Microseismic and insar fusion-based slope sliding surface identification and prediction method

Abstract

The application provides a microseismic and InSAR fusion slope sliding surface identification and prediction method, and the technical scheme comprises the following steps: S1, obtaining surface deformation through InSAR time series analysis; S2, continuously monitoring through a microseismic monitoring network; S3, three-dimensional positioning and spatial feature extraction of microseismic events; S4, spatial fitting analysis of microseismic events and surface deformation; S5, identifying a potential sliding surface; and S6, describing the geometric features of the sliding surface and determining the development stage. Through the depth space fusion analysis of the spatial distribution of microseismic events and the InSAR surface deformation field, an independent cross-validation framework is established, the reliability and credibility of the identification result of the potential sliding surface are significantly improved, the main deformation area revealed by InSAR is used as a spatial constraint condition, the interpretation direction of the microseismic data is effectively guided, the misjudgment risk caused by the dimensional limitation of the traditional single technical method is avoided, and the analysis accuracy is significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological disaster monitoring and early warning, in particular to a microseismic and InSAR (Interferometric Synthetic Aperture Radar) fusion slope sliding surface identification and prediction method, which is suitable for slope sliding surface identification and prediction under various complex geological conditions. BACKGROUND

[0002] Slope stability assessment is one of the key technologies in the geological disaster prevention system. Its core lies in accurately identifying the spatial position and three-dimensional geometric feature of the potential sliding surface inside the slope, so as to realize scientific prediction and effective early warning of landslide disasters.

[0003] Microseismic monitoring technology can realize dynamic and real-time perception of the progressive failure process inside the slope by capturing the elastic wave signals released by the rock mass during the deformation and failure process. However, the practical application of this technology in complex geological environments faces significant bottlenecks: due to factors such as velocity structure uncertainty, topographic effects and signal interference, the accuracy of traditional microseismic positioning methods often cannot meet the needs of fine depiction of the sliding surface, and the positioning results have a large spatial dispersion, which restricts the accurate depiction of the spatial distribution characteristics of the sliding surface.

[0004] InSAR technology has become a powerful tool for obtaining regional surface deformation information due to its advantages of large range, non-contact and spatial continuous coverage, and can effectively identify the main surface deformation area and deformation trend of the slope. However, InSAR technology is essentially a surface observation method, and the displacement information obtained by it reflects the comprehensive deformation result of the surface, and cannot directly reveal the depth and shape of the internal sliding and failure surface of the slope.

[0005] In existing technical practices, microseismic monitoring and InSAR technology are usually applied independently, and there is a lack of deep collaborative analysis framework. This single application mode leads to two limitations: on the one hand, the spatial reliability and accuracy of the microseismic positioning results cannot be fully evaluated due to the lack of external effective constraints and verification; on the other hand, the rich surface deformation information revealed by InSAR cannot be effectively utilized to guide and constrain the spatial search and identification process of the deep sliding surface.

[0006] Therefore, in order to break through the inherent limitations of single technical means, it is necessary to develop an innovative technology fusion method to combine the analysis of internal failure process by microseismic monitoring with the judgment of surface deformation field by InSAR. By constructing a deep correlation and mutual verification mechanism between the data of the two, the spatial position and geometric feature of the potential sliding surface of the slope are collaboratively inverted and accurately identified, thereby significantly improving the accuracy and reliability of the slope stability assessment results, and providing a more solid scientific basis for landslide disaster risk management. SUMMARY

[0007] The purpose of the present application is to provide a microseismic and InSAR fusion slope sliding surface identification and prediction method, which verifies the accuracy of microseismic positioning results by fitting and analyzing the spatial distribution of microseismic events and the main deformation area of InSAR, and identifies and characterizes the sliding surface inside the slope based on the three-dimensional spatial distribution characteristics of microseismic events.

