Landslide accelerated deformation starting point identification method and product for landslide monitoring and early warning

Abstract

The embodiment of the invention provides a landslide accelerated deformation starting point identification method and product for landslide monitoring and early warning, and relates to the technical field of landslide monitoring data processing and early warning and forecasting. In the embodiment of the invention, the statistical characteristics of the landslide speed data are analyzed in real time, and the OOA point of the landslide is dynamically identified according to the set judgment criterion. Specifically, the normal fluctuation range of the landslide steady-state creep stage speed is represented by accurately estimating the speed confidence interval, the mutual relation between the current speed and the calculated speed confidence interval is evaluated in real time according to the judgment criterion, and when the current speed exceeds the confidence interval, the current speed is determined as the potential precursor of the acceleration stage. And taking the corresponding point as a landslide accelerated deformation starting point. The OOA point identification method provided by the invention has universality, and has good performance in 15 landslide cases using different monitoring methods considered in the invention. The method provides key and necessary technical support for a landslide early warning system, and has important practical application value.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of real-time landslide monitoring and early warning, and in particular, to a method and product for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning. Background Art

[0002] Currently, in the face of huge landslide risks, an early warning system (EWS) that promotes the timely evacuation of high-risk populations has become an important part of landslide risk management. Although the EWS consists of various interacting components, one of its most critical tasks is to reliably predict the time of landslide instability.

[0003] Before slope instability, it often exhibits accelerated creep behavior, which is characterized by "the strain rate rapidly increases with time and ultimately leads to instability failure". This accelerated creep is a key precursor feature of impending instability, and several instability time prediction models have been proposed based on this. Related technologies have found through triaxial tests that the logarithm of the instability time is proportional to the logarithm of the strain rate. Later, this relationship was extended to the accelerated creep stage, and an empirical model (i.e., the Satio model) for predicting the instability time was proposed. It was also found in related technologies that the Satio model may not be applicable to some landslides, and it was modified (called the YAM model) to obtain a more accurate prediction. In addition, through slope model tests in related technologies, it was found that the logarithm of the deformation rate before slope instability is proportional to the logarithm of the acceleration, and then the inverse velocity (INV) model was proposed. For simplicity, a simplified inverse velocity (SINV) model with a linear trend is more widely used in practice. The slope (SLO) model was developed based on the previous basis of related research, and this model provides a safer prediction than INV. Although these models provide the ability to interpret the failure time from monitoring data, their reliable application in engineering still faces some practical challenges.

[0004] First, the effective application of the above prediction model depends on the accurate definition of the transition point from the steady state to the accelerating creep stage (i.e., the OOA point). As the starting point of the prediction, the prediction results are highly sensitive to the identification of the OOA point. However, the identification of the OOA point in prior art often relies on subjective judgment, which may lead to incorrect OOA settings and thus misleading predictions. Moreover, the lack of automation also limits the engineering application of this method. To solve this problem, several criteria for identifying the starting point of accelerating deformation (the OOA point) have been defined in related research: detecting the OOA point by analyzing short-term and long-term deformation trends; using the convergence and divergence of the displacement moving average to detect changes in the deformation trend and thus detect the OOA point. Although these methods perform well in some cases, they may also detect non-optimal OOAs or false positives, and some parameters may involve subjective values. Related research also provides a method for identifying the OOA point by assuming that the velocity follows a normal distribution. However, the actual monitoring data may not always conform to the normal distribution, which poses challenges to the application of these methods in practice. Generally speaking, the current monitoring technology is advanced, but the OOA identification method has not kept up. There is an urgent need for a simple and general procedure to automatically and real-time identify the OOA point. Summary of the Invention

[0005] An embodiment of the present invention provides a landslide accelerating deformation starting point identification method and product for landslide monitoring and early warning to at least partially solve the above problems.

[0006] In a first aspect of an embodiment of the present invention, a landslide accelerating deformation starting point identification method for landslide monitoring and early warning is provided. The method includes: Obtaining landslide deformation monitoring data; Determining the landslide deformation velocity corresponding to each moment according to the landslide deformation monitoring data; Determining a velocity confidence interval according to the landslide deformation velocity corresponding to each moment, where the velocity confidence interval represents the velocity fluctuation range of the landslide steady-state creep stage; Determining the landslide accelerating deformation starting point according to the landslide deformation velocity at each moment and the velocity confidence interval.

[0007] Optionally, determining the landslide accelerating deformation starting point according to the landslide deformation velocity at each moment and the velocity confidence interval includes: When the landslide deformation velocity at the target moment is greater than the velocity confidence interval, determining the target moment as the landslide accelerating deformation starting point.

