Landslide monitoring and early warning method with moisture content monitoring assisted by mutual verification of rainfall and earth surface displacement

Abstract

The invention discloses a landslide monitoring and early warning method based on potential sliding surface middle water content monitoring assisted by rainfall and earth surface displacement mutual verification. The method specifically comprises the following steps: S1, obtaining a real earth surface point cloud; s2, acquiring structural information of a rock core and an underground rock stratum; s3, carrying out three-dimensional geological modeling; s4, establishing a relation model of the water content and the physical and mechanical parameters; s5, inverting the initial state of the slope; s6, installing a rain gauge, simulating a rainfall process, calculating a stability coefficient, and predicting a landslide range; s7, setting an early warning threshold value; s8, installing an earth surface displacement meter and an early warning broadcast; s9, linkage early warning broadcast automatic early warning is carried out; and S10, performing mutual verification by taking moisture content early warning as a main part and taking rainfall and surface displacement monitoring as an auxiliary part. According to the method, through high-precision three-dimensional geologic modeling, 'surface-based general 'of slope profile analysis is improved; the potential sliding surface is simulated and calculated according to the mechanical behavior characteristic numerical value of the rock-soil body, and the defects that the rock-soil body is regarded as a rigid body and the sliding surface is assumed in a strip division method are avoided.

Description

Technical Field

[0001] The invention relates to the technical field of natural disaster monitoring, in particular to a landslide monitoring and early warning method that combines water content monitoring with mutual verification of rainfall and surface displacement. Background Art

[0002] Landslide is one of the most important geological disasters at home and abroad. Every year, landslide disasters cause huge economic losses and casualties around the world. Rainfall is generally considered to be the main factor inducing landslide disasters. Some research statistics even show that 90% of landslides are directly or indirectly related to rainfall. However, the current rainfall landslide warning has a high false alarm rate and a low success rate. For this reason, landslide disasters occur frequently during the flood season. Rainfall landslides are caused by the infiltration of rainwater and the rise of groundwater levels, which lead to an increase in the moisture content of underground rock and soil, causing the rock and soil to soften. When the softening reaches a certain degree, landslides occur. In this process, the moisture in the rock and soil plays a key role.

[0003] The patent with application number 201910362587.3 discloses a parameter and method for predicting the landslide moisture content load increase response ratio. The slope profile cannot represent the complete slope; the slope is divided into strips, and there is a common defect in the strip division method that the slope rock and soil are regarded as a rigid body and an assumed sliding surface; a moisture content sensor is arranged on the surface of each soil strip, and a "moisture meter" is arranged in the middle and bottom of the highest and lowest soil strips, that is, at the two ends of the soil strip considered to be the landslide range. The number of arrangements is relatively large and not economical.

[0004] Patent application number 201921901873.4 discloses a high-level remote landslide warning device based on deformation and moisture content. Patent application number 201911076957.3 discloses a high-level remote landslide warning device and method based on deformation and moisture content. It mainly introduces the functional structure and assembly method of the device, and does not involve the requirements for the installation location. However, the installation location is crucial for monitoring and early warning.

[0005] The patent with application number 202120694611.6 discloses a device for measuring the moisture content of soil in the transient saturated zone of a deep landslide body, which is used to measure the moisture content of soil in the transient saturated zone of a deep landslide body, rather than being arranged at the potential sliding surface and the bottom of each rock layer above the potential sliding surface.

[0006] The patent with application number 202311429947.X discloses a shallow soil landslide early warning method based on precipitation and soil moisture content. The patent with application number 202311429950.1 discloses a landslide rainfall segmentation method based on soil moisture content and its application, which is suitable for shallow soil landslides, and its moisture content is calculated by formula 2 in its claims, rather than the actual measured value. Obviously, its accuracy is affected by regional differences and soil types and has deviations.

