A method and device for ultra-high-level and ultra-long-distance geological disaster chain monitoring based on multiple physical quantities

Abstract

In order to solve the problem of full coverage monitoring of key parameters in key deformation areas, the present invention provides a super-high-position and super-long-distance geological disaster chain monitoring method and device based on multi-physical quantities, wherein the monitoring method comprises: obtaining remote sensing interpretation results of the geological disaster chain, and dividing the area where the geological disaster chain is located according to the remote sensing interpretation results; dividing the area where the geological disaster chain is located into a number of monitoring units, and deploying monitoring equipment in each monitoring unit; calculating the risk coefficient and risk criterion of each monitoring unit, and comparing the risk coefficient with the risk criterion to determine whether the monitoring unit is in a high-risk state; comparing the disaster-causing factors of the monitoring unit in a high-risk state with the disaster-causing criterion to determine whether it is in a stable state or an under-stable state; calculating the early warning state criterion with a high probability according to the combined weight of the risk coefficient, and grading the hazard state of the geological disaster chain according to the early warning state criterion.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a method and device for super-high-position and super-long-distance geological disaster chain monitoring based on multiple physical quantities. Background Art

[0002] Due to active geological structures, frequent earthquakes and strong surface erosion, some areas are areas where super-high geological disaster chains (a series of geological disaster events occurring in high mountains and extremely high mountain areas, which have mutual influence and interaction, forming a continuous disaster process) frequently occur, posing a huge challenge to human engineering activities and the ecological environment. This disaster chain usually starts with high-level collapse and landslide phenomena, and then through long-distance movement, potential energy conversion and dynamic processes, it eventually leads to a series of secondary disasters, such as barrier lakes, debris flows, mud-rock flows, etc.

[0003] At present, physical model experiments are mostly used to study the kinematic laws and dynamic characteristics of ultra-high-altitude and ultra-long-range geological disaster chains. However, the model parameters provided by the physical model experiments may deviate from the actual monitoring data of the geological disaster chain, and the conclusions drawn cannot meet the requirements of real-time monitoring and early warning.

[0004] Due to the complex and harsh outdoor environment, it is difficult for technical personnel to deploy monitoring equipment in all areas. They can only deploy monitoring equipment in the formation and circulation areas of the disaster chain, and cannot achieve full coverage monitoring. In addition, the monitoring parameters of ultra-high-altitude and ultra-long-distance geological disaster chains are relatively single, and full coverage monitoring of key parameters in key deformation areas cannot be achieved. They can only make macro judgments through InSAR surface monitoring methods, and then simulate the occurrence process of the disaster chain by building a physical model. Moreover, it is impossible to achieve effective comprehensive analysis of the monitoring between key deformation areas of ultra-high-altitude and ultra-long-distance geological disaster chains, resulting in limited accuracy in monitoring and early warning of geological disaster chains.

[0005] At the same time, the prediction of geological disaster chains is a process of comprehensive judgment. Geological disasters are often triggered from the triggering area, forming ice and rock avalanches, debris flows, and mudslides, so it is necessary to monitor from the source. Summary of the invention

[0006] The purpose of the present invention is to provide a method and device for monitoring a geological disaster chain at an ultra-high position and ultra-long distance based on multiple physical quantities, which can realize full coverage monitoring of key parameters in key deformation areas, and at the same time can also realize effective comprehensive analysis to improve the monitoring and early warning accuracy of the geological disaster chain.