[0008] Optionally, determining the landslide accelerating deformation starting point according to the landslide deformation velocity at each moment and the velocity confidence interval includes: When the landslide deformation speed at the first moment is within the speed confidence interval, it is determined that the landslide is in the steady creep stage at the first moment; When the landslide deformation speed at the second moment first exceeds the speed confidence interval, and the landslide deformation speed at the third moment exceeds the speed confidence interval, and the landslide deformation speed at the third moment is greater than the landslide deformation speed at the second moment, it is determined that the landslide enters the accelerated deformation stage, and the third moment is the next moment after the second moment; When the landslide deformation speeds at the second moment, the third moment, and the fourth moment all exceed the speed confidence interval, and the linear fitting slope is greater than 0, it is determined that the landslide enters the accelerated deformation stage and is in the uniform acceleration stage; the fourth moment is the next moment after the third moment; The second moment is determined as the starting point of the landslide accelerated deformation.

[0009] Optionally, determining the landslide deformation speed corresponding to each moment according to the landslide deformation monitoring data includes: Obtaining time series monitoring data according to the landslide deformation monitoring data; Obtaining the landslide speed corresponding to each moment according to the time series monitoring data; Performing filtering processing on the landslide speeds corresponding to each moment to obtain the landslide deformation speeds corresponding to each moment after filtering.

[0010] Optionally, determining the speed confidence interval according to the landslide deformation speeds corresponding to each moment includes: For large sample landslide monitoring data obtained by high-frequency monitoring means (such as Global Navigation Satellite System, crack meter, ground-based synthetic aperture radar, etc.), arranging the landslide deformation speeds corresponding to each moment in ascending order, and using the following formula to determine the lower limit of the speed confidence interval corresponding to the significance level α and the upper limit : ; ; where Percentile(·) represents the calculation process using the percentile method, represents the landslide deformation speed time series .

[0011] Optionally, determining the speed confidence interval according to the landslide deformation speeds corresponding to each moment includes: For small sample landslide monitoring data obtained by low-frequency monitoring means (such as total station, manual measurement, satellite synthetic aperture radar, etc.), arranging the landslide deformation speeds corresponding to each moment in ascending order, and using the following formula to determine the significance level αLower limit of the corresponding velocity confidence interval and upper limit : ; ; wherein N is the number of Bootstrap subsamples randomly sampled from the original sample ; represents the landslide deformation velocity time series ; (i = 1, 2,..., N ) represents the i th Bootstrap subsample; Percentile(·) represents the calculation process using the percentile method.

[0012] In the second aspect of the embodiments of the present invention, a landslide accelerated deformation starting point identification device for landslide monitoring and early warning is provided. The landslide accelerated deformation starting point identification device for landslide monitoring and early warning includes: An acquisition module for acquiring landslide deformation monitoring data; A first determination module for determining the landslide deformation velocity corresponding to each moment according to the landslide deformation monitoring data; A second determination module for determining a velocity confidence interval according to the landslide deformation velocity corresponding to each moment, where the velocity confidence interval represents the velocity fluctuation range of the landslide steady creep stage; A third determination module for determining the landslide accelerated deformation starting point according to the landslide deformation velocity at each moment and the velocity confidence interval.

[0013] In the third aspect of the embodiments of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes, it implements the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning as described in the first aspect of the present invention.

[0014] In the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by the processor, it implements the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning as described in the first aspect of the present invention.

[0015] In the fifth aspect of the embodiments of the present invention, a computer program product is provided, including a computer program / instructions. When the computer program / instructions are executed by the processor, it implements the steps in the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning as described in the first aspect of the present invention.

[0016] The method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiments of the present invention uses statistical methods to evaluate the relationship between the current speed and the historical speed of a certain landslide, so as to establish real-time OOA point identification criteria.

[0017] The real-time OOA point identification program proposed by the embodiments of the present invention can accurately identify OOA points consistent with the actual acceleration trend in both synthetic datasets and multiple historical landslide cases, demonstrating its robustness and applicability in different landslide scenarios. In addition, for landslide scenarios with multiple acceleration stages, the technical solution provided by the embodiments of the present invention can evaluate the latest OOA points based on real-time monitoring data, which is crucial in actual early warning scenarios. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 is the flowchart of the steps of the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiments of the present invention; Figure 2 is a schematic diagram of the OOA point identification result of a case in the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiments of the present invention; Figure 3 is the deformation monitoring data and OOA point identification results of Cases 1 to 8 in the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiments of the present invention; Figure 4 is the deformation monitoring data and OOA point identification results of Cases 9 to 15 in the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiments of the present invention; Figure 5 is a schematic diagram of the OOA point identification result of a case in the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiments of the present invention; Figure 6 is a schematic diagram of the OOA point identification result of a case in the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiments of the present invention; Figure 7 is a schematic diagram of the OOA point identification result of a case in the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiments of the present invention; Figure 8It is a structural block diagram of a landslide accelerated deformation starting point identification device provided by an embodiment of the present invention for landslide monitoring and early warning. Detailed implementation manners

