CN112597689A - Landslide process analysis method, process numerical value reconstruction method and application - Google Patents

Landslide process analysis method, process numerical value reconstruction method and application Download PDF

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CN112597689A
CN112597689A CN202011453490.2A CN202011453490A CN112597689A CN 112597689 A CN112597689 A CN 112597689A CN 202011453490 A CN202011453490 A CN 202011453490A CN 112597689 A CN112597689 A CN 112597689A
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landslide
signal
energy
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CN112597689B (en
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崔一飞
严炎
熊广林
尹述遥
郭剑
李丽
田鑫
田继枫
朱兴华
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INSTITUTE OF GEOPHYSICS CHINA EARTHQUAKE ADMINISTRATION
Tsinghua University
Southwest Jiaotong University
Changan University
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INSTITUTE OF GEOPHYSICS CHINA EARTHQUAKE ADMINISTRATION
Tsinghua University
Southwest Jiaotong University
Changan University
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    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/282—Application of seismic models, synthetic seismograms
    • G—PHYSICS
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    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/30—Analysis
    • G—PHYSICS
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    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
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Abstract

The invention discloses a landslide process analysis method, a process numerical reconstruction method and application. The landslide disaster process analysis method provided by the invention is characterized in that an original landslide seismic signal is taken as a basis, IMF (inertial measurement function) principal component signals are extracted, time-frequency spectrum transformation is carried out, and then 5 stages of a landslide process are divided according to frequency energy characteristics of different time periods in the landslide seismic signal. The method extracts the transition stage of the landslide process for the first time. The invention provides an application of a landslide disaster process analysis method in landslide disaster analysis, landslide disaster emergency response scheme design and landslide event mathematical modeling. The invention also provides a landslide process numerical simulation reconstruction method, which is used for carrying out the numerical simulation reconstruction of the landslide process on the basis of the energy data evolution of the landslide seismic signals. And the application of the reconstruction method in landslide disaster analysis and landslide disaster emergency response scheme design. The method improves the abundance degree of landslide disaster basic data and the effectiveness of the disaster prevention and control emergency scheme, and has remarkable social benefit.

Description

Landslide process analysis method, process numerical value reconstruction method and application
Technical Field
The invention relates to a signal analysis technology, in particular to a technology for analyzing a landslide disaster process and reconstructing a disaster process numerical value by utilizing seismic signals and application thereof, belonging to the technical fields of signal analysis and processing technology and geological disaster monitoring.
Background
A seismic observation station network all over the world monitors every day to obtain massive seismic signals. The seismic signal is essentially seismic waveform information generated by various natural and unnatural seismic events, wherein the seismic waveform information comprises information generated by various geological disaster events. The earthquake motion signal public resource, and the sharing of global earthquake information is primarily realized. Therefore, various technologies for preventing and controlling geological disasters by using seismic signals can greatly save the monitoring cost of the geological disasters, especially the monitoring cost for large areas.
Landslide is a geological disaster which occurs under the comprehensive influence of various environmental factors, is an independent geological disaster and is one of derivative disasters of other types of geological disasters, so that the landslide has high incidence rate and is listed as a major geological disaster by many countries in the world. However, monitoring of landslide disasters, especially monitoring of disasters in the whole process, is one of the difficulties in the field of geological disaster prevention and control. In order to monitor landslide occurrence, the main idea of the prior art is to arrange a sensor network in a mountain monitoring area, combine with a GPRS communication technology to realize remote real-time monitoring of the monitoring area, analyze and process acquired data to judge whether landslide is about to occur in advance, and to provide service for early warning and forecasting work of landslide. However, the implementation of this type of technology has three distinct drawbacks: first, as described above, since a landslide disaster is an independent disaster or a derivative disaster, the occurrence frequency is high and the location is not fixed. The prior art can not implement the sensor network monitoring technology to the wide region, can only select some key hidden danger places to monitor. Therefore, the commonness target of providing disaster forecast early warning for unspecified people cannot be realized in a real sense. Secondly, for a certain important hidden danger slope body with a built sensor monitoring network, the time for landslide is indefinite, the situation that the same place is repeated rarely occurs, meanwhile, ground facilities are possibly destroyed by once landslide, and the factors cause the cost of the sensor monitoring network technology to be extremely high and difficult to popularize in a large range. Can only be limited to scientific research. Thirdly, as landslide can directly damage ground facilities in a certain area around a slope body, all sensors often cannot acquire landslide signals and cannot record the whole course of a landslide event, so that effective monitoring data of landslide disasters are lacked, development of landslide disaster forecasting technology is restricted on one hand, and possibility of retrospective analysis of landslide process is restricted on the other hand.