[0007] In order to solve the above problems, the first aspect of the present invention provides an ultra-high position and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities, which is characterized by comprising the following steps:

[0008] Steps for dividing the area where the geological disaster chain is located: obtain the remote sensing interpretation results of the geological disaster chain, and divide the area where the geological disaster chain is located according to the remote sensing interpretation results;

[0009] Steps for deploying monitoring equipment: Divide the area where the geological disaster chain is located into several monitoring units, and deploy monitoring equipment in each monitoring unit;

[0010] Data acquisition steps: acquiring surface displacement data, flow velocity data, microseismic data, strong earthquake data and rainfall data of each monitoring unit;

[0011] Steps for calculating risk coefficient: Calculate the risk coefficient of each monitoring unit based on surface displacement data, flow velocity data, microseismic data and strong earthquake data;

[0012] Steps for calculating disaster factors: Calculate the disaster factors of the monitoring units in high-risk states based on surface displacement data, flow velocity data, microseismic data and rainfall data;

[0013] Risk judgment steps: Calculate the risk criterion based on the disaster factors, and compare the risk coefficient with the risk criterion. If the risk coefficient is greater than or equal to the risk criterion, the monitoring unit is in a low-risk state; if the risk coefficient is less than the risk criterion, the monitoring unit is in a high-risk state;

[0014] The step of judging the stable state is to compare the disaster factor of the monitoring unit in the high-risk state with the disaster criterion. If the disaster factor is less than the disaster criterion, the monitoring unit is in a stable state. If the disaster factor is greater than or equal to the disaster criterion, the monitoring unit is in an unstable state.

[0015] Hazard status classification steps: According to the high probability calculation of the combined weight of risk factors, the warning status criterion is obtained, and the hazard status of the geological disaster chain is classified according to the warning status criterion.

[0016] Furthermore, the calculation formula of the disaster factor in the ultra-high-level and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities is as follows:

[0017] CC j =W j ×V j ,j=1,2,3,…N

[0018] Among them, CC j is the disaster factor of the jth monitoring unit, W j is the weight corresponding to each monitoring device in the jth monitoring unit, V j is the probability that each monitoring device in the jth monitoring unit detects that the monitoring unit is in a stable state, where W j =[W1,W2,W3,…,W N], and W1+W2+…+W N =1,

[0019] at the same time

[0020] Among them, P jN is the stable state probability of the rainfall monitoring equipment, MS jN is the stable state probability of the microseismic monitoring equipment, FV jN is the stable state probability of the flow rate monitoring device, SD jN is the stable state probability of the surface displacement monitoring equipment.

[0021] Furthermore, the calculation formula for the risk criterion in the ultra-high-level and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities is as follows:

[0022]

[0023] Among them, RR j is the risk criterion of the jth monitoring unit, CC j is the disaster factor of the jth monitoring unit.

[0024] Furthermore, the calculation formula of the risk coefficient in the ultra-high position and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities is as follows:

[0025] RC j =A j ×FV j +B j ×MS j +C j ×SPS j ,j=1,2,3,…N

[0026] Among them, FV j For flow rate data, MS j is microseismic data, SPS j For strong earthquake data, RC j is the risk factor of the jth monitoring unit, A j is the first risk weight probability of the jth monitoring unit, B j is the second risk weight probability of the jth monitoring unit, C j is the third risk weight probability of the jth monitoring unit, and

[0027] Furthermore, the specific steps for grading the hazard status of the geological disaster chain in the above-mentioned ultra-high-level and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities are as follows:

[0028] When the warning state criterion is greater than or equal to the first threshold, the area where the geological disaster chain is located is in the first hazard state;

[0029] When the warning state criterion is less than the first threshold and greater than or equal to the second threshold, the area where the geological disaster chain is located is in the second hazard state;

[0030] When the warning state criterion is less than the second threshold and greater than or equal to the third threshold, the area where the geological disaster chain is located is in the first stable state;

[0031] When the warning state criterion is less than the first threshold and greater than or equal to the second threshold, the area where the geological disaster chain is located is in the second stable state.

[0032] Furthermore, the calculation formula for the early warning status criterion in the ultra-high-level and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities is as follows:

[0033] RCS=RR j ×C j T

[0034] Among them, C j T is the weighted probability of the occurrence of a disaster in the geological disaster chain corresponding to the jth monitoring unit determined based on the on-site investigation, RR j is the risk criterion of the j-th monitoring unit.

[0035] Furthermore, in the above-mentioned ultra-high-level and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities, the stability criterion of the monitoring unit in a stable state is updated.

