CN114135277A - Tunnel advanced geological prediction method and system based on geochemical feature while-drilling perception - Google Patents
Tunnel advanced geological prediction method and system based on geochemical feature while-drilling perception Download PDFInfo
- Publication number
- CN114135277A CN114135277A CN202111333726.3A CN202111333726A CN114135277A CN 114135277 A CN114135277 A CN 114135277A CN 202111333726 A CN202111333726 A CN 202111333726A CN 114135277 A CN114135277 A CN 114135277A
- Authority
- CN
- China
- Prior art keywords
- tunnel
- dimensional grid
- geochemical
- drilling
- grid model
- 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
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000005553 drilling Methods 0.000 title claims abstract description 48
- 230000008447 perception Effects 0.000 title claims description 8
- 239000011435 rock Substances 0.000 claims abstract description 69
- 230000002159 abnormal effect Effects 0.000 claims abstract description 22
- 238000004458 analytical method Methods 0.000 claims abstract description 21
- 238000012360 testing method Methods 0.000 claims abstract description 14
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 15
- 239000011707 mineral Substances 0.000 claims description 15
- 230000005856 abnormality Effects 0.000 claims description 5
- 238000004876 x-ray fluorescence Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000001069 Raman spectroscopy Methods 0.000 claims description 3
- 238000011161 development Methods 0.000 claims description 2
- 238000013277 forecasting method Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 8
- 239000002893 slag Substances 0.000 description 8
- 229910001919 chlorite Inorganic materials 0.000 description 7
- 229910052619 chlorite group Inorganic materials 0.000 description 7
- QBWCMBCROVPCKQ-UHFFFAOYSA-N chlorous acid Chemical compound OCl=O QBWCMBCROVPCKQ-UHFFFAOYSA-N 0.000 description 7
- 239000000843 powder Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000005641 tunneling Effects 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000002441 X-ray diffraction Methods 0.000 description 2
- 229910052626 biotite Inorganic materials 0.000 description 2
- 230000007797 corrosion Effects 0.000 description 2
- 238000005260 corrosion Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 239000010433 feldspar Substances 0.000 description 2
- 239000010438 granite Substances 0.000 description 2
- 239000003550 marker Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000010453 quartz Substances 0.000 description 2
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 229910002703 Al K Inorganic materials 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000000556 factor analysis Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Geology (AREA)
- Mining & Mineral Resources (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- Chemical & Material Sciences (AREA)
- Fluid Mechanics (AREA)
- General Physics & Mathematics (AREA)
- Geochemistry & Mineralogy (AREA)
- Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Remote Sensing (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
- Excavating Of Shafts Or Tunnels (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention provides a tunnel advanced geological forecasting method and system based on geochemical feature while-drilling sensing, which comprises the following steps: performing advanced drilling on a plurality of preset hole sites; collecting rock samples obtained from each hole site in a staged manner in the drilling process; performing geochemical test on the collected rock samples to obtain the geochemical characteristics of the rock samples at different hole sites; based on the position information and the geological features of the rock samples acquired in stages, a three-dimensional grid model of the front geology of the tunnel face is acquired through a data fitting and spatial interpolation method; and determining an abnormal region in the three-dimensional grid based on the three-dimensional grid model and the local singularity analysis method, and realizing the prediction of the tunnel advance geology. The method is based on a plurality of rock samples drilled in advance, a three-dimensional grid model of the front geology of the tunnel face is constructed by combining geological feature data and position information of different segmented rock samples, and accurate prediction of the tunnel advance geology is achieved by determining abnormal areas in the three-dimensional grid model.
Description
Technical Field
The invention belongs to the technical field of tunnel advanced geological prediction, and particularly relates to a tunnel advanced geological prediction method and system based on geochemical feature while drilling sensing.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Tunnel construction often encounters many unfavorable geological phenomena, and then causes gushing water, collapse card machine etc. causes the safety problem, drags the construction progress slowly. The advanced detection and identification analysis of geological conditions in front of tunnel faces at home and abroad attach great importance, so that the advanced geological prediction method in tunnels mainly including a geological analysis method, an advanced drilling method, a geophysical detection method (including a seismic wave method, a geological radar method, a direct current method, a transient electromagnetic method) and the like is developed.
