CN113310916A - System and method for identifying and forecasting geological anomaly in tunnel based on element inversion minerals - Google Patents
System and method for identifying and forecasting geological anomaly in tunnel based on element inversion minerals Download PDFInfo
- Publication number
- CN113310916A CN113310916A CN202110565717.0A CN202110565717A CN113310916A CN 113310916 A CN113310916 A CN 113310916A CN 202110565717 A CN202110565717 A CN 202110565717A CN 113310916 A CN113310916 A CN 113310916A
- Authority
- CN
- China
- Prior art keywords
- minerals
- data
- surrounding rock
- geological
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 229910052500 inorganic mineral Inorganic materials 0.000 title claims abstract description 89
- 239000011707 mineral Substances 0.000 title claims abstract description 89
- 238000000034 method Methods 0.000 title claims abstract description 44
- 239000011435 rock Substances 0.000 claims description 68
- 230000003595 spectral effect Effects 0.000 claims description 25
- 239000000843 powder Substances 0.000 claims description 20
- 230000002159 abnormal effect Effects 0.000 claims description 19
- 238000005553 drilling Methods 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 17
- 238000012360 testing method Methods 0.000 claims description 13
- 230000005856 abnormality Effects 0.000 claims description 12
- 238000009826 distribution Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 10
- 238000010276 construction Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000013499 data model Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 230000002547 anomalous effect Effects 0.000 claims description 4
- 230000007246 mechanism Effects 0.000 claims description 4
- 238000005315 distribution function Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 2
- 230000005540 biological transmission Effects 0.000 description 12
- 238000007405 data analysis Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 11
- 238000004590 computer program Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000001228 spectrum Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000007789 sealing Methods 0.000 description 4
- 238000004611 spectroscopical analysis Methods 0.000 description 4
- 238000004876 x-ray fluorescence Methods 0.000 description 4
- 230000009194 climbing Effects 0.000 description 3
- 238000013277 forecasting method Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 2
- 238000005422 blasting Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000007873 sieving Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000005641 tunneling Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000002734 clay mineral Substances 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000011010 flushing procedure Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004898 kneading Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000004846 x-ray emission Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/02—Devices for withdrawing samples
- G01N1/04—Devices for withdrawing samples in the solid state, e.g. by cutting
- G01N1/08—Devices for withdrawing samples in the solid state, e.g. by cutting involving an extracting tool, e.g. core bit
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/22—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material
- G01N23/223—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material by irradiating the sample with X-rays or gamma-rays and by measuring X-ray fluorescence
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/07—Investigating materials by wave or particle radiation secondary emission
- G01N2223/076—X-ray fluorescence
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/10—Different kinds of radiation or particles
- G01N2223/101—Different kinds of radiation or particles electromagnetic radiation
- G01N2223/1016—X-ray
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/60—Specific applications or type of materials
- G01N2223/616—Specific applications or type of materials earth materials
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention provides a tunnel geological anomaly recognition and prediction system and method based on element inversion minerals.
Description
Technical Field
The invention belongs to the technical field of tunnel unfavorable geological recognition and prediction, and relates to a tunnel geological anomaly recognition and prediction system and method based on element inversion minerals.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The unfavorable geology is the biggest threat in the tunnel construction process, seriously influences the progress and the tunnel construction safety of tunnel construction, has for a long time restricted the construction of tunnel and other underground engineering. Therefore, accurate and reliable geological anomaly identification and geological forecast in the tunnel are important guarantees for tunnel construction. Common bad geologic bodies in the tunnel comprise fault broken zones, altered zones, karsts and the like. The nature of these poor geologic bodies is the destruction and reconstruction of rock mass by tectonic action, and stress, hydrothermal fluid and the like in the process of tectonic action can destroy the composition of rock, leading to the change of elements and minerals composing the rock, and then the rock mass is destroyed as a whole to form poor geology, so the abnormal characteristics of the element minerals in the poor geology rock mass and the rock mass in the surrounding environment are very obvious.
