High-position landslide stability analysis method based on microseismic signal recognition

Abstract

The present application relates to high landslide stability analysis technical field, especially in kind based on microseismic signal identification's high landslide stability analysis method, the present application includes: collecting the slope rock sample carries out the destructive experiment and adopts the acoustic emission sensor to collect the test microseismic signal, constructs the test characteristic database, obtains the main frequency characteristic vector of fracture;Collecting hydrological characteristic parameter calculates the hydrological characteristic characteristic value and divides the hydrological influence grade of target area, determines the frequency of collection microseismic signal;Actual microseismic signal is collected to construct the field characteristic database, and the actual main frequency characteristic vector is obtained;The similarity of the actual main frequency characteristic vector and the corresponding main frequency characteristic vector of fracture is calculated;The similarity is judged based on high landslide stability grade;Based on the high landslide stability grade, determine whether to trigger emergency evacuation, and determine whether to start drainage equipment and grouting equipment.The present application significantly improves the high landslide early warning accuracy.

Description

Technical Field

[0001] This invention relates to the field of high-altitude landslide stability analysis technology, and in particular to a high-altitude landslide stability analysis method based on microseismic signal identification. Background Technology

[0002] With the development of geological disaster monitoring technology, people are paying increasing attention to the early warning of high-altitude landslides. Existing methods collect microseismic signals generated by rock mass fracturing on slopes and analyze signal characteristics to determine landslide stability. At the same time, some technologies combine multi-source monitoring data from air, space, ground, and underground sources, and use statistical methods and machine learning models to comprehensively assess landslide risk, attempting to consider the impact of hydrological factors such as rainfall and groundwater levels on landslides, thus providing more comprehensive data support for stability analysis.

[0003] However, existing technologies for analyzing microseismic signals do not fully consider the impact of different hydrological conditions on rock mass fracture characteristics. For example, the frequency characteristics of microseismic signals generated during tensile or shear failure of rock masses at different water contents will change, and traditional methods have not established effective correction models for such changes. Furthermore, the lack of dynamic adaptation to the hydrological cycle when determining microseismic signal acquisition frequencies and early warning parameters results in monitoring data that cannot accurately reflect actual landslide risks, limiting the accuracy of stability analysis and making it difficult to achieve reliable early warnings under complex hydrological conditions.

[0004] Chinese Patent Publication No. CN119575460A discloses a slope stability evaluation method based on the dual-dominant-frequency mechanism of microseismic signals. This invention relates to materials analysis and applications. The method is based on the objective principle that the dominant frequency characteristics of rock acoustic emission and microseismic signals are consistent and correlated, and on the dual-dominant-frequency phenomenon present in both acoustic emission tests and microseismic monitoring. It uses the dominant frequency characteristic patterns of rock samples obtained from acoustic emission tests to process the waveform data of microseismic signals obtained from microseismic monitoring. This allows for the acquisition of the dominant frequency characteristic evolution law of slope rock failure through microseismic signals, and a more accurate judgment of the stress state of the slope rock based on the dominant frequency characteristic evolution law, thereby evaluating the slope stability and significantly improving the accuracy of the evaluation results. This evaluation method combines acoustic emission tests and microseismic monitoring, offering advantages such as high evaluation accuracy and broad applicability, making it suitable for large-scale application in the monitoring and prevention of geological disasters such as landslides.

[0005] Chinese Patent Publication No. CN115691058A discloses a holographic three-dimensional network-based intelligent landslide early warning method based on multiple monitoring scenarios. This invention relates to the technical field of geological disaster early warning methods, and particularly to a holographic three-dimensional network-based intelligent landslide early warning method based on multiple monitoring scenarios. This invention's holographic three-dimensional network-based intelligent landslide early warning method, through conducting holographic and all-round monitoring of landslides in multiple monitoring fields (air, space, ground, and underground), constructs a comprehensive landslide early warning criterion coupling multiple characteristics such as surface deformation displacement, rate, and acceleration; deep deformation displacement, rate, and acceleration; groundwater level; microseismic intensity; rainfall; and soil moisture content. This effectively overcomes the limitations of currently used single or dual criteria, which suffer from large errors, low accuracy, and high false alarm rates, significantly improving early warning accuracy.

[0006] Therefore, the following problems exist in the existing technology:

[0007] The dynamic changes in the dominant frequency characteristics of microseismic signals during rock mass fracturing due to differences in soil moisture content caused by different rainfall amounts in different seasons were not considered, and the dynamic adjustment of the microseismic signal acquisition frequency based on hydrological characteristic parameters was not taken into account. This resulted in low identification rate and low accuracy of early warning for landslide risks in high-risk hydrological scenarios. Summary of the Invention

[0008] To address this, the present invention provides a method for analyzing the stability of high-altitude landslides based on microseismic signal identification. This method overcomes the problems in existing technologies that fail to consider the dynamic changes in the dominant frequency characteristics of microseismic signals when rock masses fracture under different water content conditions, and also fail to consider the dynamic adjustment of the microseismic signal acquisition frequency based on hydrological characteristic parameters. These shortcomings result in low identification rates and low accuracy of early warnings for landslide risks in high-risk hydrological scenarios.

