CN111882829A - Improved landslide and critical-sliding time forecasting method - Google Patents
Improved landslide and critical-sliding time forecasting method Download PDFInfo
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Abstract
The invention discloses an improved landslide imminent-slip time forecasting method, which aims at solving the problem that the calculation intervals are not uniform in the conventional landslide time forecasting process, and firstly determines a landslide deformation rate time smooth curve; then, carrying out dimensionless processing on the landslide deformation curve through coordinate transformation, and converting the landslide deformation curve into a tangent angle change curve, so that various landslide deformation curves can be compared under the same tangent angle characteristic; further according to the formula
Calculating the speed reciprocal, and further drawing a speed reciprocal time curve; further analyzing the deformation data, taking the deformation data corresponding to the tangent angle of 80-85 degrees as a calculation interval, and analyzing according to a speed inverse prediction model; finally, linear and nonlinear simultaneous simulation is adoptedAnd taking the time corresponding to the time when the reciprocal of the speed is 0 as the predicted landslide occurrence time according to the fitting result, and selecting the result with earlier time as the final judgment. The invention has the advantages of early warning and natural disaster prevention.
Description
Technical Field
The invention relates to the field of natural disaster prevention and control, in particular to an improved landslide impending slide time forecasting method.
Background
The landslide time prediction has undergone a lengthy development from the earliest vegetarian rattan model (MSaito, Forecastingthei of the landscaping failure. proceedings software 6th International Conferenceon SoilMecang and FoundationationEngineering.1965: 537 541), the mapping epitaxy model (Hoeke, BrayJ. Rocksloeenneering. Institutering. Institutenng of Miningand Metallurgy, PublitionofTituningInunofMiningMiningand Metallurgy,1977,14(4): 494), to the gray prediction model successively proposed by a large number of students (Chengdong. the third geological society of engineering, 1988: 1226. pages 1243), the national damage prediction model (Verlag. Louis. Persian. Securitympassin. Personal. Personant. Perfection prediction method, the third geological society of SecurityIndustrie, FastkutsingHubei, Fanghuiyu. Perfect et, the national damage prediction method of Seatsu of Seikationary change, the third geological society of Minikationary of Minikationg, 1988, the national damage prediction of Minik et Chaohuginese, the national damage prediction method, and Fanghuik Hubei et Chaohu et No. 713, zhang mourning, nonlinear engineering geology guidance, Chengdu: southwest university of transportation publishers, 1993), Verhulst inverse function models (liebin, the fisher hass inverse function model method for predicting landslide time, geologic hazard and environmental protection, 1996,7(3): pages 13-17), inverse velocity models (SegaliniA, valley ttaa, carria, landslide-of-failurespace and reshold), etc., the types of forecasting models are numerous, but most of them are retrospective forecasting, i.e., the methods for post-analysis construction and verification of forecasting are not considered, and the uncertainty of data in real-time forecasting and dynamic judgment of accelerated trend change are not considered; due to the application of refined high-frequency monitoring equipment, deformation data of the sudden landslide accelerated deformation stage cannot be acquired, so that a time forecasting model established by predecessors is mainly a medium-short term forecasting model, forecasting is generally carried out for several months to tens of days in advance, and the error can reach several days or even more than tens of days. Particularly, it is difficult to accurately predict a sudden landslide in which the accelerated deformation stage is only several tens of minutes to several tens of hours. Meanwhile, even if the time forecast values obtained by the same forecasting model in different calculation areas are different, the time forecast results are difficult to unify, and great difficulty is brought to work needing accurate time forecast, such as emergency rescue, emergency evacuation and the like of sudden landslide.