As described above, the seismic signals collected by the stations of the global seismic monitoring network include landslide disaster signals and disaster global signals. The richness and the integrity of the signals are incomparable with the signals acquired by most daily landslide monitoring sensor networks. Because the seismic signal public resource preliminarily realizes the global seismic information sharing, if the complete landslide process signal recorded in the seismic signal is effectively utilized, the whole process research on landslide disasters can be developed on the basis of low signal acquisition cost, and the disaster process analysis and numerical reconstruction are completed.
A landslide dynamics mechanism based on seismic signal inversion published in journal geodetic survey and geodynamics, 2019 and 10 months provides a scheme for solving the stress state of a landslide region in a certain landslide process based on seismic signal inversion, dividing frequency bands of different landslide sub-events by using time-frequency analysis, and reducing the landslide process by combining classical mechanical analysis. The disadvantages of this method are mainly: firstly, the landslide event is finally divided into 'still-acceleration-deceleration-integral re-acceleration-still after front end reverse inclination', compared with the actual landslide process, the method is too general, and the aim of restoring the disaster process by numerical simulation cannot be achieved; secondly, inverting the low-frequency landslide seismic signal to obtain a force-time function of a landslide source region, performing classical mechanical analysis on the function to obtain the motion characteristic of a landslide body, but processing the signal in the early stage in the inversion process is fuzzy, and errors exist in the force-time function completed on the basis; the method mainly focuses on data such as the slope of a slide bed, the direction of the slide and displacement of a slide body obtained by seismic signal inversion, and combines stress-time function analysis and time-frequency analysis and on-site monitoring data, the final result is only approximately a landslide process, a numerical model cannot be further introduced to complete numerical reconstruction on the landslide process, and therefore possibility cannot be provided for more accurate landslide disaster research. Meanwhile, the specific flow of the division stage is not given in the text, and the method cannot belong to the technical scheme called by the patent.
Disclosure of Invention
The invention aims to provide a landslide disaster process reconstruction method and application thereof aiming at the defects of the prior art. The method can restore and reconstruct the landslide disaster process by utilizing the landslide disaster signals recorded in the seismic signals.
In order to achieve the above object, the present invention firstly provides a landslide hazard process analysis method, which has the following technical scheme:
a landslide process analysis method utilizes landslide seismic signals to divide different stages of a landslide process, and is characterized in that:
firstly, obtaining an original landslide seismic signal which is a landslide event signal in seismic signals issued by a seismic observation table network;
secondly, EMD decomposition is carried out on the original signal, IMF principal component signals are extracted, FFT conversion and STFT conversion are carried out, and time-frequency-energy signals are obtained and are marked as signals A;
filtering the signal A by taking the frequency range characteristic and the amplitude range characteristic of the micro-seismic signal as conditions, recording the filtered signal as a signal B, and intercepting a relatively low-frequency low-amplitude stable signal section from a signal spectrum of the micro-seismic signal after the peak shape of the second signal;
thirdly, identifying the appearing energy amplitude peak value signals for the signal B, and respectively marking the energy amplitude peak value signals of the two times of the previous and next times as a 1 st main signal and a 2 nd main signal when the condition that the energy amplitude peak value of a certain suddenly rising energy amplitude peak is smaller than the energy amplitude peak value of the energy amplitude peak which rises suddenly next time is met;
finally, the landslide seismic signals are divided into 5 stages, including:
and (3) a static stage: the stage before the 1 st main signal,
a slipping stage: in the 1 st phase of the main signal,
a transition stage: the phase between the 1 st and 2 nd main signals,
entrainment-transport phase: in the 2 nd main signal phase, the phase,
and (3) deposition stage: stage after the 2 nd main signal.