The existing adverse geology forecasting method commonly used in the tunnel is a recognition and forecasting method based on geophysics, and the purpose of the forecasting method is to detect and recognize the position, the form, the type and the scale of an adverse geologic body. The method can only develop and forecast a certain unfavorable geology and give a result, and the forecast of the surrounding environment is difficult to judge and lacks basis.
Disclosure of Invention
The invention aims to solve the problems and provides a tunnel geological anomaly identification and prediction system and method based on element inversion minerals.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a tunnel geological anomaly identification and prediction method based on element inversion minerals comprises the following steps:
acquiring the percentage content of various elements of each collected point rock sample of the face surrounding rock;
acquiring surrounding rock spectroscopy information;
analyzing the percentage content of the elements, inverting the percentage content into mineral content, judging geological anomaly and unfavorable geological forecast of the surrounding rock, and constructing a surrounding rock mineral three-dimensional information point cloud data model by combining individual mineral information in the surrounding rock spectroscopy information;
and identifying and forecasting unfavorable geology according to the three-dimensional point cloud model of the surrounding rock mineral.
As an alternative embodiment, the percentage of elements is analyzed and inverted to mineral content using a combined CIPW standard mineral calculation and barter-nigeri standard mineral calculation.
As an alternative embodiment, the specific process of determining the geological anomaly and the unfavorable geology of the surrounding rock according to the percentage content of the elements comprises the following steps: describing a random variable based on a probability density function or a probability distribution function, and judging whether the surrounding rock element data sample conforms to normal distribution; if the element data do not accord with the normal distribution, abnormal points are removed to enable the data to accord with the normal distribution; and if the elements accord with normal distribution, calculating the lower limit of enrichment abnormality and the lower limit of loss abnormality of the elements.
By way of further limitation, the enrichment anomaly lower limit of the element is the sum of the product of the standard deviation of the sample and the correlation coefficient and the mean of the sample.
By way of further limitation, the loss anomaly lower limit for the element is the sample mean minus the product of the sample standard deviation and the correlation coefficient.
As an alternative embodiment, the construction process of the three-dimensional point cloud data model of the surrounding rock mineral comprises the following steps:
acquiring surrounding rock element data and spectral data on a tunnel face point by point;
the method comprises the steps that plane information is obtained for each scanning of a tunnel face or tunnel surrounding rock, elements and mineral information of the current scanning are established, data information of each plane is one-dimensional plane information, the scanning is continuously pushed along with continuous tunneling of a tunnel, the data information of a plurality of planes is continuously superposed, deep surrounding rock mineral three-dimensional point cloud data information is established, data integration is carried out, and a three-dimensional point cloud model is formed.
As an alternative embodiment, the specific process of identifying and forecasting the unfavorable geology according to the three-dimensional point cloud model of the surrounding rock mineral comprises the following steps: when the data is processed, firstly, the element data is calculated, the element data is expanded and inverted, the element anomaly and the mineral anomaly are respectively detected, when the anomaly point is detected, the spectral data of the point is further processed, whether the anomaly exists in the spectral data or not is identified, the anomalous mineral identified by the spectral data and the mineral inverted by the element are integrated and finally overlapped on the normal point to form the anomaly point, and after continuous scanning surface overlapping, the anomaly range is defined, so that the identification and the forecast of the unfavorable geology are carried out.
An in-tunnel geological anomaly identification and prediction system based on element inversion minerals, comprising:
the moving mechanism is used for bearing the measuring device to perform point-to-point acquisition on the surrounding rock of the tunnel face;
the testing device comprises a laser positioning and image acquiring module, a sampling module, an element data acquiring module and a spectrum data acquiring module, wherein the laser positioning and image acquiring module is used for determining the positions of element testing, spectrum testing and powder sampling;
the sampling module is used for acquiring rock sample powder;
the element data acquisition module is used for determining the percentage content of each element;
the spectral data acquisition module is used for acquiring surrounding rock spectroscopy information;
and the data processing module is used for acquiring the acquired data of the testing device, analyzing the element percentage content, inverting the element percentage content into the mineral content, judging the geological abnormality and unfavorable geological forecast of the surrounding rock, constructing a surrounding rock mineral three-dimensional information point cloud data model by combining the individual mineral information in the surrounding rock spectroscopy information, and performing advanced geological forecast according to the surrounding rock mineral three-dimensional point cloud model.
