CN114483025A - Tunnel advanced lithology identification system and method based on geochemical feature while-drilling test - Google Patents
Tunnel advanced lithology identification system and method based on geochemical feature while-drilling test Download PDFInfo
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- CN114483025A CN114483025A CN202111550356.9A CN202111550356A CN114483025A CN 114483025 A CN114483025 A CN 114483025A CN 202111550356 A CN202111550356 A CN 202111550356A CN 114483025 A CN114483025 A CN 114483025A
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- 238000005553 drilling Methods 0.000 title claims abstract description 127
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000012360 testing method Methods 0.000 title claims abstract description 48
- 239000002893 slag Substances 0.000 claims abstract description 48
- 239000011435 rock Substances 0.000 claims abstract description 45
- 238000003756 stirring Methods 0.000 claims abstract description 18
- 238000003825 pressing Methods 0.000 claims abstract description 16
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 14
- 239000012716 precipitator Substances 0.000 claims description 13
- 239000002244 precipitate Substances 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 10
- 239000002245 particle Substances 0.000 claims description 5
- 239000000843 powder Substances 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 4
- 238000010801 machine learning Methods 0.000 description 4
- 239000010459 dolomite Substances 0.000 description 3
- 229910000514 dolomite Inorganic materials 0.000 description 3
- 230000005641 tunneling Effects 0.000 description 3
- 235000019738 Limestone Nutrition 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000010433 feldspar Substances 0.000 description 2
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- 239000004579 marble Substances 0.000 description 2
- 239000011044 quartzite Substances 0.000 description 2
- 239000013049 sediment Substances 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 229910052612 amphibole Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
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- 229910052611 pyroxene Inorganic materials 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000010435 syenite Substances 0.000 description 1
Images
Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP 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
- E21B49/02—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 by mechanically taking samples of the soil
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B21/00—Methods or apparatus for flushing boreholes, e.g. by use of exhaust air from motor
- E21B21/06—Arrangements for treating drilling fluids outside the borehole
- E21B21/063—Arrangements for treating drilling fluids outside the borehole by separating components
- E21B21/065—Separating solids from drilling fluids
- E21B21/066—Separating solids from drilling fluids with further treatment of the solids, e.g. for disposal
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
Abstract
The invention discloses a tunnel advanced lithology recognition system and method based on geochemical feature while drilling test, which comprises the following steps: the advanced drilling module is used for drilling the tunnel face in advance; the element data acquisition module is used for acquiring drilling return water and return slag in the drilling process, separating, crushing, stirring and pressing the acquired drilling return water and return slag in the drilling process, and performing geological feature test on the obtained pressed sheet to obtain the element content of the rock mass in front of the tunnel face; and the processing and analyzing module is used for obtaining a lithology recognition result by adopting a pre-trained advanced lithology recognition model according to the element content of the rock mass in front of the tunnel face. The advanced drilling of the tunnel face is realized and the front rock sample is collected through the while-drilling test, the content of the rock sample element is obtained through the geochemical characteristic test, and the rock sample element content is input into the constructed advanced lithology identification model, so that the aim of quickly and accurately identifying the lithology of the stratum is fulfilled.
Description
Technical Field
The invention relates to the technical field of tunnel advanced lithology identification, in particular to a tunnel advanced lithology identification system and method based on geochemical feature while drilling testing.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Lithology recognition is the basic work in the engineering fields of rock mechanics and engineering, tunnel and underground engineering and the like, and the recognition result directly influences the selection of a tunnel construction scheme. In the construction process of tunnels and underground engineering, the construction method is influenced by surrounding rocks with different lithology, and faces different engineering problems and risks, such as easy rock burst of hard rocks, easy abrasion of TBM cutter head, easy deformation of the surrounding rocks of soft rocks, easy blocking of the TBM, low tunneling speed and the like. Therefore, the lithology recognition is carried out on the rock body in front of the tunnel face on the basis of the advanced drilling, the tunnel and underground engineering construction and disaster prevention and control can be effectively guided, and the method has important significance for reducing engineering safety risks and guaranteeing personnel and property safety.