The inventor finds that the traditional geological analysis method belongs to a macroscopic qualitative forecasting method, and although the traditional geological analysis method has the advantages of no interference to construction and wide applicability, the traditional geological analysis method is difficult to make specific accurate forecasting; although the advanced drilling method can directly reveal geological information in front of the tunnel face, the inherent problem of 'one hole observation' often exists (namely, only one sampling hole is used for judging that the geological information in front of the tunnel face is not completely reliable); the geophysical prospecting method is a detection method based on the difference of certain physical properties (such as elastic properties, conductive properties, heat conduction properties and the like) of geological media such as a front broken rock body or a water-containing body and the like, and although the geophysical prospecting method has the advantages of wide detection range and long detection distance, the ambiguity of a detection result has a great influence on the accuracy of a forecast result.
Disclosure of Invention
The scheme is based on a plurality of rock samples drilled in advance, a three-dimensional model of geological information in front of a tunnel face is constructed by combining geological feature data and position information of different segmented rock samples, and visual display and accurate prediction of tunnel advanced geology are achieved by determining abnormal areas in the three-dimensional model.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a tunnel advanced geological prediction method based on geochemical feature while drilling perception comprises the following steps:
performing advanced drilling on a plurality of preset hole sites on the tunnel face of the tunnel;
collecting rock blocks or rock slag samples obtained from each hole site in a staged manner in the drilling process;
performing geochemical test on the collected rock block or rock slag sample to obtain the geochemical characteristics of the rock block or rock slag sample at different hole sites;
constructing a three-dimensional grid model of the front geology of the tunnel face based on the position information and the geological features of the rock sample acquired in stages;
and determining an abnormal region in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, and realizing advanced prediction of unfavorable geology in front of the tunnel face.
As an alternative embodiment, the three-dimensional grid model is constructed by adopting a data fitting and spatial interpolation method.
As an alternative, the method is implemented by constructing a tunnel space coordinate system in advance, which includes but is not limited to a rectangular coordinate system, a polar coordinate system or a cylindrical coordinate system.
As an alternative embodiment, when the advanced drilling is performed on a plurality of preset hole sites, the number of the preset hole sites is not less than 3.
Alternatively, the rock samples obtained at each well site are collected in stages, and the collection method includes, but is not limited to, equidistant collection, isochronous collection, or cross-collection.
As an alternative embodiment, the geochemical test employs equipment including, but not limited to, a portable X-ray fluorescence spectrometer, a portable X-ray diffractometer or a portable Raman spectrometer.
According to a second aspect of the embodiments of the present disclosure, there is provided a tunnel advanced geological prediction system based on geochemical feature sensing while drilling, including:
the drilling unit is used for performing advanced drilling on a plurality of preset hole sites on the tunnel face of the tunnel;
the rock sample collecting unit is used for collecting the rock sample obtained at each hole site in a staged manner in the drilling process;
the geochemistry testing unit is used for performing geochemistry testing on the collected rock samples to obtain the geochemistry characteristics of the rock samples with different hole sites;
the three-dimensional model building unit is used for building a three-dimensional grid model of the front geology of the tunnel face based on the rock sample position information and the geological features acquired in stages;
and the advanced geology forecasting unit is used for determining an abnormal area in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, so as to realize the forecasting of the advanced geology of the tunnel.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory for execution, the processor implementing the following steps when executing the program:
constructing a three-dimensional grid model of geological information in front of a tunnel face based on the position information and the geochemical characteristics of the rock sample acquired in stages;
and determining an abnormal region in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, and realizing the advanced geological forecast of the tunnel.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of:
constructing a three-dimensional grid model of geology in front of a tunnel face based on the position information and the geochemical characteristics of the rock sample acquired in stages;
and determining an abnormal region in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, and realizing the advanced geological forecast of the tunnel.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) the scheme is based on a plurality of rock samples drilled in advance, a three-dimensional grid model of the front geology of a tunnel face is constructed by combining geological feature data and position information of different segmented rock samples, and the advance geology of the tunnel is forecasted by determining an abnormal area in the three-dimensional grid model; on one hand, the scheme improves the defect of 'one-hole-observation' of the traditional drilling method for advanced prediction; on the other hand, the advance forecast is carried out by utilizing the attribute abnormality in the geochemical characteristics, so that the geological analysis of the drill hole is more quantitative and the accuracy is higher.
