Fault zone identification and prediction method and system based on in-situ testing and analysis of in-hole minerals

Abstract

The present invention discloses a method and system for identifying and predicting fault zones based on in-situ testing and analysis of in-hole minerals. The method comprises: drilling a tunnel face in an area at risk of a fault fracture zone; dynamically adjusting the step size of data acquisition based on the integrity of the surrounding rock and changes in its mineral content; extracting the absorption characteristics of marker minerals based on the surrounding rock spectral data at each measuring point in the hole, and obtaining the marker mineral content at each measuring point based on a constructed spectrum-mineral content inversion model; then fitting the distribution of marker mineral content in the surrounding rock throughout the borehole; marking abnormal boreholes based on the marker mineral content in the borehole, and identifying whether there is a risk of a fault fracture zone based on the number of abnormal boreholes and image information of the surrounding rock in the borehole. The method of the present invention can achieve in-situ testing of in-hole minerals and rapid identification of mineral content, as well as integrated intelligent analysis, identification, and prediction of fault zones.

Description

Technical Field

[0001] The present invention relates to the technical field of adverse geological identification and prediction, and in particular to a method and system for fault zone identification and prediction based on in-situ testing and analysis of in-hole minerals. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Fault fracture zones are one of the most common geological challenges in tunnel construction. When TBMs tunnel through these zones, they are prone to geological hazards and accidents such as machine jams, water and mud inrush, and large deformations of the surrounding rock. In the relatively cramped and poorly lit tunnel environment, failure to promptly, effectively, and accurately identify these zones can easily lead to casualties, construction delays, and significant economic losses. Therefore, the identification and real-time prediction of fault fracture zones are crucial for safe and efficient tunnel construction.

[0004] From a structural geology perspective, fault fracture zones are often accompanied by an enrichment of characteristic clay minerals such as kaolinite, illite, and chlorite, while the corresponding rock-forming minerals, such as mica, feldspar, and hornblende, are reduced in content. Under tectonic stress, the surrounding rock forms fragmented, brecciated, or mylonitic structures, with well-developed joints and fissures, resulting in poor surrounding rock integrity. In the original rock, however, tectonic stress is relatively weak, and rock-forming minerals are largely not converted into characteristic clay minerals. Joints and fissures are undeveloped, resulting in good surrounding rock integrity. Based on these geological patterns, fault fracture zones can be identified in excavated areas and predicted in unexcavated areas by testing the content and types of characteristic minerals.

[0005] However, according to the inventors' understanding, due to the complex and changeable tunnel construction environment, traditional mineral analysis methods often rely on laboratories and are often limited by a series of cumbersome processes such as sampling, sample transportation, sample grinding, and testing. The time cost is high, and it is difficult to establish a quantitative model for data analysis. It is difficult to achieve in-situ, efficient, and intelligent mineral content identification and dynamic prediction, as well as fault fracture zone identification and intelligent prediction. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a fault zone identification and prediction method and system based on in-situ testing and analysis of in-hole minerals. By constructing a dynamically adjustable spectral-mineral content inversion model, it is possible to quickly and accurately identify the mineral content in the hole, and then further mark abnormal boreholes. Combined with neural network processing, it can ultimately achieve rapid and accurate identification of fault fracture zones in unexcavated areas and intelligent prediction of fault fracture zones in untested areas.

[0007] In some embodiments, the following technical solutions are adopted:

[0008] A fault zone identification method based on in-situ testing and analysis of in-hole minerals, comprising:

[0009] Evenly arrange quadrilateral grids on the tunnel face in the risk area of ​​fault fracture zone, take the vertices of the quadrilateral as the predetermined drilling position, and drill holes;

[0010] The detection device obtains image information and spectral data of the surrounding rock in the hole, and dynamically adjusts the step size of data acquisition based on the integrity of the surrounding rock and changes in the surrounding rock mineral content;

[0011] Based on the surrounding rock spectral data of each measuring point in the hole, the absorption characteristics of the marker minerals are extracted. Based on the constructed spectrum-mineral content inversion model, the marker mineral content of each measuring point is obtained; then the distribution of the marker mineral content in the surrounding rock of the entire borehole is fitted;

[0012] Abnormal drill holes are marked based on the content of marker minerals in the holes. Based on the number of abnormal drill holes and the image information of the surrounding rock in the holes, the risk of fault fracture zones can be identified.

[0013] As an optional solution, the size of the quadrilateral grid is dynamically adjusted according to the number of abnormal marked drilling marks. If the ratio of the number of abnormal drilling holes marked in the initial test to the total number of drilling holes exceeds the set threshold, the number of quadrilateral grids is increased to achieve drilling encryption.

