CN116910886A - Impact energy early warning method and system for karst region - Google Patents
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Abstract
The application discloses a method and a system for early warning impact energy in karst areas, wherein the flow comprises the following steps: collecting historical engineering geological data of a mine to be mined; arranging exploration points based on historical engineering geological data, and constructing a building foundation type; constructing a simulated mine based on the building foundation pattern; performing preliminary prediction on the simulated mine to obtain a preliminary prediction result; and constructing a prediction model based on the preliminary prediction result to obtain a final prediction result, and carrying out early warning on impact energy based on the final prediction result. According to the application, through simulating the change of the geological structure and the physical property, the release and accumulation process of impact energy is comprehensively mastered, and the geological disaster risk is accurately judged. Through data analysis and processing, the information originally needed to be monitored is converted into a feasible early warning standard, the early warning information is issued in time, and the early warning accuracy of geological disasters is improved.
Description
Technical Field
The application relates to the field of impact energy early warning, in particular to an impact energy early warning method and system for karst areas.
Background
Karst areas are areas rich in karst cave and erosion forms in stratum, and various geological disasters such as ground subsidence, sudden groundwater gushing, earthquake and the like are easy to occur in the areas due to the special structure and mineral composition of rock layers. Most of the current geological disaster monitoring methods adopt traditional geological exploration means, lack of a timely and accurate impact energy early warning method, and bring difficulty to effectively preventing and relieving disasters in karst areas.
Disclosure of Invention
According to the application, the release and accumulation process of impact energy is identified by simulating the change of the geological structure and physical property of the karst area, so that the geological disaster of the karst area is pre-warned in advance.
In order to achieve the above purpose, the present application provides a method for early warning impact energy in karst areas, the flow comprises:
collecting historical engineering geological data of a mine to be mined;
arranging exploration points based on the historical engineering geological data, and planning a building foundation pattern;
constructing a simulated mine based on the building foundation pattern;
performing preliminary prediction on the simulated mine to obtain a preliminary prediction result;
and constructing a prediction model based on the preliminary prediction result to obtain a final prediction result, and carrying out early warning on impact energy based on the final prediction result.
Preferably, the method for planning the basic form of the building comprises the following steps:
acquiring historical data for locally treating karst, soil holes and collapse through the historical engineering geological data;
based on the historical data, a load size of the building foundation pattern is determined.
Preferably, when mine conditions are complex, each independent foundation is provided with exploration points, and the exploration points are arranged column by column in a column-by-pile mode.
Preferably, the method for constructing the simulated mine comprises the following steps: and establishing a mesoscopic particle flow model based on the exploration results of the exploration points, and constructing the simulated mine.
Preferably, the method for obtaining the prediction result comprises the following steps: and obtaining the prediction result by using an empirical analogy method and a microseism monitoring method.
Preferably, the method for constructing the prediction model comprises the following steps: and constructing the prediction model by utilizing a random forest model based on the prediction result.
The application also provides an impact energy early warning system for karst areas, which comprises: the system comprises an acquisition module, a planning module, a calibration module, a prediction module and a construction module;
the acquisition module is used for acquiring historical engineering geological data of a mine to be mined;
the modeling block is used for arranging exploration points and modeling a building foundation pattern based on the historical engineering geological data;
the calibration module is used for constructing a simulated mine based on the building foundation type;
the prediction module is used for carrying out preliminary prediction on the simulated mine to obtain a preliminary prediction result;
the construction module is used for constructing a prediction model based on the preliminary prediction result to obtain a final prediction result, and early warning of impact energy is carried out based on the final prediction result.
Preferably, the impact energy early warning system for karst areas is characterized in that the working flow of the proposed module comprises:
knowing historical data of karst, soil holes and collapse locally through the historical engineering geological data;
based on the historical data, a load size of the building foundation pattern is determined.