[0036] The second aspect of the present invention provides an ultra-high position and ultra-long-distance geological disaster chain monitoring device based on multiple physical quantities, comprising the following modules:

[0037] Module for dividing the area where the geological disaster chain is located: used to obtain the remote sensing interpretation results of the geological disaster chain and divide the area where the geological disaster chain is located according to the remote sensing interpretation results;

[0038] Deployment of monitoring equipment module: used to divide the area where the geological disaster chain is located into several monitoring units, and deploy monitoring equipment in each monitoring unit;

[0039] Data acquisition module: used to obtain surface displacement data, flow velocity data, microseismic data, strong earthquake data and rainfall data of each monitoring unit;

[0040] Risk coefficient calculation module: used to calculate the risk coefficient of each monitoring unit based on surface displacement data, flow velocity data, microseismic data and strong earthquake data;

[0041] Disaster factor calculation module: used to calculate the disaster factor of the monitoring unit in a high-risk state based on surface displacement data, flow velocity data, microseismic data and rainfall data;

[0042] Risk judgment module: used to calculate the risk criterion according to the disaster-causing factors, and compare the risk coefficient with the risk criterion. If the risk coefficient is greater than or equal to the risk criterion, the monitoring unit is in a low-risk state; if the risk coefficient is less than the risk criterion, the monitoring unit is in a high-risk state;

[0043] Stable state judgment module: used to compare the disaster factor of the monitoring unit in a high-risk state with the disaster criterion. If the disaster factor is less than the disaster criterion, the monitoring unit is in a stable state; if the disaster factor is greater than or equal to the disaster criterion, the monitoring unit is in an unstable state;

[0044] Hazard status classification module: used to calculate the early warning status criterion based on the high probability of the combined weight of the risk coefficient, and to classify the hazard status of the geological disaster chain based on the early warning status criterion.

[0045] The remote sensing interpretation results are used to divide the area where the geological disaster chain is located, and the area is divided into several monitoring units. Monitoring equipment is deployed in the monitoring units to obtain monitoring data, and the risk coefficient and disaster factor of each monitoring unit are calculated. By comparing the risk coefficient and risk criterion, it is determined whether the monitoring unit is in a high-risk state. The disaster factor and disaster criterion of the monitoring unit in a high-risk state are further judged to determine whether the monitoring unit is in an unstable state, thereby achieving full coverage monitoring of key parameters in key deformation areas and improving the accuracy of monitoring and early warning of geological disaster chains. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of an embodiment of the present invention;

[0047] Figure 2 is a live image acquired by a drone in an embodiment of the present invention;

[0048] Figure 3 It is a structural block diagram showing another embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention. In the description of the present utility model, it should be noted that the terms "first", "second" and "third" are used only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0050] refer to Figure 1, the following is a description of the embodiment of the present invention. This embodiment specifically includes the following steps:

[0051] Steps for dividing the area where the geological disaster chain is located: obtain on-site images through hyperspectral imaging technology or high-altitude drones, such as Figure 2 As shown, after analyzing and processing the on-site images, the remote sensing interpretation results of the geological disaster chain are obtained, and the area where the geological disaster chain is located is divided according to the remote sensing interpretation results.

[0052] Steps for deploying monitoring equipment: Divide the area where the geological disaster chain is located into several monitoring units, and deploy monitoring equipment in appropriate areas within each monitoring unit. Therefore, fewer monitoring units can be deployed in targeted areas where the geological disaster chain may occur, thereby achieving full monitoring of the entire area.

[0053] Data acquisition step: Acquire the surface displacement data, flow velocity data, microseismic data, strong earthquake data and rainfall data of each monitoring unit. In this embodiment, there are N monitoring units in total, and the surface displacement data SD of the jth monitoring unit is j , flow rate data FV j , microseismic data MS j , Strong earthquake data SPS j and rainfall data P j .