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0021] An embodiment of the present invention provides a step flowchart of a method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning, as Figure 1 shown. Specifically, the method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning includes the following steps: S101, obtaining landslide deformation monitoring data; S102, determining the landslide deformation speed corresponding to each moment according to the landslide deformation monitoring data; S103, determining a speed confidence interval according to the landslide deformation speed corresponding to each moment, where the speed confidence interval represents the speed fluctuation range of the landslide steady-state creep stage; S104, determining the starting point of landslide accelerated deformation according to the landslide deformation speed at each moment and the speed confidence interval.

[0022] In an embodiment of the present invention, various common monitoring technologies can be used to record landslide deformation monitoring data, including tape measures, total stations, crack meters, slope synthetic aperture radar (S-SAR), and spaceborne synthetic aperture radar interferometry (InSAR). The method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the embodiment of the present invention can be applied to the landslide deformation monitoring data set collected by common monitoring technologies.

[0023] In an embodiment of the present invention, considering that the scattering in the landslide monitoring data obscures the speed information, data filtering can reduce the influence of noise on the detection of the starting point of landslide accelerated deformation and enhance the reliability of landslide instability time prediction. Related research has evaluated the filtering performance of common filters and pointed out that the Savitzky-Golay (SG) filter has better performance. The SG filter uses a weighted average method and uses the least square equation to define the weights of polynomial fitting.

[0024] In an optional embodiment, the step S102 includes the following sub-steps: S1021, obtaining time series monitoring data according to the landslide deformation monitoring data; S1022, obtaining the landslide speed corresponding to each moment according to the time series monitoring data; S1023, performing filtering processing on the landslide speed corresponding to each moment to obtain the landslide deformation speed corresponding to each moment after filtering.

[0025] Let S i represent the landslide deformation monitoring data at time t i ( i = 1, 2, …, n ), then the time - series monitoring data can be expressed as follows. From the following formula, we can get t i the landslide speed at time v i : (1) In the embodiment of the present invention, the step S1023 includes: Filter the landslide speed at each time using the following formula to obtain the landslide deformation speed corresponding to each time: (2) where C 1j is the first row of the weight matrix C=(J T J) -1 J T , T is the transpose operator of the matrix, and J is a Vandermonde matrix with m rows and k + 1 columns (m is the length of the filtering window, and k is the order of the polynomial); is the landslide deformation speed corresponding to the i th time obtained by filtering, v j is the j th original speed within the corresponding filtering window.

[0026] For the SG filtering of non - uniformly spaced monitoring data, it can be converted to uniformly spaced data through interpolation techniques.

[0027] The polynomial degree and the filtering window length are key parameters affecting the filtering performance. Considering the computational cost and filtering performance, cubic polynomials are usually used. The selection of the filtering window length is affected by many factors. Generally, the potential window length is between 5 and 15 points, and in practical applications, it should be determined by evaluating the filtering effect of each research case. If the noise effect is significant, the window length can be increased.

[0028] Before a landslide occurs, the slope deformation usually exhibits the classic three - stage creep evolution characteristics. According to the classic interpretation, the speed in the steady - state creep stage of the landslide is constant under ideal conditions. However, due to various noise effects in the actual application scenario, the actual speed often fluctuates around this constant value. When the landslide enters the acceleration stage, the speed continuously increases and deviates from the normal fluctuation range (NFR). Therefore, the key to real - time identification of the OOA point is to determine whether the current landslide speed deviates from the NFR.

[0029] The velocity NFR of each landslide varies during the steady-state creep stage and lacks a unified definition. Based on the statistical characteristics of the filtered velocity data of a specific landslide the velocity NFR of the steady-state creep stage can be defined by the velocity confidence interval (CI). Since the landslide velocity may exhibit non-normal statistical characteristics, in the embodiments of the present invention, the generalized percentile method is used to calculate its CI.

[0030] Specifically, in an alternative embodiment, the step S103 includes: arranging the landslide deformation velocities corresponding to each moment in ascending order, and using the following formula to determine the lower limit α and the upper limit of the velocity confidence interval corresponding to the significance level : (3) (4) where Percentile(·) represents the calculation process using the percentile method, represents the time series of the landslide deformation velocity .

[0031] Although this method performs well with a large sample size ( n > 50), it may not provide a reliable estimate with a small sample size ( n ≤ 50).