[0014] As an optional solution, the data acquisition step size can be dynamically adjusted based on the integrity of the surrounding rock and the changes in the surrounding rock mineral content. Specifically:

[0015] The integrity of the surrounding rock is obtained through the image information of the surrounding rock in the hole, and the initial step size is set. During the test, the mineral content change is obtained through the surrounding rock spectral data at multiple consecutive step positions. If the mineral content is stable, the test step size is increased. If the mineral content changes abnormally, the acquisition step size is tightened. When the current step size is increased or tightened, the new step size is set as a multiple of the original step size.

[0016] As an optional solution, when obtaining the spectral data of the surrounding rock in the hole, the spectral data of each measuring point for one week is obtained, and the average of all the spectral data is used as the spectral data of this measuring point.

[0017] As an optional solution, the construction of the spectrum-mineral content inversion model is specifically as follows:

[0018] Select fault zone marker minerals, extract the absorption characteristics of the marker minerals based on the spectral data and reflectivity obtained from the excavated section, measure the mineral content corresponding to the absorption characteristics, establish a database corresponding to the spectral absorption characteristics and mineral content, and construct a spectrum-mineral content inversion model based on the linear correspondence between the absorption characteristic peak size and mineral content;

[0019] Data from subsequent excavations and tests are continuously added to the database to optimize the inversion model.

[0020] As an optional solution, the process of identifying whether there is a risk of a fault rupture zone is as follows:

[0021] If the surrounding rock in the borehole shows abnormal phenomenon indicating the enrichment of mineral content and exceeds the set threshold, the borehole will be marked as an abnormal borehole;

[0022] If the ratio of the number of abnormal boreholes to the total number of boreholes exceeds the set threshold, and there are obvious sudden changes in the rock formation dip, jointing and cleavage zones, and a sharp increase in small folds in the surrounding rock within the hole, it is determined that there is a risk of a fault fracture zone.

[0023] As an optional solution, for the drilling risk section that has been determined to have a fault fracture zone, based on the distribution of mineral content in all abnormal drill holes, the location of the measuring point at the same depth in the drill hole group is regarded as a milepost point, and the content of the marker minerals at each milepost point is averaged. Finally, the data of each milepost are fitted to obtain the curve of the relationship between the section marker mineral content and the mileage. Based on this, the area where the anomaly occurs is delineated, and the location and impact range of the fault fracture zone are determined. Combined with the type and content distribution of the marker minerals, the occurrence, location and range of the fault fracture zone can be identified.

[0024] In other embodiments, the following technical solutions are adopted:

[0025] A fault zone prediction method based on in-situ testing and analysis of in-hole minerals, comprising:

[0026] Based on the above-mentioned fault zone identification method based on in-situ testing and analysis of in-hole minerals, the existence, location and range of the fault fracture zone in the test area are identified;

[0027] Based on the obtained data set of section marker mineral content and mileage point information, a database of marker mineral spectral data and mileage point information was constructed. The data set in the database was normalized, and the normal data after removing outliers was dimensionlessly processed using range normalization to obtain a normalized value between [0,1]. This value was input into the LSTM-RNN neural network model to predict the marker mineral content of the untested section ahead. Combined with the mileage point information and the delineation of abnormal areas, the fault fracture zone was analyzed and predicted. The prediction results served as the basis for whether to conduct drilling tests during subsequent excavation.

[0028] In other embodiments, the following technical solutions are adopted:

[0029] A fault zone identification system based on in-situ testing and analysis of in-hole minerals, comprising:

[0030] The tunnel face drilling module is used to evenly arrange quadrilateral grids on the tunnel face in the risk area of ​​fault fracture zone, and take the vertices of the quadrilaterals as the predetermined drilling positions for drilling;

[0031] The data acquisition module is used to obtain image information and spectral data of the surrounding rock in the hole through the detection device, and dynamically adjust the step size of data acquisition based on the integrity of the surrounding rock and the changes in the mineral content of the surrounding rock;

[0032] The marker mineral content distribution fitting module is used to extract the absorption characteristics of marker minerals based on the surrounding rock spectral data of each measuring point in the hole, and obtain the marker mineral content of each measuring point based on the constructed spectrum-mineral content inversion model; and then fit the marker mineral content distribution of the surrounding rock in the entire borehole;

[0033] The fault fracture zone risk identification module is used to mark abnormal drilling holes based on the content of marker minerals in the holes, and to identify whether there is a fault fracture zone risk based on the number of abnormal drilling holes and the image information of the surrounding rock in the holes.

[0034] Fault fracture zone location identification module, used to identify the existence, location and range of fault fracture zones;

[0035] The fault fracture zone prediction module is used to predict the content of marker minerals in the untested section ahead based on the neural network model, thereby realizing the analysis and prediction of the fault fracture zone.

[0036] As an optional solution, the detection device includes:

[0037] The rotating shaft can penetrate deep into the borehole, and a support rod is set in the middle position of the rotating shaft. The support rod carries a rotating bearing, and the rotating bearing is connected to the test optical fiber through a connector. When the rotating shaft rotates, it can drive the test optical fiber to rotate around the rotating bearing, realizing 360° spectral data collection.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) The method of the present invention can realize in-situ testing and analysis of minerals in the hole and rapid identification of mineral content, as well as integrated intelligent analysis, identification and prediction of fault zones, providing a strong guarantee for avoiding the occurrence of major disasters and accidents.