At present, the main methods for lithology identification can be divided into a direct observation method, a slice identification method, an experimental analysis method and an image identification method, the direct observation method depends on the subjective judgment of technicians on rock samples, and the method has low accuracy and large differentiation; the slice identification method is time-consuming and labor-consuming, and the identification process is complex, so that the requirements of short actual engineering period and fast tunneling are difficult to meet; the experimental analysis method is limited by the precision of experimental equipment and the insufficiency of observation data quantity, and the identification result is often multi-solution and inaccurate; the image recognition method cannot reflect all characteristics of rocks, is more difficult to judge particularly under the condition that the drilled rock sample is in a slag powder state, and moreover, adverse conditions such as uneven illumination, large dust and the like in a tunnel influence the image quality. And when the tunnel is tunneled to a certain depth, engineering field personnel and geology experts have too long time to enter and exit the tunnel, and the accuracy and the timeliness of lithology identification are seriously influenced.
Disclosure of Invention
In order to solve the problems, the invention provides a tunnel advanced lithology recognition system and method based on geological feature while-drilling test.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a tunnel advanced lithology identification system based on geochemical feature while drilling test, including:
the advanced drilling module is used for performing advanced drilling on the tunnel face of the tunnel;
the element data acquisition module is used for acquiring drilling return water and return slag in the drilling process, separating, crushing, stirring and pressing the drilling return water and the return slag, and performing geochemical characteristic test on the obtained pressed sheet to obtain the element content of the rock mass in front of the tunnel face;
and the processing and analyzing module is used for obtaining a lithology recognition result by adopting a pre-trained advanced lithology recognition model according to the element content of the rock mass in front of the tunnel face.
The invention provides a tunnel advanced lithology recognition system based on a geochemical characteristic while-drilling test, which can treat drilling backwater and return slag generated in the drilling process in the tunnel advanced drilling process, and obtain element components and content through the geochemical characteristic test.
In an alternative embodiment, the advance drilling module comprises a drill rod and a drill bit, wherein the drill bit is arranged at the top of the drill rod and used for advance drilling of the tunnel face.
As an alternative embodiment, the element data acquisition module comprises a drilling return water and return slag collecting device, a centrifugal precipitator, a crushing stirrer and a sample press;
the drilling backwater and return slag collecting device is used for collecting the drilling backwater and return slag in the drilling process;
the centrifugal precipitator is used for separating drilling return water and return slag;
the crushing stirrer is used for crushing and stirring the drilling return slag obtained after separation to obtain a precipitate of the drilling return slag;
the press is used for consolidating the precipitate and pressing the precipitate into a sample with a specific thickness and diameter.
As an alternative embodiment, in the process of crushing and stirring the separated drilling return slag by the crushing and stirring machine, the separated drilling return slag is subjected to particle size refinement and powder uniformity distribution treatment.
As an alternative embodiment, the tail end of the drilling backwater and return slag collecting device is connected with the tail part of the drill rod of the advanced drilling module through a spring, and the top end of the drilling backwater and return slag collecting device is abutted against the tunnel face.
As an alternative embodiment, the drilling backwater and return slag collecting device is connected with the centrifugal precipitator through a sample input pipeline; the centrifugal precipitator is connected with the crushing stirrer; the crushing and stirring machine is connected with the sample pressing machine through a conveying belt.
As an alternative embodiment, the processing and analyzing module is configured with a pre-trained advanced lithology recognition model, and the training process of the advanced lithology recognition model includes:
acquiring element content and lithology information of the excavated stratum;
and constructing an advanced lithology recognition model according to the preprocessed element content and lithology information, and training the advanced lithology recognition model.
As an alternative embodiment, the process of preprocessing the element content and lithology information of the excavated section of the stratum comprises the following steps: and establishing a regional stratum information database for the element content and the lithological information of the excavated stratum, and performing range division on the content of the relevant characteristic elements of various lithologies in the regional stratum information database to obtain the characteristic element combination and the content range of each lithology.
