A method for identifying adverse geology based on deep shaft TBM excavation and related equipment

Abstract

The present invention discloses a method and related equipment for identifying bad geology based on deep shaft TBM excavation. A deep shaft model is constructed by analyzing the geometric characteristics and ground stress levels of rock masses in different lithology sections; the destructiveness of rock masses in different lithology sections is analyzed using TBM mechanical data and applied to the deep shaft model, and the rock masses in different lithology sections in the deep shaft model are destructively analyzed and integrated to determine bad geological characteristics; based on real-time TBM excavation data, the bad geological characteristics are used to identify bad geology in the deep shaft model, and the type, location and scale of bad geology existing in the deep shaft during the TBM excavation process are determined. The present invention makes adaptive adjustments to the bad geology identification of deep shafts using TBM excavation under complex terrain conditions, and overcomes the problem that the existing bad geology identification method cannot accurately identify the bad geology of deep shafts in TBM excavation under complex geological conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of adverse geological conditions identification, and in particular to an adverse geological conditions identification method based on deep shaft TBM excavation and related equipment. Background Art

[0002] In order to improve the level of mechanized construction of underground projects, TBM (Tunnel Boring Machine) has been explored and applied in drainage corridors, traffic channels, flow channel inclined shafts, etc. However, due to various factors, TBM application is still in a small-scale use state, with high cost, low efficiency, and limited application scope. How to achieve a leap in the mechanized construction capacity of underground projects is still the biggest problem facing the current underground project construction.

[0003] A comprehensive analysis of the current status of pumped-storage construction shows that in addition to the limited structural characteristics of the underground cavern engineering of the pumped-storage power station, it is also related to the limitations of the existing application research technology and implementation path. It is an urgent need for the current construction to analyze and improve the performance of existing TBM equipment and form a complete set of technologies that can be used for TBM excavation in pumped-storage power stations from small sections to large sections, from horizontal tunnels to inclined and vertical shafts.

[0004] At present, the method for identifying adverse geological conditions based on TBM excavation data is still imperfect. The patent document with application publication number CN115034446A discloses a method for identifying and predicting adverse geological conditions based on TBM operation data. However, it ignores the fact that in the process of using TBM for large-diameter deep shaft excavation, it is usually under complex geological conditions. The adverse geological result factors identified by merely calculating and analyzing the TBM operation data lack adaptive adjustment for complex geological conditions, resulting in low accuracy of adverse geological identification results. Summary of the invention

[0005] Based on the problems raised by the above background technology, the purpose of the present invention is to provide a method and related equipment for identifying adverse geology based on deep shaft TBM excavation, which solves the problem that the adverse geology result factors identified by simply calculating and analyzing TBM operation data lack adaptive adjustment for complex geological conditions, thereby resulting in low accuracy of adverse geology identification results.

[0006] The present invention is achieved through the following technical solutions:

[0007] The first aspect of the present invention provides a method for identifying adverse geological conditions based on deep shaft TBM excavation, comprising the following steps:

[0008] Based on the geological exploration data, the deep shaft is divided into lithological sections according to geological characteristics, and the structural surface geometric characteristics and rock mass stress level of different lithological sections are analyzed. Based on the analysis results, a deep shaft model is constructed;

[0009] Obtaining TBM mechanical data, and calculating the rock mass destructiveness of the TBM mechanical data to rock masses of different lithology sections;

[0010] The rock mass destructibility is integrated with the deep shaft model to generate a deep shaft failure model, and the deep shaft failure model is analyzed to determine adverse geological features during TBM excavation;

[0011] Acquire real-time TBM excavation data, and based on the real-time TBM excavation data, use the adverse geological characteristics in the TBM excavation process to identify the adverse geological conditions on the deep shaft model, so as to obtain the adverse geological type, adverse geological location and adverse geological scale;

[0012] Among them, the deep shaft is divided into lithological sections according to geological characteristics based on geological exploration data, including the following steps:

[0013] The geological exploration data are calculated using the Walsh transformation method to obtain a stratigraphic stratification basis, and the deep vertical shaft is divided into stratigraphic groups based on the stratigraphic stratification basis;

[0014] Extract mineral elements from the deep vertical shaft after stratum division to obtain multiple mineral elements;

[0015] Calculate the weights of multiple mineral elements to obtain the weights of multiple mineral elements;

[0016] The lithology of each stratum is determined based on the weights of the multiple mineral elements, and the deep vertical well after the stratum division is divided into lithology sections according to the lithology of each stratum.