The main reason of the existing improved critical-slip time forecasting method based on a speed reciprocal method model is that after a landslide enters a critical-slip stage, deformation is further increased sharply on the basis of the existing accelerated increase, the accumulated displacement curve is shown to have obvious upwarp characteristics and does not accord with a smooth curve obtained by a general forecasting model, so that the short-term landslide time forecasting is too late, and a plurality of refined landslide overall-process deformation curves obtained by intelligent monitoring equipment in recent years prove that the deformation of the landslide deformation curve can be increased sharply again in the critical-slip stage, and the early forecasting time is likely to bring potential threats to disaster prevention work such as emergency rescue and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, adapt to the actual needs, and provide an improved landslide critical slip time forecasting method.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
an improved landslide critical-slip time forecasting method is characterized by comprising the following steps:
(1) determining a time smooth curve of the landslide deformation rate;
(2) carrying out dimensionless processing on the landslide deformation curve through coordinate transformation, and converting the landslide deformation curve into a tangent angle change curve, so that various landslide deformation curves can be compared under the same tangent angle characteristic;
(3) according to the formula
Calculating the speed reciprocal, and further drawing a speed reciprocal time curve;
(4) analyzing the deformation data, taking a tangent angle corresponding to deformation at 80-85 degrees as a calculation interval, selecting corresponding deformation data, and analyzing according to a speed inverse prediction model;
(5) linear and nonlinear (quadratic polynomial) fitting are adopted simultaneously, and finally, the result with earlier time is selected as the final judgment, so that the reliability of the prediction result is ensured, and the prediction time is obtained after fitting.
Further, the step (1) is specifically as follows: and calculating a landslide deformation rate time curve according to the landslide deformation time curve, wherein a landslide deformation rate V is (Vt + Vt-1 … + Vt-N)/(N), the V is a speed value after smoothing by a moving average method, the Vt is the speed data at the latest moment, the Vt-N is the Nth speed value from the latest speed data, and the N is the total number of the monitoring data subjected to averaging.
Further, the step (2) is specifically as follows: and calculating a tangent angle change characteristic B corresponding to the deformation rate for B ═ arctan (V/V1) according to a tangent angle calculation formula, wherein V is the actual deformation rate of the landslide, V1 is the deformation rate of the uniform deformation stage of the landslide, and the value range of B is 0-90 degrees.
Still further, α is a dimensionless quantity.
The invention has the beneficial effects that: the method unifies the calculation areas of the landslide critical-sliding time forecast, so that the deformation curves of different landslides have the same calculation area, the result is different because the critical-sliding time forecast value is not influenced by human, the precision of the landslide critical-sliding time forecast is improved, and the error of the existing model is reduced. The invention has the advantages of early warning and natural disaster prevention.
Drawings
FIG. 1 is a graph of landslide deformation rate versus time smoothed;
FIG. 2 is a schematic diagram of a calculation interval corresponding to a tangent angle;
FIG. 3 is a graph of inverse velocity of a black square landslide group;
FIG. 4 determines a velocity reciprocal fit plot for the calculation interval.
Detailed Description
In order to facilitate understanding of the features of the invention, the invention will be further described with reference to the following figures and examples:
experimental example 1: a method for forecasting the critical time of landslide, referring to fig. 1 to 3, comprising the steps of:
(1) determining a landslide displacement time curve and a deformation rate time curve; and calculating a landslide deformation rate time curve according to the landslide deformation time curve, wherein the landslide deformation rate V is (Vt + Vt-1 … + Vt-N)/(N). Wherein, V is the velocity value after the smoothing processing by the moving average method, Vt is the velocity data at the latest moment, Vt-N is the nth velocity value from the latest velocity data onward, N is the total number of the monitoring data subjected to the averaging processing, the moving average is not more than twice, and the moving interval N is determined according to the frequency of the landslide monitoring data.
(2) Carrying out dimensionless processing on the landslide deformation curve through coordinate transformation, and converting the landslide deformation curve into a tangent angle change curve, so that various landslide deformation curves can be compared under the same tangent angle characteristic; calculating a tangent angle change characteristic α corresponding to the deformation rate for α ═ arctan (V/V1) according to a tangent angle calculation formula, wherein: v is the actual deformation rate of the landslide, V1 is the deformation rate of the landslide in the uniform deformation stage, and the value range of alpha is 0-90 degrees.
(3) According to the formula
Calculating the speed reciprocal, and further drawing a speed reciprocal time curve;
(4) analyzing deformation data, and calculating according to an improved speed inverse prediction model by taking a tangent angle corresponding to deformation at 80-85 degrees as a calculation interval;
(5) and simultaneously fitting by adopting linear regression and nonlinearity, and finally selecting the result with earlier time as the final judgment so as to ensure the reliability of the prediction result and obtain the prediction time after fitting.