As an alternative embodiment, the sampling module comprises a drilling device for drilling a powder sample, a transport device for receiving and transporting the powder sample to a sample collection device for collection of the sample, and a sample collection device.
As a further limitation, the drilling apparatus, the elemental data acquisition module, and the spectral data acquisition module are mounted on a retractable structure.
As an alternative embodiment, the moving mechanism includes a wall climbing robot, a mechanical arm, a slide rail, a fixing member, and the like.
A computer readable storage medium having stored therein instructions adapted to be loaded by a processor of a terminal device and to perform the steps of a method for identifying and forecasting geological anomalies within tunnels based on elemental inversion of minerals.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions adapted to be loaded by a processor and to execute the steps of the method for identifying and forecasting geological anomalies in tunnels based on element-based inversion of minerals.
Compared with the prior art, the beneficial effect of this disclosure is:
the method and the device can acquire the geological abnormal information in the tunnel face or the rock mass near the tunnel face, and forecast the unfavorable geology of the region based on the information. The unfavorable geological anomaly is revealed and forecasted by element inversion of minerals, research is conducted on the basis of rock change, and the defect determined by single mineral anomaly is overcome.
The method can continuously and massively acquire the surrounding rock data near the tunnel face of the tunnel and analyze the data in real time without manual sampling and processing analysis.
According to the method, geological anomaly identification and unfavorable geological research are developed from the aspect of geological mechanism of unfavorable geological formation, the coverage range is wider, and the identification effect is better.
According to the method for mineral inversion by elements, the accuracy of identifying the geological anomaly of the surrounding rock by singly depending on the elements or the minerals is improved.
The method can be used for analyzing and forecasting the geological anomaly of the tunnel in different environments in TBM construction and drilling and blasting construction, and has good adaptability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a flow chart of a data acquisition process;
FIG. 2 is an overall apparatus diagram of the present embodiment;
fig. 3(a) is a schematic diagram of the effect applied in a TBM tunnel;
FIG. 3(b) is a schematic diagram of the effect of the application in the drilling and blasting tunnel;
wherein, 1-powder receiving disc; 2-hyperspectral tester; 3-X-ray fluorescence spectroscopy; 4-a small drill; 5-laser positioning and image acquisition device; 6-sample collection box; 7-powder sample chute; 8-powerful blower; 9-sieving; 10-a transfer hinge; 11-sample valve bag; 12-a coding machine; 13-valve bag control instrument; 14-a laser sensor; 15-hand grip; 16-tunnel inner arch frame; 17-tunnel surrounding rock; 18-computer data analysis transmission and control system; 19-TBM tunnel; 20-TBM tunnel normal area; 21-TBM tunnel abnormal area; 22-drilling a blast tunnel; 23-drilling a blast hole abnormal area; and 24-drilling a normal area of the blast hole.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings 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 disclosure 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 disclosure. 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.
The tunnel geological anomaly identification and advance geological prediction system based on element inversion minerals mainly comprises the following parts: the device comprises a positioning and image acquisition module, a sampling module, an element data acquisition module, a spectrum data acquisition module and a computer data analysis, transmission and control module.
The positioning and image module (5) integrates laser positioning and high-definition photographing, the laser position is used for determining the specific position needing sampling and testing, and the high-definition photographing is used for acquiring surrounding rock information before sampling and testing.