In the landslide process analysis method, the landslide seismic signals are landslide event signals recorded in public seismic signals issued by the seismic observation station network. The landslide disaster process analysis method is to analyze the landslide process by utilizing landslide earthquake motion signals and divide different stages of the landslide process. The method is based on an original landslide seismic signal, EMD analysis is carried out on the original landslide seismic signal, IMF main component signals are extracted from a system IMF component signal, FFT (fast Fourier transform) and STFT (fast Fourier transform) are carried out on the IMF main component signals in sequence, so that a time-frequency-energy signal (signal A) of a landslide event is obtained, and 5 stages of a landslide process are divided according to frequency and energy characteristics of different time periods in the landslide seismic signal.
The technical principle of the process analysis method is as follows: by performing EMD analysis on the original landslide seismic signals, noise reduction can be achieved, a higher signal-to-noise ratio is achieved, main signal features are extracted, so that stages can be clearly divided in the following process, and the effect of clearly dividing landslide process stages can be achieved through time-frequency-energy signals obtained by FFT (fast Fourier transform) and STFT (fast Fourier transform).
In the stage 5 of the landslide process divided by the method, the transition stage is determined firstly in the analysis and research of the landslide process. In past studies, this phase was not identified as an independent phase of the landslide process and was most likely divided into slip phases. The earlier stage research of the invention finds that the energy in the transition stage is continuously and most stable in the landslide process, and the centralized display of the energy characteristics in the stage can be improved by dividing the energy in the transition stage and the glide stage with the short preceding duration, so that the energy data in the stage has higher utilization value. For example, the energy data change at the stage is taken as a reference frame, which is beneficial to quantitative analysis of the landslide process, and is also beneficial to coupling the computer-simulated landslide process and the real landslide process with each other, thereby providing data for further research on the high-speed landslide motion mechanism.
Because the method can be used for dividing a stage which cannot be found and is clearly defined in the conventional landslide process research, more accurate results can be obtained by carrying out landslide disaster analysis and landslide disaster emergency response research on the basis of the method. Thus, the present invention provides the following:
the landslide disaster process analysis method is applied to landslide disaster analysis and applied to landslide disaster emergency response scheme design.
By utilizing the energy characteristic advantages of the transition stage extracted by the landslide process analysis method, the invention provides the application of the landslide process numerical reconstruction method in the landslide disaster research on the mathematical modeling of the landslide event, and the technical scheme is as follows:
the landslide process numerical reconstruction method is applied to landslide event mathematical modeling, and is characterized in that:
firstly, dividing a landslide process 5 stage from landslide seismic signals, and extracting transition stage signals;
secondly, analyzing to obtain the energy variation data of the transition stage along the time axis;
thirdly, establishing a mathematical model based on the landslide seismic signal, wherein the mathematical model is related to the energy change of the landslide event along a time axis;
thirdly, checking the accuracy of the mathematical model by taking the energy variation data of the transition stage along the time axis as a true value and taking the energy variation data calculated by the mathematical model along the time axis as a value to be checked;
and finally, receiving the mathematical model according to the inspection accuracy result, or backtracking and revising the mathematical model until the mathematical model meets the inspection standard.
On the basis of the landslide process analysis method, the invention further provides a landslide process numerical simulation reconstruction method, and the technical scheme is as follows:
a landslide process numerical simulation reconstruction method realized by the landslide process analysis method utilizes landslide seismic signals to carry out numerical simulation reconstruction of a landslide process, and is characterized in that: firstly, dividing a landslide process 5 stage from landslide seismic signals; secondly, calculating the signal power spectrum density PSD of the signal B at each stage to obtain the amplitude energy data of the landslide process along the time axis, and performing numerical simulation on the landslide process by adopting a discrete element method to obtain the normalized kinetic energy change data and the particle vertical speed change data of the landslide process along the time axis; thirdly, intercepting a t 1-t 2 sample in the landslide process, using time-energy as an index, verifying the accuracy of numerical simulation result energy data by using PSD result energy data in t 1-t 2 time, if the accuracy meets a verification standard, receiving the established numerical simulation result, otherwise, backtracking to adjust setting parameters in the numerical simulation process in the previous step, and executing the data simulation process again until the accuracy verification standard is met; and finally, according to the received numerical simulation result, establishing a numerical model of the landslide process by using the speed change of the landslide body as a variable.