[0032] In practice, small sample monitoring data is often encountered, such as deformation monitoring information from satellite-based InSAR, total station, and 3D laser scanning technology. In this case, the step S103 includes: determining the velocity confidence interval based on the landslide deformation velocities corresponding to each moment, including: Arranging the landslide deformation velocities corresponding to each moment in ascending order, and using the following formula to determine the lower limit α and the upper limit of the velocity confidence interval corresponding to the significance level : (5) (6) where N is the number of Bootstrap subsamples randomly sampled from the original sample ; represents the time series of the landslide deformation velocity ; (i = 1, 2,..., N) represents the i th Bootstrap subsample; Percentile(·) represents the calculation process using the percentile method.

[0033] Based on the accurate estimation of the velocity CI in the steady-state creep stage, by determining the current velocity v i Whether it is within the calculated CI range, the OOA point can be identified in real time. In particular, in the embodiments of the present invention, a significance level α = 0.05 is used for the CI (i.e., 95% CI) to define the NFR.

[0034] In order to improve the reliability of the technical solution proposed in the embodiments of the present invention for engineering practitioners in actual scenarios, the following five criteria are set in the embodiments of the present invention to evaluate the current deformation state of the landslide identified by OOA: Criterion 0: Current v i Within the CI range, that is, the landslide is in the steady-state creep stage; Criterion 1: Current v i First exceeds the CI, which may be a potential precursor to the acceleration stage or may also be spike noise; Criterion 2: At least two consecutive velocities v i 、 v i+1 Exceed the CI and v i < v i+1 , indicating that the landslide is very likely to enter the acceleration stage; Criterion 3: At least three consecutive velocities v i 、 v i+1 、 v i+2 Exceed the CI, and the slope of their linear fit is greater than 0, indicating that the landslide has entered the acceleration stage and is in the uniform acceleration stage; Criterion 4: At least four consecutive velocities v i 、 v i+1 、 v i+2 、 v i+3 Exceed the CI, and the quadratic coefficient of their parabolic fit is greater than 0, indicating that the landslide has entered the acceleration stage and is in the super-acceleration stage; When it is determined that the landslide is in the acceleration stage (i.e., at least reaching Criterion 3), the point where Criterion 1 is last reached is regarded as the starting point of the accelerated deformation of the landslide.

[0035] Thus, in an alternative embodiment, step S104 includes: when the landslide deformation speed at the target moment is greater than the speed confidence interval, determining the target moment as the starting point of landslide accelerated deformation.

[0036] In an alternative embodiment, step S104 includes: S1041, when the landslide deformation speed at the first moment is within the speed confidence interval, determining that the landslide is in the steady creep stage at the first moment; S1042, when the landslide deformation speed at the second moment exceeds the speed confidence interval for the first time, and the landslide deformation speed at the third moment exceeds the speed confidence interval, and the landslide deformation speed at the third moment is greater than the landslide deformation speed at the second moment, determining that the landslide enters the accelerated deformation stage, where the third moment is the next moment after the second moment.

[0037] S1043, when the landslide deformation speeds at the second moment, the third moment, and the fourth moment all exceed the speed confidence interval, and the linear fitting slope is greater than 0, determining that the landslide enters the accelerated deformation stage and is in the uniform acceleration stage; the fourth moment is the next moment after the third moment.

[0038] S1044, determining the second moment as the starting point of landslide accelerated deformation.

[0039] To verify the landslide deformation starting point recognition method proposed in the embodiments of the present invention, in the embodiments of the present invention, to illustrate the implementation and performance of the proposed method, an example of real-time recognition of OOA points using an artificial synthetic monitoring data set is given. The advantage of synthetic data is that the true values of the monitoring data can be known, so as to better verify the performance of the method. The speed monitoring data is generated using a piecewise function based on the YAM model, expressed as follows: (7) where v represents the speed data at time t , where t ∈[0, 99]. The parameters a and β of the YAM model are set to 2500 and 2 respectively. The OOA point time t OOA is defined as 75; the fault time t c is set to 100. The generated monitoring data consists of discrete points that follow the continuous mathematical equation provided by formula (7) and are spaced one unit of time apart. In addition, random values following the standard normal distribution (i.e., mean of 0 and standard deviation of 1) are added to simulate the noise in the monitoring data.

