indicates that the seepage deformation anomaly propagates along the baseline direction, i.e., the direction of construction progress, which conforms to the physical propagation law of construction disturbance. Therefore, identifying controlling coordinated abrupt events is a crucial step in achieving a leap from single-point anomaly identification to multi-point coordinated, spatially chained risk identification.

[0065] S56. If the time difference between the occurrence of the two main co-mutation events simultaneously meets the following conditions, then the point pair is determined to constitute a spatial continuous anomaly unit, and the two measurement points are simultaneously marked as spatial co-mutation anomaly measurement points.

[0066] i) The time difference between the occurrence of the master co-mutation event is less than or equal to the time difference threshold.

[0067] ii) The time difference between the occurrence of the master co-mutation event is greater than zero.

[0068] Condition i) above reflects that the occurrence of a controlling cooperative mutation event between adjacent measurement points must satisfy temporal tightness and response continuity.

[0069] Condition ii) reflects that the occurrence of the master-controlled coordinated mutation event has a clear temporal sequence, that is, the abnormal response of the subsequent measurement point occurs later than that of the previous point, which reflects the spatiotemporal evolution characteristics of the abnormal signal being transmitted in an orderly manner from upstream to downstream along the baseline.

[0070] As a time correlation criterion in condition i), the time difference threshold is used to determine whether the main co-current mutation events between adjacent measurement points occur sequentially within a reasonable time scale. Specifically, the theoretical propagation time can be calculated based on the construction progress speed and the distance between measurement points.

[0071] In one application example, let the distance between adjacent measurement points be... The average construction speed is The theoretical time for the construction work face to propagate from the upstream point to the downstream point is: This time can be used as a reference benchmark for the time difference threshold. The actual threshold can be taken as 1.5 to 2 times the theoretical time to accommodate the seepage hysteresis effect of the formation response.

[0072] Measurement points that simultaneously satisfy both temporal and spatial coordination anomalies are identified as coordination anomaly measurement points.

[0073] This invention significantly enhances the ability to identify the coupling mechanism of seepage-induced deformation by constructing a spatiotemporally matched multi-source dataset of displacement and water level and designing a collaborative response criterion with directional characteristics. On this basis, in the process of identifying abnormal measurement points by utilizing the dynamic temporal correlation between surface displacement and groundwater level, a dual verification mechanism of time and space dimensions is introduced to achieve a leap from single-point isolated anomalies to multi-point collaborative evolution of risk identification.

[0074] S6. Spatial clustering of collaborative abnormal measurement points generates anomaly clusters, and the spatial distribution range of the anomaly clusters is used as the seepage deformation risk zone, triggering an early warning.

[0075] See Figure 3 As shown, spatial clustering analysis is performed on the collaborative anomaly measurement points to generate anomaly point clusters as follows: the locations of all measurement points identified as collaborative anomalies are extracted, and spatial clustering algorithms are used to classify them.

[0076] Based on the clustering results, multiple candidate point clusters composed of collaborative anomaly measurement points are generated, each cluster representing a potential high-risk response area with spatial connectivity.

[0077] To eliminate random interference, the clustering results need to be screened for physical rationality. Therefore, the following criteria are set: a lower limit for the number of measurement points within a cluster and a judgment criterion for the spatial geometry of the cluster are set.

[0078] The lower limit for the number of measurement points is recommended to be 3 to ensure statistical significance.

[0079] The criteria for determining the spatial geometry of the cluster are as follows: The spatial distribution of measurement points within the cluster satisfies any of the following conditions: (1) The spatial distribution outline of the cluster is strip-shaped, and its extension direction is parallel to the baseline direction.

[0080] (2) The maximum Euclidean distance between all point pairs in the cluster does not exceed the distance threshold. The distance threshold can be set according to the characteristic scale of the project, such as span, diameter or excavation width. Its essence is to control the boundary of the spatial scale of abnormal response and ensure that the identified clusters are located in the same mechanical domain.