[0040] (2) The present invention constructs a spectrum-mineral content inversion model, establishes a model through the spectral absorption characteristics of the measured marker minerals and the corresponding mineral content database, and can obtain mineral content information based on the spectral data without the need for complex processes such as sampling, transportation, grinding, and testing, thereby realizing in-situ, rapid, and accurate identification of the mineral content in the measured borehole.

[0041] (3) The present invention proposes a dynamic step adjustment mechanism based on the in-hole image information and mineral content distribution information. The integrity of the rock in the hole is preliminarily judged according to the in-hole image information, and then the step length is determined for mineral testing. The test step length is dynamically adjusted according to the changes in the content of the marker minerals at the positions of similar measuring points, so as to achieve more accurate and intelligent identification of mineral content and fault fracture zones, while improving the identification efficiency.

[0042] (4) The present invention can quickly identify the position and range of the fault fracture zone by analyzing the distribution of marker mineral content in the face drill holes and delineating the abnormal range. It builds a database based on the existing marker mineral spectral data and mileage information, and realizes intelligent prediction of the marker mineral type and content distribution in the untested area ahead and the occurrence, position and range of the fault zone based on the neural network model.

[0043] (5) The present invention designs an integrated system and device for in-situ fault zone identification and prediction in a hole, which can realize water removal, anti-shake, light source provision, lighting, positioning, image acquisition, mineral testing, data analysis and prediction, and can greatly improve work efficiency and reduce work difficulty.

[0044] Other features and advantages of additional aspects of the present invention will be given in part in the following description and in part will become obvious from the following description or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 1 is a schematic diagram of a fault zone identification and prediction method based on in-situ testing and analysis of in-hole minerals according to an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of a quadrilateral grid arrangement of drill holes on a tunnel face in an embodiment of the present invention;

[0047] Figure 3 Schematic diagram of a fault zone identification and prediction system based on in-situ testing and analysis of in-hole minerals in an embodiment of the present invention;

[0048] Figure 4 Schematic diagram of a fault zone identification and prediction device based on in-situ testing and analysis of in-hole minerals in an embodiment of the present invention;

[0049] Figure 5 1 is a schematic side structural diagram of a borehole detection device in an embodiment of the present invention;

[0050] Figure 6 1 is a schematic plan view of a borehole detection device according to an embodiment of the present invention;

[0051] Among them, 1. Operating gimbal; 2. Track; 3. Lifting device; 4. Test gimbal; 5. Detection device; 6. Ground feature spectrometer; 7. Intelligent operation platform; 8. Distribution box; 9. Lighting device; 10. Position sensor; 11. Water removal nozzle; 12. Constant temperature heating device; 13. Test optical fiber; 14. Support anti-shake device; 15. LED light; 16. Miniature camera; 17. Protective case. DETAILED DESCRIPTION

[0052] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0053] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0054] Example 1

[0055] In one or more embodiments, a fault zone identification method based on in-situ testing and analysis of in-hole minerals is disclosed, combined with Figure 1 , specifically including the following process:

[0056] S101: Reference Figure 2 , a quadrilateral grid is evenly arranged on the tunnel face in the risk area of ​​fault fracture zone, and each vertex of the quadrilateral is taken as the predetermined drilling position for drilling;

[0057] Specifically, based on the previous geological survey data combined with the analysis results of the changes in the content of mineral markers in the excavated surrounding rock and the prediction analysis results of the tested sections, quadrilateral grids can be evenly arranged on the tunnel face in the risk area of ​​the fault fracture zone, and the vertices of the quadrilaterals can be taken as the predetermined drilling positions. Manual drilling combined with multi-arm drilling rigs can be used to arrange the predetermined drilling positions on the tunnel face. The size of the quadrilateral grid division is dynamically adjusted according to the number of abnormal marked drill holes. If, after the initial test is completed, the proportion of the number of marked abnormal drill holes to the total number of drill holes exceeds the set threshold, the tunnel face will be drilled more densely to ensure the accuracy of the identification results.

[0058] S102: Acquire image information and spectral data of the surrounding rock in the hole through the detection device 5, and dynamically adjust the step size of data acquisition based on the integrity of the surrounding rock and the change in the mineral content of the surrounding rock;

[0059] In this embodiment, before the detection device 5 enters the borehole, the halogen light source, LED lighting source and micro camera 16 carried on the in-hole detection device 5 are first turned on. After the detection device 5 enters the hole smoothly, the in-hole water removal and drying device is turned on, and the rock spectral data in the hole is collected according to the initial step length. The water content in the hole is determined based on the detection results. After the water removal is completed, the detection begins.