[0009] To achieve the above objectives, this invention provides a method for analyzing the stability of high-altitude landslides based on microseismic signal identification, comprising:

[0010] Slope rock samples from the target area were collected for destructive verification experiments under different water content conditions. Acoustic emission sensors were used to collect test microseismic signals at various water contents.

[0011] A test feature database is constructed based on the test microseismic signals at each water content, and the rupture dominant frequency feature vector at each water content is obtained.

[0012] Collect hydrological characteristic parameters of the target area within a historical period, and calculate hydrological characteristic values ​​based on the hydrological characteristic parameters of the target area within the historical period;

[0013] Based on the hydrological characteristic values, the hydrological impact level of the target area is divided, and the frequency of microseismic signal acquisition is determined based on the hydrological impact level.

[0014] Based on the frequency of the acquired microseismic signals, the actual microseismic signals of the target area are acquired. Based on the actual microseismic signals and soil moisture content, a field feature database is constructed to obtain the actual dominant frequency feature vector.

[0015] Calculate the similarity between the actual dominant frequency feature vector of the target area and the corresponding rupture dominant frequency feature vector under the water content, and determine the stability level of the high-level landslide based on the similarity.

[0016] Based on the stability level of the high-level landslide, determine whether to trigger an emergency evacuation, and whether to activate drainage and grouting equipment;

[0017] After completing drainage and grouting adjustment, the actual microseismic signals of the adjusted target area are collected, the corresponding actual main frequency feature vector is obtained, the corresponding updated similarity is obtained, and the stability level of the corresponding high-level landslide is determined based on the similarity.

[0018] The hydrological characteristic parameters include soil moisture content, groundwater level variation, and rainfall.

[0019] Furthermore, the process of constructing a test feature database based on the test microseismic signals at each water content, and obtaining the rupture dominant frequency feature vector at each water content, includes:

[0020] The acquired microseismic signals at various water contents are used to generate a visualization image of energy distribution in a two-dimensional time-frequency coordinate system.

[0021] Based on the energy distribution visualization image, the fracture main frequency feature vector at each water content is obtained;

[0022] The fractured main frequency feature vector includes the energy peak frequency, the ratio of main frequency energy to total energy, and the bandwidth.

[0023] Furthermore, the process of calculating the hydrological characteristic values ​​based on the hydrological characteristic parameters of the target area within a historical period includes,

[0024] Obtain soil moisture content, groundwater level changes, and rainfall in the target area over a historical period;

[0025] The ratio of soil moisture content within a single cycle to a predetermined soil moisture content threshold is determined as the first hydrological characteristic parameter.

[0026] The ratio of the groundwater level change amplitude within a single cycle to a predetermined groundwater level change amplitude threshold is determined as the second hydrological characteristic parameter.

[0027] The ratio of rainfall within a single cycle to a predetermined rainfall threshold is determined as the third hydrological characteristic parameter.

[0028] The sum of the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter is determined as the hydrological characteristic representation value.

[0029] Furthermore, the process of classifying the hydrological impact level of the target area based on the aforementioned hydrological characteristic values ​​includes,

[0030] If the hydrological characteristic value of the target area is greater than the predetermined hydrological characteristic value threshold, the hydrological impact level of the target area is determined to be the first hydrological impact level.

[0031] If the hydrological characteristic value of the target area is less than or equal to the predetermined hydrological characteristic threshold, the hydrological impact level of the target area is determined to be the second hydrological impact level.

[0032] Furthermore, the process of determining the frequency of the acquired microseismic signal based on the hydrological impact level of the target area includes,

[0033] If the hydrological impact level of the target area is the first level, then the frequency for collecting microseismic signals should be a relatively high frequency.

[0034] If the hydrological impact level of the target area is the second hydrological impact level, then the frequency for collecting microseismic signals should be determined to be a lower frequency.

[0035] Furthermore, the process of constructing a field feature database based on the actual microseismic signals and soil moisture content of the target area, and obtaining the actual dominant frequency feature vector, includes:

[0036] The actual microseismic signals of the target area and the soil moisture content are used to generate a visualization image of energy distribution in a two-dimensional time-frequency coordinate system;

[0037] Based on the energy distribution visualization image, the actual main frequency feature vector is obtained;

[0038] The actual main frequency feature vector includes the peak energy frequency, the ratio of main frequency energy to total energy, and the bandwidth.