[0081] The spatial continuity distribution reflected in condition (1) is based on the high consistency between this morphology and the spatial pattern of engineering construction disturbance and seepage field diffusion. In actual engineering, engineering construction often forms linear or strip-shaped influence zones along the baseline direction. Therefore, the axial strip-shaped arrangement of anomalies indicates that its response has a clear spatial orientation and propagation path, which is consistent with the physical mechanism of the disturbance source-response zone expanding along the baseline direction, and is a significant characterization of spatial continuity.

[0082] Condition (2) embodies the spatial continuity distribution based on the mathematical expression of spatial proximity constraints. This condition ensures that the distance between any two points within a cluster is within the local scale range, avoiding the misclassification of cross-regional, disconnected discrete points as the same risk unit.

[0083] The establishment of the above-mentioned criteria for determining the spatial geometry of clusters aims to eliminate isolated, discrete, or randomly distributed misjudged point groups based on spatial distribution characteristics, and to ensure that the identified anomalous clusters reflect deformation responses controlled by common disturbances.

[0084] After analyzing each candidate point cluster generated by clustering, the candidate point clusters that simultaneously meet the requirements for the number of measurement points and the spatial geometric shape are identified as outlier point clusters.

[0085] This invention utilizes a set of previously identified collaborative anomaly measurement points to conduct spatial clustering analysis, revealing the spatial aggregation characteristics and distribution patterns of seepage deformation anomalies, which serves as the basis for subsequent risk zone boundary delineation and early warning triggering.

[0086] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0087] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0088] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0089] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0090] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simultaneous measurement and correlation analysis of surface displacement and seepage parameters, characterized in that, include: The area to be measured is delineated along a preset baseline, and an initial measurement network is established at equal intervals within the area to be measured. After construction begins, elevation data of each measurement point is collected and compared with the initial elevation to obtain surface displacement data. Curve fitting and differential processing are performed on the surface displacement data to generate curvature-distance curves. Based on the curvature characteristics of the curvature-distance curve, the area to be measured is divided into high-variance, medium-variance, and low-variance zones, and the initial measurement network is optimized within each zone. In the optimized measurement network, surface displacement data and adjacent groundwater level data of each measurement point are collected synchronously to construct a displacement-water level multi-source dataset. Spatiotemporal correlation analysis was performed using displacement-water level multi-source datasets to identify collaboratively anomalous measurement points; Within each zone, measurement points are arranged in an orderly manner along the baseline direction according to mileage. During the observation period, the surface displacement sequence and the adjacent groundwater level depth sequence of each measurement point are extracted simultaneously. The time series is divided into consecutive adjacent time pairs, and the surface displacement change and groundwater level change of each adjacent time pair are calculated. When the groundwater level change of an adjacent time pair is negative and the surface displacement change is positive, the later time in that adjacent time pair is recorded as the co-mutation time point. The proportion of co-mutation time points for each measurement point throughout the entire observation period is statistically analyzed and compared with the proportion limit. If... If the proportion limit is reached, the measurement point is recorded as a time-coordinated anomalous measurement point; in the ordered sequence of measurement points, adjacent measurement point pairs are defined. If both adjacent measurement point pairs are identified as time-coordinated anomalous measurement points, the occurrence time difference of the dominant coordinated mutation event between the later and earlier measurement points in the adjacent measurement point pair is calculated; if the occurrence time difference of the dominant coordinated mutation events of the two points simultaneously meets the following conditions, the point pair is determined to constitute a spatial continuous anomalous unit, and both measurement points are simultaneously marked as spatial coordinated anomalous measurement points; i) The occurrence time difference of the dominant coordinated mutation event is less than or equal to the time difference threshold; ii) The time difference between the occurrence of the master-controlled cooperative mutation event is greater than zero; The observation period is divided into equal-length time windows; for the identified time-coordinated anomaly measurement points, all coordinated mutation time points identified during the observation period are mapped to the corresponding time windows; the number of coordinated mutation time points occurring in each window is counted; the time window with the highest frequency of coordinated mutation time points is extracted, and the coordinated mutation time point at the center of the window is taken as the master coordinated mutation event. Spatial clustering of collaborative anomaly measurement points generates anomaly point clusters, and the spatial distribution range of the anomaly point clusters is used as the seepage deformation risk zone, triggering an early warning.