[0007] Patent application number 201910519884.4 discloses a three-dimensional loading landslide test device with different moisture contents, which is mainly used for indoor test research rather than for field landslide monitoring and early warning. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides a landslide monitoring and early warning method that combines moisture content monitoring with mutual verification of rainfall and surface displacement, which solves the problems of high false alarm rate and inaccurate early warning of rainfall landslides.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a landslide monitoring and early warning method with moisture content monitoring supplemented by rainfall and surface displacement mutual verification, specifically comprising the following steps:

[0010] S1. Use a drone equipped with a LiDAR device to scan the surface of the work area to obtain a ground point cloud, and then remove vegetation to obtain a true surface point cloud;

[0011] S2. Arrange drilling holes according to landslide investigation specifications to obtain core and underground rock structure information;

[0012] S3, combining the surface and underground rock formation information in S1 and S2 to perform three-dimensional geological modeling, and importing it into three-dimensional numerical simulation software that can be used for secondary development;

[0013] S4, measuring the physical and mechanical parameters of the cores obtained during the drilling process at different water contents, establishing a relationship model between water content and physical and mechanical parameters, and implanting the relationship model into the aforementioned three-dimensional numerical simulation software through programming;

[0014] S5. In the aforementioned three-dimensional numerical simulation software, the initial state of the slope is inverted according to the physical and mechanical parameters and groundwater level data measured by the core;

[0015] S6. According to the rainfall data monitored by the rain gauge arranged in the open area of ​​the work area, or the rainfall data artificially assumed for prediction needs, input into the upper boundary of the aforementioned three-dimensional numerical model, simulate the rainfall process, calculate the stability coefficient, and predict the range of the landslide;

[0016] S7. Arrange a moisture content monitoring point in the middle of the potential sliding surface in the numerical model, record the moisture content value that changes with the duration of simulated rainfall, and analyze it together with the stability coefficient in S6 to guide the setting of monitoring and early warning thresholds;

[0017] S8. Drill a hole to the middle of the potential sliding surface at the work site according to the location of the moisture content monitoring point in the numerical simulation, bury the moisture content monitoring instrument, and then seal the hole, and install a surface displacement meter and early warning broadcast on the surface;

[0018] S9, setting the warning threshold of the moisture content monitor according to the warning threshold in step 7, and automatically issuing a landslide warning by linking the warning broadcast;

[0019] S10. Take water content warning as the main measure, supplemented by rainfall and surface displacement monitoring for mutual verification. When the water content monitor reaches the warning value, check the rainfall and surface displacement data at this time. If there is no rainfall and no obvious displacement, it can be preliminarily judged as a false alarm and further observed and confirmed; if there is rainfall and no obvious displacement, it can be judged as a normal warning and observation should be strengthened; if there is no rainfall and obvious displacement, it can be judged as a normal warning and observation should be strengthened; if there is rainfall and obvious displacement, it can be judged as a normal warning and close observation should be carried out.

[0020] Preferably, the number of boreholes in S2 should be no less than 5, and should be arranged at least in the four corners and the middle of the working area. The cumulative length of the cores taken from each layer of rock should meet the needs of mechanical parameter testing of specimens with different water contents in S4. If not, the number of boreholes should be increased. The depth of the boreholes in S2 should pass through the potential slip surface and enter the stable bedrock for at least 2m.

[0021] Preferably, the three-dimensional numerical simulation software described in S3 must be capable of secondary development, such as FLAC3D, 3DEC and other numerical simulation software.

[0022] Preferably, the physical and mechanical parameters in S4 include but are not limited to density, water content, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, permeability, and porosity.

[0023] Preferably, the infiltration boundary of the input rainfall data in S6 is set as follows:

[0024] When there is no runoff on the surface, all rainfall infiltrates, and its boundary flow satisfies:

[0025]

[0026] In the formula, is the saturated permeability tensor; k r (h) is the relative permeability coefficient; h is the hydraulic head; x j is the coordinate; n i is the slope normal vector; p is the rainfall intensity.

[0027] The boundary head satisfies:

[0028] h≤0 (2)

[0029] When runoff occurs on the surface, the rainfall cannot be fully infiltrated, and the uninfiltrated part forms runoff on the surface. When studying landslides, the slope surface usually has a certain slope, so assuming there is no water accumulation, the boundary water head should meet the following requirements:

[0030] h=0 (3)

[0031] The boundary flow should meet the following requirements:

[0032]

[0033] Preferably, the simulated rainfall process in S6 involves saturated seepage and unsaturated seepage of rock and soil, and the permeability coefficient evolves according to the following formula during saturated seepage:

[0034]

[0035] Where k0 is the initial permeability; n0 is the initial porosity; ε v is the volumetric strain.