[0039] Furthermore, the process of calculating the similarity between the actual dominant frequency feature vector of the target region and the corresponding rupture dominant frequency feature vector at the corresponding water content includes,

[0040] Obtain the actual dominant frequency feature vector and the corresponding rupture dominant frequency feature vector extracted from the test model at the corresponding water content;

[0041] The calculation formula calculates the similarity between the actual dominant frequency feature vector of the target region and the rupture dominant frequency feature vector at the corresponding water content.

[0042] Furthermore, the process of determining the stability level of a high-altitude landslide based on the aforementioned similarity includes,

[0043] If the similarity is greater than the first similarity threshold when the hydrological impact level of the target area is the first hydrological impact level, then the stability level of the high-level landslide is determined to be high-risk.

[0044] If the similarity is greater than the second similarity threshold when the hydrological impact level of the target area is the second hydrological impact level, then the stability level of the high-level landslide is determined to be the warning level.

[0045] Furthermore, based on the stability level of the high-altitude landslide, the process of determining whether to trigger an emergency evacuation includes,

[0046] If the stability level of the high-level landslide in the target area is high-risk, then the audible and visual alarm device will be activated and an evacuation order will be sent.

[0047] If the stability level of the high-level landslide in the target area is at the warning level, then the frequency for collecting microseismic signals is determined to be a relatively high frequency to monitor the actual microseismic signals in the target area.

[0048] Furthermore, it also includes an intelligent terminal for a high-altitude landslide stability analysis method based on microseismic signal identification, the intelligent terminal including a data acquisition module, a data analysis module, a level assessment module, and an early warning module.

[0049] Compared with existing technologies, the advantages of this invention lie in providing a method for analyzing the stability of high-altitude landslides based on microseismic signal identification. By conducting tensile and shear failure experiments on rock samples under different water content conditions, corresponding microseismic signals are obtained and a test feature database is constructed. This allows for the accurate capture of the fracture characteristics of rock masses under different hydrological environments, overcoming the shortcomings of traditional methods that ignore the influence of hydrological conditions on rock mass fracture. Simultaneously, by combining historical hydrological characteristic parameters to calculate characterization values ​​and classify levels, the microseismic signal acquisition frequency is determined, enabling dynamic adjustment of the monitoring frequency. This makes monitoring more targeted, ensuring monitoring effectiveness while avoiding resource waste. Furthermore, by repeatedly acquiring and analyzing adjusted microseismic signals, a closed-loop monitoring and evaluation system is formed, enabling timely understanding of changes in the stability of high-altitude landslides, providing a scientific basis for subsequent decision-making, and significantly improving the accuracy of high-altitude landslide early warning.

[0050] In particular, this invention generates a visual image of the energy distribution in a two-dimensional time-frequency coordinate system from the test microseismic signals at various water contents. This allows complex microseismic signals to be presented in an intuitive visual form, facilitating in-depth signal analysis. Based on the visualized energy distribution image, a fracture dominant frequency feature vector is extracted, which includes the peak energy frequency, the ratio of dominant frequency energy to total energy, and the bandwidth, comprehensively reflecting the essential characteristics of the microseismic signal during rock mass fracture. This method not only improves the accuracy and comprehensiveness of feature extraction but also provides a reliable basis for the comparative analysis of rock mass fracture characteristics at different water contents. Compared to traditional methods that only perform simple analysis of microseismic signals, this process can more accurately capture subtle changes during rock mass fracture, thus providing a more solid foundation for subsequent high-altitude landslide stability analysis and contributing to improving the reliability and effectiveness of the entire analysis method.

[0051] In particular, this invention processes multi-dimensional hydrological characteristic parameters such as soil moisture content, groundwater level fluctuations, and rainfall over historical periods, transforming these dispersed parameters into a comprehensive hydrological characteristic value. First, the ratio of each parameter to its corresponding predetermined threshold is calculated, achieving standardization and making parameters of different magnitudes comparable. Then, the ratios of the three parameters are added to obtain a comprehensive characteristic value, which fully reflects the overall hydrological characteristics of the target area, avoiding the one-sidedness of single-parameter analysis. This method considers the combined impact of multiple hydrological factors on high-altitude landslides, making the calculated hydrological characteristic value more accurately reflect the hydrological conditions of the target area. This provides scientific and reasonable data support for subsequent hydrological impact level classification, contributing to improved accuracy in high-altitude landslide stability analysis.