Compared with the prior art, the application has the following beneficial effects:
according to the application, through simulating the change of the geological structure and the physical property, the release and accumulation process of impact energy is comprehensively mastered, and the geological disaster risk is accurately judged. Through data analysis and processing, the information originally needed to be monitored is converted into a feasible early warning standard, the early warning information is issued in time, and the early warning accuracy of geological disasters is improved.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present application;
fig. 2 is a schematic diagram of a system structure according to the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
Example 1
In the geological study, the early warning of the impact energy can be essentially summarized as the prediction of the rock burst, so the embodiment uses the mode of predicting the rock burst to early warn the impact energy.
As shown in fig. 1, a flow chart of the method of the present embodiment is shown, where the flow chart includes:
s1, collecting historical engineering geological data of a mine to be mined.
Karst regions, so-called karst features, are mainly characterized by erosion and also include mechanical erosion processes such as erosion and diving of running water, collapse and the like. Therefore, due to the specificity, the survey points cannot be directly arranged in a conventional mode, and dangers are easy to occur, so that engineering geological data (namely, historical engineering geological data) of the periphery of the planned site needs to be collected first.
S2, arranging exploration points based on historical engineering geological data, and simulating a building foundation type.
Before the karst area is surveyed, engineering geological data around the planned site should be fully collected, and historical data of local treatment karst, soil holes and collapse are obtained. The following problems should be noted in karst area survey workload placement:
foundation type of building
To understand the size of the load of the building to be built, the exploration points should be arranged for the foundation pattern to be adopted. When the conditions are complex, each independent foundation should be provided with exploration points, and when a pile-by-pile foundation is adopted, exploration holes should be arranged column by column.
Influence of karst stability
The arrangement of the investigation work should be diversified and the development of the process dynamic mountain in underground karst has the characteristics of concealment, diversity and large spatial position change, and a method combining a plurality of means should be adopted when the workload arrangement is carried out.
For the cover type and bare karst foundations with shallow burial, well detection, groove detection and other methods can be adopted to find out the form of the shallow karst hole gap. For a buried karst foundation, technicians should acquire the information of the rock and soil layers in time in the process of investigation, and if a site with serious karst development occurs, means such as rock and soil testing, static detection, geophysical prospecting and the like should be added according to actual conditions, so as to further find out the buried positions of loose cracks, buried soil holes and the like. Meanwhile, the depth of the exploration point should be appropriately increased or decreased according to actual conditions so as to meet the requirements of basic design and stability checking calculation.
S3, constructing a simulated mine based on the building foundation type.
And then, according to the results obtained by the exploration points, adopting a parallel bonding model to establish a single-axis compressed particle flow number model (simulating a mine) of a standard rock sample around the mine, and obtaining mesomechanics parameters through a trial-and-error method to obtain a calibration result. In this embodiment, the calibration result may be divided into: formation impact propensity and stope support stress zone characteristics, by predicting these two mesoscopic parameters, subsequent predictions are obtained.
S4, carrying out preliminary prediction on the simulated mine to obtain a preliminary prediction result.
Estimating formation impact propensity using empirical analogy
The method for safely mining the rock burst coal seam of the mine or other mines under similar conditions is called an empirical analogy method by summarizing the regularity of the past empirical teaching and training.
In this embodiment, the following factors should be considered as emphasis: the current situation and the development trend of rock burst of the ore and the neighboring ore; the coal seam or the adjacent layer and the adjacent area have overshoot rock burst; the old roof of the coal bed is a hard rock stratum with the thickness of more than 5m and the uniaxial compressive strength of more than 70 MPa; island-shaped or peninsula-shaped coal pillars; supporting the pressure affected zone; the upper part or the lower part is left with coal pillars or stoping boundaries, and the coal seam thickness or the dip angle suddenly changes in the area; a buckling or breaking structural strap, etc.