[0040] The proposed OOA point recognition method was executed on a workstation (Intel® Core™ i5-14600KF CPU @ 3.5 GHz, 32 GB RAM). Whenever new monitoring data is obtained, the method takes less than 0.2 seconds to evaluate the landslide deformation stage each time, almost completing in real time. Figure 2 It shows the synthetic data and the filtered synthetic data in the embodiments of the present invention, as well as the OOA points determined using the method proposed in the embodiments of the present invention. Among them, part (a) is the synthetic data, (b) is the OOA point recognition result based on the synthetic data, and part (c) is the OOA point recognition result based on the filtered data. Among them, in part (a), the orange points represent the original velocity data, the green points represent the filtered velocity data, the blue line represents the ideal velocity curve, the red dashed line corresponds to the landslide instability time, and the purple dashed line corresponds to the true OOA point. In part (b), the green points correspond to criterion 0, the yellowish-green points correspond to criterion 1, the yellow points correspond to criterion 2, the orange points correspond to criterion 3, the red points correspond to criterion 4, the blue dashed line corresponds to the ideal velocity curve, the red dashed line corresponds to the instability time, and the orange dashed line corresponds to the OOA point determined using the method proposed in the embodiments of the present invention. In part (c), the green points correspond to criterion 0, the yellowish-green points correspond to criterion 1, the yellow points correspond to criterion 2, the orange points correspond to criterion 3, the red points correspond to criterion 4, the blue dashed line corresponds to the ideal velocity curve, the red dashed line corresponds to the instability time, and the orange dashed line corresponds to the OOA point determined using the method proposed in the embodiments of the present invention.

[0041] by Figure 2 It can be clearly seen that the original data with noise obscures the velocity trend and may interfere with the judgment of professionals. According to the original monitoring data, the identified OOA point is 87, which is 12 unit times later than the true OOA point (75) due to the influence of noise (as shown in part b of Figure 2 ). In contrast, data filtering significantly reduces the noise and clarifies the deformation trend. The OOA point identified using the filtered data is 78, which only differs from the true value (75) by 3 unit times (as shown in part c of Figure 2 ). This small error is acceptable because during the transition stage between the steady state and the accelerating state, the increment of velocity is small, resulting in the confusion of the obvious boundary between these two stages. In this case, it is challenging to accurately determine the OOA by any method (including the method based on expert experience). In summary, the tests based on the synthetic monitoring data examples show that data filtering can significantly improve the data quality, and the OOA point recognition method proposed in the embodiments of the present invention has good performance.

[0042] In the embodiments of the present invention, a historical landslide dataset is established, which includes 15 historical landslide cases with complete monitoring data reported in the literature. The cases in the dataset use a variety of different monitoring methods (total station, spaceborne InSAR, crack meter, etc.), cover a variety of slope types (natural, artificial fill, and mining slopes), and vary in volume, material, failure mechanism, and triggering factors. The monitoring data of most cases is obtained by digitizing scientific literature using WebPlotDigitizer software. The monitoring period of each case is long enough to include the transition between the stable creep stage and the accelerating creep stage. Table 1 summarizes the detailed information of the landslide cases.

[0043] Table 1. Information on the Landslide Case Dataset

[0044] Before determining the OOA point, the landslide velocity monitoring data needs to be preprocessed using the SG filtering method of formula (2). It can be seen from the results that the SG filter effectively reduces the noise in the monitoring data and clearly reveals the deformation trend of the landslide. This provides a good data basis for subsequent determination of the OOA point.

[0045] Based on the filtered velocity data, the OOA point identification method proposed in the embodiments of the present invention determines the landslide deformation stage in real time. Since landslide monitoring is a dynamically updated process, the technical solution provided in the embodiments of the present invention can automatically identify the OOA point in real time to ensure timely warning.

[0046] In the embodiments of the present invention, when evaluating the landslide deformation state, only the monitoring data available before the current moment is used to simulate the actual application scenario. The OOA identification results of the 15 landslide cases considered in the embodiments of the present invention are as Figure 3 and Figure 4 shown, Figure 3 showing the deformation monitoring data and OOA point identification results of Cases 1 to 8, Figure 4 showing the deformation monitoring data and OOA point identification results of Cases 9 to 15. It can be seen that in the 15 cases, the OOA points related to the final landslide instability are found early enough (an average of 36.20 days) to enable timely prediction of the instability time. Among them, the OOA point of the Cadia tailings dam landslide monitored by satellite InSAR was determined 95.78 days before the instability, and the latest accelerating trend leading to the collapse of the No. 1 open-pit mine landslide monitored by GB-SAR was found 0.92 days before the instability. The results show that for the 15 landslide cases with different monitoring instruments and monitoring frequencies, the proposed method can successfully identify the OOA point in a real-time scenario.

[0047] In addition, it is also necessary to timely identify the latest landslide deformation trend. The method proposed in the embodiments of the present invention detected repeated acceleration stages in multiple landslide cases, such as the Iron Mine landslide, the Letlhakane landslide, the 1# open-pit mine landslide, and the Preonoz landslide. These findings are consistent with the actual deformation trend, verifying the excellent performance of the proposed method. Landslides are affected by rainfall, human activities, and stability degradation, resulting in accelerated deformation. However, when the influence of external factors is mitigated or weakened by the self-organization effect, it may cause the landslide to reach a new steady state. The method proposed in the embodiments of the present invention can use the latest deformation monitoring data to update the current deformation state in real time. When the landslide deformation continues to accelerate under the positive feedback mechanism and leads to instability, the method can also successfully identify the final OOA point.