[0052] In particular, this invention classifies the hydrological impact level of a target area by using hydrological characteristic values, enabling a clear and precise definition of the hydrological conditions of the target area. By setting predetermined threshold values ​​for hydrological characteristic values, the hydrological impact level of the target area is divided into first and second levels, making complex hydrological conditions simple and easy to understand, facilitating subsequent appropriate measures. This classification method can quickly determine the degree of influence of the target area's hydrological conditions on the stability of high-altitude landslides. When the hydrological characteristic value is greater than the threshold, it indicates that the hydrological conditions have a greater impact on the high-altitude landslide, requiring more stringent monitoring and prevention measures; conversely, the impact is relatively small, and the monitoring strategy can be adjusted appropriately. This classification method provides a direct basis for determining the frequency of microseismic signal acquisition, helping to improve the targeting and effectiveness of high-altitude landslide monitoring, thereby enhancing the practicality of the entire stability analysis method.

[0053] In particular, this invention uses a cosine similarity formula to calculate the similarity between the actual dominant frequency eigenvector and the corresponding rupture dominant frequency eigenvector at the corresponding water content, enabling an objective and quantitative measurement of the degree of similarity between the two. Cosine similarity reflects the similarity of two vectors by calculating the angle between them in space; a larger value indicates greater similarity. This calculation method comprehensively considers the influence of various parameters in the eigenvector, avoiding the limitations of comparing a single parameter, making the similarity calculation results more scientific and reliable. Through this quantitative similarity value, the closeness between the rock mass condition and the rupture state of the current target area can be more accurately determined, providing a clear numerical basis for judging the stability level of high-altitude landslides. This helps improve the objectivity and accuracy of the judgment results, reduces errors from human judgment, and thus enhances the credibility of the entire high-altitude landslide stability analysis method.

[0054] In particular, this invention determines the stability level of high-altitude landslides by combining similarity and hydrological influence levels, fully considering the combined effects of the rock mass's own condition and external hydrological conditions, resulting in a more comprehensive and reasonable assessment. Different similarity thresholds are used for different hydrological influence levels, reflecting the crucial role of hydrological conditions in the stability of high-altitude landslides. When the hydrological influence level is Level 1, it indicates a high risk of external hydrological conditions; if the similarity exceeds the first similarity threshold, it is classified as a high-risk level, enabling timely identification of high-risk conditions. When the hydrological influence level is Level 2, external hydrological conditions are relatively stable; if the similarity exceeds the second similarity threshold, it is classified as a warning level, facilitating early preventative measures. This assessment method avoids the one-sidedness of relying solely on a single factor for level determination, improving the accuracy of high-altitude landslide stability assessment. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the steps of the high-altitude landslide stability analysis method based on microseismic signal identification according to an embodiment of the present invention.

[0056] Figure 2 The flowchart of the steps of constructing a test feature database based on the test microseismic signals at each water content and obtaining the rupture dominant frequency feature vector at each water content in this embodiment of the invention is as follows:

[0057] Figure 3 A flowchart illustrating the steps of calculating hydrological characteristic values ​​based on the hydrological characteristic parameters of the target area within a historical period, according to an embodiment of the present invention;

[0058] Figure 4 This invention provides a logic diagram for determining the hydrological impact level of a target area based on the hydrological characteristic values. Detailed Implementation

[0059] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0060] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0061] Please see Figure 1 The diagram shows the steps of a high-altitude landslide stability analysis method based on microseismic signal identification according to an embodiment of the present invention. The present invention provides a high-altitude landslide stability analysis method based on microseismic signal identification, comprising:

[0062] Slope rock samples from the target area were collected for destructive verification experiments under different water content conditions. Acoustic emission sensors were used to collect test microseismic signals at various water contents.

[0063] A test feature database is constructed based on the test microseismic signals at each water content, and the rupture dominant frequency feature vector at each water content is obtained.

[0064] Collect hydrological characteristic parameters of the target area within a historical period, and calculate hydrological characteristic values ​​based on the hydrological characteristic parameters of the target area within the historical period;

[0065] Based on the hydrological characteristic values, the hydrological impact level of the target area is divided, and the frequency of microseismic signal acquisition is determined based on the hydrological impact level.

[0066] Based on the frequency of the acquired microseismic signals, the actual microseismic signals of the target area are acquired. Based on the actual microseismic signals and soil moisture content, a field feature database is constructed to obtain the actual dominant frequency feature vector.

[0067] Calculate the similarity between the actual dominant frequency feature vector of the target area and the corresponding rupture dominant frequency feature vector under the water content, and determine the stability level of the high-level landslide based on the similarity.

[0068] Based on the stability level of the high-level landslide, determine whether to trigger an emergency evacuation, and whether to activate drainage and grouting equipment;

[0069] After completing drainage and grouting adjustment, the actual microseismic signals of the adjusted target area are collected, the corresponding actual main frequency feature vector is obtained, the corresponding updated similarity is obtained, and the stability level of the corresponding high-level landslide is determined based on the similarity.

[0070] The hydrological characteristic parameters include soil moisture content, groundwater level variation, and rainfall.

[0071] It is understood that the rupture mechanisms of the aforementioned destructive testing include tensile failure and shear failure.