[0017] In the above technical solution, the deep shaft model is constructed by first dividing the deep shaft into lithological sections according to the past geological exploration data. The purpose of dividing the lithological sections is that in the process of using TBM to excavate a large-diameter deep shaft, it is usually under complex geological conditions. The rock mass in the large-diameter deep shaft is different at different depths and positions. The lithological section division can well reflect the rock mass category of the deep shaft and improve the accuracy of poor geological identification. The geometric characteristics and ground stress levels of the rock masses in different lithological sections after the lithological section division are analyzed, and the deep shaft model for poor geological identification is constructed according to its geometric characteristics and ground stress levels.

[0018] After conducting a destructive analysis of the TBM mechanical data on rock masses of different lithology sections, it is necessary to apply it to the deep shaft model. Through the destructive analysis and fusion of the rock masses of different lithology sections in the deep shaft model, a deep shaft failure model can be generated, that is, the destructiveness of rock masses of different lithology sections when subjected to TBM excavation is integrated into the original deep shaft model. The resulting deep shaft failure model can characterize the possibility of TBM mechanical damage during deep shaft excavation, thereby determining the adverse geological features that may exist during TBM excavation.

[0019] After conducting a destructive analysis of the TBM mechanical data on rock masses of different lithology sections, it is necessary to apply it to the deep shaft model. Through the destructive analysis and fusion of the rock masses of different lithology sections in the deep shaft model, a deep shaft failure model can be generated, that is, the destructiveness of rock masses of different lithology sections when subjected to TBM excavation is integrated into the original deep shaft model. The resulting deep shaft failure model can characterize the possibility of TBM mechanical damage during deep shaft excavation, thereby determining the adverse geological features that may exist during TBM excavation.

[0020] After determining the adverse geological features that may exist during TBM excavation, it is necessary to apply them to the detection of actual deep shafts, where real-time TBM excavation data includes real-time TBM mechanical data, rock mass data at the excavation site, and rock slag data at the excavation site. Based on real-time TBM excavation data, the adverse geological features during TBM excavation are used to identify the adverse geology of the deep shaft model, and the type, location, and scale of the adverse geology that exists in the deep shaft during TBM excavation can be determined.

[0021] In an optional embodiment, the Walsh transformation method is used to calculate the geological exploration data to obtain the basis for stratigraphic stratification, including the following steps:

[0022] A. extracting measurement curves including three-directional logging curves, induction logging curves, and neutron logging curves from the geological exploration data, and performing Walsh filtering on the measurement curves to obtain Walsh filtering curves;

[0023] B. Selecting the initial interface depth of the deep vertical shaft as the starting stratum interface of the deep vertical shaft, moving a step length and calculating the interpolation average value of the logging average value of the logging curve within the step length and the average value of the logging curve within the previous step length, and calculating the weighted average value of the interpolation average value;

[0024] C. Select any value between 0 and 1 as the threshold value. If the weighted average value is greater than or equal to the threshold value, the interface depth after one step in step B is taken as the deep shaft formation interface. Otherwise, repeat step B.

[0025] D. Determine the zero-crossing point by performing wavelet transformation on the measurement curve corresponding to the largest weighted average value among the weighted average values ​​in step B;

[0026] E. Using the logging data on both sides of the deep vertical well stratum interface obtained in step C as the stratum stratification pending points, matching the stratum stratum specific points with the zero-crossing points to obtain stratum stratum points.

[0027] In an optional embodiment, analyzing the geometric characteristics of the structural surface and the ground stress level of the rock mass in different lithology sections includes the following steps:

[0028] Calculating the rock mass in-situ stress level of each lithology section, and determining the rock mass strain energy release rate of each lithology section based on the rock mass in-situ stress level;

[0029] The amount of crack formation and the direction of crack propagation on the structural surface are determined based on the rock mass strain energy release rate.

[0030] In an optional embodiment, calculating the rock mass in-situ stress level of each lithology segment, and determining the rock mass strain energy release rate of each lithology segment based on the rock mass in-situ stress level includes:

[0031]

[0032]

[0033] In the above formula, F represents rock stress, t represents deviatoric stress, p represents hydrostatic pressure, p0 represents the critical value of hydrostatic pressure, k represents rock cohesion, α represents rock friction angle, μ represents rock Poisson's ratio, G represents rock strain energy release rate, K1 represents stress intensity factor when shear failure occurs, and K2 represents stress intensity factor when tensile failure occurs.