[0036] There is matrix suction in unsaturated rock and soil. According to the Van Genuchten model, the functional relationship between the water content w of the rock and soil and the matrix suction ψ is:

[0037]

[0038] Where w is the volumetric water content; w s is the volumetric moisture content; w r is the residual volume water content; ψ is the matrix suction, ψ=u a -u w ,u a is the pore gas pressure, u w is the pore water pressure; a, n, and m are related fitting parameters, a is the matrix suction corresponding to the inflection point when the soil-water characteristic curve changes from a saturated state to an unsaturated state. Generally, a is 100 for soil and 10 for rock. n is the slope of the soil-water characteristic curve at the inflection point, which means the rate of change of water content in the initial air intake stage; m is the soil parameter corresponding to the residual water content of the soil, m = 1-1 / n.

[0039] Combining the relationship between soil moisture content and saturation, the relationship between soil saturation and matrix suction is obtained:

[0040]

[0041] Where, s is the saturation of rock and soil; s r is the residual saturation; the rest are the same as before.

[0042] When the residual saturation s r = 0,

[0043]

[0044] Substituting the Van Gennuchten model into the statistical conductivity model proposed by Burdine and Mualem, the relationship between matrix suction and relative permeability under unsaturated conditions can be obtained:

[0045]

[0046] In terms of saturation, it is:

[0047] k r =s 1 / 2 [1-(1-s 1 / m ) m ] 2 (10)

[0048] According to the relationship between permeability coefficient and relative permeability coefficient, the relationship between permeability coefficient and saturation of rock and soil in unsaturated state is obtained:

[0049] k=k s s 1 / 2 [1-(1-s 1 / m ) m ] 2 (11)

[0050] The above formula is implanted into the numerical simulation software through programming language to realize the simulation of rainfall infiltration process.

[0051] Preferably, during the simulated rainfall process in S6, the stability coefficient of the slope is calculated using the strength reduction method (Formula 12, Formula 13) at intervals of a certain seepage time.

[0052] c F =c / F trial (12)

[0053]

[0054] In the formula, c is the cohesive force; is the internal friction angle; c F is the reduced cohesion; is the reduced internal friction angle; F trial is the reduction factor, which is also the stability factor.

[0055] Preferably, the landslide monitoring warning threshold in S7 is set with two groups of warning values ​​for synchronous warning, the first group is an absolute value warning based on numerical simulation results, and its threshold can be considered as:

[0056] Blue warning: The water content value corresponding to the stability coefficient of 1.05 obtained by numerical simulation calculation;

[0057] Yellow warning: The water content value corresponding to the stability coefficient of 1.03 obtained by numerical simulation calculation;

[0058] Orange warning: The moisture content value corresponding to the stability coefficient of 1.01 obtained by numerical simulation;

[0059] Red warning: The water content value corresponding to the stability coefficient of 1.00 obtained by numerical simulation calculation;

[0060] Alternatively, the moisture content value corresponding to the stability coefficient of 1.00 obtained by simulation of the count value is V,

[0061] Blue warning: moisture content value = 0.7V;

[0062] Yellow warning: moisture content value = 0.8V

[0063] Orange warning: moisture content value = 0.9V;

[0064] Red warning: moisture content value = 1.0V;

[0065] The second group is based on the early warning of the moisture content growth rate measured on site, and its threshold can be considered as:

[0066] Blue warning: water content growth rate = a / h;

[0067] Yellow warning: water content growth rate = b / h;

[0068] Orange warning: water content growth rate = c / h;

[0069] Red warning: water content growth rate = 0 / h;

[0070] Among them, a, b, and c represent different moisture content growth rates, which are summarized after long-term monitoring of the working area; h represents hours.

[0071] Preferably, moisture content monitors are buried along the borehole for burying moisture content monitors at the potential sliding surface (as core monitoring points) and at the bottom of each rock layer above the potential sliding surface (as auxiliary monitoring points) in S8. When the thickness of a certain rock layer is relatively large, multiple moisture content monitors can be buried therein at a certain depth. The model of the moisture content monitor in S8 is not limited, but it must be able to be placed in the borehole. The diameter of the borehole should not exceed 91 mm, and it should be resistant to certain overburden pressure, corrosion and durability.

[0072] Preferably, the sealing material in S8 should be selected with a material having a permeability coefficient equivalent to that of the adjacent rock formation. It may be considered to put the core taken out of the drilling hole back to its original position and cement it with a cementing material with a comparable permeability coefficient. The surface displacement meter in S8 may use a displacement monitoring device such as GNSS or a crack meter.