[0072] It is understood that the process of acquiring the test microseismic signals includes obtaining the acoustic emission signals of the rock sample during the tensile failure experiment and the shear failure experiment through an acoustic emission sensor; the actual microseismic signals are acquired in the target area through microseismic monitoring equipment.

[0073] By conducting tensile and shear failure experiments on rock samples under different moisture contents, a test feature database was constructed using corresponding microseismic signals. This allows for the precise capture of rock mass fracture characteristics under varying hydrological environments, overcoming the shortcomings of traditional methods that neglect the influence of hydrological conditions on rock mass fracture. Simultaneously, characterization values ​​were calculated and categorized based on historical hydrological parameters to determine the microseismic signal acquisition frequency, enabling dynamic adjustment of the monitoring frequency and making monitoring more targeted. This ensures monitoring effectiveness while avoiding resource waste. Furthermore, by repeatedly acquiring and analyzing adjusted microseismic signals, a closed-loop monitoring and evaluation system was formed. This system can promptly grasp changes in the stability of high-altitude landslides, providing a scientific basis for subsequent decision-making and significantly improving the accuracy of high-altitude landslide early warning.

[0074] Please see Figure 2 The diagram shows the steps of constructing a test feature database based on the test microseismic signals at various water cuts and obtaining the rupture dominant frequency feature vector at each water cut, according to an embodiment of the present invention. The process of constructing a test feature database based on the test microseismic signals at various water cuts and obtaining the rupture dominant frequency feature vector at each water cut includes:

[0075] The acquired microseismic signals at various water contents are used to generate a visualization image of energy distribution in a two-dimensional time-frequency coordinate system.

[0076] Based on the energy distribution visualization image, the fracture main frequency feature vector at each water content is obtained;

[0077] The fractured main frequency feature vector includes the energy peak frequency, the ratio of main frequency energy to total energy, and the bandwidth.

[0078] In this embodiment, environmental noise is removed from the acquired test microseismic signals at various water contents, and the effective signal segments are retained to generate a visualization image of energy distribution in a two-dimensional time-frequency coordinate system. This image visually displays the energy distribution of the microseismic signals in the time and frequency dimensions, determines the frequency range where energy is concentrated, and uses a peak detection algorithm to extract the peak energy frequency, the ratio of dominant frequency energy to total energy, and the bandwidth. This constructs a rupture dominant frequency feature vector at each water content, providing a standardized data format for subsequent analysis.

[0079] By generating energy distribution visualization images in a two-dimensional time-frequency coordinate system from test microseismic signals at various water contents, complex microseismic signals can be presented in an intuitive visual form, facilitating in-depth signal analysis. Based on these energy distribution visualization images, a fracture dominant frequency feature vector is extracted, which includes the peak energy frequency, the ratio of dominant frequency energy to total energy, and the bandwidth, comprehensively reflecting the essential characteristics of the microseismic signal during rock mass fracture. This method not only improves the accuracy and comprehensiveness of feature extraction but also provides a reliable basis for comparative analysis of rock mass fracture characteristics at different water contents. Compared to traditional methods that only perform simple analysis of microseismic signals, this process can more accurately capture subtle changes during rock mass fracture, thus providing a more solid foundation for subsequent high-altitude landslide stability analysis and contributing to improving the reliability and effectiveness of the entire analysis method.

[0080] Please see Figure 3 The diagram shows a flowchart illustrating the steps of calculating hydrological characteristic values ​​based on the hydrological characteristic parameters of a target area within a historical period, according to an embodiment of the present invention. The process of calculating hydrological characteristic values ​​based on the hydrological characteristic parameters of a target area within a historical period includes:

[0081] The process of calculating hydrological characteristic values ​​based on the hydrological characteristic parameters of the target area within a historical period includes:

[0082] Obtain soil moisture content, groundwater level changes, and rainfall in the target area over a historical period;

[0083] The ratio of soil moisture content within a single cycle to a predetermined soil moisture content threshold is determined as the first hydrological characteristic parameter.

[0084] The ratio of the groundwater level change amplitude within a single cycle to a predetermined groundwater level change amplitude threshold is determined as the second hydrological characteristic parameter.

[0085] The ratio of rainfall within a single cycle to a predetermined rainfall threshold is determined as the third hydrological characteristic parameter.

[0086] The sum of the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter is determined as the hydrological characteristic representation value.

[0087] In this embodiment, the soil moisture content threshold is pre-determined, calculated by acquiring the soil moisture content during the rainy season over a historical period of twelve months, and the average value is set as the soil moisture content threshold to 75% of the average value. The groundwater level fluctuation threshold is pre-determined, calculated by acquiring the daily groundwater level fluctuation over a historical period of three months, and the average value is set as the groundwater level fluctuation threshold to twice the average value. The rainfall threshold is pre-determined, calculated by acquiring the rainfall during the rainy season over a historical period of twelve months, and the average rainfall threshold is set as 75% of the average value. A single period is set to 24 hours.