[0060] The micro camera 16 is used to visualize the in-hole detection process. By taking pictures at fixed times and fixed points, the image information of the rock in the hole is obtained. The step length is set according to the dynamic adjustment mechanism of the step length, and the image information is stored as one of the bases for later identification of the broken zone.

[0061] In this embodiment, the step size of data collection in the borehole is dynamically and intelligently adjusted through a step size dynamic adjustment mechanism. The detection device 5 moves according to the set step size. There is a measuring point at each step size position. The average value of the spectral data of each measuring point for a week is taken as the measuring point data.

[0062] First, based on the surrounding rock image information within the hole and the integrity of the surrounding rock, the initial step size is set. If the surrounding rock is intact, a large step size is used; if the surrounding rock is relatively intact, a medium step size is used; if the surrounding rock is relatively broken, a small step size is used. Specifically, during the test, the original step size is adjusted by analyzing the changes in mineral content in the surrounding rock spectral data at multiple set step sizes. The measurement point step size is adjusted using a multiple gradient adjustment mechanism. If the current step size is to be encrypted or tightened, the new step size is set as a multiple of the original step size. If the mineral content is stable, the test step size is gradually increased for data collection. If there are abnormal changes in the mineral content, the collection step size is tightened to achieve a scientific arrangement of the hole measurement points within the hole.

[0063] S103: Based on the surrounding rock spectral data of each measuring point in the hole, the absorption characteristics of the marker minerals are extracted, and based on the constructed spectrum-mineral content inversion model, the marker mineral content of each measuring point is obtained; and then the distribution of the marker mineral content of the surrounding rock in the entire borehole is fitted;

[0064] In this embodiment, spectral data of the surrounding rock in the hole is collected. The position information measured by the position sensor 10 ensures that the spectral data collection device is accurately positioned at the set measuring point position. By virtue of the rotation of the bearing device carrying the optical fiber, the spectral data of multiple hole positions at a single measuring point (each measuring point is provided with multiple hole positions because the spectral data of a circle of the measuring point position needs to be measured) is automatically collected. The spectral data of multiple hole positions at a single measuring point are averaged as the spectral data of this position. This step is repeated to obtain the spectral data of each measuring point position in the borehole.

[0065] The acquired spectral reflectance data of the surrounding rock are preprocessed to extract the absorption characteristics of the marker minerals. The absorption characteristics are input into the pre-built spectrum-mineral content inversion model to obtain the marker mineral content. The measured mineral content and absorption characteristics are saved in the database to expand the database and improve the model accuracy.

[0066] The process of establishing the spectrum-mineral content inversion model in this embodiment is specifically as follows: selecting fault zone marker minerals, extracting the absorption characteristics of the marker minerals from the spectral data-reflectivity obtained from the excavated section, measuring the corresponding mineral content, establishing a spectral absorption characteristic-mineral content database, and constructing a spectrum-mineral content inversion model based on the linear correspondence between the absorption characteristic peak size and the mineral content. While continuous excavation and mineral testing are being carried out, the database is continuously supplemented, which can achieve continuous optimization and dynamic adjustment of this model.

[0067] S104: Mark abnormal drill holes based on the content of marker minerals in the holes. Based on the number of abnormal drill holes and the image information of the surrounding rocks in the holes, identify whether there is a risk of fault fracture zone.

[0068] Specifically, after identifying the mineral content data at each measuring point, the distribution of marker mineral content in the entire borehole surrounding rock is fitted through data fitting. If the borehole surrounding rock shows abnormal phenomena such as enrichment of marker mineral content, and exceeds the set threshold, such boreholes are marked as abnormal.

[0069] On this basis, multi-hole testing is carried out, and the distribution of the marker mineral content of the arranged drill hole group is obtained through the above-mentioned testing method, and the drill hole group is judged to be abnormal and marked as abnormal; in this embodiment, all the drill holes arranged on the tunnel face are regarded as one drill hole group.

[0070] The ratio of the number of marked abnormal boreholes to the number of boreholes is compared with the set threshold to determine whether it exceeds the set threshold. If it exceeds the threshold, the surrounding rock image information inside the borehole is combined to determine whether there is a risk of fault fracture zone in this unexcavated section.

[0071] S105: For risk sections that have been determined to have fault fracture zones (sections refer to the depth of the drill hole group drilled for each inspection), the marker mineral content of each mileage point is averaged according to the distribution of mineral content in all abnormal drill holes. Finally, the data of each mileage point is fitted with a curve of the relationship between the section marker mineral content and the mileage. Based on this, the area where the anomaly occurs is circled, and the position and impact range of the fault fracture zone are determined. Combined with the type and content distribution of the marker minerals, the occurrence, position and range of the fault fracture zone can be accurately identified.