The technical principle of the landslide process numerical reconstruction method is as follows: and calculating the energy change characterized by vibration in the landslide process by using the Power Spectral Density (PSD) of the signal. Meanwhile, a numerical simulation is carried out on the landslide process by adopting a discrete element method numerical analysis method, and the energy change represented by the particle speed and the normalized energy in the landslide process is obtained. On the basis, the energy value changing along the time axis is used as an intermediate quantity, the accuracy of the numerical model can be checked by using a PSD result, and the model is guided to be corrected, so that the acceptable numerical model in the landslide process is obtained finally. And finally, according to the received time particle velocity data of the numerical model, a numerical model of the landslide process can be established, and the dynamic process of the landslide process can be described. In particular, the accuracy of the numerical model is checked by using the PSD result, and a method for calculating relative errors can be adopted.
The optimization scheme of the landslide process numerical reconstruction method is that when the accuracy of the numerical model is tested by using the PSD result, the intercepted t 1-t 2 samples are in a relative stable stage of normalized kinetic energy in the PSD result, and the time duration is not less than 25% of the total time duration of the landslide process as far as possible. According to the earlier research, the transition stage in the 5 stage of the landslide process is selected as the t 1-t 2 sample, and based on the advantages of the landslide process energy data, a more scientific standard for testing the accuracy of the numerical model can be provided.
The invention also provides the application of the landslide process numerical simulation reconstruction method in landslide hazard analysis and the application in landslide hazard emergency response scheme design.
Compared with the prior art, the invention has the beneficial effects that: (1) the landslide process analysis method and the process numerical reconstruction method are methods for completely analyzing the landslide disaster process by using landslide seismic signals, and the method can be used for developing disaster research by using seismic signal resources and greatly reducing the cost of landslide disaster process monitoring for providing a foundation for landslide process research, so that the disaster process research can be carried out without being limited by early-stage hardware construction, more landslide disasters in more quantity and more places and the enrichment degree of landslide disaster basic data is greatly improved. (2) The landslide process analysis method provided by the invention divides and defines the transition stage from the landslide process for the first time, and improves the accuracy of analysis and restoration of the landslide process, so that the effectiveness of landslide disaster prevention and treatment research and disaster emergency response scheme design can be provided. (3) The landslide process numerical reconstruction method provides a method for carrying out numerical simulation on the whole landslide process, and the obtained landslide disaster staged numerical model can provide an ideal basis for more accurate landslide disaster research. (4) The earthquake motion signals are the existing public resources purchased by public finance payment, so that the utilization efficiency of the public signal resources and the utilization efficiency of the public finance are improved by utilizing the resources to develop a new disaster research field, and the earthquake motion signals have obvious social benefits.
Drawings
Fig. 1 is a schematic flow chart of a landslide process analysis method.
FIG. 2 is an original landslide seismic signal.
Fig. 3 is a principal component signal (IMF 4).
FIG. 4 is a time-frequency diagram of IMF4 after FFT and STFT transformation.
Fig. 5 shows the 1 st main signal and the 2 nd main signal.
Fig. 6 is a flow chart of a numerical simulation reconstruction method in a landslide process.
Fig. 7 is a transition phase power spectral density PSD graph.
FIG. 8 is a diagram of a discrete element method building particle sliding process.
Fig. 9 is a graph of normalized kinetic energy versus time.
Fig. 10 is a graph of the vertical average velocity of the particles (showing the transition phase).
Detailed Description
Preferred embodiments of the present invention will be further described with reference to the accompanying drawings.
Example one
As shown in fig. 1 to 5, the method of the present invention is used to complete the analysis of the landslide disaster process.