As an alternative embodiment, the process of processing the lithology recognition result in the analysis module includes: according to the element content of the rock mass in front of the tunnel face of the tunnel, adopting a trained advanced lithology recognition model to judge the confidence coefficient of the output result, and if the confidence coefficient is met, outputting the lithology recognition result; and if the confidence coefficient is not met, the advanced lithology recognition model is trained again until the lithology recognition result meeting the confidence coefficient is output.
In a second aspect, the invention provides a tunnel advanced lithology identification method based on a geochemical feature while-drilling test, which comprises the following steps:
acquiring element content and lithology information of the excavated stratum;
constructing an advanced lithology recognition model according to the preprocessed element content and lithology information and training the advanced lithology recognition model;
performing advanced drilling on the tunnel face, separating, crushing, stirring and pressing the obtained drilling return water and return slag in the drilling process, and performing geochemical characteristic test on the obtained pressed sheet to obtain the element content of the rock mass in front of the tunnel face;
and obtaining a lithology recognition result by adopting a pre-trained advanced lithology recognition model according to the element content of the rock mass in front of the tunnel face.
The invention provides a tunnel advanced lithology identification method based on geochemical characteristic while drilling test, which is characterized in that an advanced lithology identification model is established by utilizing the relation between rock characteristic element content data and lithology information, the accurate identification of the lithology of a front rock body is realized only by different grades of the characteristic element content data, and the speed and the accuracy of the lithology identification are improved.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a tunnel advanced lithology recognition system and method based on geochemical characteristics while drilling test, which realize advanced drilling of a tunnel face and acquisition of a front rock sample through the while drilling test, acquire element data of the rock sample through the geochemical test, input the element data into a rock lithology recognition model in front of the tunnel face established based on a machine learning method, achieve the aim of rapid and accurate recognition of stratum lithology, have the advantages of high recognition accuracy, high recognition speed, strong engineering applicability and the like, realize lithology recognition of the rock in front of the tunnel face, ensure the accuracy and timeliness of recognition results, and have better application prospects.
The invention provides a tunnel advanced lithology recognition system and method based on geochemical characteristics while drilling test, which solves the problem of tunnel advanced lithology recognition, performs advanced lithology recognition on a tunnel at an unearthed section, provides accurate lithology data reference for the following forecasting and tunneling work, and meets the requirement of real-time lithology judgment in front of a tunnel face in construction; compared with traditional methods such as a direct observation method, a slice identification method, an experimental analysis method and an image identification method, the method provided by the invention has the advantages of strong objectivity, time and labor saving, no requirement on the state of the sample, no environmental interference and the like.
The invention provides a tunnel advanced lithology recognition system and method based on geochemical characteristic while-drilling test, which are more refined and faster in lithology naming, control the range of accuracy by differentially combining characteristic elements of various lithologies and grading content data of each element, and are quick and accurate in the process from drilling to recognition result acquisition.
Advantages of additional aspects of the invention 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 invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic diagram of a tunnel advance lithology identification system device based on a geochemical feature while drilling test according to embodiment 1 of the invention;
fig. 2 is a diagram of a network construction of an advanced lithology recognition model according to embodiment 1 of the present invention;
fig. 3 is a flowchart of a tunnel advance lithology identification method based on a geochemical feature while drilling test according to embodiment 2 of the present invention;
wherein, 1, a drill rod; 2. a drill bit; 3. drilling a backwater and return slag collecting device; 4. a sample bowl; 5. a mechanical arm; 6. an element tester; 7. a host; 8. surrounding rocks; 9. a conveyor belt; 10. a centrifugal precipitator; 11. a crushing and stirring machine; 12. and (5) pressing a sample machine.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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 invention 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 exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
As shown in fig. 1, the present embodiment provides a tunnel advanced lithology identification system based on geochemical feature while drilling test, including:
the advanced drilling module is used for drilling the tunnel face in advance;
the element data acquisition module is used for acquiring drilling return water and return slag in the drilling process, separating, crushing, stirring and pressing the drilling return water and the return slag, and performing geochemical characteristic test on the obtained pressed sheet to obtain the element content of the rock mass in front of the tunnel face;
and the processing and analyzing module is used for obtaining a lithology recognition result by adopting a pre-trained advanced lithology recognition model according to the element content of the rock mass in front of the tunnel face.