[0072] Example 2

[0073] In one or more embodiments, a fault zone prediction method based on in-situ testing and analysis of in-hole minerals is disclosed, as follows:

[0074] Based on the fault zone identification method based on in-situ testing and analysis of in-hole minerals described in Example 1, the existence, location and range of the fault fracture zone in the test area are identified;

[0075] A database of marker mineral spectral data and mileage information was constructed, and the data groups in the database were normalized. After removing outliers, the normal data was dimensionlessly processed using range normalization to obtain a normalized value between [0, 1]. This value was input into the LSTM-RNN neural network model to predict the marker mineral content in the untested section ahead. Combined with mileage information and the delineation of abnormal areas, the fault fracture zone was analyzed and predicted. The prediction results served as the basis for whether to conduct drilling tests during subsequent excavation.

[0076] Example 3

[0077] In one or more embodiments, a fault zone identification system based on in-situ testing and analysis of in-hole minerals is disclosed, specifically comprising:

[0078] The tunnel face drilling module is used to evenly arrange quadrilateral grids on the tunnel face in the risk area of ​​fault fracture zone, and take the vertices of the quadrilaterals as the predetermined drilling positions for drilling;

[0079] The data acquisition module is used to obtain image information and spectral data of the surrounding rock in the hole through the detection device 5, and dynamically adjust the step size of data acquisition based on the integrity of the surrounding rock and the changes in the mineral content of the surrounding rock;

[0080] The marker mineral content distribution fitting module is used to extract the absorption characteristics of marker minerals based on the surrounding rock spectral data of each measuring point in the hole, and obtain the marker mineral content of each measuring point based on the constructed spectrum-mineral content inversion model; and then fit the marker mineral content distribution of the surrounding rock in the entire borehole;

[0081] Fault fracture zone risk identification module, which is used to mark abnormal drill holes based on the content of marker minerals in the holes, and to identify whether there is a fault fracture zone risk based on the number of abnormal drill holes and the image information of the surrounding rock in the holes;

[0082] Fault fracture zone location identification module, used to identify the existence, location and range of fault fracture zones;

[0083] The fault fracture zone prediction module is used to predict the content of marker minerals in the untested section ahead based on the neural network model, thereby realizing the analysis and prediction of the fault fracture zone.

[0084] The specific implementation of each of the above modules has been described in Example 1 and will not be described in detail here.

[0085] Example 4

[0086] As a specific implementation method, this embodiment discloses a fault zone identification system based on in-situ testing and analysis of in-hole minerals, combined with Figure 4-Figure 6 ,The system hardware structure mainly includes:

[0087] Tracked robots, Figure 4 The specific structure of the crawler robot is given. The crawler 2 can realize stable displacement of the robot; the crawler robot is provided with an operating platform 1, on which the operator can operate the robot to start and stop and collect data; the operating platform 1 is provided with a lifting device 3, which is connected to a test platform 4. The test platform 4 is placed with a detection device 5 and a ground feature spectrometer 6, and a maintenance space is reserved for personnel; in this embodiment, the lifting device 3 can drive the lifting and lowering of the test platform 4 to realize comprehensive data collection at different positions of the tunnel face; the detection device 5 is used to realize data testing and collection.

[0088] Both the intelligent operating platform 7 and the power distribution box 8 are mounted on the operating platform 1. The power distribution box 8 ensures the proper storage, distribution, and utilization of the robot's electrical energy. A lighting device 9 is provided on the power distribution box 8 for forward illumination. The intelligent operating platform 7 is used for data transmission, processing, and display. The operator can use the intelligent operating platform 7 to intelligently control the robot's movement, data collection, analysis, and storage, and the raising and lowering of the lifting device 3. A display screen is also provided to visualize the entire process.

[0089] Among them, combined Figure 5 and Figure 6 The detection device 5 specifically includes: a rotating shaft that can penetrate into the borehole, a dewatering nozzle 11 is provided on the top of the rotating shaft, and the dewatering nozzle 11 discharges the moisture in the hole by spraying high-pressure gas; a constant temperature heating device 12 covers the surface of the rotating shaft for further dewatering.

[0090] The support anti-shake device 14 is made of resin material and is supported on the rotating shaft to play a supporting and buffering role, which can effectively reduce the impact of shaking during data collection.

[0091] A support rod is provided in the middle of the rotating shaft. A rotating bearing is mounted on the support rod. The rotating bearing is connected to a test optical fiber 13 via a connector. A miniature probe (miniature camera 16) is provided at the end of the test optical fiber 13.

[0092] A protective housing 17 is installed at the end of the rotating shaft near the dewatering nozzle 11, supporting the shaft's circumference. An LED light 15 is mounted on the inner surface of the housing 17, providing illumination for the detection device 5 to enter the hole and for the micro-camera 16 to capture image information. A hole is provided in the housing 17, through which the micro-probe passes. Rotation of the shaft drives the test fiber 13 around the rotating bearing, enabling 360-degree spectral data acquisition.