[0073] Beneficial Effects

[0074] The present invention provides a landslide monitoring and early warning method that combines water content monitoring with rainfall and surface displacement verification. Compared with the prior art, it has the following beneficial effects:

[0075] (1) This landslide monitoring and early warning method, which uses moisture content monitoring supplemented by rainfall and surface displacement to verify each other, improves the "generalization of the whole by the surface" analysis based on slope profiles through high-precision three-dimensional geological modeling; the potential sliding surface is calculated based on the mechanical behavior characteristics of the rock and soil body through numerical simulation, avoiding the defects of the strip method that regards the slope rock and soil body as a rigid body and assumes a sliding surface; burying moisture content monitoring instruments at the potential sliding surface and at the bottom of each rock layer above the potential sliding surface is better than arranging moisture content monitoring instruments at the high and low ends of the landslide range and in the transient saturated zone of the landslide body, and the number of equipment is less and more economical; the moisture content data is obtained by actual measurement, avoiding the deviation between the calculated value based on the empirical formula and the actual value; it is not only applicable to shallow soil landslides, but also to deep soil landslides and rock landslides, and has a wider range of applications.

[0076] (2) The landslide monitoring and early warning method that uses moisture content monitoring supplemented by mutual verification of rainfall and surface displacement is more accurate in terms of potential sliding surfaces and landslide ranges based on high-precision three-dimensional geological modeling and numerical simulation calculations, providing a target for landslide monitoring, early warning and prevention. Moisture content monitoring instruments are buried at the potential sliding surface and at the bottom of each rock layer above the potential sliding surface. The selected location is more optimal, the number of devices used is less, and it is more economical. It is not only applicable to shallow soil landslides, but also to deep soil landslides and rock landslides, and has a wider range of applications. Compared with the current shallow moisture content monitoring and early warning method at a depth of tens of centimeters underground, this method is more effective, supplemented by mutual verification of rainfall and surface displacement to eliminate false alarms, and has higher accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a schematic diagram of the LiDAR scanning work area of ​​the machine of the present invention;

[0078] Figure 2 This is a schematic diagram of the surface point cloud of the working area of ​​the present invention;

[0079] Figure 3 This is a schematic diagram of the model of the present invention imported into the three-dimensional numerical simulation software;

[0080] Figure 4 This is a schematic diagram of the relationship between the density of a rock layer and its water content in the present invention;

[0081] Figure 5 This is a schematic diagram of the relationship between the elastic modulus of a rock formation and its water content in the present invention;

[0082] Figure 6 This is a schematic diagram of the relationship between the Poisson's ratio of a certain rock formation and its water content in the present invention;

[0083] Figure 7 A schematic diagram of the relationship between the cohesion of a rock layer and its water content in the present invention;

[0084] Figure 8This is a schematic diagram of the relationship between the internal friction angle of a rock formation and its water content in the present invention;

[0085] Fig. 9 A schematic diagram of a screenshot of a three-dimensional numerical simulation software for programming and implantation of the present invention;

[0086] Fig.10 A schematic diagram of the landslide range predicted by calculating the stability coefficient according to the present invention;

[0087] Fig.11 A schematic diagram of arranging water content monitoring points in the middle of a potential sliding surface according to the present invention;

[0088] Fig.12 This is a schematic diagram of the water content-stability coefficient curve guiding monitoring and early warning of the present invention;

[0089] Fig.13 This is a schematic diagram of early warning according to the stability coefficient of the present invention;

[0090] Fig.14 This is a schematic diagram of early warning according to water content of the present invention;

[0091] Fig.15 It is the technical roadmap of the present invention. DETAILED DESCRIPTION

[0092] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0093] See also Figure 1-15 The present invention provides a technical solution: a landslide monitoring and early warning method using water content monitoring supplemented by rainfall and surface displacement mutual verification, specifically comprising the following steps:

[0094] S1. Use a drone equipped with a LiDAR device to scan the work area ( Figure 1 ) Obtain ground point cloud data from the ground surface, and remove vegetation to obtain real surface point cloud data ( Figure 2 );