(2) According to the tunnel face front geological model constructed by the scheme, the geological characteristic data can be continuously and visually displayed along the tunneling direction, so that the distribution condition of the geological characteristic of the rock mass is more visual.
(3) According to the scheme disclosed by the invention, the forecast precision can be adjusted by adjusting the number of the advanced drilling holes and the interpolation density, different requirements can be met, and the robustness is stronger.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
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 flowchart of a tunnel advanced geological prediction method based on geochemical feature while drilling sensing according to a first embodiment of the disclosure;
fig. 2 is a schematic diagram of a tunnel space rectangular coordinate system according to a first embodiment of the disclosure;
FIG. 3 is a schematic diagram of a three-dimensional mesh model of the geology in front of a tunnel face according to a first embodiment of the present disclosure;
fig. 4(a) is a schematic diagram of a cross-sectional abnormal delineation result of a tunnel bottom plate according to a first embodiment of the present disclosure (taking Si element as an example);
fig. 4(b) is a schematic diagram of the abnormal delineation result of the tunnel floor cross section (taking chlorite minerals as an example) in the first embodiment of the disclosure.
Detailed Description
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 embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The first embodiment is as follows:
the components of the rock mass can be visually reflected on the basis of the geochemical characteristics of the tunnel surrounding rock, and the element and mineral contents of the rock are quantifiable parameters. Further, in order to overcome the defect that the traditional geological analysis can only be qualitative, improve the problem of 'one-hole observation' of advanced drilling and achieve the purpose of accurate visual prediction of poor geologic bodies, as shown in fig. 1, the disclosure provides a tunnel advanced geological prediction method based on geological feature while-drilling perception, which comprises the following steps:
step 1: performing advanced drilling on a plurality of preset hole sites on the tunnel face of the tunnel;
a tunnel space coordinate system including, but not limited to, a rectangular coordinate system, a polar coordinate system, or a cylindrical coordinate system needs to be constructed in advance before the step 1 is performed.
As an optional implementation manner, in this embodiment, a three-dimensional rectangular coordinate system is established with the horizontal direction as an X axis, the tunneling direction as a Y axis, and the vertical direction as a Z axis.
Meanwhile, position coordinates of a plurality of preset hole sites need to be determined in advance, in the embodiment, 3 hole sites are selected for advanced drilling, and it can be understood that the number of the hole sites can be any integer number not less than 3, such as 4, 5, 6, 7, and the like, and of course, when the number of the hole sites is too large, the processing efficiency is seriously affected while the geological prediction precision is improved, and although the processing efficiency is improved when the number of the hole sites is too small, the geological prediction precision is affected.
Preferably, the hole site location coordinates are set to satisfy as uniform a distribution as possible.
Specifically, when the tunneling is stopped, a plurality of hole sites are drilled in advance on the tunnel face (taking 3 as an example), and the hole number (such as ZK 1; ZK 2; ZK3) and the hole site coordinates (such as (X1, Z1; X2, Z2; X3, Z3)) of each drilled hole are recorded.
Step 2: collecting rock samples (namely rock slag rock powder) obtained from each hole site in a staged manner in the drilling process;
the rock samples obtained at each hole site are collected in a staged manner, and the collection manner includes but is not limited to equidistant collection, isochronous collection or cross collection of the two manners.
Specifically, the equidistant collection means that the drill drills once after drilling forward a certain distance; the isochronous collection means that the collection is performed once every time the drilling machine drills forward for a specific period of time on the premise of knowing the drilling efficiency.