[0054] Calculation steps of risk factor: Based on surface displacement data SD j , flow rate data FV j , microseismic data MS j , Strong earthquake data SPS j Calculate the risk coefficient RC of monitoring unit j j ;

[0055] Calculation steps of disaster factors: Based on the surface displacement data SD j , flow rate data FV j , microseismic data MS j and rainfall data P j Calculate the disaster factor CC of monitoring unit j j , the calculation formula is:

[0056] CC j =W j ×V j ,j=1,2,3,…N

[0057] Among them, W j is the weight corresponding to each monitoring device in monitoring unit j, V j is the probability that each monitoring device in the jth monitoring unit detects that the monitoring unit is in a stable state, W j The calculation formula is:

[0058] W j =[W1,W2,W3,…,W N ], and W1+W2+…+W N =1

[0059] V j The calculation formula is:

[0060]

[0061] Among them, P jN is the stable state probability of the rainfall monitoring equipment, MS jN is the stable state probability of the microseismic monitoring equipment, FV jN is the stable state probability of the flow rate monitoring device, SD jN is the stable state probability of the surface displacement monitoring equipment.

[0062] Risk assessment steps: Based on the hazard factor CC j Calculate the risk criterion RR of monitoring unit j j , risk criterion RR j The calculation formula is:

[0063]

[0064] The risk factor RC j and risk criterion RR j By comparison, if the risk factor RC j Greater than or equal to the risk criterion RR j The monitoring unit is in a low risk state. If the risk coefficient RC j Less than risk criterion RR j The monitoring unit is in a high-risk state.

[0065] Steps for judging stable state: Set the disaster factor CC of the monitoring unit in high risk state j Compared with the disaster criterion DCC, the disaster criterion DCC is the possibility of the monitoring unit causing a disaster in the geological disaster chain. Usually, a technician sets an initial value. If the disaster factor is less than the disaster criterion DCC, the monitoring unit is in a stable state, that is, there is a risk of geological disasters in the monitoring unit, but it is still in a stable state for a short period of time and no geological disasters will break out if the triggering conditions are not met, and the stability criterion SC of the monitoring unit is updated; the stability criterion SC can be adaptively adjusted according to the data sampling frequency of the monitoring equipment in each monitoring unit on site. For example, when the stability criterion SC is small, the data sampling frequency of the monitoring equipment can be collected every 2 hours; when the stability criterion SC is large, the data sampling frequency of the monitoring equipment is autonomously adjusted to collect data every 10 minutes. If the disaster factor CCj If it is greater than or equal to the disaster criterion DCC, the monitoring unit is in an unstable state.

[0066] Hazard status classification steps: According to the risk factor RC j The combined weight high probability calculation is used to obtain the warning status criterion RCS, and the calculation formula is:

[0067] RCS=RR j ×C j T

[0068] Among them, C j T The weighted probability of a disaster occurring in the geological disaster chain corresponding to the monitoring unit j is determined based on the on-site investigation.

[0069] And according to the early warning status criterion RCS, the hazard status of the geological disaster chain is graded:

[0070] When the warning state criterion is greater than or equal to the first threshold, the area where the geological disaster chain is located is in the first hazard state;

[0071] When the warning state criterion is less than the first threshold and greater than or equal to the second threshold, the area where the geological disaster chain is located is in the second hazard state;

[0072] When the warning state criterion is less than the second threshold and greater than or equal to the third threshold, the area where the geological disaster chain is located is in the first stable state;

[0073] When the warning state criterion is less than the first threshold and greater than or equal to the second threshold, the area where the geological disaster chain is located is in the second stable state.

[0074] In this embodiment, the first threshold is 20%, the second threshold is 40%, the third threshold is 60%, and the fourth threshold is 80%.

[0075] W = [0.20 0.35 0.35 0.10]

[0076]

[0077] Therefore: CC = [0.37 0.29 0.175 0.165], and then it can be calculated that the probability that the monitoring unit is in an unstable state is 37%.

