CN108226998B - Geological advanced prediction method based on TSP (Total suspended particulate) system and rock mass random discontinuous surface three-dimensional network - Google Patents
Geological advanced prediction method based on TSP (Total suspended particulate) system and rock mass random discontinuous surface three-dimensional network Download PDFInfo
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- 239000011435 rock Substances 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000012545 processing Methods 0.000 claims abstract description 6
- 238000001514 detection method Methods 0.000 claims abstract description 4
- 230000008901 benefit Effects 0.000 claims abstract description 3
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 12
- 238000004088 simulation Methods 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 8
- 238000007689 inspection Methods 0.000 claims description 7
- 238000009412 basement excavation Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 4
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000005422 blasting Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
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- 238000007619 statistical method Methods 0.000 description 1
- 230000005641 tunneling Effects 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/282—Application of seismic models, synthetic seismograms
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/612—Previously recorded data, e.g. time-lapse or 4D
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/64—Geostructures, e.g. in 3D data cubes
- G01V2210/641—Continuity of geobodies
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Abstract
The invention discloses a geological advanced prediction method based on a TSP system and a three-dimensional network of random discontinuous surfaces of rock masses, wherein the TSP system collects data on site and judges the validity of the data; processing and rechecking the data to obtain three-dimensional network model data of the surrounding rock in front and obtain exploration data in the previous period; dividing a rock mass structure statistical homogeneous region and a discontinuous surface advantage group number in the statistical homogeneous region; simulating and generating a discontinuous surface space three-dimensional network numerical model according to the data corresponding to each group of discontinuous surfaces; and comprehensively predicting the stability of the front rock mass in advance by combining the exploration data, the TSP analysis result and the discontinuous surface space three-dimensional network numerical model. The method takes the detection result of the TSP as the original data, and fuses the three-dimensional network of the random discontinuous surface of the rock mass to accurately predict and early warn the stability of the rock mass in front of the underground engineering, thereby improving the prediction accuracy, powerfully guiding the field construction of the underground engineering and ensuring the first-line construction safety of the engineering.
Description
Technical Field
The invention relates to a geological advanced prediction method based on a TSP system and a three-dimensional network of random discontinuous surfaces of rock masses.
Background
The technology level and the comprehensive national power of China are continuously improved, the traffic engineering such as highways, railways, urban subways and the like has attracted attention, and meanwhile, the construction of underground engineering is driven to enter a cross-type development track. A batch of large and large tunnels with complex geological conditions and great difficulty in construction technology are built successively. At present, China is the country with the most tunnels and underground projects, the fastest development speed and the most complex geological and structural forms in the world, and countless underground projects such as mountain tunnels, subway tunnels, underwater tunnels, hydroelectric pressure tunnels and the like are built and to be built. In the underground engineering excavation engineering, because rock mass is cut into various rock masses with different sizes and shapes by a structural plane, along with the continuous excavation of the underground engineering, the original static balance state of the surrounding rock is broken, and some unstable blocks exposed on the face of the hollow face can slide along the soft structural plane and cause local block falling, serious even chain reaction is generated, so that the local collapse and instability of the surrounding rock are caused within a certain range of the underground engineering, the engineering progress and the safety of personnel and equipment are seriously influenced. Therefore, in order to ensure the construction safety of underground engineering and improve the efficiency of engineering construction, advanced geological detection in underground engineering is more important.
At present, a TSP advanced geological prediction system adopts the seismic wave reflection principle, is simple to operate and can predict the geological condition in front of the underground engineering tunnel face in a long distance. The micro blasting is applied to the drill hole in a certain distance behind the tunneling surface to emit signals, seismic waves caused by blasting are transmitted to the periphery in the rock body in a spherical mode, one part of the seismic waves are transmitted to the front of underground engineering, when the seismic waves encounter rock wave impedance difference interfaces (such as faults, broken zones, lithological changes, karst caves, underground water and the like), part of the seismic signals are reflected back, the reflected signals are converted into electric signals through a receiving sensor and amplified, and the related parameters of the bad geologic body can be determined by correspondingly calculating the reflected signals.