Fig. 1 is a schematic flow chart of a landslide process analysis method. And performing low-frequency analysis in a range of 0Hz to 5 Hz.
1. Original landslide seismic signal
In 2017, a landslide event occurs in a place in Sichuan, and the landslide event is monitored and collected by a broadband seismograph of a seismic monitoring station of a Sichuan seismic table network. And extracting the original landslide seismic signals of the landslide event from the original seismic waveform signals recorded by the broadband seismograph (recording the frequency range of 0.001 Hz-500 Hz) of the monitoring station. The time length of the original landslide seismic signal is about 150s, two obvious amplitude peaks can be seen in the oscillogram, and the second peak can be basically seen to be larger than the first peak. FIG. 2 is an original landslide seismic signal.
2. Extract signal A
Performing EMD on the original landslide seismic signal to obtain a series of IMF component signals, extracting IMF principal component signals, performing FFT on the IMF principal component signals to obtain frequency-amplitude signals, and performing STFT to obtain time-frequency-energy signals, which are recorded as signals A.
In the example, the original landslide seismic signals are subjected to EMD decomposition to obtain 10 component signals IMF, wherein IMF4 is the IMF component with the highest content and the clearest and obvious characteristics and belongs to IMF principal component signals, and therefore IMF4 is selected to continue subsequent analysis. Fig. 3 is a principal component signal (IMF4), and fig. 4 is a time-frequency diagram of IMF4 after FFT and STFT transformation.
3. Extracting the 1 st and 2 nd main signals
The signal A has a high signal-to-noise ratio and can distinguish different stages of the signal. In the signal A, a relatively low-frequency and low-amplitude stable signal section is cut from a signal spectrum after the second signal peak shape and is marked as a micro-seismic signal, and a frequency range characteristic index and an amplitude range characteristic index of the micro-seismic signal are extracted. And filtering the signal A by taking the frequency range characteristic index and the energy characteristic index of the microseism signal as conditions, and marking the filtered signal as a signal B.
For signal B, the energy amplitude peak signal present is identified. When the energy amplitude peak value of a certain suddenly rising energy amplitude peak is smaller than the energy amplitude peak value of a second suddenly rising energy amplitude peak, the energy amplitude peak value signals of the two times before and after are respectively marked as a 1 st main signal and a 2 nd main signal.
In this example, the micro-seismic signals are signals in a period of 110-150 s, the frequency range characteristic index is 0 Hz-0.5 Hz, and the amplitude range characteristic index is not more than +/-800 × 10-3mm/s. Conditional filtering is performed to mark signal B. Further, the front and rear peaks of the signal B, which are formed by sudden changes in energy amplitude, are identified as the 1 st main signal and the 2 nd main signal. Fig. 5 shows the 1 st main signal and the 2 nd main signal.
4. Landslide staging
Dividing the landslide seismic signals into 5 stages, sequentially:
(1) and (3) a static stage: stage before the 1 st main signal. Basically, the frequency and the amplitude are not existed, and the landslide kinetic energy is close to zero. In this example, 0s to 18 s.
(2) A slipping stage: 1 st main signal phase. Both the frequency and amplitude become high and reach a first peak. In this example, 18s to 28 s.
(3) A transition stage: the phase between the 1 st and 2 nd main signals. The frequency and amplitude are gradually reduced compared with the peak value of the previous stage, and the low amplitude and low frequency characteristics are restored. In this example 28s to 68 s.
(4) Entrainment-transport phase: the 2 nd main signal phase. The frequency and the amplitude have a second peak value, the amplitude is shown to increase firstly and then decrease, the frequency range is shown to be wide firstly and then narrow, and the landslide energy is increased firstly and then decreased later than at the last stage. In this example 68s to 110 s.
(5) And (3) deposition stage: stage after the 2 nd main signal. The amplitude and frequency are gradually reduced at the end of the upper stage, and the landslide energy is gradually reduced at the end of the upper stage. In this example, 110s to 150 s.