The sampling module comprises: the powder receiving disc (1) is used for receiving a powder sample drilled by a drilling machine and conveying the powder sample to a sieve for screening through a chute; a small-sized drilling machine (4) for drilling a powder sample in the surrounding rock; the sample collecting box (6) is used for collecting the sealed sample of the self-sealing belt; a chute (7) for conveying the powder sample to the sieve; a powerful blower (8) for flushing the chute and the powder remaining in the sieve; a sieve (9) with different specifications for sieving powder samples with proper sizes; a transfer hinge (10) for transferring the bagged powder to the sample collection box; the sample self-sealing bag (11) is used for containing the sample screened by the sieve; a coding machine (12) for coding numbers on the self-sealing bags; and the laser sensor (14) is used for controlling the amount of the packaged samples in the self-sealing bag.
The element data acquisition module comprises an X-ray fluorescence spectrometer (3) connected to the shaft, and can acquire the element content of the surrounding rock through a flying contact mode.
The spectral data acquisition module comprises a hyperspectral meter (2) linked on an axis.
The rotating shaft connecting the X-ray fluorescence spectrometer and the hyperspectral instrument can be extended and freely rotated.
The computer data analysis transmission and control module (18) is used for controlling the sampling and testing process of the device and storing and transmitting data.
In the TBM tunnel, the whole system is fixed on a tunnel arch frame by using hand grips at two sides, the hand grips can be replaced according to different arch frame sizes, and the hand grips are provided with sliding and locking devices and can slide or be clamped on the arch frame.
The system can be carried on a robot dog or a wall climbing robot according to actual conditions in the drilling blast hole so as to directly carry out work on the tunnel face.
The working method of the tunnel geological anomaly identification and advanced geological prediction system based on element inversion minerals mainly comprises the following steps:
1. the apparatus is mounted on a robot dog or a wall climbing robot, or is mounted on a tunnel arch. Calculating parameters according to different tunnel sizes and the surrounding rock range to be tested, and inputting the parameters into a control system;
2. according to the set first parameter, the positioning and image module (5) carries out positioning and obtains a surrounding rock image;
3, the X-ray fluorescence spectrometer (3) carries out test at a positioning point and transmits data to a computer data analysis transmission and control module (18);
4. the high-speed spectrograph (2) carries out a test at a positioning point and transmits data to a computer data analysis transmission and control module (18);
5. the sampling module begins work, valve bag control appearance (13) struts valve bag (11), code printer (12) are beaten the sign indicating number, small-size rig (4) begin to bore at first point and get the powder appearance, the powder is accepted dish (1) and is received the sample, and send the sample to sieve (9) through chute (7), rig work drives sieve (9) and sieves the sample extremely from sample bag (11), when the height that highly reaches laser sensor and set for of sample in the valve bag, the sensor feeds back control system (18) rig stop work.
6. The valve bag controller (13) is used for kneading the sample valve bag (11) to close, and the conveying hinge (10) conveys the valve bag to the upper part of the sample collecting box and releases the valve bag. The powerful blower (8) starts to work at the same time, and cleans the chute (7) and the sieve (9).
7. And the positioning and image module (5) carries out next positioning and acquires a surrounding rock image. And repeating the steps 3-6.
8. The image data is transmitted to a computer data analysis, transmission and control system (18) for storage, the element and spectrum data is transmitted to the computer data analysis, transmission and control system (18) for data anomaly analysis and mapping, and the system performs poor geological anomaly analysis and prediction.
The computer data analysis transmission and control system (18) analyzes the element percentage content acquired by the element data acquisition module, inverts the element percentage content into mineral content for judging geological anomaly and unfavorable geological forecast of the surrounding rock, and then combines the individual mineral information acquired by the spectral information to construct a surrounding rock mineral three-dimensional information point cloud data model.
The following method can be used for constructing the three-dimensional information point cloud data model of the surrounding rock minerals:
the method comprises the steps of acquiring surrounding rock element data and spectral data on a tunnel face according to points, acquiring mineral data of the points through inversion after acquiring the element data, and acquiring spectroscopic information of the points, wherein the spectroscopic information contains information of abnormal minerals such as specific clay minerals and altered minerals.