[0088] By processing multi-dimensional hydrological characteristic parameters such as soil moisture content, groundwater level fluctuations, and rainfall over historical periods, these dispersed parameters are transformed into a comprehensive hydrological characteristic value. First, the ratio of each parameter to its corresponding predetermined threshold is calculated, achieving parameter standardization and making parameters of different magnitudes comparable. Then, the ratios of the three parameters are added to obtain the comprehensive characteristic value, which can fully reflect the overall hydrological characteristics of the target area and avoid the one-sidedness of single-parameter analysis. This method considers the combined impact of multiple hydrological factors on high-altitude landslides, making the calculated hydrological characteristic value more realistically reflect the hydrological conditions of the target area. This provides scientific and reasonable data support for subsequent hydrological impact level classification and helps improve the accuracy of high-altitude landslide stability analysis.

[0089] Please see Figure 4 As shown, this is a logic diagram for determining the hydrological impact level of a target area based on the hydrological characteristic values ​​in an embodiment of the present invention. The process of determining the hydrological impact level of a target area based on the hydrological characteristic values ​​in the present invention includes:

[0090] The process of classifying the hydrological impact level of the target area based on the aforementioned hydrological characteristic values ​​includes:

[0091] If the hydrological characteristic value of the target area is greater than the predetermined hydrological characteristic value threshold, the hydrological impact level of the target area is determined to be the first hydrological impact level.

[0092] If the hydrological characteristic value of the target area is less than or equal to the predetermined hydrological characteristic threshold, the hydrological impact level of the target area is determined to be the second hydrological impact level.

[0093] In this embodiment, the threshold value for hydrological characteristics is obtained in advance and takes a value in the range of [2.85, 2.95].

[0094] Classifying the hydrological impact level of a target area by using hydrological characteristic values ​​allows for a clear and precise definition of the hydrological conditions in that area. By setting predetermined threshold values ​​for hydrological characteristic values, the hydrological impact level of the target area is divided into first and second levels, simplifying complex hydrological conditions and facilitating subsequent appropriate measures. This classification method can quickly determine the degree of influence of the target area's hydrological conditions on the stability of high-altitude landslides. When the hydrological characteristic value is greater than the threshold, it indicates a significant impact of the hydrological conditions on the high-altitude landslide, requiring more stringent monitoring and prevention measures; conversely, the impact is relatively small, and monitoring strategies can be adjusted appropriately. This classification method provides a direct basis for determining the frequency of microseismic signal acquisition, helping to improve the targeting and effectiveness of high-altitude landslide monitoring, thereby enhancing the practicality of the entire stability analysis method.

[0095] Specifically, the process of determining the frequency of microseismic signal acquisition based on the hydrological impact level of the target area includes,

[0096] If the hydrological impact level of the target area is the first level, then the frequency for collecting microseismic signals should be a relatively high frequency.

[0097] If the hydrological impact level of the target area is the second hydrological impact level, then the frequency for collecting microseismic signals should be determined to be a lower frequency.

[0098] In this embodiment, at the first hydrological impact level, a higher frequency microseismic signal is acquired, using 500Hz sampling, for high-risk hydrological periods where high-frequency damage signals need to be captured. At the second hydrological impact level, a lower frequency microseismic signal is acquired, using 100Hz sampling, for drought or low hydrological risk periods, meeting basic monitoring needs while considering the endurance of the monitoring equipment.

[0099] Specifically, the process of constructing a field feature database based on the actual microseismic signals and soil moisture content of the target area, and obtaining the actual dominant frequency feature vector, includes the following steps:

[0100] The actual microseismic signals of the target area and the soil moisture content are used to generate a visualization image of energy distribution in a two-dimensional time-frequency coordinate system;

[0101] Based on the energy distribution visualization image, the actual main frequency feature vector is obtained;

[0102] The actual main frequency feature vector includes the peak energy frequency, the ratio of main frequency energy to total energy, and the bandwidth.

[0103] In this embodiment, environmental noise is removed from the actual microseismic signal of the target area, and the effective signal segment is retained to generate a visualization image of energy distribution in a two-dimensional time-frequency coordinate system. This image shows the energy distribution of the actual microseismic signal in the time and frequency dimensions under different soil moisture conditions. The frequency range of energy concentration is determined, and a peak detection algorithm is used to extract the peak energy frequency, the ratio of dominant frequency energy to total energy, and the bandwidth. An actual dominant frequency feature vector is constructed, which represents the current actual rock mass microseismic characteristics of the target area and is used for comparative analysis with the feature vector in the test model.