In the embodiment, the advanced drilling module performs advanced drilling work in the trenchless section of the tunnel, and is used for performing advanced drilling on the surrounding rock 8 in front of the tunnel face of the tunnel so as to generate drilling backwater and return slag in the drilling process; the drilling device comprises a drill rod 1 and a drill bit 2, wherein the drill bit 2 is arranged at the top of the drill rod 1 and is used for advancing drilling on a tunnel face.
As a further limitation, the drill bit 2 is a percussion drill bit.
In this embodiment, the element data acquisition module is configured to collect and process drilling backwater and return slag generated in a drilling process, and specifically includes: and separating, crushing and stirring drilling backwater and returned slag, solidifying the obtained precipitate, pressing the precipitate into a sample with specific thickness and diameter, and performing geochemical characteristic test on the sample to obtain rock element content data.
In this embodiment, the element data acquisition module comprises a drilling backwater and return slag collection device 3, a sample bowl 4, a mechanical arm 5, an element tester 6, a centrifugal precipitator 10, a crushing and stirring machine 11 and a sample pressing machine 12;
the drilling backwater and return slag collecting device 3 is connected with a centrifugal precipitator 10 through a sample input pipeline; the centrifugal precipitator 10 is connected with a crushing stirrer 11; the crushing stirrer 11 is connected with a sample pressing machine 12 through a conveyor belt 9;
the drilling backwater and return slag collecting device 3 is used for obtaining drilling backwater and return slag generated in the drilling process, conveying the drilling backwater and the return slag to a centrifugal precipitator 10 through a sample input pipeline to be separated, conveying the separated precipitate to a crushing stirrer 11 to be subjected to particle size refinement and powder uniformity distribution, conveying the precipitate to a sample press 12, solidifying the precipitate by using the sample press 12, pressing the precipitate into a sample with a specific thickness and a specific diameter, putting the sample into a sample bowl 4 through a mechanical arm 5, and placing the sample bowl 4 at a sample bin of an element tester 6 through the mechanical arm 5 during geological test so as to perform geological characteristic test on the sample to obtain the rock element content.
By way of further limitation, the sample bowl 4, the robotic arm 5, the elemental tester 6, the conveyor belt 9, the centrifugal settler 10, the pulverizer mixer 11, and the sample press 12 are all mounted to the body of the drilling machine.
As a further limitation, the drilling backwater and return slag collecting device 3 is connected with an advanced drilling module.
As a further limitation, the tail end of the drilling backwater and return slag collecting device 3 is connected with the tail part of the drill rod 1 through a spring, the top end of the drilling backwater and return slag collecting device 3 is abutted against the tunnel face, and the drilling backwater and return slag collecting device 3 is connected with other components of the element data acquisition module through a pipeline inside the drilling backwater and return slag collecting device.
As a further limitation, the pulverizer 11 can refine the particle size of the sediment, and can make the particle space distribution uniform, compared with the unevenness of the rock block surface, the sample preparation by the sample press 12 can keep the surface of the sample to be tested flat, so that the test data of the element tester 6 is more accurate, and the accuracy of the result is improved.