According to the motion mechanism of the landslide, the specific motion characteristics of the landslide process represented by the method for dividing the 5-stage landslide process are as follows:
(1) and (3) a static stage: slight looseness of the landslide occurred with essentially no appreciable displacement.
(2) A slipping stage: the landslide mass begins to slide downwards gradually, and the speed is increased gradually.
(3) A transition stage: the landslide body slides downwards more stably, and the energy gradually becomes stable compared with the upper stage. This stage is a stage that has been discovered in the prior art.
(4) Entrainment-transport phase: the slope is dragged and mobilized, and the landslide scale is obviously increased compared with the last stage.
(5) And (3) deposition stage: and (3) moving the landslide body to the valley bottom, flattening the terrain, widening the migration path, quickly slowing down the moving speed, finally depositing the landslide body, and finishing the landslide process.
Example two
As shown in fig. 6 to 10, in addition to the first embodiment, the numerical simulation reconstruction in the present landslide disaster process is continuously completed by the method of the present invention.
Fig. 6 is a flow chart of a numerical simulation reconstruction method in a landslide process.
1. Calculating the PSD of the Power spectral Density of Signal B
And for each stage of the landslide process, calculating the PSD of the signal power spectral density of the signal B to obtain the energy distribution condition of each stage.
In this example, taking analysis of the transition stage signals (28s to 68s) as an example, a frequency value corresponding to each second time of the transition stage signals in the landslide process in the signal B is substituted into formula 1, and an energy distribution situation represented by the power spectral density PSD of the transition stage is calculated. Fig. 7 is a transition phase power spectral density PSD graph.
Figure BDA0002832432490000111
In the formula, f represents frequency, and t represents time.
2. Numerical simulation
Using PFC2D5.0 simulating the landslide process of the friction particle material. The DEM discretizes the particulate material into individual particles, associates the contact forces with the intersection points through a stiffness model, ignores momentum transfer in non-planar directions, sets numerical simulation parameters to conform to the landslide field material characteristics of this embodiment, constructs the landslide using field site terrain information, and then performs the simulation, terminating the simulation when all material is deposited downstream. Then, the average kinematic energy of the whole process is back calculated to obtain a particle sliding process diagram (fig. 8). And finally, normalizing the average motion energy to obtain normalized kinetic energy and time relation data and particle vertical average velocity data. Fig. 9 is a graph of normalized kinetic energy versus time for a landslide process, and fig. 10 is a graph of particle vertical mean velocity (illustrating a transition phase).
In this example, PFC2D5.0 concrete operation process: dividing research materials into source materials and slope scouring materials, firstly calibrating the binding strength and rigidity in a DEM according to the traditional laboratory test result, calibrating the source materials according to the rock sample triaxial laboratory test result, then distributing contact binders to a loose deposition slope according to the simple direct shear test result, calibrating the friction coefficient of the source surface and the slope to be close to the field landslide, and setting other parameters as follows:
the friction coefficient of the wall is 0.7, and the general wall rigidity is 1 multiplied by 109N/m, shear wall stiffness 1X 109N/m；
Raw materials: the particle radius is 2.5 m-3 m, the particle density is 2650kg/m3 Normal particle stiffness 1X 1010N/m, particle shear stiffness 1X 1010N/m, particle friction coefficient 0.4, normal parallel bond stiffness 2X 109N/m, parallel bond shear stiffness 1X 109N/m, normal strength of parallel bond 100MPa, flatThe shear strength of the row key is 50 MPa.