[0095] S2. Arrange drilling holes according to the landslide investigation specifications to obtain core and underground rock structure information. The number of drilling holes should be no less than 5, and they should be arranged in the four corners and the middle of the working area. The cumulative length of the cores of each layer of rock should meet the requirements of the mechanical parameter test of the specimens with different water contents in step 4. If not, increase the number of drilling holes. The drilling depth should pass through the potential slip surface and enter the stable bedrock for at least 2m;

[0096] S3, combine the surface and underground rock information in S1 and S2 to perform three-dimensional geological modeling, and import it into three-dimensional numerical simulation software that can be used for secondary development, such as FLAC3D, 3DEC and other numerical simulation software, see Figure 3 As shown;

[0097] S4. Measure the physical and mechanical parameters of the cores obtained during the drilling process at different water contents, including but not limited to density, water content, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, permeability, and porosity, and establish a relationship model between water content and physical and mechanical parameters, see Figures 4 to 8 As shown, the relationship model is implanted into the aforementioned three-dimensional numerical simulation software through programming. Fig. 9 As shown;

[0098] S5. In the aforementioned three-dimensional numerical simulation software, the initial state of the slope is inverted according to the physical and mechanical parameters and groundwater level data measured by the core;

[0099] S6. According to the rainfall data monitored by the rain gauges arranged in the open space of the work area, or the rainfall data artificially assumed for prediction needs, input it into the upper boundary of the above-mentioned three-dimensional numerical model, simulate the rainfall process, calculate the stability coefficient, and predict the range of landslide, see Fig.10 As shown in the figure, the infiltration boundary of rainfall data is set as follows:

[0100] When there is no runoff on the surface, all rainfall infiltrates, and its boundary flow satisfies:

[0101]

[0102] In the formula, is the saturated permeability tensor; k r (h) is the relative permeability coefficient; h is the hydraulic head; x j is the coordinate; n i is the slope normal vector; p is the rainfall intensity.

[0103] The boundary head satisfies:

[0104] h≤0 (2)

[0105] When runoff occurs on the surface, the rainfall cannot be fully infiltrated, and the uninfiltrated part forms runoff on the surface. When studying landslides, the slope surface usually has a certain slope, so assuming that there is no water accumulation, the boundary water head should satisfy:

[0106] h=0 (3)

[0107] The boundary flow should meet the following requirements:

[0108]

[0109] The rainfall process involves saturated seepage and unsaturated seepage in rock and soil. During saturated seepage, the permeability coefficient evolves according to the following formula:

[0110]

[0111] Where k0 is the initial permeability; n0 is the initial porosity; ε v is the volumetric strain.

[0112] There is matrix suction in unsaturated rock and soil. According to the Van Genuchten model, the functional relationship between the water content w of the rock and soil and the matrix suction ψ is:

[0113]

[0114] Where w is the volumetric water content; w s is the volumetric moisture content; w r is the residual volume water content; ψ is the matrix suction, ψ=u a -u w ,u a is the pore gas pressure, u w is the pore water pressure; a, n, and m are related fitting parameters, a is the matrix suction corresponding to the inflection point when the soil-water characteristic curve changes from a saturated state to an unsaturated state. Generally, a is 100 for soil and 10 for rock. n is the slope of the soil-water characteristic curve at the inflection point, which means the rate of change of water content in the initial air intake stage; m is the soil parameter corresponding to the residual water content of the soil, m = 1-1 / n.

[0115] Combining the relationship between soil moisture content and saturation, the relationship between soil saturation and matrix suction is obtained:

[0116]

[0117] Where, s is the saturation of rock and soil; s r is the residual saturation; the rest are the same as before.

[0118] When the residual saturation s r = 0,

[0119]

[0120] Substituting the Van Gennuchten model into the statistical conductivity model proposed by Burdine and Mualem, the relationship between matrix suction and relative permeability under unsaturated conditions can be obtained:

[0121]

[0122] In terms of saturation, it is:

[0123] kr =s 1 / 2 [1-(1-s 1 / m ) m ] 2 (10)

[0124] According to the relationship between permeability coefficient and relative permeability coefficient, the relationship between permeability coefficient and saturation of rock and soil in unsaturated state is obtained:

[0125] k=k s s 1 / 2 [1-(1-s 1 / m ) m ] 2 (11)

[0126] The above formula is implanted into the numerical simulation software through programming language to realize the simulation of rainfall infiltration process.