The area where rock burst may occur can be delineated by analysis of geological conditions and mining specifications throughout the mine. As the depth of extraction increases, rock burst may occur when the coal rock mass stresses meet the strength conditions. The depth of the originating impact is often referred to as the critical depth. From the depth of initiation, rock burst may occur in the coal pillar, coal seam bulge and upper and lower coal seam sections adjacent the coal pillar, and as the mining level increases, the location and extent of rock burst occurrence increases. All areas near the face, areas where the thickness and inclination of the coal layer suddenly change, and geologic zones may be dangerous areas where rock burst occurs.
Estimating the characteristics of the stope supporting stress zone by adopting a microseismic monitoring method
And monitoring and recording the times of the impact of more than 0.5 level and the energy released by the rock burst by using a short-period seismometer. And predicting the recent rock burst occurrence trend and the stress release condition by using the trend. Prior to the positioning system being built, current monitoring using current seismometers is employed. Through the flow, a preliminary prediction result is obtained.
Through preliminary prediction, combining the particularity of karst landforms, the critical depth should not exceed 668m; the working face has a leading support pressure concentration range of 5-35 m and a stress concentration coefficient of 2.5, but the leading pressure influence range of the upper gravel layer reaches 120m. Therefore, the concentrated stress of the working face mining has obvious influence on the working face. The periodic pressure intensity of the upper layer working surface is up to 510kn/m during layer mining 2 The pressure is strong.
S5, constructing a prediction model based on the preliminary prediction result to obtain a final prediction result, and performing early warning on impact energy based on the final prediction result.
And constructing a prediction model based on the preliminary prediction result by adopting a random forest model.
First, a sample set composed of the above preliminary prediction results is collected. Then, cleaning the collected sample set by adopting a mean value substitution method; in this embodiment, the method mainly cleans the missing value, cleans the content inconsistent with the original data type, cleans the unnecessary data, and cleans the logic error data.
Then screening out characteristic data from the sample set to form a characteristic set, randomly extracting M samples from M samples of the sample set by using a bootstrap method based on the established characteristic set by using a random forest model as a sub-training set to construct a decision tree; synchronously constructing T (T > 1) decision trees by adopting the same method; randomly selecting p features from n features of the feature set as a subset of node splitting, selecting 1 feature with the smallest p feature errors as node splitting features according to square errors, and keeping node splitting until the decision tree is not split any more; in this embodiment, the 3 features are root nodes and content nodes, and each prediction result is an output, i.e., a leaf node. Splitting the T decision trees to form a random forest; training each split decision tree in M samples randomly based on the preliminary prediction result to obtain a prediction result corresponding to each decision tree; and solving the average value of the prediction results corresponding to each decision tree to obtain a final prediction result.
And finally, early warning of impact energy is carried out based on the final prediction result.
Example two
As shown in fig. 2, a system structure diagram of the present embodiment includes: the system comprises an acquisition module, a planning module, a calibration module, a prediction module and a construction module. The acquisition module is used for acquiring historical engineering geological data of the mine to be mined; the modeling block is used for arranging exploration points based on historical engineering geological data and modeling a building foundation pattern; the calibration module is used for constructing a simulated mine based on the building foundation type; the prediction module is used for carrying out preliminary prediction on the simulated mine to obtain a preliminary prediction result; the construction module is used for constructing a prediction model based on the preliminary prediction result to obtain a final prediction result, and early warning of impact energy is carried out based on the final prediction result.
In the following, the present embodiment will be described in detail to solve the technical problems in actual life.
Firstly, historical engineering geological data of a mine to be mined is collected by utilizing a collection module.
Karst regions, so-called karst features, are mainly characterized by erosion and also include mechanical erosion processes such as erosion and diving of running water, collapse and the like. Therefore, due to the specificity, the survey points cannot be directly arranged in a conventional mode, and dangers are easy to occur, so that engineering geological data (namely, historical engineering geological data) of the periphery of the planned site needs to be collected first.
Then, the modeling block arranges exploration points based on the historical engineering geological data to model the foundation pattern of the building.