In this embodiment, the processing and analyzing module is configured with a pre-trained advanced lithology recognition model, as shown in fig. 2, a training process of the advanced lithology recognition model includes:
(1) acquiring stratum element content data and lithology information of an excavated section, and processing the element content data;
specifically, lithological information and corresponding characteristic element content of a regional stratum are obtained through drilling test and survey information of an excavated section in an early construction period, a regional stratum information database is established, and the related characteristic element content of various lithological properties in the database is subjected to fine range division;
the range division method for the fine content of the characteristic elements comprises the following steps:
in this embodiment, 14 kinds of common rocks in the engineering field are selected, which are tuff, shale, siltstone, feldspar sandstone, limestone, marble, dolomite, syenite, basalt, gneiss, granite, amphibole, gabbro and quartzite respectively;
combining characteristic elements of each lithology according to the early-stage data, and listing the specific content range of each element; the limestone-dolomite complex comprises the following components, wherein the characteristic element combination of tuff comprises 10-15% of Al, 5-10% of K, 0-5% of Na, 70-80% of Si, the characteristic element combination of mud shale comprises 15-25% of Al, 5-10% of Fe, 0-5% of K, 52-65% of Si, the characteristic element combination of siltstone comprises 10-15% of Al, 70-75% of Si, the characteristic element combination of feldspar sandstone comprises 5-10% of Al, 5-10% of K, 80-90% of Si, the characteristic element combination of limestone comprises 0-5% of Al, 85-100% of Ca, 0-5% of Si, the characteristic element combination of marble comprises 85-100% of Ca, 0-5% of Si, the characteristic element combination of dolomite comprises 70-85% of Ca and 20-25% of Mg, and the characteristic element combination of long rock comprises 15-25% of Al, K (10-15%), Si (52-65%), the characteristic element combination of basalt is Al (10-15%), Ca (5-10%), Fe (10-15%), Mg (5-10%), Si (45-52%), the characteristic element combination of gneiss is Al (15-25%), K (5-10%), Na (0-5%), Si (65-70%), the characteristic element combination of granite is Al (10-15%), K (5-10%), Na (0-5%), Si (70-75%), the characteristic element combination of amphibolite is Al (15-25%), Ca (5-10%), Fe (5-10%), Mg (0-5%), Si (52-65%), the characteristic element combination of pyroxene is Al (15-25%), Ca (5-10%), Fe (10-15%), (Al), Mg (5-10 percent), Si (45-52 percent) and the characteristic element of quartzite is Si (90-100 percent).
(2) Based on a machine learning method, establishing an advanced lithology identification model by using the processed element content data and lithology information;
based on a machine learning method, the characteristic element content data subjected to fine range division in a regional stratum information database and corresponding lithological information are used as data sets, the data sets are divided into training sets and testing sets, and the data are learned and trained to obtain an advanced lithological identification model.
In this embodiment, the processing and analyzing module adopts a trained advanced lithology recognition model according to the element content of the rock mass in front of the tunnel face to perform confidence judgment on the output result, and if the confidence is met, the lithology recognition result is output; and if the confidence coefficient is not met, returning the corresponding data to the advanced lithology recognition model for learning and training until a lithology recognition result meeting the confidence coefficient is output.
The confidence coefficient is the reliability of the model constructed when training is carried out through the early-stage mass element content data and lithology information based on a deep learning method to different lithology recognition accuracy rates, and the reliability of the model is trained to a value which meets engineering requirements and reaches an accurate standard when the model is trained.
In this embodiment, the tunnel advanced lithology recognition system based on the geochemical feature while drilling test further comprises a control module, wherein the control module comprises a host 7, receives information feedback of the advanced drilling module, the element data acquisition module and the processing and analyzing module, and controls the operation and the stop of the advanced drilling module, the element data acquisition module and the processing and analyzing module.
The embodiment provides a tunnel advanced lithology recognition system based on geochemical characteristic while-drilling test, can realize in the tunnel advanced drilling process, handle the drilling return water that produces and return the sediment in the drilling process to obtain element composition and content data through the geochemical test, equipment degree of automation is high, and data acquisition is rapid, and recognition rate is fast, and the lithology discernment degree of accuracy is high, has better application prospect.