[0104] Specifically, the process of calculating the similarity between the actual dominant frequency feature vector of the target region and the corresponding rupture dominant frequency feature vector at the corresponding water cut includes,

[0105] Obtain the actual dominant frequency feature vector and the corresponding rupture dominant frequency feature vector extracted from the test model at the corresponding water content;

[0106] The calculation formula calculates the similarity between the actual dominant frequency feature vector of the target region and the rupture dominant frequency feature vector at the corresponding water content.

[0107] Understandably, the cosine similarity calculation formula is an existing technology that measures similarity by comparing the directions of two vectors. The calculation first calculates the sum of the product of corresponding elements of the two vectors, then calculates the square root of the sum of the squares of the elements of each vector, and finally divides the sum of the squares by the product of the two vectors. The closer the result is to 1, the more consistent the directions of the two vectors are, and the higher the similarity.

[0108] By employing the cosine similarity formula to calculate the similarity between the actual dominant frequency eigenvector and the corresponding rupture dominant frequency eigenvector at the corresponding water content, the degree of similarity between the two can be objectively and quantitatively measured. Cosine similarity reflects the similarity between two vectors by calculating the angle between them in space; a larger value indicates greater similarity. This calculation method comprehensively considers the influence of various parameters in the eigenvectors, avoiding the limitations of comparing a single parameter, making the similarity calculation results more scientific and reliable. Through this quantitative similarity value, the degree of proximity between the rock mass condition and the rupture state in the current target area can be more accurately determined, providing a clear numerical basis for judging the stability level of high-altitude landslides. This helps improve the objectivity and accuracy of the judgment results, reduces errors from human judgment, and thus enhances the credibility of the entire high-altitude landslide stability analysis method.

[0109] Specifically, the process of determining the stability level of a high-altitude landslide based on the aforementioned similarity includes,

[0110] If the similarity is greater than the first similarity threshold when the hydrological impact level of the target area is the first hydrological impact level, then the stability level of the high-level landslide is determined to be high-risk.

[0111] If the similarity is greater than the second similarity threshold when the hydrological impact level of the target area is the second hydrological impact level, then the stability level of the high-level landslide is determined to be the warning level.

[0112] In this embodiment, the first hydrological impact level is a high-risk level, at which point water level interference causes the main frequency to shift naturally, and the first similarity threshold is set to 0.75. The second hydrological impact level is a low-risk level, and under low hydrological interference, the rupture features need to be strictly matched, and the second similarity threshold is set to 0.85.

[0113] The method of determining the stability level of high-altitude landslides by combining similarity and hydrological impact level fully considers the combined influence of the rock mass's own condition and external hydrological conditions, making the determination more comprehensive and reasonable. Different similarity thresholds are used for different hydrological impact levels, reflecting the important role of hydrological conditions in the stability of high-altitude landslides. When the hydrological impact level is level one, it indicates a high risk of external hydrological conditions; if the similarity is greater than the first similarity threshold, it is classified as a high-risk level, enabling timely identification of high-risk conditions. When the hydrological impact level is level two, the external hydrological conditions are relatively stable; if the similarity is greater than the second similarity threshold, it is classified as a warning level, facilitating early prevention and preparation. This method avoids the one-sidedness of determining the level based on only a single factor, improving the accuracy of high-altitude landslide stability level determination.

[0114] Specifically, the process of determining whether to trigger an emergency evacuation based on the stability level of the high-altitude landslide includes:

[0115] If the stability level of the high-level landslide in the target area is high-risk, then the audible and visual alarm device will be activated and an evacuation order will be sent.

[0116] If the stability level of the high-level landslide in the target area is at the warning level, then the frequency for collecting microseismic signals is determined to be a relatively high frequency to monitor the actual microseismic signals in the target area.

[0117] Specifically, it also includes an intelligent terminal for a high-altitude landslide stability analysis method based on microseismic signal identification. The intelligent terminal includes a data acquisition module, a data analysis module, a level assessment module, and an early warning module.