[0127] During the simulated rainfall process, the stability coefficient of the slope is calculated using the strength reduction method (Equation 12, Equation 13) at certain seepage time intervals (such as 1h, 2h, etc.).

[0128] c F =c / F trial (12)

[0129]

[0130] In the formula, c is the cohesive force; is the internal friction angle; c F is the reduced cohesion; is the reduced internal friction angle; F trial is the reduction factor, which is also the stability factor;

[0131] S7. Arrange moisture content monitoring points in the middle of the potential sliding surface in the numerical model. Fig.11 As shown, record the moisture content value that changes with the simulated rainfall duration, and analyze it together with the stability coefficient in step 6. Fig.12 As shown, the monitoring warning threshold setting is guided, and two sets of warning values ​​for synchronous warning are set. The first set is the absolute value warning based on the numerical simulation results. The threshold can be considered (or set by yourself according to the actual working area) as follows:

[0132] Blue warning: The water content value corresponding to the stability coefficient of 1.05 obtained by numerical simulation is

[0133] Yellow warning: The water content value corresponding to the stability coefficient of 1.03 obtained by numerical simulation calculation is

[0134] Orange warning: The water content value corresponding to the stability coefficient of 1.01 obtained by numerical simulation is

[0135] Red warning: The water content value corresponding to the stability coefficient of 1.00 obtained by numerical simulation is shown in Fig.13 As shown;

[0136] Alternatively, the moisture content value corresponding to the stability coefficient of 1.00 obtained by simulation of the count value is V,

[0137] Blue warning: water content value = 0.7V,

[0138] Yellow warning: water content value = 0.8V,

[0139] Orange warning: water content value = 0.9V,

[0140] Red warning: moisture content value = 1.0V, see Fig.14 As shown;

[0141] The second group is based on the early warning of the moisture content growth rate measured on site, and its threshold can be considered as:

[0142] Blue warning: water content growth rate = a / h;

[0143] Yellow warning: water content growth rate = b / h;

[0144] Orange warning: water content growth rate = c / h;

[0145] Red warning: water content growth rate = 0 / h;

[0146] Among them, a, b, and c represent different moisture content growth rates, which are summarized after long-term monitoring of the work area; h represents hours;

[0147] S8. Drill holes to the middle of the potential sliding surface at the work site according to the location of the water content monitoring point in the numerical simulation. Bury water content monitors along the borehole at the potential sliding surface (as the core monitoring point) and at the bottom of each rock layer above the potential sliding surface (as an auxiliary monitoring point). When a certain rock layer is thick, multiple water content monitors (as auxiliary monitoring points) can be buried at a certain depth (such as 1m) therein. The model of the water content monitor is not limited, but it must be able to be placed in the borehole (the borehole diameter should not exceed 91mm), withstand a certain overburden pressure, corrosion and durability, and then seal the hole. The sealing material should be selected from materials with a permeability coefficient equivalent to that of the adjacent rock layer. It can be considered to put the core taken out of the borehole back to its original position and cement it with a cementing material with a comparable permeability coefficient. Install a surface displacement meter (displacement monitoring equipment such as GNSS or crack meters can be used) and an early warning broadcast on the surface;

[0148] S9, setting the warning threshold of the moisture content monitor according to the warning threshold in step 7, and automatically issuing a landslide warning by linking the warning broadcast;

[0149] S10. Water content warning is mainly used, supplemented by rainfall and surface displacement monitoring for mutual verification. When the moisture content monitor reaches the warning value, check the rainfall and surface displacement data at this time. If there is no rainfall and no obvious displacement, it can be initially judged as a false alarm and further observed and confirmed; if there is rainfall and no obvious displacement, it can be judged as a normal warning and strengthened observation; if there is no rainfall and obvious displacement, it can be judged as a normal warning and strengthened observation; if there is rainfall and obvious displacement, it can be judged as a normal warning and close observation. The technical route of the whole process is shown in Fig.15 shown.