Slope scouring material: the particle radius is 2.5 m-3 m, the particle density is 2600kg/m316MN of normal strength of contact bonding, 8MN of shear strength of contact bonding, 0.1 of damping coefficient and 9.81m/s of gravity acceleration2。
3. Numerical simulation reconstruction of landslide process
Intercepting the landslide process for a period of time t 1-t 2, verifying the accuracy of time-normalized kinetic energy data (shown in figure 9) of the numerical simulation and normalized analysis results by using time-amplitude energy data (shown in figure 7) of PSD analysis results in t 1-t 2, receiving the established numerical simulation results if the numerical simulation results meet accuracy verification standards, otherwise, backtracking to adjust setting parameters in the numerical simulation process in the previous step, and re-executing the data simulation process until the accuracy verification standards are met. Specifically, a relative error δ of the PFC result data (time-normalized energy value) with respect to the PSD result data (time-amplitude energy value) per second between t1 and t2 is calculated according to equation 2, and a relative error δ per second between t1 and t2 is obtained. In the period from t1 to t2, if delta not less than 80% of the time is less than or equal to 10% (namely delta not less than 80% is not more than 10%), judging that the numerical simulation meets the precision test standard; otherwise, judging that the numerical simulation does not meet the precision test standard, backtracking the setting parameters in the numerical simulation process of the last step, and executing the data simulation process again until the test standard is met.
Figure BDA0002832432490000131
In this example, the numerical simulation reconstruction landslide process continues to be completed with the transition phase signals (28 s-68 s). For each second of the duration of the transition phase 40s, the relative error δ of the normalized kinetic energy value of the numerical simulation result with respect to the amplitude energy value of the PSD calculation result is calculated as equation 2, and the results are shown in table 1. As can be seen from the results in Table 1, the relative error is less than 10% in 40s of the transition stage
Figure BDA0002832432490000132
Figure BDA0002832432490000133
Therefore, the numerical simulation meets the precision test standard, and the simulation result can be used for further depicting the landslide process to complete the landslide process reconstruction.
TABLE 1 transition phase numerical simulation relative error data
Serial number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Relative error% 9.6 3.3 9.2 5.3 9.2 16.1 9.2 9.3 9.5 9.4 8.8 8.1 9.8 2.5 5.6 8.5 18.6 9.6 25.1 9.4
Serial number 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Relative error% 15.3 9.5 9.7 8.3 8.5 8.2 5.5 9.9 25.1 21.4 2.7 16.5 8.2 5.7 9.3 9.5 2.7 4.6 1.8 8.1
The direction and speed of particles in different time periods in the landslide process can be determined by using the numerical simulation result (figure 8), so that the landslide disaster process can be reconstructed by depicting:
(1) and (3) a static stage: 0s to 18 s. The energy at this stage shows a tendency to be substantially 0. The average speed of the sliding mass is increased from t to 0s, v to 0m/s to t to 18s, v to 1 m/s. The landslide body loosens along the slope, and tiny deformation occurs and tiny speed occurs.
(2) A slipping stage: 18s to 28 s. The energy at this stage tends to increase rapidly. The average speed of the sliding mass is increased from t to 18s, v to 1m/s to t to 28s, and v to 15 m/s. The landslide body slides downwards along the slope, and kinetic energy is rapidly increased.
(3) A transition stage: 28s to 68 s. The energy in this stage tends to be more stable. The average speed of the sliding mass is changed from t to 28s, v to 15m/s, t to 45s, v to 14m/s, t to 68s, v to 16 m/s.
(4) Entrainment-transport phase: 68s to 110 s. The energy at this stage tends to increase first and then decrease. The average speed of the sliding mass is increased from t 68s, v 16m/s to t 85s, v 20m/s, and the peak speed occurs when t 85s, and then is decreased from t 85s, v 20m/s to t 110s, and v 14 m/s.
(5) And (3) deposition stage: 110s to 150 s. The energy at this stage shows a gradually decreasing trend. The average speed of the sliding mass is reduced from t to 110s, v to 14m/s to t to 150s, and v to 0 m/s.
The numerical reconstruction result can be displayed as a landslide process dynamic image.