Example 2
As shown in fig. 3, this embodiment provides a tunnel advance lithology identification method using the tunnel advance lithology identification system based on geochemical feature while drilling test described in embodiment 1, and the method includes the following steps:
step 1: acquiring the stratum element content data and lithology information of the excavated segment, and processing the stratum element content data of the excavated segment;
step 2: based on a machine learning method, establishing an advanced lithology identification model by using the processed element content data and lithology information;
and step 3: performing advanced drilling on the tunnel face, separating, crushing, stirring and pressing the obtained drilling return water and return slag in the drilling process, and performing geochemical characteristic test on the obtained pressed sheet to obtain the element content of the rock mass in front of the tunnel face;
and 4, step 4: and obtaining a lithology recognition result by adopting a trained advanced lithology recognition model according to the element content of the rock mass in front of the tunnel face.
The embodiment provides a tunnel advanced lithology identification method based on geochemical feature while drilling test, which is characterized in that an advanced lithology identification model is established by utilizing the relation between the content data of the rock characteristic elements and the lithology information, the accurate identification of the lithology of the front rock body is realized only through different grades of the content data of the characteristic elements, and the speed and the accuracy of the lithology identification are greatly improved.
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 (10)
1. A tunnel advanced lithology recognition system based on geochemical characteristics while drilling test is characterized by comprising:
the advanced drilling module is used for drilling the tunnel face in advance;
the element data acquisition module is used for acquiring drilling return water and return slag in the drilling process, separating, crushing, stirring and pressing the drilling return water and the return slag, and performing geochemical characteristic test on the obtained pressed sheet to obtain the element content of the rock mass in front of the tunnel face;
and the processing and analyzing module is used for obtaining a lithology recognition result by adopting a pre-trained advanced lithology recognition model according to the element content of the rock mass in front of the tunnel face.
2. The tunnel advanced lithology identification system based on the geochemical characteristic while drilling test as recited in claim 1, wherein the advanced drilling module comprises a drill rod and a drill bit, and the drill bit is arranged at the top of the drill rod and is used for performing advanced drilling on the tunnel face.
3. The tunnel advanced lithology recognition system based on geochemical feature while drilling test as claimed in claim 1, wherein the element data acquisition module comprises a drilling return water and return slag collection device, a centrifugal precipitator, a crushing stirrer and a sample press;
the drilling backwater and return slag collecting device is used for collecting the drilling backwater and return slag in the drilling process;
the centrifugal precipitator is used for separating drilling return water and return slag;
the crushing stirrer is used for crushing and stirring the separated drilling return slag to obtain a precipitate;
the press is used for consolidating the precipitate and pressing the precipitate into a sample with a specific thickness and diameter.
4. The tunnel advanced lithology recognition system based on geochemical feature while drilling test as recited in claim 3, wherein the crushing and stirring machine is used for performing particle size refinement and powder uniformity distribution treatment on the separated drilling return slag during the process of crushing and stirring the separated drilling return slag.
5. The tunnel advanced lithology recognition system based on geochemical characteristics while drilling test as claimed in claim 3, wherein the tail end of the drilling backwater and return slag collecting device is connected with the tail part of the drill rod of the advanced drilling module through a spring, and the top end of the drilling backwater and return slag collecting device is abutted against the tunnel face.
6. The tunnel advanced lithology recognition system based on geochemical feature while drilling testing as described in claim 3, wherein the drilling backwater and return slag collection device is connected with the centrifugal precipitator through a sample input pipeline; the centrifugal precipitator is connected with the crushing stirrer; the crushing and stirring machine is connected with the sample pressing machine through a conveying belt.
7. The tunnel advanced lithology recognition system based on geochemical feature while drilling test as claimed in claim 1, wherein the processing and analyzing module is configured with a pre-trained advanced lithology recognition model, and the training process of the advanced lithology recognition model comprises:
acquiring element content and lithology information of the excavated stratum;
and constructing an advanced lithology recognition model according to the preprocessed element content and lithology information, and training the advanced lithology recognition model.