[0008] To achieve the above purpose, the present application provides the following technical scheme, specifically including the following six steps:

[0009] S1: InSAR time series analysis and surface deformation acquisition. Using InSAR time series analysis technology, select appropriate time span to ensure annual scale analysis accuracy, process radar satellite image sequence covering the monitoring area. Combined with the geological background of the study area, set the displacement threshold (which can be dynamically adjusted according to the lithological characteristics), through image registration, interferogram generation, phase unwrapping and other processes, obtain the cumulative ground displacement field with certain spatial resolution, identify the spatial range and deformation order of the main deformation area. Usually, small baseline set (SBAS) technology is used to process multi-temporal SAR images covering the monitoring area, and the calculation formula is:

[0010]

[0011] In the formula, φ 1 and φ 2 are the interference phases obtained at the imaging time of the two SAR images respectively; λ is the radar wavelength; d 1 and d 2 are the displacement amounts of the ground objects at the imaging time of the two SAR images respectively; Δφ toperror is the residual topographic phase error; φ atm is the atmospheric delay phase; φ noi is the noise phase.

[0012] S2: Lay out microseismic monitoring network and continuously monitor. According to the position of the main deformation area determined by InSAR, scientifically lay out the microseismic monitoring network in the target slope area, and implement continuous and real-time microseismic signal acquisition. The microseismic monitoring network includes long-term continuous monitoring stations and short-term intensive monitoring stations. The short-term intensive monitoring stations need to be densely laid out with a spacing of about 50 m, and collect environmental noise signals for more than 72 hours for inversion of high-resolution three-dimensional velocity structure. The long-term monitoring system consists of multiple fixed stations to form a full-enclosure monitoring network, and uses high-precision data acquisition instruments to realize all-weather real-time monitoring and positioning of microseismic events.

[0013] S3: Microseismic event 3D location and spatial feature extraction. The collected microseismic events are processed for accurate 3D location to obtain the spatial 3D coordinates and distribution characteristics of the microseismic events. Specifically, it includes: identifying effective microseismic events based on the signal-to-noise ratio and power spectral density double criteria, and eliminating teleseisms and environmental noise; extracting the arrival time difference between each station pair through waveform cross-correlation analysis, with an accuracy of milliseconds; constructing a theoretical travel time model considering complex stratum velocity structure and topographic effect; using a grid search algorithm to determine the source location that minimizes the theoretical and actual arrival time difference residual, and the relevant positioning formula is as follows:

[0014]

[0015] wherein, ΔT theo x,y,z,S i ,S j is the theoretical arrival time difference of the grid node ( x,y,z ) to the station pair ( S i ,S j ) in the theoretical arrival time difference database; ΔT obs S i ,S j is the actual observed arrival time difference of the station pair obtained by cross-correlation analysis; the weight w ij is obtained by the following formula:

[0016]

[0017] wherein, GAP ij is the azimuth difference of the station pair relative to the source, SNR ij is the average signal-to-noise ratio of the station pair, CC ij is the maximum cross-correlation coefficient, α, β, γ is the weight coefficient, and its optimal value is determined by experience or experiment.

[0018] ​​S4: Spatial fitting analysis of microseismic events and surface deformation. The spatial distribution data of microseismic events obtained by three-dimensional positioning are superimposed and correlated with the main surface deformation area identified by InSAR. The spatial correlation degree between the two is quantitatively evaluated to verify whether the microseismic event distribution can effectively reflect the existence of the internal sliding surface of the slope. The specific method is as follows: the number of microseismic events in the main deformation area and the peripheral reference area is counted, the density ratio is calculated to determine whether the distribution difference is significant, and the correlation coefficient of the spatial distribution of microseismic events and the surface deformation intensity is calculated. When the microseismic event density in the main deformation area is significantly higher than that outside the area, and the spatial correlation coefficient shows strong correlation, it reflects that there is a close internal relationship between microseismic activity and surface deformation, the microseismic positioning result is reliable, the two monitoring methods are mutually verified, and the deep structural variation mechanism affecting surface deformation is revealed. If the correlation is not significant, it indicates that the microseismic event cannot effectively reflect the characteristics of the sliding surface, and the monitoring scheme needs to be re-evaluated. When calculating the correlation coefficient, the Pearson spatial correlation coefficient is used to calculate the spatial correlation degree, and the specific calculation formula is:

[0019]

[0020] wherein, n is the number of divided spatial units; x i the microseismic event density and intensity value of each unit i y i the surface deformation intensity value of each unit i x is the average value of microseismic event density and intensity; y is the average value of surface deformation intensity 。

[0021] S5: Potential sliding surface identification. According to the results of spatial fitting analysis, the aggregation distribution characteristics of microseismic events in the main deformation area are focused on, and the frequency characteristics of microseismic events are combined to identify the potential sliding fracture surface inside the slope. The sliding surface determination needs to meet four cooperative indicators: ① Microseismic events are concentrated in a certain depth range in the form of a band or a plane; ② The microseismic event concentration area coincides with the main surface deformation area identified by InSAR in the plane position; ③ The main frequency of microseismic events is concentrated in a certain frequency band, indicating the existence of a similar scale of fracture mechanism; ④ The energy of microseismic events is uniformly distributed, and there is no sudden high-energy event. When the above conditions are met, it is determined that there is a potential sliding surface at this depth.