Preferably, in the embodiment, an equidistant collection mode is adopted, and the rock slag rock powder discharged from each drill hole is collected in real time at preset intervals in the drilling process, and the position information of each rock slag rock powder is recorded (for example, ZK 1: Y1, Y2..);
in one or more embodiments, the collection mode adopts an isochronous collection mode, and the rock slag rock powder discharged from each drill hole is collected in real time at preset time intervals in the drilling process, and the position information of each rock slag rock powder is recorded; it is understood that the single equidistant collection method and the isochronous collection method may not satisfy the actual requirement, and therefore, the equidistant collection method and the isochronous collection method may be considered to be used alternately.
And step 3: performing geochemical test on the collected rock samples to obtain the geochemical characteristics of the rock samples at different hole sites;
as an alternative embodiment, the equipment used for the geochemical testing of the rock sample includes, but is not limited to, a portable X-ray fluorescence spectrometer, a portable X-ray diffractometer or a portable raman spectrometer.
Specifically, in this embodiment, portable XRF (X-Ray Fluorescence) and XRD (X-Ray Diffraction) are adopted to perform geochemical test on the obtained rock sample, so as to obtain information on the types and contents of elements and minerals in the sample;
preferably, the obtained geochemical feature data is subjected to noise and missing value processing, and the geochemical feature data with the content lower than the detection limit is deleted from the data;
specifically, the obtained geochemical characteristics are required to be processed as follows:
it is known that in the formation process of geobodies, certain elements/minerals often show similar geochemical behaviors so as to present a certain symbiotic combination relationship, so that for improving analysis efficiency, for massive data obtained from rock samples, methods such as cluster analysis, robust factor analysis and the like are utilized to reduce the dimension of the data, and then sensitive elements and marker mineral data of different bad geobodies are selected;
preferably, in this embodiment, for geological feature data (specifically, content data of 7 elements including Si, Al, Ca, Fe, K, Mg, and Mn in a rock sample, and content data of 4 minerals including quartz, feldspar, chlorite, and biotite) with deleted contents lower than the detection limit, R-type clustering analysis is used to generate a clustering dendrogram, and based on a preset distance coefficient (set to 15 in this embodiment, the smaller the distance coefficient between two variables is, the stronger the symbiotic relationship is), the elements are divided into two combinations of Si-Al-K and Ca-Fe-Mg-Mn, and the minerals are divided into two combinations of quartz-feldspar and chlorite-biotite, so that data of Si/Ca + quartz/chlorite can be selected as sensitive element and marker mineral data;
and the acquired data is stored in a csv format or a grd format, so that the subsequent use is facilitated.
And 4, step 4: constructing a three-dimensional grid model of the front geology of the tunnel face based on the position information and the geological feature data of the rock sample acquired in stages;
specifically, based on the obtained rock sample position information and geochemical feature data, gridding the measured geochemical data by using a data fitting and spatial interpolation method, so that the element mineral content value of each grid point represents element mineral observed values of grid units which take the grid point as the center and respectively take the step length and the scanning line interval as the width and the height, and outputting a geochemical feature three-dimensional grid model in front of a tunnel face;
preferably, before the data gridding, the tunnel hole diameter size and the number and distribution of the sample points are comprehensively considered, and then an appropriate grid unit size is specified.
And 5: and determining an abnormal region in the three-dimensional grid based on the three-dimensional grid model and the local singularity analysis method, and realizing the prediction of the tunnel advance geology.
Specifically, the determining the abnormal region in the three-dimensional grid by using the local singularity analysis method specifically includes: firstly, determining a series of continuous contour lines which are closed successively through an observed value of a grid unit, calculating the average content rho of various element minerals in each contour line, then calculating the area A surrounded by the contour lines, respectively taking log (rho) and logA as two variables, obtaining the estimation of a singular value number through a least square method, and performing interception and delineation of an abnormal area in a three-dimensional model by using a singular index projection point.