Before the karst area is surveyed, engineering geological data around the planned site should be fully collected, and historical data of local treatment karst, soil holes and collapse are obtained. The following problems should be noted in karst area survey workload placement:
foundation type of building
To understand the size of the load of the building to be built, the exploration points should be arranged for the foundation pattern to be adopted. When the conditions are complex, each independent foundation should be provided with exploration points, and when a pile-by-pile foundation is adopted, exploration holes should be arranged column by column.
Influence of karst stability
The arrangement of the investigation work should be diversified and the development of the process dynamic mountain in underground karst has the characteristics of concealment, diversity and large spatial position change, and a method combining a plurality of means should be adopted when the workload arrangement is carried out.
For the cover type and bare karst foundations with shallow burial, well detection, groove detection and other methods can be adopted to find out the form of the shallow karst hole gap. For a buried karst foundation, technicians should acquire the information of the rock and soil layers in time in the process of investigation, and if a site with serious karst development occurs, means such as rock and soil testing, static detection, geophysical prospecting and the like should be added according to actual conditions, so as to further find out the buried positions of loose cracks, buried soil holes and the like. Meanwhile, the depth of the exploration point should be appropriately increased or decreased according to actual conditions so as to meet the requirements of basic design and stability checking calculation.
The calibration module constructs a simulated mine based on the building foundation pattern.
And then, according to the results obtained by the exploration points, adopting a parallel bonding model to establish a single-axis compressed particle flow number model (simulating a mine) of a standard rock sample around the mine, and obtaining mesomechanics parameters through a trial-and-error method to obtain a calibration result. In this embodiment, the calibration result may be divided into: formation impact propensity and stope support stress zone characteristics, by predicting these two mesoscopic parameters, subsequent predictions are obtained.
The prediction module performs preliminary prediction on the simulated mine to obtain a preliminary prediction result.
Estimating formation impact propensity using empirical analogy
The method for safely mining the rock burst coal seam of the mine or other mines under similar conditions is called an empirical analogy method by summarizing the regularity of the past empirical teaching and training.
In this embodiment, the following factors should be considered as emphasis: the current situation and the development trend of rock burst of the ore and the neighboring ore; the coal seam or the adjacent layer and the adjacent area have overshoot rock burst; the old roof of the coal bed is a hard rock stratum with the thickness of more than 5m and the uniaxial compressive strength of more than 70 MPa; island-shaped or peninsula-shaped coal pillars; supporting the pressure affected zone; the upper part or the lower part is left with coal pillars or stoping boundaries, and the coal seam thickness or the dip angle suddenly changes in the area; a buckling or breaking structural strap, etc.
The area where rock burst may occur can be delineated by analysis of geological conditions and mining specifications throughout the mine. As the depth of extraction increases, rock burst may occur when the coal rock mass stresses meet the strength conditions. The depth of the originating impact is often referred to as the critical depth. From the depth of initiation, rock burst may occur in the coal pillar, coal seam bulge and upper and lower coal seam sections adjacent the coal pillar, and as the mining level increases, the location and extent of rock burst occurrence increases. All areas near the face, areas where the thickness and inclination of the coal layer suddenly change, and geologic zones may be dangerous areas where rock burst occurs.
Estimating the characteristics of the stope supporting stress zone by adopting a microseismic monitoring method
And monitoring and recording the times of the impact of more than 0.5 level and the energy released by the rock burst by using a short-period seismometer. And predicting the recent rock burst occurrence trend and the stress release condition by using the trend. Prior to the positioning system being built, current monitoring using current seismometers is employed. Through the flow, a preliminary prediction result is obtained.
Through preliminary prediction, combining the particularity of karst landforms, the critical depth should not exceed 668m; the working face has a leading support pressure concentration range of 5-35 m and a stress concentration coefficient of 2.5, but the leading pressure influence range of the upper gravel layer reaches 120m. Thus, the working face is mining concentrated stress to workThe effect of the surface is obvious. The periodic pressure intensity of the upper layer working surface is up to 510kn/m during layer mining 2 The pressure is strong.