Each time, a piece of plane information is acquired for the tunnel face or the tunnel surrounding rock, the element and mineral information of the current scanning face are established, the data information of each plane is one-dimensional face information, the data information of a plurality of faces is continuously superimposed by the scanning face along with the continuous tunneling of the tunnel, the deep surrounding rock mineral three-dimensional point cloud data information can be established, and a three-dimensional point cloud model is formed through computer data integration, as shown in fig. 2.
Based on the principle provided by the above, when the computer processes data, the element data is firstly calculated, the system carries out expansion and inversion on the element data and respectively detects element abnormality and mineral abnormality, when the abnormal point is detected, the spectral data of the point is further processed, whether the abnormality exists in the spectral data is identified, the abnormal mineral identified by the spectral data and the mineral inverted by the element are integrated, and finally the abnormal mineral and the mineral are superposed on the normal point to form the abnormal point. After the continuous scanning surfaces are overlapped, an abnormal range can be defined so as to identify and forecast unfavorable geology.
In this embodiment, the element and spectrum information acquiring device and the sampling device are controlled by the freely movable telescopic mechanical arm to move within the corresponding range, so as to control the distance between the corresponding unit and the tunnel face.
In the embodiment, the diameter of the drill bit of the small drilling machine (4) is not more than 2 times of the maximum light spot in the X-ray fluorescence spectrometer or the hyperspectral spectrometer. The sampling device is provided with a sample collecting device and is provided with sieves with different meshes (such as 200 meshes, 100 meshes and the like) to prevent blocky samples from mixing, and the collected samples are coded by using a valve bag and an automatic coding machine. After one sample is screened, the sieve can be flushed by a strong blower so as to ensure that the sample cannot be polluted later.
The method for the element inversion of the computer data analysis transmission and control system (18) adopts a calculation method combining CIPW standard mineral calculation and Balt-Nigery standard mineral calculation.
The geological anomaly recognition and geological forecast of the computer data analysis, transmission and control system (18) are divided into two parts, and the geological anomaly is recognized through the acquired element characteristics and the mineral characteristics of element inversion. And then, developing advanced geological forecast according to a surrounding rock mineral three-dimensional point cloud model constructed according to the mineral characteristics and the spectral characteristics.
The following are the abnormal judgment method and steps of the element data, when the computer data analysis transmission and control system (18) receives the element percentage, the analysis will be automatically carried out:
describing the random variables based on a probability density function or a probability distribution function:
judging whether the surrounding rock element data sample conforms to normal distribution;
if the element data do not accord with normal distribution, the system removes abnormal points by using an EDA method so that the data accord with normal distribution;
if the elements accord with normal distribution, the system calculates the lower limit of enrichment abnormality and the lower limit of loss abnormality of the elements;
the anomaly threshold value is calculated using the mean standard deviation method according to the following formula:
C1＝C0+K·S
C2＝C0-K·S
in the formula (I), the compound is shown in the specification,
is the mean of the samples, S is the standard deviation of the samples, C0As a background value, C1For enrichment lower anomaly limit, C2 is for loss lower anomaly limit. In practical application, the selection of the K value needs to be combined with tunnel geological conditions.
The spectral identification is very sensitive to the characteristic extraction of altered or stressed minerals, and a corresponding spectral database can be automatically constructed based on different tunnel geological conditions to identify abnormal minerals.
When the elements are used for mineral inversion, the minerals obtained through inversion are necessarily abnormal when the elements are abnormal, and the three-dimensional point cloud model of the surrounding rock minerals, which is established through identification of the element inversion mineral abnormality and the spectrum mineral abnormality, is used for advanced geological prediction.