[0022] ​​S6: Sliding surface geometry feature characterization and development stage determination. Based on the three-dimensional spatial distribution pattern and parameters of the microseismic events on the identified potential sliding surface, the key geometric features of the sliding surface are determined, and the development and maturity stage of the sliding surface is determined. The determination method of the geometric features of the sliding surface specifically includes: ①statistical depth distribution of microseismic events, and the depth interval corresponding to the event number peak is taken as the depth of the sliding surface; ②spatial clustering analysis is performed on the microseismic events, the dominant occurrence is fitted, and parameters such as inclination and dip angle are determined; ③the density contour of the microseismic event projection in the horizontal plane is used to determine the boundary range of the sliding surface; ④the spatial interpolation method is used to fit the microseismic event point cloud, and the three-dimensional morphology of the sliding surface is obtained.

[0023] Further, the present application also includes the determination of the development stage of the sliding surface: when the microseismic events are highly concentrated in space and significantly correspond to the surface deformation zone, it is determined that it is in the mature sliding surface stage; when the microseismic events are dispersedly distributed and have no obvious dominant depth, it is determined that it is in the early deformation stage; when there are multiple depth event concentration zones, it is determined that there are multiple sliding surface systems.

[0024] Compared with the prior art, the beneficial effects of the present application are: through the depth space fusion analysis of the spatial distribution of microseismic events and InSAR surface deformation field, an independent cross-validation framework is established, which significantly improves the reliability and reliability of the identification result of the potential sliding surface; using the main deformation area revealed by InSAR as a spatial constraint condition, the interpretation direction of the microseismic data is effectively guided, the misjudgment risk caused by the dimensional limitation of the traditional single technology method is avoided, and the analysis accuracy is significantly enhanced; based on the three-dimensional spatial distribution characteristics of the microseismic events, the depth, dip angle, range and other key parameters of the sliding surface can be quantitatively determined, and an accurate geometric model is provided for slope stability analysis; in combination with the frequency spectrum characteristics and energy release law of the microseismic events, the rupture evolution stage of the sliding surface and the overall deformation state of the slope can be identified, and the landslide early warning ability based on deep rupture precursor is formed; the method integrates surface deformation and deep rupture information, realizes three-dimensional monitoring of slope deformation, and provides a new technical means for slope stability evaluation under complex geological conditions. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 The microseismic and InSAR fusion slope sliding surface identification and prediction method flowchart provided for the embodiments of the present application;

[0026] Figure 2 The InSAR surface deformation distribution and microseismic monitoring network layout superimposed graph in the embodiments of the present application;

[0027] Figure 3 The microseismic event density distribution and InSAR surface deformation spatial correlation analysis graph in the embodiments of the present application;

[0028] Figure 4A microseismic event three-dimensional spatial distribution and a sliding surface fitting result graph in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The present application provides a microseismic and InSAR fusion slope sliding surface identification and prediction method. The upper SQS (SQS) creep slope in the Jinsha River basin of the Hengduan Mountains in the eastern Tibet Autonomous Region is selected as the test site in the embodiment. The terrain in this area fluctuates more than 700 m, and the geological structure is complex, which is an ideal place to test the method of the present application.

[0030] Step S1: Obtain surface deformation by InSAR time series analysis. Collect Sentinel-1 satellite ascending SAR images covering the study area, with a time span of 2014-2022. Process using SBAS-InSAR technology, including interference pair optimization generation: based on preset spatio-temporal baseline constraints, construct high coherence interference image pair combination; differential interference processing: eliminate the terrain phase contribution derived from the digital elevation model (DEM), extract the target deformation phase component; atmospheric phase correction: separate and suppress the phase noise caused by atmospheric delay effect through time domain filtering technology; time series deformation inversion: based on the least squares principle to calculate the time series evolution process of the surface deformation in the study area. Obtain the 8-year cumulative surface displacement distribution of the study area, identify the main deformation area with a cumulative displacement of 300-455mm, as shown in Figure 2 .