[0118] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for analyzing the stability of high-altitude landslides based on microseismic signal identification, characterized in that, include: Slope rock samples from the target area were collected for destructive verification experiments under different water content conditions. Acoustic emission sensors were used to collect test microseismic signals at various water contents. A test feature database is constructed based on the test microseismic signals at each water content, and the rupture dominant frequency feature vector at each water content is obtained. Collect hydrological characteristic parameters of the target area within a historical period, and calculate hydrological characteristic values ​​based on the hydrological characteristic parameters of the target area within the historical period; The hydrological impact level of the target area is determined based on the aforementioned hydrological characteristic values. If the hydrological characteristic value of the target area is greater than the predetermined hydrological characteristic value threshold, the hydrological impact level of the target area is determined to be the first hydrological impact level. If the hydrological characteristic value of the target area is less than or equal to the predetermined hydrological characteristic threshold, the hydrological impact level of the target area is determined to be the second hydrological impact level. The frequency of microseismic signal acquisition is determined based on the hydrological impact level. Based on the frequency of the acquired microseismic signals, the actual microseismic signals of the target area are acquired. Based on the actual microseismic signals and soil moisture content, a field feature database is constructed to obtain the actual dominant frequency feature vector. Calculating the similarity between the actual dominant frequency feature vector of the target area and the corresponding rupture dominant frequency feature vector under the water content, and judging the degree of similarity between the rock mass condition and the rupture state of the current target area based on the similarity, in order to determine the stability level of the high-altitude landslide, includes: Obtain the actual dominant frequency feature vector and the corresponding rupture dominant frequency feature vector extracted from the test model at the corresponding water content; The calculation formula calculates the similarity between the actual dominant frequency feature vector of the target area and the corresponding rupture dominant frequency feature vector at the water content. If the similarity is greater than the first similarity threshold when the hydrological impact level of the target area is the first hydrological impact level, then the stability level of the high-level landslide is determined to be high-risk. If the similarity is greater than the second similarity threshold when the hydrological impact level of the target area is the second hydrological impact level, then the stability level of the high-level landslide is determined to be the warning level. Based on the stability level of the high-level landslide, determine whether to trigger an emergency evacuation, and whether to activate drainage and grouting equipment; After completing drainage and grouting adjustment, the actual microseismic signals of the adjusted target area are collected, the corresponding actual main frequency feature vector is obtained, the corresponding updated similarity is obtained, and the stability level of the corresponding high-level landslide is determined based on the similarity. The hydrological characteristic parameters include soil moisture content, groundwater level variation, and rainfall.

2. The method for high-altitude landslide stability analysis based on microseismic signal identification according to claim 1, characterized in that, The process of constructing a test feature database based on the test microseismic signals at each water cut and obtaining the rupture dominant frequency feature vector at each water cut includes: The acquired microseismic signals at various water contents are used to generate an energy distribution visualization image in a two-dimensional time-frequency coordinate system. Based on the energy distribution visualization image, the fracture main frequency feature vector at each water content is obtained; The fractured main frequency feature vector includes the energy peak frequency, the ratio of main frequency energy to total energy, and the bandwidth.

3. The method for high-altitude landslide stability analysis based on microseismic signal identification according to claim 1, characterized in that, The process of calculating hydrological characteristic values ​​based on the hydrological characteristic parameters of the target area within a historical period includes: Obtain soil moisture content, groundwater level changes, and rainfall in the target area over a historical period; The ratio of soil moisture content within a single cycle to a predetermined soil moisture content threshold is determined as the first hydrological characteristic parameter. The ratio of the groundwater level change amplitude within a single cycle to a predetermined groundwater level change amplitude threshold is determined as the second hydrological characteristic parameter. The ratio of rainfall within a single cycle to a predetermined rainfall threshold is determined as the third hydrological characteristic parameter. The sum of the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter is determined as the hydrological characteristic representation value.

4. The method for high-altitude landslide stability analysis based on microseismic signal identification according to claim 1, characterized in that, The process of determining the frequency of microseismic signal acquisition based on the hydrological impact level of the target area includes the following steps: If the hydrological impact level of the target area is the first level, then the frequency for collecting microseismic signals should be a relatively high frequency. If the hydrological impact level of the target area is the second hydrological impact level, then the frequency for collecting microseismic signals should be determined to be a lower frequency.

5. The method for high-altitude landslide stability analysis based on microseismic signal identification according to claim 1, characterized in that, The process of constructing a field feature database based on actual microseismic signals and soil moisture content in the target area, and obtaining the actual dominant frequency feature vector, includes the following steps: The actual microseismic signals of the acquired target area are compared with the soil moisture content to generate a visualization image of energy distribution in a two-dimensional time-frequency coordinate system; Based on the energy distribution visualization image, the actual main frequency feature vector is obtained; The actual main frequency feature vector includes the peak energy frequency, the ratio of main frequency energy to total energy, and the bandwidth.

6. The method for high-altitude landslide stability analysis based on microseismic signal identification according to claim 1, characterized in that, Based on the stability level of the high-altitude landslide, the process of determining whether to trigger an emergency evacuation includes, If the stability level of the high-level landslide in the target area is high-risk, then the audible and visual alarm device will be activated and an evacuation order will be sent. If the stability level of the high-level landslide in the target area is at the warning level, then the frequency for collecting microseismic signals is determined to be a relatively high frequency to monitor the actual microseismic signals in the target area.

7. The method for high-altitude landslide stability analysis based on microseismic signal identification according to claim 1, characterized in that, The intelligent terminal used in the method for identifying the stability of high-altitude landslides includes a data acquisition module, a data analysis module, a level assessment module, and an early warning module.

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