[0093] A position sensor 10 is provided on the side wall of the hole of the protective shell 17 for collecting the distance between the detection device 5 and the top of the borehole; the protective shell 17 is used to protect the optical fiber, ensure the accuracy of data collection, and at the same time prevent loose gravel from falling and affecting the detection device 5, and play a supporting role.

[0094] Combine Figure 3 The entire system specifically includes the following parts:

[0095] (1) The intelligent control part consists of a central control unit, a hole position capture unit, a device posture control unit, and a remote control unit;

[0096] Among them, the central control unit is mainly composed of the operating host, which can realize the direct connection of various systems, achieve the optimal scheduling and reasonable integration of various systems, and achieve the optimal power consumption configuration of the device and system during use;

[0097] The hole position positioning and capture unit consists of a camera and a light source. The camera is installed on a working platform to continuously capture the tunnel face to be measured, obtain image information of the tunnel face to be measured, and perform feature extraction through the image processing unit to achieve hole position capture and lock. The light source ensures the imaging and capture effects of the camera.

[0098] The device attitude control unit consists of a shock-absorbing and wear-resistant track 2, a working platform, an electromagnetic signal transceiver, and a telescopic guide tube. The shock-absorbing and wear-resistant track 2 minimizes jitter interference to the device and system caused by uneven ground conditions, and its wear resistance ensures a sufficiently long service life. The working platform can rotate 360 ​​degrees, making the various devices on the platform more flexible. The electromagnetic signal transceiver can emit electromagnetic waves to the side rock wall. The propagation time of the electromagnetic waves can be used to determine the distance between the electromagnetic wave transceiver and the side rock wall, thereby accurately controlling the distance between the device and the face. The telescopic guide tube can adjust the position of the detection device 5 within the hole and perform data testing at different mileage positions in the borehole. The sensor installed on it can visualize the attitude and position of the internal test optical fiber 13, enabling real-time adjustment and optimization of test parameters. The anti-shake device installed on it can effectively avoid spectral data errors caused by optical fiber instability due to jitter during the test process, achieving stable rotation of the optical fiber and ensuring accurate test results.

[0099] The remote control unit can realize remote control from a mobile terminal, feedback the system operation status through the wireless signal transceiver in the control unit, realize the start and stop and remote adjustment settings of the device and system, and realize remote control of the intelligent control system, integrated test system, data collection, storage and processing system, data analysis and result output system, and visual information viewing and acquisition of data.

[0100] (2) Integration test part

[0101] It consists of an illumination unit, a water removal unit, a step size dynamic adjustment unit, and a data acquisition unit;

[0102] The lighting unit consists of an LED lighting component and a halogen lamp. The LED lighting component is turned on when the detection device 5 enters the hole, and is used to provide sufficient light source for the micro camera 16 to collect images, ensuring the quality of the collected images, and also realizing real-time visualization of the complex situation in the hole. The halogen lamp provides a reliable light source for spectral data collection, ensuring the quality of spectral data collection.

[0103] The dehydration unit consists of a dehydration nozzle 11, a constant temperature heating device 12 and a humidity sensing unit. The dehydration nozzle 11 uses a high-pressure jet hole to blow out the water vapor in the hole. At the same time, the constant temperature heating device 12 is turned on to achieve heating in the hole and evaporation of water vapor; the humidity sensing unit analyzes the spectral data during the spectral data test process and determines whether the water has been completely removed by judging whether there is a characteristic peak of water, so as to effectively eliminate the interference of moisture on the spectral test data.

[0104] The step-size dynamic adjustment unit consists of a position sensor 10, a micro-camera 16 provided by the data acquisition module, and a spectral detection device 5. The position sensor 10 realizes real-time position perception and enables accurate positioning of the detection device 5. The initial step-size is determined based on the image information of the surrounding rock in the hole captured by the micro-camera 16. If the surrounding rock is intact, a large step-size is used; if the surrounding rock is relatively intact, a medium step-size is used; if the surrounding rock is relatively fragmented, a small step-size is used. During the test, the test step-size is automatically set based on the continuity of mineral content information at adjacent measuring points after data collection and processing. If the mineral content is stable, the test step-size is gradually increased for data collection. If there are abnormal changes in the mineral content, the collection step-size is tightened.

[0105] The data acquisition unit consists of a position sensor 10, a micro camera 16 and a spectral detection device 5. The position sensor 10 is mounted on the front end of the probe and is used to accurately locate the measuring point position in combination with the step size setting of the step size dynamic adjustment module, which will automatically realize the transmission, collection, processing and storage of mileage information; the micro camera 16 obtains image information by continuously shooting the surrounding rock, and obtains the complete information of the surrounding rock in the hole through image feature extraction, which provides effective opinions for the comprehensive identification of the fault fracture zone; the spectral data acquisition unit realizes the spectral data acquisition of the surrounding rock of the measuring point through the rotation of the optical fiber.