As an alternative embodiment, based on the abnormal regions in the determined three-dimensional grid, the development position and scale of the poor geologic body can be forecasted through the distribution of the specific abnormal regions; and the prediction of the type of the poor geologic body can be made through the abnormality of the types and the contents of the specific element minerals.
As shown in fig. 4(a) and 4(b), in advance geological forecast of a tunnel of granite (normal granite has characteristics of high Si content and low chlorite content), based on the method disclosed by the present disclosure, a drilling sample is input to measure the content of element Si and mineral chlorite in data, and the content is displayed in an output geochemical characteristic three-dimensional grid model cross section, it can be found that significant Si element loss abnormality and chlorite mineral enrichment abnormality exist 2-4 meters ahead of the left side of a tunnel face, indicating that a surrounding rock corrosion phenomenon occurs at the position, resulting in Si element loss and chlorite corrosion, and when the area is excavated, the surrounding rock strength and integrity should be paid attention to, and reinforcement measures should be taken in advance to avoid accidents.
Example two:
corresponding to a forecasting method in an embodiment of the present disclosure, the embodiment provides a tunnel advanced geology forecasting system based on geochemical feature while-drilling sensing, including:
the drilling unit is used for performing advanced drilling on a plurality of preset hole sites on the tunnel face of the tunnel;
the rock sample collecting unit is used for collecting the rock sample obtained at each hole site in a staged manner in the drilling process;
the geochemistry testing unit is used for performing geochemistry testing on the collected rock samples to obtain the geochemistry characteristics of the rock samples with different hole sites;
the three-dimensional model building unit is used for building a three-dimensional grid model of the front geology of the tunnel face based on the rock sample position information and the geological features acquired in stages;
and the advanced geology forecasting unit is used for determining an abnormal area in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, so as to realize the forecasting of the advanced geology of the tunnel.
The tunnel advanced geological prediction method and system based on the geochemical feature while-drilling sensing can be realized, and have wide application prospects.
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.
Claims (10)
1. The tunnel advanced geological prediction method based on the geochemical characteristics while drilling perception is characterized by comprising the following steps of:
performing advanced drilling on a plurality of preset hole sites on the tunnel face of the tunnel;
collecting rock samples obtained from each hole site in a staged manner in the drilling process;
performing geochemical test on the collected rock samples to obtain the geochemical characteristics of the rock samples at different hole sites;
constructing a three-dimensional grid model of the front geology of the tunnel face based on the position information and the geological features of the rock sample acquired in stages;
and determining an abnormal region in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, and realizing the prediction of the tunnel advance geology.
2. The method for tunnel advanced geological prediction while drilling based on geochemical features as recited in claim 1, wherein the three-dimensional grid model is constructed by adopting a data fitting and spatial interpolation method.
3. The method for advance geological prediction of tunnels based on geochemical feature while drilling perception according to claim 1, wherein the method is implemented by constructing a spatial coordinate system of the tunnel in advance, wherein the spatial coordinate system of the tunnel comprises but is not limited to a rectangular coordinate system, a polar coordinate system or a cylindrical coordinate system.
4. The advanced geological prediction method of tunnel based on geochemical feature while drilling sensing as recited in claim 1, wherein the number of the preset hole sites is not less than 3 when the plurality of preset hole sites are drilled in advance.
5. The method for advance tunnel geological prediction while drilling based on geochemical features as recited in claim 1, wherein the rock samples obtained at each hole site are collected in stages, and the collection modes include but are not limited to equidistant collection or isochronous collection or cross collection of the two modes.
6. The method for tunnel advanced geological prediction while drilling based on geochemical features as recited in claim 1, wherein the geochemical test is performed using equipment including, but not limited to, a portable X-ray fluorescence spectrometer, a portable X-ray diffractometer or a portable raman spectrometer.
7. The method for forecasting the advanced geology of the tunnel based on the geochemical feature while drilling perception as claimed in claim 1, wherein the forecasting of the advanced geology of the tunnel is realized by: based on the determined abnormal regions in the three-dimensional grid, forecasting the development position and scale of the poor geologic body through the distribution of the specific abnormal regions; or the prediction of the type of the bad geologic body is made through the abnormality of the types and the contents of the specific element minerals.