[0034] In an optional embodiment, calculating the rock mass destructiveness of the TBM mechanical data for rock masses of different lithology sections includes the following steps:

[0035] Calculate the impact strength generated by the TBM mechanical data, and determine the dynamic stress to which the rock mass of each lithology section is subjected under the impact strength and the dynamic strain under the dynamic stress;

[0036] constructing a dynamic stress-dynamic strain curve, and analyzing the dynamic stress-dynamic strain curve to determine the dynamic compressive strength;

[0037] Based on the dynamic compressive strength, a crack propagation simulation is performed to determine the crack propagation trend of each lithology section under TBM mechanical data.

[0038] In an optional embodiment, calculating the impact strength generated by the TBM mechanical data includes:

[0039]

[0040] In the above formula, E represents the impact strength, f represents the cutterhead thrust, T represents the cutterhead torque, D represents the main machine diameter, and r represents the TBM speed.

[0041] The second aspect of the present invention provides a system for identifying adverse geology based on deep shaft TBM excavation, comprising:

[0042] A model building module, which is used to divide the deep shaft into lithology sections according to geological characteristics based on geological exploration data, analyze the structural surface geometric characteristics and rock stress level of the rock mass in different lithology sections, and build a deep shaft model based on the analysis results;

[0043] A destructive analysis module, which is used to obtain TBM mechanical data and calculate the rock destructiveness of the TBM mechanical data to rock masses of different lithology sections;

[0044] A feature acquisition module, wherein the feature acquisition module is used to merge the rock mass destructiveness with the deep shaft model to generate a deep shaft failure model, analyze the deep shaft failure model, and determine adverse geological features during TBM excavation;

[0045] An identification module is used to obtain real-time TBM excavation data, and based on the real-time TBM excavation data, use the adverse geological characteristics during the TBM excavation process to identify the adverse geological conditions of the deep shaft model to obtain the adverse geological type, adverse geological location and adverse geological scale.

[0046] In an optional embodiment, the model building module includes:

[0047] A stratigraphic division unit, the stratigraphic division unit is used to calculate the geological exploration data using a Walsh transformation method to obtain a stratigraphic stratification basis, and to perform stratigraphic division on the deep vertical well based on the stratigraphic stratification basis;

[0048] An extraction unit, the extraction unit is used to extract mineral elements from the deep vertical well after the stratum is divided, to obtain a plurality of mineral elements;

[0049] A weight calculation unit, wherein the weight calculation unit is used to perform weight calculation on a plurality of mineral elements to obtain weights of the plurality of mineral elements;

[0050] A lithology segment division unit is used to determine the lithology of each stratum based on the weights of the multiple mineral elements, and to divide the deep vertical well after the stratum division into lithology segments according to the lithology of each stratum.

[0051] A third aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for identifying adverse geology based on deep shaft TBM excavation is implemented.

[0052] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying adverse geology based on deep shaft TBM excavation.

[0053] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0054] Adaptive adjustments are made to the identification of poor geology in deep vertical shafts excavated by TBM under complex terrain conditions, overcoming the problem that existing poor geology identification methods are inaccurate in identifying poor geology in deep vertical shafts excavated by TBM under complex geological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0056] Figure 1 A schematic flow chart of a method for identifying adverse geological conditions based on deep shaft TBM excavation provided in Example 1 of the present invention;

[0057] Figure 2 A schematic diagram of the structure of a system for identifying adverse geological conditions based on deep shaft TBM excavation provided in Example 2 of the present invention;

[0058] Figure 3 A schematic diagram of the structure of an electronic device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0060] Example 1

[0061] Figure 1 A schematic diagram of a flow chart of a method for identifying adverse geological conditions based on deep shaft TBM excavation provided in Example 1 of the present invention, as shown in FIG. Figure 1As shown, a method for identifying adverse geology based on deep shaft TBM excavation includes the following steps:

[0062] Step S1: divide the deep shaft into lithology sections according to geological characteristics based on geological exploration data, analyze the structural surface geometry characteristics and rock stress level of the rock mass in different lithology sections, and construct a deep shaft model based on the analysis results.

[0063] It should be noted that the identification of poor geology in deep shafts can include two aspects for evaluation, the first is the strength of the rock mass, and the second is the integrity of the rock mass. In order to evaluate the strength and integrity of the rock mass, the present invention first needs to construct a deep shaft model. Among them, the deep shaft model constructed by the present invention first divides the deep shaft into lithological sections according to past geological exploration data. The purpose of dividing the lithological sections is that in the process of using TBM to excavate a large-diameter deep shaft, it is usually under complex geological conditions. The rock mass in the large-diameter deep shaft is different at different depths and positions. The lithological section division can well reflect the rock mass category of the deep shaft and improve the accuracy of poor geology identification. By analyzing the geometric characteristics and ground stress levels of the rock masses in different lithological sections after the lithological section division, the deep shaft model for poor geology identification to be performed by the present invention is constructed according to its geometric characteristics and ground stress levels.