The specific application of the method of the invention is as follows, see fig. 1 to 4:
the black square platform is located in the Yongjing county in Gansu province of China, the annual average rainfall amount of the black square platform is 287.6mm, and due to a series of landslide disasters caused by perennial irrigation, the agricultural land on the tableland is continuously reduced, and the continuous development of local economy and the life and property safety of nearby villagers are seriously damaged. In recent years, a plurality of displacement monitoring devices are arranged in an area where landslide is likely to occur by combining early identification work, and complete deformation data of a black square platform loess landslide for many times is successfully acquired. Chenjia No. 6 landslide is located on the right bank of the northeast side grindstone ditch of the black platform, and a sliding source area is 116m long and 165m wide and is a typical burst-type loess landslide. In 2017, in 5 and 13 months, one sliding is generated on the Chenjia No. 6 landslide, and the volume of the landslide is less than 1 km 3; 3, 4 months in 2019, Chenjia # 6 again landslides. The Dang Chuan 4# landslide and the Dang Chuan No. 7 landslide are located on the southwest side yellow river side of the Changjiang Xia of Jingjing county salt pan Changjiang province in Gansu province, the Dang Chuan 4# landslide is deformed and gradually accelerated from the 8 th month end in 2017, the neighborhood of the Dang Chuan 4# landslide continuously slides in the 1 st morning 5 in 2017, 3 grooves are formed in a landslide area, an accumulation body with the length of more than 300m is formed below the landslide, and the Dang Chuan 4# landslide occurs again in the 10 th 23 th month in 2018; no. 9 and 20 in 2019, the landslide of No. 7 from Dang Chuan enters an acceleration stage, the landslide occurs at 5 days in the morning and 4 days in 10 and 5 days in 2019, and the landslide volume exceeds 2 km 3. According to an improved landslide deformation near-slip time forecasting method based on a speed reciprocal method model, the instar 6# landslide instability time is 27 points at 5, 13 and 13 of 2017, and the difference between the instability time and the actual landslide occurrence time is only 0.42 hour at 9 points at 5, 13 and 52 points at 2017, 5 and 13 of 2017; (2) the instability time of the party Chuan No. 4 landslide is 56 minutes at 1 am of 10 and 1 months in 2017, and is different from the actual landslide occurrence time at 5 am of 10 and 1 months in 2017 by only 3 hours; (3) the instability time of the party Chuan No. 4 landslide is 11 points at 10, 23 and 2018 minutes, and is 8.7 hours different from the actual landslide occurrence time of 2017, 10, 23 days at night at 8 points at 30; (4) the instability time of the Chenjia No. 6 landslide is 9 minutes at night in 3 months and 3 days in 2019, and the difference between the instability time of the Chenjia No. 6 landslide and the instability time of the Chenjia No. 6 landslide is 4.3 hours from the instability time of the Chenjia No. 6 landslide occurring in 3 months and 4 days in morning at 0 point 19 in 2019; (5) the instability time of the party Chuan No. 7 landslide is 33 minutes at 6 am on 1 day 10 and month 1 in 2019, and is 93.9 hours different from the actual landslide occurrence time at 4 am on 5 days 10 and month 5 in 2019, and the time is 27 hours.
In conclusion, the improved landslide critical-slip time forecasting method is provided by the invention aiming at the two problems that the calculation intervals of the conventional landslide critical-slip time forecasting model are not uniform, so that the calculation points cannot be unified together, and different results can be obtained by the same forecasting method, and the critical-slip time forecasting is carried out based on the speed reciprocal forecasting model.
The above is a preferred embodiment of the present invention, but not limited to the above, and it will be understood by those skilled in the art that the idea of the present invention can be understood and various extensions and changes can be made without departing from the concept of the present invention.
Claims (4)
1. An improved landslide critical-slip time forecasting method is characterized by comprising the following steps:
(1) determining a time smooth curve of the landslide deformation rate;
(2) carrying out dimensionless processing on the landslide deformation curve through coordinate transformation, and converting the landslide deformation curve into a tangent angle change curve, so that various landslide deformation curves can be compared under the same tangent angle characteristic;
(3) according to the formula
Calculating the speed reciprocal, and further drawing a speed reciprocal time curve;
(4) analyzing the deformation data, taking a tangent angle corresponding to deformation at 80-85 degrees as a calculation interval, selecting corresponding deformation data, and analyzing according to a speed inverse prediction model;
(5) linear and nonlinear (quadratic polynomial) fitting are adopted simultaneously, and finally, the result with earlier time is selected as the final judgment, so that the reliability of the prediction result is ensured, and the prediction time is obtained after fitting.