[0048] Finally, under the condition of the same workstation (Intel® Core™ i5-14600KF CPU @ 3.5 GHz, 32 GB RAM), the computational efficiency of the proposed method was evaluated by simulating a real landslide monitoring scenario (i.e., dynamically and real-time identifying the OOA point as new monitoring data is obtained). In the scenario of small-sample monitoring data, each OOA point detection can be completed within 0.2 seconds; in the case of large-sample monitoring data, each OOA point detection can be completed within 0.1 seconds, and this process can be considered to be completed in real time. Taking the Vajont landslide (small-sample monitoring data set, 21 data points) and the Preonoz landslide (large-sample monitoring data set, 649 data points) as examples, the total time for OOA point identification is 3.50 seconds and 9.44 seconds respectively. The method has a low computational cost and excellent performance, and can provide strong support for the real-time interpretation of landslide monitoring data in practical applications.

[0049] In summary, the OOA point identification method proposed in the embodiments of the present invention is general and can achieve real-time automatic identification, and performs well among different monitoring methods for the 15 landslide cases considered. This provides the key and necessary conditions for the successful implementation of landslide warning.

[0050] The real-time OOA point identification program proposed in the embodiments of the present invention can accurately identify the OOA points consistent with the actual acceleration trend in both synthetic data sets and multiple historical landslide cases, demonstrating its robustness and applicability in different scenarios. In addition, for landslides with multiple acceleration stages, the latest OOA points can be evaluated based on real-time monitoring data, which is crucial in actual warning scenarios.

[0051] In the embodiments of the present invention, 3 representative landslide cases were selected, and different monitoring methods with different frequencies were used to elaborate in detail the performance of the method proposed in the embodiments of the present invention.

[0052] 1. Preonoz Landslide In the embodiment of the present invention, the Preonoz landslide is mainly composed of amphibolite and gneiss, and it has experienced several large-scale catastrophic damages in history. According to the landslide deformation data monitored by crack gauges starting from May 1, 2012, the landslide event was reviewed again using the proposed method. Let t = 0 represent the first measurement date, then the landslide instability failure time is 14.06 d. First, the original velocity data was preprocessed using the SG filter (as shown in part (a) of Figure 5 ). Then, the velocity NFR (i.e., 95% CI) at each monitoring moment was evaluated in real time in the embodiment of the present invention (as shown in part (a) of Figure 5 ). Combining the OOA point real-time identification criterion proposed by the present invention, we determined two key OOA points (as shown in part (b) of Figure 5 ). The first landslide acceleration event finally converged to a new steady state, while the second landslide acceleration event finally led to the landslide instability. These results are consistent with the actual decision described in this case, and the method proposed in the embodiment of the present invention detected the OOA points earlier, providing more safety margins.

[0053] Among them, Figure 5 shows the results of the Preonoz landslide case analysis. Part (a) represents the filtered velocity and its NFR (95%CI) results; part (b) represents the displacement monitoring data and the OOA point identification results.

[0054] 2. Iron Mine Landslide In this case, the deformation of the Iron Mine landslide was monitored using prism station measurements. The overall angle of this slope is about 75°, and the height is 80m. Finally, wedge-shaped sliding instability occurred due to being surrounded by structural planes cut on both sides. Figure 6 Part (a) of Figure 6 shows the Iron Mine landslide deformation data recorded by station No. 65 with a monitoring frequency of 1 d. The original data was processed using the SG filtering method, and the further OOA identification results are as shown in part (b) of t . During the entire deformation stage, a total of three landslide acceleration events were detected. The first acceleration event was characterized by a brief and slight increase in velocity, followed by stability, while the second was a brief increase and then decreased to the normal fluctuation range. The final acceleration event was detected at t = 53 d, and the velocity continued to increase until the landslide occurred at

[0055] = 70 d. The method successfully identified multiple landslide acceleration events and updated the OOA point identification results in real time according to the latest monitoring data, demonstrating the good performance of the method. Figure 6Shows the results of the Iron Mine landslide: Part (a) represents the filtered velocity and its NFR (95% CI); Part (b) represents the displacement monitoring data and the OOA point identification results.