The stability of the rock mass is controlled by discontinuities in the rock mass. Before stability analysis of rock mass, the first problem is to find out the geological conditions of rock mass, especially the spreading characteristics of discontinuous surfaces in rock mass. Because the discontinuity surface reflects the nature of the rock mass discontinuity and inhomogeneity. However, the spreading of the discontinuities in the rock mass does not have the regular following characteristics, they are randomly distributed in the rock mass, and their geometry is not predictable, and therefore, statistical inference and probabilistic methods are used for the study. The three-dimensional network simulation is based on probability theory and statistics, and the research object of the three-dimensional network simulation is the geometrical characteristics of discontinuous surface space. Simplifying the assumed geometric form of the discontinuous surface, correcting field sampling deviation by methods such as a probability statistical method and a spatial analytic geometry, solving the correct geometric parameters such as trace length, size, spatial density and occurrence distribution, and constructing the combined form of the discontinuous surface in a three-dimensional space by a Monte-Carlo method to form a three-dimensional network model.
According to traditional advanced geological prediction of underground engineering, TSP data acquired on site are processed through corresponding post-processing software, so that time profiles, depth deviation profiles, extracted reflecting layers, rock physical and mechanical parameters, energy of each reflecting layer and other results of P, SH and SV waves are obtained, the obtained results are interpreted, and the forward soft and weak zones, broken zones, faults, water-containing conditions and the like can be predicted. Although the TSP system is widely applied to the advanced prediction of underground engineering, the stability of the rock body in front of the underground engineering is not predicted by combining a rock body random discontinuous surface three-dimensional network. How to predict the stability of rock mass in advance in underground engineering based on a TSP system and a three-dimensional network of random discontinuous surfaces of rock mass is a technical problem to be solved urgently at present.
Disclosure of Invention
The invention aims to solve the problems and provides a geological advanced prediction method based on a TSP system and a three-dimensional network of random discontinuous surfaces of rock masses.
In order to achieve the purpose, the invention adopts the following technical scheme:
a geological advanced prediction method based on a TSP system and a three-dimensional network of random discontinuous surfaces of rock masses comprises the following steps:
(1) the TSP system collects data on site and judges the validity of the data;
(2) processing and rechecking the data to obtain three-dimensional network model data of the surrounding rock in front and obtain exploration data in the previous period;
(3) dividing a rock mass structure statistical homogeneous region and a discontinuous surface advantage group number in the statistical homogeneous region;
(4) simulating and generating a discontinuous surface space three-dimensional network numerical model according to the data corresponding to each group of discontinuous surfaces;
(5) and comprehensively predicting the stability of the front rock mass in advance by combining the exploration data, the TSP analysis result and the discontinuous surface space three-dimensional network numerical model.
Further, in the step (1), the data validity is judged, which means that a signal with data interference smaller than a threshold value is selected in a field test process.
Further, in the step (2), the TSP system data information includes a depth deviation map, a three-dimensional display map, occurrence information, and a 2D physical property map of the rock mass.
Further, in the step (2), the condition of the surrounding rock in front of the excavation surface is analyzed and preliminary prediction is made by combining the exploration data in the previous stage and the depth deviation map generated after the processing of the TSP system.
And (3) dividing the statistical homogeneous region of the rock mass structure, wherein different rock mass structures have different rock mass mechanical characteristics and rock mass hydraulic characteristics, and dividing the statistical homogeneous region of the rock mass structure by combining actual geological characteristics by adopting a probability correlation table Schmidt chart comparison method.
In the step (4), the process of the discontinuous surface space three-dimensional network numerical model comprises the following steps: the method comprises the steps of correcting the deviation of the discontinuous surface trace length, simulating the distance and the density of the discontinuous surface, correcting the measurement deviation of the discontinuous surface attitude or/and carrying out Monte Carlo simulation and model inspection.
In the step (4), the correction of the length of the discontinuous surface trace, the simulation of the diameter of the disc, the fitting of the distribution density function and the deviation correction of the distance measurement are carried out by adopting an end point estimator method based on probability statistics.