8. The tunnel advanced geological prediction system based on the geochemical characteristics while drilling perception is characterized by comprising the following components:
the drilling unit is used for performing advanced drilling on a plurality of preset hole sites on the tunnel face of the tunnel;
the rock sample collecting unit is used for collecting the rock sample obtained at each hole site in a staged manner in the drilling process;
the geochemistry testing unit is used for performing geochemistry testing on the collected rock samples to obtain the geochemistry characteristics of the rock samples with different hole sites;
the three-dimensional model building unit is used for building a three-dimensional grid model of the front geology of the tunnel face based on the rock sample position information and the geological features acquired in stages;
and the advanced geology forecasting unit is used for determining an abnormal area in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, so as to realize the forecasting of the advanced geology of the tunnel.
9. An electronic device comprising a memory, a processor, and a computer program stored for execution on the memory, wherein the processor when executing the program performs the steps of:
constructing a three-dimensional grid model of geology in front of a tunnel face based on the position information and the geochemical characteristics of the rock sample acquired in stages;
and determining an abnormal region in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, and realizing the advanced geological forecast of the tunnel.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the program when executed by a processor implements the steps of:
constructing a three-dimensional grid model of geology in front of a tunnel face based on the position information and the geochemical characteristics of the rock sample acquired in stages;
and determining an abnormal region in the three-dimensional grid model based on the three-dimensional grid model and the local singularity analysis method, and realizing the advanced geological forecast of the tunnel.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111333726.3A CN114135277A (en) | 2021-11-11 | 2021-11-11 | Tunnel advanced geological prediction method and system based on geochemical feature while-drilling perception |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111333726.3A CN114135277A (en) | 2021-11-11 | 2021-11-11 | Tunnel advanced geological prediction method and system based on geochemical feature while-drilling perception |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114135277A true CN114135277A (en) | 2022-03-04 |
Family
ID=80392984
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111333726.3A Pending CN114135277A (en) | 2021-11-11 | 2021-11-11 | Tunnel advanced geological prediction method and system based on geochemical feature while-drilling perception |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114135277A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116227090A (en) * | 2023-05-08 | 2023-06-06 | 山东大学 | TBM advanced geological prediction and tunneling performance prediction digital twin system and method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20030096702A (en) * | 2002-06-17 | 2003-12-31 | 코오롱건설주식회사 | Methods to measure drilling path and drilling angle of tunnel |
| CN103399354A (en) * | 2013-08-01 | 2013-11-20 | 中国建筑第四工程局有限公司 | Forecasting method and system for tunnel geology |
| 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 |
| CN110031491A (en) * | 2019-04-04 | 2019-07-19 | 山东大学 | Vehicular lithology and unfavorable geology Precursory Characters identifying system and method |
| CN110306997A (en) * | 2019-06-21 | 2019-10-08 | 中交隧道工程局有限公司南京分公司 | Advanced drilling system based on optimal theoretical hole distribution scheme and working method |
| CN111261236A (en) * | 2020-01-21 | 2020-06-09 | 山东大学 | Tunnel igneous rock weathering degree determining system and method |
| CN113310916A (en) * | 2021-05-24 | 2021-08-27 | 山东大学 | System and method for identifying and forecasting geological anomaly in tunnel based on element inversion minerals |
-
2021
- 2021-11-11 CN CN202111333726.3A patent/CN114135277A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20030096702A (en) * | 2002-06-17 | 2003-12-31 | 코오롱건설주식회사 | Methods to measure drilling path and drilling angle of tunnel |