[0106] (3) Data collection, storage and processing

[0107] It consists of a data collection unit, a data storage unit and a data processing unit;

[0108] The data collection unit collects, classifies, and transfers the measurement point mileage data, image data, and spectrum data collected by the detection device 5 during the test process, and collects data such as the device position information, pan-tilt rotation angle, attitude control parameters, and image information obtained by the pan-tilt camera.

[0109] The data storage unit is used to orderly store data collection information and program operation traces and other information for data backup, analysis, call and program upgrade detection and repair.

[0110] The data processing unit realizes the feature extraction of image information and the extraction of marker mineral absorption features of spectral test data, and inputs the extracted marker mineral absorption features into the spectrum-mineral content inversion model to realize the rapid and intelligent identification and reading of the mineral content of the drill hole.

[0111] (4) Data analysis and result output

[0112] It includes a fault zone identification unit, a fault zone intelligent prediction unit, and a result output and visualization display unit;

[0113] Among them, the fault zone identification unit obtains the mineral content of the drill hole group on the tunnel face, obtains the distribution of marker minerals in the hole through data analysis, and determines whether there is abnormal enrichment or loss of mineral distribution through the set threshold, thereby realizing the identification of the fault zone and data output.

[0114] The fault zone intelligent prediction unit inputs the marker mineral spectral data and corresponding mileage information obtained by the data processing system into the neural network model for training, and inputs the prediction results into the data processing system for processing to obtain the distribution of marker minerals in the untested section, thereby realizing intelligent prediction and data output of the distribution of mineral fault fracture zones in the untested section.

[0115] (5) Full-process visualization platform

[0116] It includes a fault fracture zone identification visualization unit, a fault fracture zone prediction visualization unit and a text data output unit: the fault fracture zone identification visualization unit can visualize the above identification results, and can view the changes in the content of marker minerals in the fault fracture zone and the position and distribution of the fault fracture zone on the device's smart display screen and mobile terminal; the fault fracture zone prediction visualization unit can visualize the content and distribution of marker minerals in the untested section and the fault fracture zone prediction results through the above equipment; the text data output unit can realize text output of data collected, processed, operated, etc. by the device and system.

[0117] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A fault zone identification method based on in-situ testing and analysis of in-hole minerals, characterized in that: include: Evenly arrange quadrilateral grids on the tunnel face in the risk area of ​​fault fracture zone, take the vertices of the quadrilateral as the predetermined drilling position, and drill holes; The size of the quadrilateral grid is dynamically adjusted according to the number of abnormal drilling marks. If the ratio of the number of abnormal drilling marks to the total number of drillings exceeds a set threshold during the initial test, the number of quadrilateral grids is increased to achieve drilling density. The detection device obtains image information and spectral data of the surrounding rock in the hole, and dynamically adjusts the step size of data acquisition based on the integrity of the surrounding rock and changes in the surrounding rock mineral content; Based on the surrounding rock spectral data of each measuring point in the hole, the absorption characteristics of the marker minerals are extracted. Based on the constructed spectrum-mineral content inversion model, the marker mineral content of each measuring point is obtained; then the distribution of the marker mineral content in the surrounding rock of the entire borehole is fitted; Abnormal drilling holes are marked based on the content of marker minerals in the holes. Based on the number of abnormal drilling holes and the image information of the surrounding rocks in the holes, the risk of fault fracture zones can be identified. The specific process of identifying whether there is a fault fracture zone risk is as follows: If the surrounding rock in the borehole shows abnormal phenomenon indicating the enrichment of mineral content and exceeds the set threshold, the borehole will be marked as an abnormal borehole; If the ratio of the number of abnormal boreholes to the total number of boreholes exceeds the set threshold, and the surrounding rock in the hole has obvious sudden changes in rock formation occurrence, jointing and cleavage zones, and a sharp increase in small folds, it is determined that there is a risk of fault fracture zone; For the risky sections of drilling holes that have been determined to have fault fracture zones, based on the distribution of mineral content in all abnormal drilling holes, the location where the measuring point exists at the same depth in the drilling hole group is regarded as a milepost. The content of the marker minerals at each milepost is averaged, and finally the data of each milepost are fitted to obtain the curve of the relationship between the section marker mineral content and the mileage. Based on this, the area where the anomalies occur is delineated, and the location and impact range of the fault fracture zone are determined. Combined with the types and content distribution of the marker minerals, the occurrence, location and range of the fault fracture zone are finally identified.

2. The fault zone identification method based on in-situ testing and analysis of in-hole minerals according to claim 1, characterized in that: Based on the integrity of the surrounding rock and the changes in the surrounding rock mineral content, the step size of data acquisition is dynamically adjusted, specifically: The integrity of the surrounding rock is obtained through the image information of the surrounding rock in the hole, and the initial step size is set. During the test, the mineral content change is obtained through the surrounding rock spectral data at multiple consecutive step positions. If the mineral content is stable, the test step size is increased. If the mineral content changes abnormally, the acquisition step size is tightened. When the current step size is increased or tightened, the new step size is set as a multiple of the original step size.