In the step (4), the simulation process of the discontinuous surface space and the density is to simulate the discontinuous surface space, and X is adopted2Performing optimal fitting of the probability density function by using a K-S inspection method to obtain the probability distribution density function of the spacing and corresponding parameters thereof; the spatial density of the discontinuity surface is calculated by the tensor method proposed by Oda.
In the step (4), different weights are assigned to each discontinuous surface according to the obtained average diameter of each group of discontinuous surfaces in the space and a weight assignment formula, and then discontinuous surface attitude measurement deviation correction is performed.
In the step (4), the monte carlo simulation and model inspection process includes:
(4-1) determining a scale of generating the network model;
(4-2) determining the number of discontinuous surfaces in the scale form of the generated network model;
(4-3) determining the spatial position of each discontinuous surface, and randomly generating the central point coordinates of each discontinuous surface by adopting a Monte Carlo simulation method;
(4-4) determining the size of each discontinuous surface diameter: simulating and generating the diameter of the discontinuous surface by a Monte Carlo method and leading the diameter of the discontinuous surface to be subjected to the known optimal probability distribution;
(4-5) determining the occurrence of the discontinuous surface;
(4-6) combining the results of all the simulations to form a complete model, and testing it.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to a rock mass stability advanced prediction method based on a TSP system and a three-dimensional network of random discontinuous surfaces of rock masses. The method takes the detection result of the TSP as original data, and combines a three-dimensional network of random discontinuous surfaces of rock masses to accurately predict and early warn the stability of the rock masses in front of the underground engineering, thereby improving the prediction accuracy, powerfully guiding the field construction of the underground engineering and ensuring the first-line construction safety of the engineering.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of a method implementation of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the present invention, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only terms of relationships determined for convenience of describing structural relationships of the parts or elements of the present invention, and are not intended to refer to any parts or elements of the present invention, and are not to be construed as limiting the present invention.
In the present invention, terms such as "fixedly connected", "connected", and the like are to be understood in a broad sense, and mean either a fixed connection or an integrally connected or detachable connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be determined according to specific situations by persons skilled in the relevant scientific or technical field, and are not to be construed as limiting the present invention.
As shown in figure 1, the geological advanced prediction method based on the TSP system and the three-dimensional network of the random discontinuous surface of the rock mass comprises the following steps:
the method comprises the following steps: the TSP system collects data on site and judges the validity of the data;
step two: processing and rechecking the data to obtain the three-dimensional network model data of the surrounding rock in front;
and after the TSP system data is processed, a depth deviation graph, a three-dimensional display graph, attitude information and a 2D (two-dimensional) physical property graph of the rock mass in front of the excavation surface can be obtained.
And (3) analyzing the surrounding rock conditions (faults, broken zones, water-containing conditions and the like) in front of the excavation surface by combining the exploration data in the previous stage and the depth deviation map generated after the TSP system is processed, and making a preliminary prediction.
And extracting corresponding three-dimensional network numerical model data by combining the analysis conclusion with the acquired occurrence information.
Step three: dividing the rock mass structure statistical homogeneity area and the number of the discontinuous surface dominance groups in the statistical homogeneity area;
different rock mass structures have different rock mass mechanical characteristics and rock mass hydraulics. And dividing the statistical homogeneous region of the rock mass structure by adopting a Schmidt chart comparison method of a probability association table and combining actual geological characteristics.
And dividing the dominant group number of the discontinuous surfaces in each statistical homogeneous area.
Step four: simulating data corresponding to each group of discontinuous surfaces to generate a discontinuous surface space three-dimensional network numerical model;
correction of discontinuous surface trace length, simulation of disc diameter size, fitting of distribution density function and deviation correction of spacing measurement. The method for correcting the deviation mainly adopts an end point estimator method based on probability statistics.
And (4) simulating the discontinuous surface spacing and density. Firstly, simulating the spacing of the discontinuous surfaces, and performing optimal fitting on a probability density function by adopting an X2 and K-S inspection method to obtain a probability distribution density function of the spacing and corresponding parameters thereof; the spatial density of the discontinuity surface is calculated by the tensor method proposed by Oda.