The computer data analysis, transmission and control system (18) establishes a database of data, and the continuous abundant data increases the precision of anomaly detection and makes the inversion effect continuous and accurate.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110565717.0A CN113310916A (en) | 2021-05-24 | 2021-05-24 | System and method for identifying and forecasting geological anomaly in tunnel based on element inversion minerals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110565717.0A CN113310916A (en) | 2021-05-24 | 2021-05-24 | System and method for identifying and forecasting geological anomaly in tunnel based on element inversion minerals |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113310916A true CN113310916A (en) | 2021-08-27 |
Family
ID=77374450
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110565717.0A Pending CN113310916A (en) | 2021-05-24 | 2021-05-24 | System and method for identifying and forecasting geological anomaly in tunnel based on element inversion minerals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113310916A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114135277A (en) * | 2021-11-11 | 2022-03-04 | 山东大学 | Method and system for advance geological prediction of tunnel based on perception while drilling of geochemical characteristics |
| CN114483025A (en) * | 2021-12-17 | 2022-05-13 | 山东大学 | Tunnel advanced lithology identification system and method based on geochemical feature while-drilling test |
| CN115656053A (en) * | 2022-10-19 | 2023-01-31 | 山东大学 | Rock Mineral Content Testing Method and System |
| CN115760698A (en) * | 2022-10-19 | 2023-03-07 | 山东大学 | Fracture identification method and system fusing image and spectral characteristic information |
| CN115753632A (en) * | 2022-10-19 | 2023-03-07 | 山东大学 | Image spectrum-based method and system for real-time judgment and identification of poor geologic body in tunnel |
| CN116539537A (en) * | 2023-03-27 | 2023-08-04 | 山东大学 | Fault zone identifying and predicting method and system based on in-situ test analysis of mineral in hole |
| WO2024083262A1 (en) * | 2022-10-19 | 2024-04-25 | 山东大学 | Space-sky-ground-tunnel-hole integrated unfavorable geology identification method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120137831A1 (en) * | 2010-12-06 | 2012-06-07 | Tekni-Plex, Inc. | Method and apparatus for leaching metal from mining ore |
| CN106530396A (en) * | 2016-08-25 | 2017-03-22 | 中国黄金集团内蒙古矿业有限公司 | Mining geological logging data three dimensional processing method |
| CN107807221A (en) * | 2017-09-22 | 2018-03-16 | 中国石油天然气集团公司 | A kind of abnormity point selective examination inspection method of geochemical reconnaisance laboratory sample analysis |
| CN110031491A (en) * | 2019-04-04 | 2019-07-19 | 山东大学 | Vehicular lithology and unfavorable geology Precursory Characters identifying system and method |
| CN111267984A (en) * | 2020-01-21 | 2020-06-12 | 山东大学 | System and method for identifying poor geologic body in tunnel based on hyperspectral technology analysis |
-
2021
- 2021-05-24 CN CN202110565717.0A patent/CN113310916A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120137831A1 (en) * | 2010-12-06 | 2012-06-07 | Tekni-Plex, Inc. | Method and apparatus for leaching metal from mining ore |
| CN106530396A (en) * | 2016-08-25 | 2017-03-22 | 中国黄金集团内蒙古矿业有限公司 | Mining geological logging data three dimensional processing method |
| CN107807221A (en) * | 2017-09-22 | 2018-03-16 | 中国石油天然气集团公司 | A kind of abnormity point selective examination inspection method of geochemical reconnaisance laboratory sample analysis |
| CN110031491A (en) * | 2019-04-04 | 2019-07-19 | 山东大学 | Vehicular lithology and unfavorable geology Precursory Characters identifying system and method |
| CN111267984A (en) * | 2020-01-21 | 2020-06-12 | 山东大学 | System and method for identifying poor geologic body in tunnel based on hyperspectral technology analysis |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114135277A (en) * | 2021-11-11 | 2022-03-04 | 山东大学 | Method and system for advance geological prediction of tunnel based on perception while drilling of geochemical characteristics |
| CN114135277B (en) * | 2021-11-11 | 2024-07-19 | 山东大学 | Tunnel advanced geological prediction method and system based on localization feature while-drilling perception |