2. The improved landslide critical-slip time forecasting method of claim 1, wherein: the step (1) is specifically as follows: and calculating a landslide deformation rate time curve according to the landslide deformation time curve, wherein a landslide deformation rate V is (Vt + Vt-1 … + Vt-N)/(N), the V is a speed value after smoothing by a moving average method, the Vt is the speed data at the latest moment, the Vt-N is the Nth speed value from the latest speed data, and the N is the total number of the monitoring data subjected to averaging.
3. The improved landslide critical-slip time forecasting method of claim 1, wherein: the step (2) is specifically as follows: and calculating a tangent angle change characteristic B corresponding to the deformation rate for B ═ arctan (V/V1) according to a tangent angle calculation formula, wherein V is the actual deformation rate of the landslide, V1 is the deformation rate of the uniform deformation stage of the landslide, and the value range of B is 0-90 degrees.
4. The improved landslide critical-slip time forecasting method of claim 1, wherein: said α is a dimensionless quantity.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113250753A (en) * | 2021-06-21 | 2021-08-13 | 云南航天工程物探检测股份有限公司 | Tunnel monitoring measurement improved tangent angle early warning method |
| CN114001703A (en) * | 2021-10-09 | 2022-02-01 | 四川轻化工大学 | Landslide deformation data real-time filtering method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103615962A (en) * | 2013-12-09 | 2014-03-05 | 三峡大学 | Landslide mass surface displacement measuring method |
| US20170069189A1 (en) * | 2015-09-08 | 2017-03-09 | Cpg Technologies, Llc | Global emergency and disaster transmission |
| CN109389808A (en) * | 2017-08-06 | 2019-02-26 | 中铁二院工程集团有限责任公司 | Lin-gang new city method based on creepage type landslide constant speed rate of deformation |
| CN109935054A (en) * | 2019-04-25 | 2019-06-25 | 成都星感智联科技有限公司 | A kind of landslide short period forecasting procedure |
| CN111445057A (en) * | 2020-02-27 | 2020-07-24 | 重庆大学 | Displacement gradient method for predicting landslide sliding direction and time |
| CN112071028A (en) * | 2020-09-18 | 2020-12-11 | 北京中地华安地质勘查有限公司 | Monitoring and early warning method and device for shallow landslide |
-
2020
- 2020-07-28 CN CN202010739421.1A patent/CN111882829A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103615962A (en) * | 2013-12-09 | 2014-03-05 | 三峡大学 | Landslide mass surface displacement measuring method |
| US20170069189A1 (en) * | 2015-09-08 | 2017-03-09 | Cpg Technologies, Llc | Global emergency and disaster transmission |
| CN109389808A (en) * | 2017-08-06 | 2019-02-26 | 中铁二院工程集团有限责任公司 | Lin-gang new city method based on creepage type landslide constant speed rate of deformation |
| CN109935054A (en) * | 2019-04-25 | 2019-06-25 | 成都星感智联科技有限公司 | A kind of landslide short period forecasting procedure |
| CN111445057A (en) * | 2020-02-27 | 2020-07-24 | 重庆大学 | Displacement gradient method for predicting landslide sliding direction and time |
| CN112071028A (en) * | 2020-09-18 | 2020-12-11 | 北京中地华安地质勘查有限公司 | Monitoring and early warning method and device for shallow landslide |
Non-Patent Citations (2)
| Title |
|---|
| 亓星 等: ""基于斋藤模型的滑坡临滑时间预报方法改进及应用"", 《工程地质学报》 * |
| 王延平 等: ""速度倒数法滑坡预警模型流变试验研究"", 《岩 土 力 学》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113250753A (en) * | 2021-06-21 | 2021-08-13 | 云南航天工程物探检测股份有限公司 | Tunnel monitoring measurement improved tangent angle early warning method |
| CN113250753B (en) * | 2021-06-21 | 2023-04-14 | 云南航天工程物探检测股份有限公司 | Tunnel monitoring measurement improved tangent angle early warning method |
| CN114001703A (en) * | 2021-10-09 | 2022-02-01 | 四川轻化工大学 | Landslide deformation data real-time filtering method |
| CN114001703B (en) * | 2021-10-09 | 2023-07-28 | 四川轻化工大学 | Landslide deformation data real-time filtering method |
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