And (5) correcting the measurement deviation of the discontinuity surface attitude. According to the obtained average diameter of each group of discontinuous surfaces in the space, different weights are distributed to each discontinuous surface by combining a weight distribution formula, and then correction is carried out.
Monte Carlo simulation and model checking. Generating a 3-D network model requires dimensioning the generated network model to be in the form of a rectangular box. The process is as follows: determining the scale of a square box; determining the number of discontinuous surfaces in the square box; determining the space position of each discontinuous surface, and randomly generating the center point coordinates of each discontinuous surface by adopting a Monte Carlo simulation method; determining the diameter of each discontinuous surface: simulating and generating the diameter of the discontinuous surface by a Monte Carlo method and leading the diameter of the discontinuous surface to be subjected to the known optimal probability distribution; determining the occurrence of the discontinuous surface; combining the models: combining the simulation results of the above steps to form a complete model, and checking the complete model.
Step five: and (4) comprehensive interpretation. And comprehensively predicting the stability of the front rock mass in advance by combining the exploration data, the TSP analysis result and the discontinuous surface space three-dimensional network numerical model.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (8)
1. A geological advanced prediction method based on a TSP system and a three-dimensional network of random discontinuous surfaces of rock masses is characterized by comprising the following steps: the method comprises the following steps:
(1) the TSP system collects data on site and judges the validity of the data;
(2) processing and rechecking the data to obtain three-dimensional network model data of the surrounding rock in front and obtain exploration data in the previous period;
(3) dividing a rock mass structure statistical homogeneous region and a discontinuous surface advantage group number in the statistical homogeneous region;
(4) simulating and generating a discontinuous surface space three-dimensional network numerical model according to the data corresponding to each group of discontinuous surfaces;
(5) combining exploration data, taking a detection result of the TSP as original data, and fusing a discontinuous plane space three-dimensional network numerical model to comprehensively predict the stability of the front rock mass in advance;
in the step (2), the TSP system data information comprises a depth deviation map, a three-dimensional display map, occurrence information and a 2D rock mass physical map;
and (2) analyzing the surrounding rock condition in front of the excavation surface and making a preliminary prediction by combining the exploration data in the previous stage and the depth deviation map generated after the TSP system processing.
2. The geological advanced prediction method based on the TSP system and the three-dimensional network of the random discontinuous surfaces of the rock mass as claimed in claim 1, which is characterized in that: in the step (1), the data validity is judged, that is, a signal with data interference smaller than a threshold value is selected in the field test process.
3. The geological advanced prediction method based on the TSP system and the three-dimensional network of the random discontinuous surfaces of the rock mass as claimed in claim 1, which is characterized in that: and (3) dividing the statistical homogeneous region of the rock mass structure, wherein different rock mass structures have different rock mass mechanical characteristics and rock mass hydraulic characteristics, and dividing the statistical homogeneous region of the rock mass structure by combining actual geological characteristics by adopting a probability correlation table Schmidt chart comparison method.
4. The geological advanced prediction method based on the TSP system and the three-dimensional network of the random discontinuous surfaces of the rock mass as claimed in claim 1, which is characterized in that: in the step (4), the process of the discontinuous surface space three-dimensional network numerical model comprises the following steps: the method comprises the steps of correcting the deviation of the discontinuous surface trace length, simulating the distance and the density of the discontinuous surface, correcting the measurement deviation of the discontinuous surface attitude or/and carrying out Monte Carlo simulation and model inspection.
5. The geological advanced prediction method based on the TSP system and the three-dimensional network of the random discontinuous surfaces of the rock mass as claimed in claim 1, which is characterized in that: in the step (4), the correction of the length of the discontinuous surface trace, the simulation of the diameter of the disc, the fitting of the distribution density function and the deviation correction of the distance measurement are carried out by adopting an end point estimator method based on probability statistics.