[0078] refer to Figure 3 The present invention also provides an ultra-high position and ultra-long-distance geological disaster chain monitoring device based on multiple physical quantities, including the following modules:

[0079] Module for dividing the area where the geological disaster chain is located: used to obtain the remote sensing interpretation results of the geological disaster chain and divide the area where the geological disaster chain is located according to the remote sensing interpretation results;

[0080] Deployment of monitoring equipment module: used to divide the area where the geological disaster chain is located into several monitoring units, and deploy monitoring equipment in each monitoring unit;

[0081] Data acquisition module: used to obtain surface displacement data, flow velocity data, microseismic data, strong earthquake data and rainfall data of each monitoring unit;

[0082] Risk coefficient calculation module: used to calculate the risk coefficient of each monitoring unit based on surface displacement data, flow velocity data, microseismic data and strong earthquake data;

[0083] Disaster factor calculation module: used to calculate the disaster factor of the monitoring unit in a high-risk state based on surface displacement data, flow velocity data, microseismic data and rainfall data;

[0084] Risk judgment module: used to calculate the risk criterion according to the disaster-causing factors, and compare the risk coefficient with the risk criterion. If the risk coefficient is greater than or equal to the risk criterion, the monitoring unit is in a low-risk state; if the risk coefficient is less than the risk criterion, the monitoring unit is in a high-risk state;

[0085] Stable state judgment module: used to compare the disaster factor of the monitoring unit in a high-risk state with the disaster criterion. If the disaster factor is less than the disaster criterion, the monitoring unit is in a stable state; if the disaster factor is greater than or equal to the disaster criterion, the monitoring unit is in an unstable state;

[0086] Hazard status classification module: used to calculate the early warning status criterion based on the high probability of the combined weight of the risk coefficient, and to classify the hazard status of the geological disaster chain based on the early warning status criterion.

[0087] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A method for monitoring a chain of ultra-high-level and ultra-long-range geological disasters based on multiple physical quantities, characterized in that: The steps include: The step of dividing the area where the geological disaster chain is located is as follows: obtaining the remote sensing interpretation result of the geological disaster chain, and dividing the area where the geological disaster chain is located according to the remote sensing interpretation result; Deploy monitoring equipment step: divide the area where the geological disaster chain is located into a number of monitoring units, and deploy monitoring equipment in each of the monitoring units; Data acquisition step: acquiring surface displacement data, flow velocity data, microseismic data, strong earthquake data and rainfall data of each monitoring unit; Calculating risk coefficient step: calculating the risk coefficient of each monitoring unit according to the surface displacement data, the flow velocity data, the microseismic data and the strong earthquake data; Calculating disaster factors: Calculating the disaster factors of the monitoring unit according to the surface displacement data, the flow velocity data, the microseismic data and the rainfall data; Risk judgment step: obtaining a risk criterion according to the disaster-causing factor, and comparing the risk coefficient with the risk criterion; if the risk coefficient is greater than or equal to the risk criterion, the monitoring unit is in a low-risk state; if the risk coefficient is less than the risk criterion, the monitoring unit is in a high-risk state; The step of judging a stable state is as follows: comparing the disaster factor of the monitoring unit in a high-risk state with a disaster criterion; if the disaster factor is less than the disaster criterion, the monitoring unit is in a stable state; if the disaster factor is greater than or equal to the disaster criterion, the monitoring unit is in an under-stable state; Hazard status classification step: obtain a warning status criterion based on the combined weight probability of the risk coefficients, and classify the hazard status of the geological disaster chain based on the warning status criterion.

2. The ultra-high position and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities according to claim 1 is characterized in that: The disaster factor calculation formula is: CC j =W j ×V j ,j=1,2,3,…N Among them, CC j is the disaster factor of the jth monitoring unit, W j is the weight corresponding to each monitoring device in the jth monitoring unit, V j is the probability that each monitoring device in the j-th monitoring unit detects that the monitoring unit is in a stable state, Among them, W j =[W1, W2, W3, ..., W N ], and W1+W2+…+W N =1, at the same time Among them, P jN is the stable state probability of the rainfall monitoring equipment, MS jN is the stable state probability of the microseismic monitoring equipment, FV jN is the stable state probability of the flow rate monitoring device, SD jN is the stable state probability of the surface displacement monitoring equipment.