2. The method for simultaneous measurement and correlation analysis of surface displacement and seepage parameters as described in claim 1, characterized in that: The process of defining the area to be measured along a preset baseline and establishing an initial measurement network at equal intervals within the area to be measured is as follows: Extend a predetermined horizontal distance to both sides along a preset baseline to form a rectangular monitoring area symmetrically distributed with the baseline as the center, which serves as the area to be measured.

3. The method for simultaneous measurement and correlation analysis of surface displacement and seepage parameters as described in claim 1, characterized in that: The curve fitting and differentiation processing based on surface displacement data to generate curvature-distance curves includes the following: Using the surface displacement data of each measurement point as the vertical axis and the distance along the baseline as the horizontal axis, numerical fitting is used to fit the discrete measurement point data into a continuous displacement-distance curve. The second derivative of the fitted displacement-distance curve is calculated to obtain the curvature-distance curve that reflects the degree of surface bending deformation.

4. The method for simultaneous measurement and correlation analysis of surface displacement and seepage parameters as described in claim 1, characterized in that: The process of dividing the area to be measured into high-variance, medium-variance, and low-variance zones based on the curvature characteristics of the curvature-distance curve is described below: On the curvature-distance curve, capture the point where the absolute value of curvature reaches the global maximum as the curvature peak point, and extract its local extreme points by calculating the first derivative of curvature as the curvature inflection point. Centered on the peak curvature point, extending along the baseline to the horizontal positions corresponding to the adjacent left and right curvature inflection points, a continuous spatial interval extending along the baseline is formed, which is determined to be a high-variation zone. The region between adjacent curvature inflection points outside the high-variation region is defined as the medium-variation region; In the curvature-distance curve, the area outside the outermost curvature inflection point is designated as the low-variation zone.

5. The method for simultaneous measurement and correlation analysis of surface displacement and seepage parameters as described in claim 1, characterized in that: The optimization of the initial measurement network within each partition is performed as follows: In areas of high variation, the spacing between measurement points is set to half the spacing between measurement points in the initial measurement network; In the variable region, the initial spacing between measurement points is kept constant; In the low-variety region, the spacing between measurement points is set to a multiple of the measurement spacing in the initial measurement network.

6. The method for simultaneous measurement and correlation analysis of surface displacement and seepage parameters as described in claim 1, characterized in that: The process of spatially clustering collaborative anomaly measurement points to generate anomaly point clusters is as follows: The locations of all measurement points identified as collaborative anomalies were extracted, and spatial clustering algorithms were used to classify them. Based on the clustering results, multiple candidate point clusters composed of collaborative anomaly measurement points are generated; Establish criteria for determining the lower limit of the number of measurement points within a cluster and the spatial geometry of the cluster; After analyzing each candidate point cluster generated by clustering, the candidate point clusters that simultaneously meet the requirements for the number of measurement points and the spatial geometric shape are identified as outlier point clusters.

7. The method for simultaneous measurement and correlation analysis of surface displacement and seepage parameters as described in claim 6, characterized in that: The criteria for determining the spatial geometry of the cluster are as follows: The spatial distribution of measurement points within the cluster satisfies any of the following conditions: (1) The spatial distribution outline of the cluster is strip-shaped, and its extension direction is parallel to the baseline direction; (2) The maximum Euclidean distance between all point pairs in the cluster does not exceed the distance threshold.

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