6. The geological advanced prediction method based on the TSP system and the three-dimensional network of the random discontinuous surfaces of the rock mass as claimed in claim 1, which is characterized in that: in the step (4), the simulation process of the discontinuous surface space and the density is to simulate the discontinuous surface space, and X is adopted2Performing optimal fitting of the probability density function by using a K-S inspection method to obtain the probability distribution density function of the spacing and corresponding parameters thereof; the spatial density of the discontinuity surface is calculated by the tensor method proposed by Oda.
7. The geological advanced prediction method based on the TSP system and the three-dimensional network of the random discontinuous surfaces of the rock mass as claimed in claim 1, which is characterized in that: in the step (4), different weights are assigned to each discontinuous surface according to the obtained average diameter of each group of discontinuous surfaces in the space and a weight assignment formula, and then discontinuous surface attitude measurement deviation correction is performed.
8. The geological advanced prediction method based on the TSP system and the three-dimensional network of the random discontinuous surfaces of the rock mass as claimed in claim 1, which is characterized in that: in the step (4), the monte carlo simulation and model inspection process includes:
(4-1) determining a scale of generating the network model;
(4-2) determining the number of discontinuous surfaces in the scale form of the generated network model;
(4-3) determining the spatial position of each discontinuous surface, and randomly generating the central point coordinates of each discontinuous surface by adopting a Monte Carlo simulation method;
(4-4) determining the size of each discontinuous surface diameter: simulating and generating the diameter of the discontinuous surface by a Monte Carlo method and leading the diameter of the discontinuous surface to be subjected to the known optimal probability distribution;
(4-5) determining the occurrence of the discontinuous surface;
(4-6) combining the results of all the simulations to form a complete model, and testing it.
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| CN110096775A (en) * | 2019-04-20 | 2019-08-06 | 青岛理工大学 | Method for determining underground engineering excavation state |
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| CN101743554A (en) * | 2007-03-30 | 2010-06-16 | 帕拉代姆地球物理有限公司 | Partitioning algorithm for building a stratigraphic grid |
| CN103513293A (en) * | 2013-10-12 | 2014-01-15 | 广西大学 | Tunnel geology comprehensive advanced forecasting expert system and implementation method thereof |
| CN103901503A (en) * | 2014-03-25 | 2014-07-02 | 中冶集团武汉勘察研究院有限公司 | Comprehensive detection method for front unfavorable geologic bodies in mine underground roadway tunneling |
| CN105334548A (en) * | 2015-10-20 | 2016-02-17 | 中冶交通建设集团有限公司 | Geological forecasting method for tunnel construction in karst area |
| CN106570287A (en) * | 2016-11-10 | 2017-04-19 | 中国人民解放军理工大学 | Method for predicting water inflow of tunnel based on three-dimensional discrete fracture network |
| CN107589471A (en) * | 2017-07-13 | 2018-01-16 | 高军 | A kind of Railway Tunnel Synthetic Geological Prediction Ahead of Construction method |
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| CN102221711B (en) * | 2011-05-23 | 2013-05-01 | 吉林大学 | Advanced prediction device for tunnel water inrush by using nuclear magnetic resonance differential detection and detection method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101743554A (en) * | 2007-03-30 | 2010-06-16 | 帕拉代姆地球物理有限公司 | Partitioning algorithm for building a stratigraphic grid |
| CN103513293A (en) * | 2013-10-12 | 2014-01-15 | 广西大学 | Tunnel geology comprehensive advanced forecasting expert system and implementation method thereof |
| CN103901503A (en) * | 2014-03-25 | 2014-07-02 | 中冶集团武汉勘察研究院有限公司 | Comprehensive detection method for front unfavorable geologic bodies in mine underground roadway tunneling |
| CN105334548A (en) * | 2015-10-20 | 2016-02-17 | 中冶交通建设集团有限公司 | Geological forecasting method for tunnel construction in karst area |
| CN106570287A (en) * | 2016-11-10 | 2017-04-19 | 中国人民解放军理工大学 | Method for predicting water inflow of tunnel based on three-dimensional discrete fracture network |
| CN107589471A (en) * | 2017-07-13 | 2018-01-16 | 高军 | A kind of Railway Tunnel Synthetic Geological Prediction Ahead of Construction method |
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