[0031] Step S2: Lay out a microseismic monitoring network and conduct continuous monitoring. Based on the main deformation area position determined by InSAR data and the on-site geological conditions, lay out a microseismic monitoring network in the target slope area. As shown in Figure 2 , 90 temporary short-term intensive monitoring stations are laid out in the main deformation area at an interval of 50-100m, and environmental noise data for more than 72 hours are collected for subsequent high-resolution velocity structure inversion; at the same time, 6 long-term monitoring stations are laid out, using a three-component seismograph to conduct continuous monitoring at a sampling rate of 250Hz (4ms interval). The stations are laid out around the main deformation area, covering different elevations (2600m-3500m), to ensure the encircling monitoring of the potential sliding area.

[0032] Step S3: Three-dimensional localization and spatial feature extraction of microseismic events. Continuous waveform data was processed, and valid events were identified using a dual criterion of signal-to-noise ratio (SNR>5) and power spectral density ratio (PSD) (signal / noise>1.5). After removing teleseismic events and environmental noise, 503 valid microseismic events were identified within one year. For each event, the arrival time difference between each station pair was extracted through waveform cross-correlation analysis, with a correlation coefficient threshold set to 0.8 to achieve millisecond-level accuracy. A velocity model was constructed using environmental noise data from short-term stations, and a three-dimensional S-wave velocity structure at a depth of 0-150m was obtained through surface wave tomography, with a velocity range of 200-2500m / s and a resolution of 5m×5m×2m. A theoretical travel time database incorporating topographic and velocity heterogeneity was constructed, and a grid search algorithm was used to find the source location that minimizes the residual time difference between theoretical and observed data. Controlled blasting tests demonstrated a localization accuracy of 1.5m within the dense array coverage area.

[0033] Step S4: Spatial Fit Analysis of Microseismic Events and Surface Deformation. The 503 located microseismic events were overlaid with InSAR deformation maps for analysis. The distribution of microseismic events inside and outside the main deformation zone was statistically analyzed. 362 microseismic events were located within the main deformation zone (accounting for 72% of all microseismic events), while 141 microseismic events were located outside the main deformation zone (accounting for 28% of all microseismic events). Using the Pearson correlation coefficient method, the spatial correlation coefficient between the distribution of microseismic events and the intensity of surface deformation was calculated to be 0.82, indicating a strong correlation between the two. The density of microseismic events within the main deformation zone was 8 times that outside the zone, far exceeding the 3-fold threshold. These results indicate that the microseismic location is accurate and reliable, and that microseismic activity and surface deformation have good spatial consistency.

[0034] Step S5: Identify potential slip surfaces. Based on the successful verification in S4, identify slip surfaces according to the spatial distribution characteristics of microseismic events. For example... Figure 3 As shown in (a), microseismic events within the main deformation zone are distributed within a depth range of 0-148m, with a significant peak at a depth of 2-12m (a total of 181 microseismic events). They exhibit a clear planar distribution, highly consistent with the planar location of the main deformation zone at the surface. The dominant frequencies of these microseismic events within this depth range are concentrated in the 4-9Hz range, indicating a similar rupture mechanism, consistent with the dominant frequency characteristics of in-situ rupture deformation of the rock mass. The PSD values ​​are uniformly distributed between -165 and -135 dB / Hz, with no abnormally high-energy events. Based on these findings, a main slip surface exists at a depth of 2-12m.