| CN114483025A (en) * | 2021-12-17 | 2022-05-13 | 山东大学 | Tunnel advanced lithology identification system and method based on geochemical feature while-drilling test |
| CN115656053A (en) * | 2022-10-19 | 2023-01-31 | 山东大学 | Rock Mineral Content Testing Method and System |
| CN115760698A (en) * | 2022-10-19 | 2023-03-07 | 山东大学 | Fracture identification method and system fusing image and spectral characteristic information |
| CN115753632A (en) * | 2022-10-19 | 2023-03-07 | 山东大学 | Image spectrum-based method and system for real-time judgment and identification of poor geologic body in tunnel |
| CN115760698B (en) * | 2022-10-19 | 2024-02-13 | 山东大学 | Crack identification method and system integrating image and spectral feature information |
| WO2024083262A1 (en) * | 2022-10-19 | 2024-04-25 | 山东大学 | Space-sky-ground-tunnel-hole integrated unfavorable geology identification method and system |
| CN115656053B (en) * | 2022-10-19 | 2024-05-31 | 山东大学 | Rock mineral content testing method and system |
| CN115753632B (en) * | 2022-10-19 | 2024-05-31 | 山东大学 | Real-time identification method and system of unfavorable geological bodies in tunnels based on image spectrum |
| CN116539537A (en) * | 2023-03-27 | 2023-08-04 | 山东大学 | Fault zone identifying and predicting method and system based on in-situ test analysis of mineral in hole |
| CN116539537B (en) * | 2023-03-27 | 2025-09-16 | 山东大学 | Fault zone identifying and predicting method and system based on in-situ test analysis of mineral in hole |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113310916A (en) | System and method for identifying and forecasting geological anomaly in tunnel based on element inversion minerals | |
| Khaksar et al. | Rock strength from core and logs, where we stand and ways to go | |
| US8217337B2 (en) | Evaluating a reservoir formation | |
| Manzoor et al. | Establishing relationships between structural data from close-range terrestrial digital photogrammetry and measurement while drilling data | |
| Rostami et al. | Rock characterization while drilling and application of roof bolter drilling data for evaluation of ground conditions | |
| CN109826623B (en) | Geophysical well logging identification method for tight sandstone reservoir bedding joints | |
| CN112946780B (en) | Method and device for determining running and sliding fracture activity period | |
| WO2013162400A1 (en) | Determining physical properties of solid materials suspended in a drilling fluid | |
| CN119047156A (en) | A Slope Reliability Evaluation Method | |
| AU2024203735B1 (en) | An intelligent prediction method of geomechanical parameters of surrounding rock based on deep learning | |
| RU2553739C2 (en) | Method and device for determination of local spatial extent of phase of valuable material mineral in rock | |
| Babaei Khorzoughi | Use of measurement while drilling techniques for improved rock mass characterization in open-pit mines | |
| CN112801035A (en) | Knowledge and data dual-drive-based carrying type lithology intelligent identification method and system | |
| Hansen et al. | Improving face decisions in tunnelling by machine learning‐based MWD analysis | |
| Entezari et al. | A data-driven approach to predict shear wave velocity from CPTu measurements | |
| Harraden et al. | Proposed methodology for utilising automated core logging technology to extract geotechnical index parameters | |
| Hansen et al. | Building and analysing a labelled measure while drilling dataset from 15 hard rock tunnels in Norway | |
| CN109143397B (en) | Carbonate reservoir fracture-cave filling identification method and system | |
| Orpen et al. | Error-proofing diamond drilling and drill core measurements | |
| US11216700B2 (en) | Automated material classification by structural features | |
| van As | Geotechnical engineering for mass mining | |
| CN117684990A (en) | Anomaly identification device and method based on elemental mineral detection while drilling in TBM tunnels | |
| Vezhapparambu et al. | Estimating the blast sill thickness using changepoint analysis of MWD data | |
| US20140372041A1 (en) | Validation of physical and mechanical rock properties for geomechanical analysis | |
| Medinac | Advances in pit wall mapping and slope assessment using unmanned aerial vehicle technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210827 |
|
| RJ01 | Rejection of invention patent application after publication |