Claims (10)

1. The landslide process analysis method is characterized in that different stages of a landslide process are divided by utilizing landslide seismic signals, and the landslide process analysis method is characterized in that:
firstly, obtaining an original landslide vibration signal, wherein the landslide seismic signal is a landslide event signal in seismic signals issued by a seismic observation station network;
secondly, EMD decomposition is carried out on the original signal, IMF principal component signals are extracted, FFT conversion and STFT conversion are carried out, and time-frequency-energy signals are obtained and are marked as signals A;
filtering the signal A by taking the frequency range characteristic and the amplitude range characteristic of the micro-seismic signal as conditions, recording the filtered signal as a signal B, and intercepting a relatively low-frequency low-amplitude stable signal section from a signal spectrum of the micro-seismic signal after the peak shape of the second signal;
thirdly, for the signal B, identifying an appearing energy amplitude peak value signal, and when the energy amplitude peak value of a certain suddenly rising energy amplitude peak is smaller than the energy amplitude peak value of a next suddenly rising energy amplitude peak value, respectively marking the energy amplitude peak value signals of the previous and next two times as a 1 st main signal and a 2 nd main signal;
finally, the landslide seismic signals are divided into 5 stages, including:
and (3) a static stage: the stage before the 1 st main signal,
a slipping stage: in the 1 st phase of the main signal,
a transition stage: the phase between the 1 st and 2 nd main signals,
entrainment-transport phase: in the 2 nd main signal phase, the phase,
and (3) deposition stage: stage after the 2 nd main signal.
2. The landslide process analysis method of claim 1, wherein: the signal A is processed in the frequency range of 0 Hz-0.5 Hz and the amplitude range of less than or equal to +/-800 multiplied by 10-3And mm/s is conditional filtering.
3. The application of the landslide disaster process analysis method according to claim 1 or 2 in landslide disaster analysis and in landslide disaster emergency response scheme design.
4. The application of the landslide disaster process analysis method of claim 1 or 2 in the mathematical modeling of landslide events, wherein:
firstly, dividing a landslide process 5 stage from landslide seismic signals, and extracting transition stage signals;
secondly, analyzing to obtain the energy variation data of the transition stage along the time axis;
thirdly, establishing a mathematical model based on the landslide seismic signal, wherein the mathematical model is related to the energy change of the landslide event along a time axis;
thirdly, checking the accuracy of the mathematical model by taking the energy variation data of the transition stage along the time axis as a true value and taking the energy variation data calculated by the mathematical model along the time axis as a value to be checked;
and finally, receiving the mathematical model according to the inspection accuracy result, or backtracking and revising the mathematical model until the mathematical model meets the inspection standard.
5. A landslide process numerical simulation reconstruction method implemented by the landslide process analysis method of claim 1 or 2, wherein:
firstly, dividing a landslide process 5 stage from landslide seismic signals;
secondly, calculating the signal power spectrum density PSD of the signal B at each stage to obtain the amplitude energy data of the landslide process along the time axis, and performing numerical simulation on the landslide process by adopting a discrete element method to obtain the normalized kinetic energy change data and the particle vertical speed change data of the landslide process along the time axis;
thirdly, intercepting a t 1-t 2 sample in the landslide process, using time-energy as an index, verifying the accuracy of numerical simulation result energy data by using PSD result energy data in t 1-t 2 time, if the accuracy meets a verification standard, receiving the established numerical simulation result, otherwise, backtracking to adjust setting parameters in the numerical simulation process in the previous step, and executing the data simulation process again until the accuracy verification standard is met;
and finally, according to the received numerical simulation result, establishing a numerical model of the landslide process by using the speed change of the landslide body as a variable.
6. The landslide process numerical simulation reconstruction method of claim 5, wherein: the accuracy test of the numerical simulation result takes the relative error delta of the PFC result data relative to the PSD result data at every second from t1 to t2 as a test index.
7. The landslide process numerical simulation reconstruction method of claim 6, wherein: the accuracy test standard of the numerical simulation result is as follows: and in the period from t1 to t2, if delta not less than 80% of the time is not more than 10%, the numerical simulation is judged to meet the precision test standard.
8. The landslide process numerical simulation reconstruction method of claim 5, wherein: the t 1-t 2 samples are the relative stable phase of the normalized kinetic energy in the PSD result and the time length is not less than 25% of the total time length of the landslide process.
9. The landslide process numerical simulation reconstruction method of claim 5, wherein: and selecting a transition stage of the landslide process from the t1 sample to the t2 sample.
10. The landslide process numerical simulation reconstruction method of claim 5 applied to landslide hazard analysis and design of landslide hazard emergency response scheme.
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