3. The fault zone identification method based on in-situ testing and analysis of in-hole minerals according to claim 1, characterized in that: When obtaining the spectral data of the surrounding rock in the hole, the spectral data of each measuring point for one week is obtained, and the mean value of all the spectral data is used as the spectral data of this measuring point.

4. The fault zone identification method based on in-situ testing and analysis of in-hole minerals according to claim 1, characterized in that: The construction of the spectrum-mineral content inversion model is specifically as follows: Select fault zone marker minerals, extract the absorption characteristics of the marker minerals based on the spectral data and reflectivity obtained from the excavated section, measure the mineral content corresponding to the absorption characteristics, establish a database corresponding to the spectral absorption characteristics and mineral content, and construct a spectrum-mineral content inversion model based on the linear correspondence between the absorption characteristic peak size and mineral content; The data from subsequent excavation and testing are continuously added to the database to optimize the inversion model.

5. A fault zone prediction method based on in-situ testing and analysis of in-hole minerals, characterized in that: include: Based on the fault zone identification method based on in-situ testing and analysis of in-hole minerals according to any one of claims 1 to 4, the existence, location and range of the fault fracture zone in the test area are identified; Based on the obtained data set of section marker mineral content and mileage point information, a database of marker mineral spectral data and mileage point information was constructed. The data set in the database was normalized, and the normal data after removing outliers was dimensionlessly processed using range normalization to obtain a normalized value between [0,1]. This value was input into the LSTM-RNN neural network model to predict the marker mineral content of the untested section ahead. Combined with the mileage point information and the delineation of abnormal areas, the fault fracture zone was analyzed and predicted. The prediction results served as the basis for whether to conduct drilling tests during subsequent excavation.

6. A fault zone identification system based on in-situ testing and analysis of in-hole minerals, characterized in that: include: The tunnel face drilling module is used to evenly arrange quadrilateral grids on the tunnel face in the risk area of ​​fault fracture zone, select the vertices of each quadrilateral as the predetermined drilling position, and drill holes. The size of the quadrilateral grid is dynamically adjusted according to the number of abnormal drilling holes marked. If the ratio of the number of abnormal drilling holes marked to the total number of drilling holes exceeds a set threshold during the initial test, the number of quadrilateral grids is increased, thereby achieving denser drilling. The data acquisition module is used to obtain image information and spectral data of the surrounding rock in the hole through the detection device, and dynamically adjust the step size of data acquisition based on the integrity of the surrounding rock and the changes in the mineral content of the surrounding rock; The marker mineral content distribution fitting module is used to extract the absorption characteristics of marker minerals based on the surrounding rock spectral data of each measuring point in the hole, and obtain the marker mineral content of each measuring point based on the constructed spectrum-mineral content inversion model; and then fit the marker mineral content distribution of the surrounding rock in the entire borehole; Fault fracture zone risk identification module, which is used to mark abnormal drill holes based on the content of marker minerals in the holes, and to identify whether there is a fault fracture zone risk based on the number of abnormal drill holes and the image information of the surrounding rock in the holes; Fault fracture zone location identification module, used to identify the existence, location and range of fault fracture zones; The fault fracture zone prediction module is used to predict the content of marker minerals in the untested section ahead based on the neural network model, thereby realizing the analysis and prediction of the fault fracture zone; The specific process of identifying whether there is a fault fracture zone risk is as follows: If the surrounding rock in the borehole shows abnormal phenomenon indicating the enrichment of mineral content and exceeds the set threshold, the borehole will be marked as an abnormal borehole; If the ratio of the number of abnormal boreholes to the total number of boreholes exceeds the set threshold, and the surrounding rock in the hole has obvious sudden changes in rock formation occurrence, jointing and cleavage zones, and a sharp increase in small folds, it is determined that there is a risk of fault fracture zone; For the risky sections of drilling holes that have been determined to have fault fracture zones, based on the distribution of mineral content in all abnormal drilling holes, the location where the measuring point exists at the same depth in the drilling hole group is regarded as a milepost. The content of the marker minerals at each milepost is averaged, and finally the data of each milepost are fitted to obtain the curve of the relationship between the section marker mineral content and the mileage. Based on this, the area where the anomalies occur is delineated, and the location and impact range of the fault fracture zone are determined. Combined with the types and content distribution of the marker minerals, the occurrence, location and range of the fault fracture zone are finally identified.

7. The fault zone identification system based on in-situ testing and analysis of in-hole minerals according to claim 6, characterized in that: The detection device comprises: The rotating shaft can penetrate deep into the borehole, and a support rod is set in the middle position of the rotating shaft. The support rod carries a rotating bearing, and the rotating bearing is connected to the test optical fiber through a connector. When the rotating shaft rotates, it can drive the test optical fiber to rotate around the rotating bearing, realizing 360° spectral data collection.

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