[0150] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0151] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0152] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A landslide monitoring and early warning method using water content monitoring supplemented by rainfall and surface displacement mutual verification, characterized in that: The specific steps include: S1. Use a drone equipped with a LiDAR device to scan the surface of the work area to obtain a ground point cloud, and then remove vegetation to obtain a true surface point cloud; S2. Arrange drilling holes according to landslide investigation specifications to obtain core and underground rock structure information; S3, combining the surface and underground rock formation information in S1 and S2 to perform three-dimensional geological modeling, and importing it into three-dimensional numerical simulation software that can be used for secondary development; S4, measuring the physical and mechanical parameters of the cores obtained during the drilling process at different water contents, establishing a relationship model between water content and physical and mechanical parameters, and implanting the relationship model into the aforementioned three-dimensional numerical simulation software through programming; S5. In the aforementioned three-dimensional numerical simulation software, the initial state of the slope is inverted according to the physical and mechanical parameters and groundwater level data measured by the core; S6. According to the rainfall data monitored by the rain gauge arranged in the open area of ​​the work area, or the rainfall data artificially assumed for prediction needs, input into the upper boundary of the aforementioned three-dimensional numerical model, simulate the rainfall process, calculate the stability coefficient, and predict the range of the landslide; S7. Arrange a moisture content monitoring point in the middle of the potential sliding surface in the numerical model, record the moisture content value that changes with the duration of simulated rainfall, and analyze it together with the stability coefficient in S6 to guide the setting of monitoring and early warning thresholds; S8. Drill a hole to the middle of the potential sliding surface at the work site according to the location of the moisture content monitoring point in the numerical simulation, bury the moisture content monitoring instrument, and then seal the hole, and install a surface displacement meter and early warning broadcast on the surface; S9, setting the warning threshold of the moisture content monitor according to the warning threshold in step 7, and automatically issuing a landslide warning by linking the warning broadcast; S10. Take water content warning as the main measure, supplemented by rainfall and surface displacement monitoring for mutual verification. When the water content monitor reaches the warning value, check the rainfall and surface displacement data at this time. If there is no rainfall and no obvious displacement, it can be preliminarily judged as a false alarm and further observed and confirmed; if there is rainfall and no obvious displacement, it can be judged as a normal warning and observation should be strengthened; if there is no rainfall and obvious displacement, it can be judged as a normal warning and observation should be strengthened; if there is rainfall and obvious displacement, it can be judged as a normal warning and close observation should be carried out.

2. The landslide monitoring and early warning method of claim 1, wherein the moisture content monitoring is supplemented by the mutual verification of rainfall and surface displacement, is characterized by: The number of drill holes in S2 should be no less than 5, and should be arranged at least in the four corners and the middle of the working area. The cumulative length of the cores taken from each layer of rock should meet the needs of mechanical parameter testing of specimens with different water contents in S4. If not, the number of drill holes should be increased. The depth of the drill holes in S2 should pass through the potential slip surface and enter the stable bedrock for at least 2m.

3. The landslide monitoring and early warning method of claim 1, wherein the moisture content monitoring is supplemented by the mutual verification of rainfall and surface displacement, is characterized by: The three-dimensional numerical simulation software described in S3 must be capable of secondary development, such as FLAC3D, 3DEC and other numerical simulation software.

4. The landslide monitoring and early warning method of claim 1, wherein the moisture content monitoring is supplemented by the mutual verification of rainfall and surface displacement, is characterized by: The physical and mechanical parameters in S4 include but are not limited to density, water content, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, permeability, and porosity.

5. The landslide monitoring and early warning method of claim 1, wherein the moisture content monitoring is supplemented by the mutual verification of rainfall and surface displacement, is characterized by: The infiltration boundary of the input rainfall data in S6 is set as follows: When there is no runoff on the surface, all rainfall infiltrates, and its boundary flow satisfies: In the formula, is the saturated permeability tensor; k r (h) is the relative permeability coefficient; h is the hydraulic head; x j is the coordinate; n i is the slope normal vector; p is the rainfall intensity. The boundary head satisfies: h≤0 (2) When runoff occurs on the surface, the rainfall cannot be fully infiltrated, and the uninfiltrated part forms runoff on the surface. When studying landslides, the slope surface usually has a certain slope, so assuming there is no water accumulation, the boundary water head should meet the following requirements: h＝0 (3) The boundary flow should meet the following requirements:

6. The landslide monitoring and early warning method of claim 1, wherein the method comprises: The simulated rainfall process in S6 involves saturated seepage and unsaturated seepage of rock and soil. During saturated seepage, the permeability coefficient evolves according to the following formula: Where k0 is the initial permeability; n0 is the initial porosity; ε v is the volumetric strain. There is matrix suction in unsaturated rock and soil. According to the Van Genuchten model, the functional relationship between the water content w of the rock and soil and the matrix suction ψ is: Where w is the volumetric water content; w s is the volumetric moisture content; w r is the residual volume water content; ψ is the matrix suction, ψ=u a -u w ,u a is the pore gas pressure, u w is the pore water pressure; a, n, and m are related fitting parameters, a is the matrix suction corresponding to the inflection point when the soil-water characteristic curve changes from a saturated state to an unsaturated state. Generally, a is 100 for soil and 10 for rock. n is the slope of the soil-water characteristic curve at the inflection point, which means the rate of change of water content in the initial air intake stage; m is the soil parameter corresponding to the residual water content of the soil, m = 1-1 / n. Combining the relationship between soil moisture content and saturation, the relationship between soil saturation and matrix suction is obtained: Where, s is the saturation of rock and soil; s r is the residual saturation; the rest are the same as before. When the residual saturation s r = 0, Substituting the Van Gennuchten model into the statistical conductivity model proposed by Burdine and Mualem, the relationship between matrix suction and relative permeability under unsaturated conditions can be obtained: In terms of saturation, it is: k r =s 1 / 2 [1-(1-s 1 / m ) m ] 2 (10) According to the relationship between permeability coefficient and relative permeability coefficient, the relationship between permeability coefficient and saturation of rock and soil in unsaturated state is obtained: k=k s s 1 / 2 [1-(1-s 1 / m ) m ] 2 (11) The above formula is implanted into the numerical simulation software through programming language to realize the simulation of rainfall infiltration process.

7. The landslide monitoring and early warning method of claim 1, wherein the method comprises: In the simulated rainfall process in S6, the stability coefficient of the slope is calculated by using the strength reduction method (Formula 12, Formula 13) at intervals of a certain seepage time. c F =c / F trial (12) In the formula, c is the cohesive force; is the internal friction angle; c F is the reduced cohesion; is the reduced internal friction angle; F trial is the reduction factor, which is also the stability factor.

8. The landslide monitoring and early warning method of claim 1, wherein: The landslide monitoring warning threshold in S7 sets two groups of warning values ​​for synchronous warning. The first group is an absolute value warning based on numerical simulation results, and its threshold can be considered as: Blue warning: The water content value corresponding to the stability coefficient of 1.05 obtained by numerical simulation calculation; Yellow warning: The water content value corresponding to the stability coefficient of 1.03 obtained by numerical simulation calculation; Orange warning: The moisture content value corresponding to the stability coefficient of 1.01 obtained by numerical simulation; Red warning: The water content value corresponding to the stability coefficient of 1.00 obtained by numerical simulation calculation; Alternatively, the moisture content value corresponding to the stability coefficient of 1.00 obtained by simulation of the count value is V, Blue warning: moisture content value = 0.7V; Yellow warning: moisture content value = 0.8V Orange warning: moisture content value = 0.9V; Red warning: moisture content value = 1.0V; The second group is based on the early warning of the moisture content growth rate measured on site, and its threshold can be considered as: Blue warning: water content growth rate = a / h; Yellow warning: water content growth rate = b / h; Orange warning: water content growth rate = c / h; Red warning: water content growth rate = 0 / h; Among them, a, b, and c represent different moisture content growth rates, which are summarized after long-term monitoring of the working area; h represents hours.

9. The landslide monitoring and early warning method of claim 1, wherein: In the S8, moisture content monitors are buried along the boreholes for burying moisture content monitors at the potential sliding surface (as core monitoring points) and at the bottom of each rock layer above the potential sliding surface (as auxiliary monitoring points). When the thickness of a certain rock layer is relatively large, multiple moisture content monitors can be buried therein at certain depths. The model of the moisture content monitor in S8 is not limited, but it must be able to be placed in the borehole. The diameter of the borehole should not exceed 91 mm, and it should be resistant to certain overburden pressure, corrosion and durability.

10. The landslide monitoring and early warning method of claim 1, wherein: The sealing material in S8 should be selected with a material with a permeability coefficient equivalent to that of the adjacent rock formation. It is possible to consider putting the core taken out of the drilling hole back to its original position and cementing it with a cementing material with a comparable permeability coefficient. The surface displacement meter in S8 can use a displacement monitoring device such as GNSS or a crack meter.

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