[0035] Step S6: Characterization of sliding surface geometry and determination of development stage. Geometric parameters of the sliding surface are extracted based on the three-dimensional distribution of microseismic events, such as... Figure 4As shown, the microseismic events present obvious planar aggregation characteristics in three-dimensional space. According to the position of the depth concentration distribution zone of the microseismic events, it is determined that the depth of the main sliding surface is 7.7 m below the ground surface; by principal component analysis of the microseismic events in the depth range, the sliding surface is obtained, the inclination is about 22 degrees, and the tendency is about 93 degrees; the sliding surface is projected to a plane, the longitude range is 98.9251 degrees to 98.9355 degrees, the latitude range is 30.5383 degrees to 30.5489 degrees, the length of the sliding surface is about 1.17 kilometers, the width is about 0.99 kilometers, and the planar projection area is about 1.16 square kilometers; the spatial interpolation method is used to interpolate the microseismic event points, and a three-dimensional curved surface model (the middle gray curved surface) of the sliding surface is obtained, which shows that the sliding surface is spoon-shaped. Figure 4 Based on the determined sliding surface parameters, the development stage of the sliding surface is distinguished, and the evolution trend is predicted. The microseismic events in the main deformation area are densely distributed, and the distribution form is highly corresponding to the surface deformation area, so it is determined that the sliding surface is a mature sliding surface, and it is predicted that the slope is in a stable creep stage, and will continue to develop into an accelerated deformation stage, so it needs to be focused on and monitored.

[0036] The data processing of the present application can be realized by the following methods:

[0037] InSAR data processing: the whole process of SBAS-InSAR time series analysis is completed by using professional SAR processing software; high spatial resolution ground surface deformation raster data set is exported, including time series displacement field and cumulative deformation field.

[0038] Microseismic data acquisition and processing: high-fidelity signal acquisition is implemented by using a professional wideband seismic data acquisition system, and intelligent detection, seismic phase accurate picking and initial positioning calculation of microseismic events are completed by relying on a professional seismic analysis platform.

[0039] Microseismic positioning result and InSAR data fusion analysis: the microseismic positioning result and InSAR deformation data are spatially superimposed in a geographic information system; the spatial statistical method is used to analyze the microseismic event distribution characteristics in the main deformation area; according to the known distribution characteristics of the microseismic events, a three-dimensional model of the sliding surface is constructed by using a curved surface simulation method.

[0040] Sliding surface identification and prediction: a three-dimensional visualization model is established by using professional software, the geometric parameters and morphological characteristics of the sliding surface are determined, the development stage is distinguished according to the related parameters, and the risk evaluation is performed.

[0041] The present application realizes high-precision identification and parameterized characterization of the slope sliding surface by fusing microseismic monitoring and InSAR technology. The spatial consistency of microseismic and ground deformation verifies the reliability of the method, and provides a new technical means for slope stability evaluation under complex geological conditions. The method has been successfully applied in many slope engineering projects, and significantly improves the prediction and early warning ability of landslide disasters.

[0042] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying and predicting a slope sliding surface by microseismic and InSAR fusion, characterized in that, Comprising the following steps: S1: InSAR time series analysis and surface deformation acquisition; InSAR time series analysis is performed on the target area to obtain the cumulative deformation distribution of the slope surface in the study area, and the position of the main deformation area is determined; S2: Laying out a microseismic monitoring network and continuous monitoring; The microseismic monitoring network is laid out around the main deformation area in a full-encircling manner, and continuous microseismic monitoring is performed; S3: Three-dimensional positioning of microseismic events and extraction of spatial characteristics; accurate three-dimensional positioning of microseismic events is performed to obtain the spatial distribution characteristics of microseismic events; S4: Spatial fitting analysis of microseismic events and surface deformation; the spatial distribution of microseismic events is verified and analyzed with the position of the InSAR main deformation area, including calculating the microseismic event density ratio inside and outside the main deformation area and the spatial correlation coefficient of microseismic event density and surface deformation intensity, to determine whether the microseismic event distribution can effectively indicate the existence of the internal sliding surface of the slope; The double verification analysis process includes: first, counting the number of microseismic events inside and outside the main deformation area identified by InSAR, and calculating the microseismic event density ratio inside and outside the main deformation area; second, based on the spatial density distribution of microseismic events and the surface deformation intensity of the main deformation area, the spatial correlation coefficient is calculated by using Pearson spatial correlation; To determine whether microseismic events can effectively indicate the sliding surface, two conditions must be met to verify the internal consistency between microseismic events and the mechanical process of slope deformation, and to confirm that the microseismic event distribution can effectively indicate the position of the internal sliding surface, which can enter step S5 for sliding surface identification, the conditions include Condition one: the microseismic event density ratio inside and outside the main deformation area is greater than 3, indicating that the microseismic activity has significant spatial concentration; Condition two: the spatial correlation coefficient r of microseismic event density and surface deformation intensity is greater than 0.7, indicating that the two have strong correlation; S5: Potential sliding surface identification; on the premise that microseismic events can indicate the sliding surface, the specific position of the potential sliding surface is identified based on the concentrated distribution characteristics of microseismic events in the main deformation area, and the three-dimensional spatial structure of the sliding surface is described by analyzing the spatial distribution pattern, geometric distribution characteristics and frequency characteristics of microseismic events; On the basis of verifying that microseismic events can effectively indicate the sliding surface, the specific criteria for identifying the potential sliding surface include: Microseismic events in a specific range of the slope exhibit obvious strip distribution or face-like aggregation, which suggests the existence of a continuous rupture surface in the internal slope; The microseismic event concentration area coincides with the InSAR-identified surface main deformation area in planar projection, indicating that underground microseismic activity is related to surface deformation; The frequency characteristics of microseismic events are consistent, indicating that they are derived from the same rupture mechanism; S6: Geometric feature description and development stage determination of sliding surface; based on the identified geometric features of the sliding surface and the spatio-temporal evolution law of microseismic events, the development stage of the sliding surface is determined in combination with the degree of agreement with surface deformation, and the future evolution trend of the slope is predicted.