3. The ultra-high position and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities according to claim 1 is characterized in that: The calculation formula of the risk criterion is: Among them, RR j is the risk criterion of the jth monitoring unit, CC j is the disaster factor of the j-th monitoring unit.

4. The ultra-high position and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities according to claim 1 is characterized in that: The calculation formula of the risk coefficient is: RC j =A j ×FV j +B j ×MS j +C j ×SPS j ,j=1,2,3,…N Among them, FV j For flow rate data, MS j is microseismic data, SPS j For strong earthquake data, RC j is the risk factor of the jth monitoring unit, A j is the first risk weight probability of the jth monitoring unit, B j is the second risk weight probability of the jth monitoring unit, C j is the third risk weight probability of the jth monitoring unit, and 5. The ultra-high position and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities according to claim 1 is characterized in that: The specific steps for grading the hazard status of the geological disaster chain are: When the warning state criterion is greater than or equal to a first threshold value, the area where the geological disaster chain is located is in a first hazard state; When the warning state criterion is less than the first threshold value and greater than or equal to the second threshold value, the area where the geological disaster chain is located is in the second hazard state; When the warning state criterion is less than the second threshold value and greater than or equal to the third threshold value, the area where the geological disaster chain is located is in the first stable state; When the warning state criterion is less than the first threshold and greater than or equal to the second threshold, the area where the geological disaster chain is located is in the second stable state.

6. The ultra-high position and ultra-long distance geological disaster chain monitoring method based on multiple physical quantities according to claim 3 is characterized by: The calculation formula of the early warning state criterion is: RCS=RR j ×C j T Among them, C j T is the weighted probability of the occurrence of a disaster in the geological disaster chain corresponding to the jth monitoring unit determined based on the on-site investigation, RR j is the risk criterion of the j-th monitoring unit.

7. The ultra-high position and ultra-long-distance geological disaster chain monitoring method based on multiple physical quantities according to claim 1 is characterized in that: A stability criterion of the monitoring unit in a stable state is updated.

8. An ultra-high-level and ultra-long-distance geological disaster chain monitoring device based on multiple physical quantities, characterized in that: Includes the following modules: Module for dividing the area where the geological disaster chain is located: used to obtain the remote sensing interpretation results of the geological disaster chain, and divide the area where the geological disaster chain is located according to the remote sensing interpretation results; Deployment of monitoring equipment module: used to divide the area where the geological disaster chain is located into several monitoring units, and deploy monitoring equipment in each of the monitoring units; Data acquisition module: used to acquire surface displacement data, flow velocity data, microseismic data, strong earthquake data and rainfall data of each monitoring unit; A risk coefficient calculation module: used for calculating the risk coefficient of each monitoring unit according to the surface displacement data, the flow velocity data, the microseismic data and the strong earthquake data; A disaster factor calculation module is used to calculate the disaster factor of the monitoring unit according to the surface displacement data, the flow velocity data, the microseismic data and the rainfall data; A risk judgment module is used to calculate the risk criterion according to the disaster-causing factor, and compare the risk coefficient with the risk criterion. If the risk coefficient is greater than or equal to the risk criterion, the monitoring unit is in a low-risk state; if the risk coefficient is less than the risk criterion, the monitoring unit is in a high-risk state; A stable state judgment module is used to compare the disaster factor of the monitoring unit in a high-risk state with the disaster criterion. If the disaster factor is less than the disaster criterion, the monitoring unit is in a stable state; if the disaster factor is greater than or equal to the disaster criterion, the monitoring unit is in an under-stable state. Hazard status classification module: used to calculate the warning status criterion according to the combined weight probability of the risk coefficient, and classify the hazard status of the geological disaster chain according to the warning status criterion.

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