| CN103399354A (en) * | 2013-08-01 | 2013-11-20 | 中国建筑第四工程局有限公司 | Forecasting method and system for tunnel geology |
| 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 |
| CN110031491A (en) * | 2019-04-04 | 2019-07-19 | 山东大学 | Vehicular lithology and unfavorable geology Precursory Characters identifying system and method |
| CN110306997A (en) * | 2019-06-21 | 2019-10-08 | 中交隧道工程局有限公司南京分公司 | Advanced drilling system based on optimal theoretical hole distribution scheme and working method |
| CN111261236A (en) * | 2020-01-21 | 2020-06-09 | 山东大学 | Tunnel igneous rock weathering degree determining system and method |
| CN113310916A (en) * | 2021-05-24 | 2021-08-27 | 山东大学 | System and method for identifying and forecasting geological anomaly in tunnel based on element inversion minerals |
Non-Patent Citations (2)
| Title |
|---|
| 倪丽萍，蒋欣，郭亨波: "《城市地下空间信息基础平台建设与管理》", 31 October 2018, 同济大学出版社, pages: 161 * |
| 邓庆阳，王树理: "《岩土工程勘察技术》", 31 January 2016, 中国地质大学出版社, pages: 53 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116227090A (en) * | 2023-05-08 | 2023-06-06 | 山东大学 | TBM advanced geological prediction and tunneling performance prediction digital twin system and method |
| CN116227090B (en) * | 2023-05-08 | 2023-09-01 | 山东大学 | TBM advanced geological prediction and tunneling performance prediction digital twin system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Rogers et al. | Volumetric fracture intensity measurement for improved rock mass characterisation and fragmentation assessment in block caving operations | |
| Cafaro et al. | Large sample spacing in evaluation of vertical strength variability of clayey soil | |
| CN103122762B (en) | Method and device for detecting effective fractured interval of unconventional shale oil and gas reservoir | |
| CN110109895A (en) | Fender graded unified prediction and application suitable for TBM driving tunnel | |
| Vazaios et al. | Integration of lidar-based structural input and discrete fracture network generation for underground applications | |
| CN103824133A (en) | Comprehensive prediction method for prospective area of granite type uranium mine field | |
| CN104656124A (en) | Multi-parameter comprehensive rock burst predicting method based on geophysical exploration method | |
| KR20160056359A (en) | GIS-based real time earthquake prediction method | |
| CA2753131A1 (en) | Method for predicting fluid flow | |
| Elmo et al. | Guidelines for the quantitative description of discontinuities for use in discrete fracture network modelling | |
| CN103135131A (en) | Device for interpreting fractured reservoir prediction | |
| CN102590874A (en) | Method for detecting ground surface crack of upland coal-mining subsidence paddy field | |
| Ray et al. | Characterizing and modeling natural fracture networks in a tight carbonate reservoir in the Middle East: A methodology | |
| De Vargas et al. | Influence of geological discontinuities on the groundwater flow of the Serra Geral Fractured Aquifer System | |
| CN114135277A (en) | Tunnel advanced geological prediction method and system based on geochemical feature while-drilling perception | |
| CN110968840A (en) | Method for judging grade of tunnel surrounding rock based on magnetotelluric sounding resistivity | |
| Qi et al. | Two-dimensional prediction of the interface of geological formations: A comparative study | |
| CN108593531A (en) | Rapid quantitative evaluation method for rock weathering degree and application thereof | |
| CN102436014A (en) | Method for evaluating multi-parameter three-dimensional geologic structure of underground water sealed cave depot | |
| CN112285773B (en) | Quantitative characterization method for binary structure of buried fault | |
| Bandpey et al. | Validation of 3D discrete fracture network model focusing on areal sampling methods-a case study on the powerhouse cavern of Rudbar Lorestan pumped storage power plant, Iran | |
| Zhang et al. | Thickness Identification of Tunnel Lining Structure by Time-Energy Density Analysis based on Wavelet Transform. | |
| EP3807679B1 (en) | Method for determining the geometry of an area of a reservoir | |
| Liu et al. | Extraction and imaging of indicator elements for non-destructive, in-situ, fast identification of adverse geology in tunnels | |
| Palleske | A study of biases, assumptions and practical consideration for the use of discrete fracture networks in geomechanical practice |
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 |