2. The microseismic and InSAR fusion-based slope sliding surface identification and prediction method according to claim 1, characterized in that, In step S1, SBAS-InSAR technology is used for InSAR time series analysis, multi-temporal radar image data are processed, space-time incoherence and atmospheric delay error are effectively suppressed, high-precision resolution cumulative ground surface displacement field in the monitoring area on the inter-annual scale is obtained, so as to accurately delineate the spatial boundary of the main deformation area and characterize the deformation magnitude.

3. The microseismic and InSAR fusion-based slope sliding surface identification and prediction method according to claim 1, characterized in that, The microseismic monitoring network in step S2 includes short-term intensive monitoring stations and long-term continuous monitoring stations.

4. The microseismic and InSAR fusion-based slope sliding surface identification and prediction method according to claim 1, characterized in that, The three-dimensional positioning of the microseismic event in step S3 specifically includes: Identifying and screening effective microseismic events originating from the inside of the slope from the original signal, eliminating teleseisms and various environmental noise interference; accurately obtaining the first arrival time of the key seismic phase recorded by the effective microseismic event at each monitoring station, and extracting the arrival time difference information of the microseismic event; Considering the complex stratum velocity structure and topographic relief effect of the slope, the arrival time difference information is used for three-dimensional spatial positioning calculation to obtain the spatial coordinates of the microseismic event, including longitude, latitude and depth information.

5. The microseismic and InSAR fusion-based slope sliding surface identification and prediction method according to claim 1, characterized in that, The geometric features of the sliding surface for prediction and analysis in step S6 include: According to the concentrated depth of the microseismic event in the vertical direction, the depth position of the sliding surface is determined, reflecting the development level of the sliding surface in the vertical space; Based on the dominant occurrence of the spatial distribution of the microseismic event, the dip and inclination parameters of the sliding surface are calculated, reflecting the inclination of the sliding surface; According to the distribution boundary of the microseismic event in the horizontal plane, the range of the sliding surface is determined, and the extension scale of the sliding surface in the horizontal direction is calculated; The three-dimensional spatial distribution of the microseismic event is fitted and analyzed to obtain the overall morphology of the sliding surface, and the spatial geometric features of the sliding surface are fully presented.

6. The microseismic and InSAR fusion-based slope sliding surface identification and prediction method according to claim 1, characterized in that, In step S6, the evolution trend of the slope is predicted by judging the development stage of the sliding surface, relying on the analysis of the coincidence degree of the three-dimensional spatial distribution of the microseismic event and the geometric shape of the surface main deformation area: When the microseismic event presents a highly concentrated distribution state in space, and the corresponding relationship with the surface deformation area is significant, it is indicated that the sliding surface has formed a relatively complete structure, and the sliding surface is determined as a mature sliding surface; When the microseismic event is distributed dispersedly, and the correlation between the surface deformation and the surface deformation is low, it is indicated that the sliding surface is in the initial development period, and the internal structure is not stable, and the sliding surface is determined as an early deformation stage; When there are multiple independent microseismic event concentration zones in different depth intervals, it is indicated that there is a complex deformation evolution process in the internal of the slope, and it is determined as a multi-stage sliding surface system.

Images