[0056] 3. Cadia Tailings Dam Landslide In this case, the historical deformation of the Cadia Tailings Dam landslide since 2017 was analyzed using the satellite InSAR method and Sentinel-1 data (revisit time of 12 d), and its velocity record is shown in Figure 7 part (a) below. For simplicity, November 16, 2016 was set as t t = 0 d, and the failure time was 431.78 d. According to the established procedure, the original velocity data was filtered, and the OOA points were determined using the proposed method. Figure 7 Part (b) below shows the OOA point identification results. The first OOA point appeared at t t = 12 d, and then the landslide velocity dropped to the normal fluctuation range, indicating that the landslide entered the steady-state creep stage. Subsequently, at t t = 336 d, the last OOA point was detected, and the velocity continued to increase until the final failure. This finding is consistent with the object-oriented analysis results in the related technology, demonstrating the effectiveness of the proposed method.

[0057] The embodiments of the present invention relate to the technical field of landslide monitoring data processing and early warning forecasting. In the embodiments of the present invention, by analyzing the statistical characteristics of landslide velocity data in real time, the starting point of the accelerated deformation of the landslide is dynamically identified according to the set discrimination criteria. Specifically, the present invention accurately estimates the confidence interval to characterize the normal fluctuation range of the velocity in the steady-state creep stage of the landslide, and evaluates the mutual relationship between the current velocity and the calculated velocity confidence interval in real time according to the discrimination criteria. When the current velocity exceeds the confidence interval, it is determined as a potential precursor of the acceleration stage, and the corresponding point is used as the starting point of the accelerated deformation of the landslide. The OOA point identification method proposed by the present invention is universal and performs well in 15 landslide cases using different monitoring methods considered in the present invention. This method provides key and necessary technical support for the landslide early warning system and has important practical application value.

[0058] Based on the same inventive concept, the embodiments of the present invention also provide a device for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning, as shown in Figure 8 below, which shows the structural block diagram of the device for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning. The device 800 for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning includes: An acquisition module 801, configured to acquire landslide deformation monitoring data; The first determination module 802 is configured to determine the landslide deformation speed corresponding to each moment according to the landslide deformation monitoring data; The second determination module 803 is configured to determine a speed confidence interval according to the landslide deformation speed corresponding to each moment, where the speed confidence interval represents the speed fluctuation range in the steady creep stage of the landslide; The third determination module 804 is configured to determine the starting point of landslide accelerated deformation according to the landslide deformation speed at each moment and the speed confidence interval.

[0059] Optionally, the third determination module 804 is specifically configured to: When the landslide deformation speed at the target moment is greater than the speed confidence interval, determine that the target moment is the starting point of landslide accelerated deformation.

[0060] Optionally, the third determination module 804 is specifically configured to: When the landslide deformation speed at the first moment is within the speed confidence interval, determine that the landslide is in the steady creep stage at the first moment; When the landslide deformation speed at the second moment exceeds the speed confidence interval for the first time, and the landslide deformation speed at the third moment exceeds the speed confidence interval, and the landslide deformation speed at the third moment is greater than the landslide deformation speed at the second moment, determine that the landslide enters the accelerated deformation stage, where the third moment is the next moment after the second moment; When the landslide deformation speeds at the second moment, the third moment, and the fourth moment all exceed the speed confidence interval, and the linear fitting slope is greater than 0, determine that the landslide enters the accelerated deformation stage and is in the uniform acceleration stage; the fourth moment is the next moment after the third moment; Determine the second moment as the starting point of landslide accelerated deformation.

[0061] Optionally, the first determination module 802 is specifically configured to: Obtain time series monitoring data according to the landslide deformation monitoring data; Obtain the landslide speed corresponding to each moment according to the time series monitoring data; Perform filtering processing on the landslide speeds corresponding to each moment to obtain the landslide deformation speeds corresponding to each moment after filtering.

[0062] Optionally, the second determination module 803 is specifically configured to: For large sample landslide monitoring data obtained by high-frequency monitoring means (such as Global Navigation Satellite System, crack meter, ground-based synthetic aperture radar, etc.), arrange the landslide deformation speeds corresponding to each moment in ascending order, and use the following formula to determine the lower limit and the upper limit : ; ; Among them, Percentile(·) represents the calculation process using the percentile method, represents the landslide deformation speed time series .

[0063] Optionally, the second determination module is specifically configured to: For the small-sample landslide monitoring data obtained by low-frequency monitoring means (such as total station, manual measurement, satellite synthetic aperture radar, etc.), arrange the landslide deformation speeds corresponding to each moment in ascending order, and use the following formula to determine the lower limit and the upper limit : ; ; Among them, N is the number of Bootstrap subsamples randomly sampled and constructed from the original sample ; represents the landslide deformation speed time series ; (i = 1, 2,..., N) represents the i th Bootstrap subsample; Percentile(·) represents the calculation process using the percentile method.

[0064] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes, it implements the steps in the landslide accelerated deformation starting point recognition method for landslide monitoring and early warning as described in any one of the above embodiments.

[0065] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps in the landslide accelerated deformation starting point recognition method for landslide monitoring and early warning as described in any one of the above embodiments.