8. The system for identifying advanced lithology of tunnel based on geochemical feature while drilling test as claimed in claim 7, wherein the process for preprocessing the element content and lithology information of the excavated section of stratum comprises: and establishing a regional stratum information database for the element content and the lithology information of the excavated stratum, and performing range division on the content of various lithology related characteristic elements in the regional stratum information database to obtain a characteristic element combination and a content range of each lithology.
9. The tunnel advanced lithology identification system based on geochemical feature while drilling test as claimed in claim 1, wherein the identification process of the lithology identification result in the processing and analyzing module comprises: according to the element content of the rock mass in front of the tunnel face of the tunnel, adopting a trained advanced lithology recognition model to judge the confidence coefficient of the output result, and if the confidence coefficient is met, outputting the lithology recognition result; and if the confidence coefficient is not met, the advanced lithology recognition model is trained again until the lithology recognition result meeting the confidence coefficient is output.
10. A tunnel advanced lithology identification method based on geochemical feature while drilling test is characterized by comprising the following steps:
acquiring element content and lithology information of the excavated stratum;
constructing an advanced lithology recognition model according to the preprocessed element content and lithology information and training the advanced lithology recognition model;
performing advanced drilling on the tunnel face, separating, crushing, stirring and pressing the obtained drilling return water and return slag in the drilling process, and performing geochemical characteristic test on the obtained pressed sheet to obtain the element content of the rock mass in front of the tunnel face;
and obtaining a lithology recognition result by adopting a pre-trained advanced lithology recognition model according to the element content of the rock mass in front of the tunnel face.
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| CN202111550356.9A CN114483025A (en) | 2021-12-17 | 2021-12-17 | Tunnel advanced lithology identification system and method based on geochemical feature while-drilling test |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105866006A (en) * | 2016-05-25 | 2016-08-17 | 中国石油天然气股份有限公司 | Tight sandstone crack detecting device and method |
| CN205786410U (en) * | 2016-05-11 | 2016-12-07 | 中石化石油工程技术服务有限公司 | A kind of XRF landwaste Atomic Absorption SpectrophotometerICP |
| KR101841073B1 (en) * | 2016-11-23 | 2018-03-22 | 황종덕 | Offshore drilling waste recycling apparatus |
| CN109872311A (en) * | 2019-02-01 | 2019-06-11 | 中国铁建重工集团有限公司 | A kind of Rock Mass Integrality sentences knowledge method |
| CN110031491A (en) * | 2019-04-04 | 2019-07-19 | 山东大学 | Vehicular lithology and unfavorable geology Precursory Characters identifying system and method |
| US10927671B1 (en) * | 2018-01-26 | 2021-02-23 | Diversified Well Logging, Llc | Method and apparatus for drill cutting analysis |
| 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 |
| CN113779690A (en) * | 2021-11-11 | 2021-12-10 | 山东大学 | Advanced geological prediction method and system based on while-drilling sensing |
-
2021
- 2021-12-17 CN CN202111550356.9A patent/CN114483025A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205786410U (en) * | 2016-05-11 | 2016-12-07 | 中石化石油工程技术服务有限公司 | A kind of XRF landwaste Atomic Absorption SpectrophotometerICP |
| CN105866006A (en) * | 2016-05-25 | 2016-08-17 | 中国石油天然气股份有限公司 | Tight sandstone crack detecting device and method |
| KR101841073B1 (en) * | 2016-11-23 | 2018-03-22 | 황종덕 | Offshore drilling waste recycling apparatus |
| US10927671B1 (en) * | 2018-01-26 | 2021-02-23 | Diversified Well Logging, Llc | Method and apparatus for drill cutting analysis |
| CN109872311A (en) * | 2019-02-01 | 2019-06-11 | 中国铁建重工集团有限公司 | A kind of Rock Mass Integrality sentences knowledge method |
| CN110031491A (en) * | 2019-04-04 | 2019-07-19 | 山东大学 | Vehicular lithology and unfavorable geology Precursory Characters identifying 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 |
| CN113779690A (en) * | 2021-11-11 | 2021-12-10 | 山东大学 | Advanced geological prediction method and system based on while-drilling sensing |
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