And finally, a construction module constructs a prediction model based on the preliminary prediction result to obtain a final prediction result, and early warning of impact energy is carried out based on the final prediction result.
And constructing a prediction model based on the preliminary prediction result by adopting a random forest model.
First, a sample set composed of the above preliminary prediction results is collected. Then, cleaning the collected sample set by adopting a mean value substitution method; in this embodiment, the method mainly cleans the missing value, cleans the content inconsistent with the original data type, cleans the unnecessary data, and cleans the logic error data.
Then screening out characteristic data from the sample set to form a characteristic set, randomly extracting M samples from M samples of the sample set by using a bootstrap method based on the established characteristic set by using a random forest model as a sub-training set to construct a decision tree; synchronously constructing T (T > 1) decision trees by adopting the same method; randomly selecting p features from n features of the feature set as a subset of node splitting, selecting 1 feature with the smallest p feature errors as node splitting features according to square errors, and keeping node splitting until the decision tree is not split any more; in this embodiment, the 3 features are root nodes and content nodes, and each prediction result is an output, i.e., a leaf node. Splitting the T decision trees to form a random forest; training each split decision tree in M samples randomly based on the preliminary prediction result to obtain a prediction result corresponding to each decision tree; and solving the average value of the prediction results corresponding to each decision tree to obtain a final prediction result.
And finally, early warning of impact energy is carried out based on the final prediction result.
The above embodiments are merely illustrative of the preferred embodiments of the present application, and the scope of the present application is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present application pertains are made without departing from the spirit of the present application, and all modifications and improvements fall within the scope of the present application as defined in the appended claims.
Claims (8)
1. The impact energy early warning method for the karst area is characterized by comprising the following steps of:
collecting historical engineering geological data of a mine to be mined;
arranging exploration points based on the historical engineering geological data, and planning a building foundation pattern;
constructing a simulated mine based on the building foundation pattern;
performing preliminary prediction on the simulated mine to obtain a preliminary prediction result;
and constructing a prediction model based on the preliminary prediction result to obtain a final prediction result, and carrying out early warning on impact energy based on the final prediction result.
2. The method of claim 1, wherein the method of planning the building foundation pattern comprises:
acquiring historical data for locally treating karst, soil holes and collapse through the historical engineering geological data;
based on the historical data, a load size of the building foundation pattern is determined.
3. The impact energy early warning method for karst areas according to claim 2, wherein when mine conditions are complex, each independent foundation is provided with exploration points, and the exploration points are arranged column by column in a column-by-column manner.
4. The method of claim 1, wherein the method of constructing the simulated mine comprises: and establishing a mesoscopic particle flow model based on the exploration results of the exploration points, and constructing the simulated mine.
5. The method for early warning of impact energy in a karst region according to claim 1, wherein the method for obtaining the prediction result comprises: and obtaining the prediction result by using an empirical analogy method and a microseism monitoring method.
6. The method of claim 1, wherein the method of constructing the predictive model comprises: and constructing the prediction model by utilizing a random forest model based on the prediction result.
7. An impact energy early warning system for karst regions, comprising: the system comprises an acquisition module, a planning module, a calibration module, a prediction module and a construction module;
the acquisition module is used for acquiring historical engineering geological data of a mine to be mined;
the modeling block is used for arranging exploration points and modeling a building foundation pattern based on the historical engineering geological data;
the calibration module is used for constructing a simulated mine based on the building foundation type;
the prediction module is used for carrying out preliminary prediction on the simulated mine to obtain a preliminary prediction result;
the construction module is used for constructing a prediction model based on the preliminary prediction result to obtain a final prediction result, and early warning of impact energy is carried out based on the final prediction result.
8. The impact energy pre-warning system of a karst region of claim 7, wherein the workflow of the proposed module comprises:
knowing historical data of karst, soil holes and collapse locally through the historical engineering geological data;
based on the historical data, a load size of the building foundation pattern is determined.
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