[0066] Based on the same inventive concept, an embodiment of the present invention provides a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, it implements the steps in the landslide accelerated deformation starting point recognition method for landslide monitoring and early warning as described in any one of the above embodiments.

[0067] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0068] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0069] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the method, terminal device (device), and computer program product according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable terminal devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable terminal devices generate a device for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0070] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable terminal devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0071] These computer program instructions can also be loaded onto a computer or other programmable terminal devices, so that a series of operation steps are executed on the computer or other programmable terminal devices to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable terminal devices provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0072] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0073] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or terminal device comprising the element.

[0074] The above has introduced in detail a method for identifying the starting point of landslide accelerated deformation for landslide monitoring and early warning provided by the present invention. Specific examples are used in this text to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning, characterized in that: The method comprises: Obtain landslide deformation monitoring data; Determining the landslide deformation speed corresponding to each moment according to the landslide deformation monitoring data; Determine a velocity confidence interval according to the landslide deformation velocity corresponding to each moment, wherein the velocity confidence interval represents the velocity fluctuation range of the landslide in the steady-state creep stage; The starting point of the accelerated deformation of the landslide is determined according to the landslide deformation velocity at each moment and the velocity confidence interval.

2. The method for identifying the starting point of landslide acceleration deformation for landslide monitoring and early warning according to claim 1 is characterized in that: According to the landslide deformation velocity at each moment and the velocity confidence interval, the starting point of the landslide accelerated deformation is determined, including: When the landslide deformation velocity at the target time is greater than the velocity confidence interval, the target time is determined as the starting point of the landslide accelerated deformation.

3. The method for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning according to claim 1, characterized in that: According to the landslide deformation velocity at each moment and the velocity confidence interval, the starting point of the landslide accelerated deformation is determined, including: When the deformation velocity of the landslide at the first moment is within the velocity confidence interval, determining that the landslide is in a steady-state creep stage at the first moment; When the landslide deformation velocity at the second moment exceeds the velocity confidence interval for the first time, and the landslide deformation velocity at the third moment exceeds the velocity confidence interval, and the landslide deformation velocity at the three moments is greater than the landslide deformation velocity at the second moment, it is determined that the landslide enters the accelerated deformation stage, and the third moment is the next moment of the second moment; When the deformation speed of the landslide at the second moment, the third moment and the fourth moment all exceed the speed confidence interval and the linear fitting slope is greater than 0, it is determined that the landslide has entered the accelerated deformation stage and is in the uniform acceleration stage; the fourth moment is the next moment of the third moment; The second moment is determined as the starting point of accelerated deformation of the landslide.

4. The method for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning according to claim 1, characterized in that: Determining the landslide deformation speed corresponding to each moment according to the landslide deformation monitoring data includes: Time series monitoring data are obtained based on landslide deformation monitoring data; Obtaining the landslide velocity corresponding to each moment according to the time series monitoring data; The landslide velocity corresponding to each moment is filtered to obtain the landslide deformation velocity corresponding to each moment after filtering.

5. The method for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning according to claim 1, characterized in that: The velocity confidence interval is determined according to the landslide deformation velocity corresponding to each moment, including: The landslide deformation velocities corresponding to each moment are arranged in ascending order, and the following formula is used to determine the significance level: α The corresponding lower limit of the speed confidence interval and upper limit : ； ； Among them, Percentile(·) represents the calculation process using the percentile method, Represents the time series of landslide deformation velocity .

6. The method for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning according to claim 1, characterized in that: The velocity confidence interval is determined according to the landslide deformation velocity corresponding to each moment, including: The landslide deformation velocities corresponding to each moment are arranged in ascending order, and the significance level is determined by the following formula α The corresponding lower limit of the speed confidence interval and upper limit : ； ； in, N From the original sample The number of Bootstrap subsamples constructed by random sampling; Represents the time series of landslide deformation velocity ; (i = 1, 2, ..., N) represents the i Bootstrap subsamples; Percentile(·) indicates the calculation process using the percentile method.

7. A device for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning, characterized in that: The device for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning comprises: An acquisition module is used to acquire landslide deformation monitoring data; A first determination module, used to determine the landslide deformation speed corresponding to each moment according to the landslide deformation monitoring data; A second determination module is used to determine a velocity confidence interval according to the landslide deformation velocity corresponding to each moment, wherein the velocity confidence interval represents the velocity fluctuation range of the landslide in the steady-state creep stage; The third determination module is used to determine the starting point of the landslide accelerated deformation according to the landslide deformation velocity at each moment and the velocity confidence interval.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps in the method for identifying the starting point of accelerated deformation of a landslide for landslide monitoring and early warning as described in any one of claims 1 to 6 are implemented.

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