[0064] In summary, the purpose of this step is to construct a deep shaft model, wherein the core point of constructing the deep shaft model lies in the division of lithology segments, the geometric characteristics of the structural surface, and the analysis of the rock mass in-situ stress level.

[0065] In an optional embodiment, dividing the deep shaft into lithological sections according to geological characteristics based on geological exploration data includes the following steps:

[0066] The geological exploration data are calculated using the Walsh transformation method to obtain a stratigraphic stratification basis, and the deep vertical shaft is divided into stratigraphic groups based on the stratigraphic stratification basis;

[0067] Mineral elements are extracted from the deep vertical shaft after the stratum division to obtain multiple mineral elements;

[0068] Calculate the weights of multiple mineral elements to obtain the weights of multiple mineral elements;

[0069] The lithology of each stratum is determined based on the weights of the multiple mineral elements, and the deep vertical well after the stratum division is divided into lithology sections according to the lithology of each stratum.

[0070] It should be noted that the purpose of lithology segment division is to decompose complex geology so as to facilitate the subsequent identification of bad geology in deep vertical shafts of complex geology. The present invention adopts the technology of layer-by-layer decomposition. First, it is necessary to perform stratigraphic stratification. Since the distribution in the stratum is not uniform, the Walsh transformation method is used in the embodiment of the present invention to calculate the geological exploration data, so as to obtain the basis for stratigraphic stratification, and perform stratigraphic division based on the basis. Specifically, in this embodiment, the geological characteristics including three-dimensional logging curves, induction logging curves, and neutron logging curves in the geological exploration data are calculated. The curves are transformed by the Walsh transformation method, which can effectively perform stratigraphic stratification of complex geology and improve the accuracy of non-uniform distribution geological stratification to a certain extent. Secondly, the mineral elements in each layer of the stratum after stratification are extracted, and the same mineral elements are divided into the same category by category division. Finally, the content of the mineral element is counted, and the content of all mineral elements is normalized as the weight of the mineral element. The two mineral elements with the top two weights are selected as the main components of the rock mass, and the relationship between other mineral elements and the main components is calculated, and the stratigraphic lithology is determined based on the relationship. The basis of this step is that a rock mass has major mineral elements and minor mineral elements; secondly, the major mineral elements make a preliminary distinction between the rock mass lithology, but there is a certain error in classifying rock masses with the same or similar main components into the same lithology. For example, the main components of regional limestone and mudstone are similar. At this time, it is necessary to analyze the relationship between the minor components and the major components. For example, regional limestone is produced by the action of calcium in the stratum, and the calcium component in its minor components has a certain proportional relationship with the main component in regional limestone. Mudstone is produced by the action of sodium, magnesium, and iron in the stratum, and the sodium, magnesium, and iron components in its minor components have a certain proportional relationship with the main component in regional limestone. In this way, the lithology of each stratum can be determined.

[0071] In an optional embodiment, the Walsh transformation method is used to calculate the geological exploration data to obtain the basis for stratigraphic stratification, which includes the following steps:

[0072] A. extracting measurement curves including three-directional logging curves, induction logging curves, and neutron logging curves from the geological exploration data, and performing Walsh filtering on the measurement curves to obtain Walsh filtering curves;

[0073] B. Selecting the initial interface depth of the deep vertical shaft as the starting stratum interface of the deep vertical shaft, moving a step length and calculating the interpolation average value of the logging average value of the logging curve within the step length and the average value of the logging curve within the previous step length, and calculating the weighted average value of the interpolation average value;

[0074] C. Select any value between 0 and 1 as the threshold value. If the weighted average value is greater than or equal to the threshold value, the interface depth after one step in step B is taken as the deep shaft formation interface. Otherwise, repeat step B.

[0075] D. Determine the zero-crossing point by performing wavelet transformation on the measurement curve corresponding to the largest weighted average value among the weighted average values ​​in step B;

[0076] E. Using the logging data on both sides of the deep vertical well stratum interface obtained in step C as the stratum stratification pending points, matching the stratum stratum specific points with the zero-crossing points to obtain stratum stratum points.

[0077] In an optional embodiment, analyzing the geometric characteristics of the structural surface and the ground stress level of the rock mass in different lithology sections includes the following steps:

[0078] Calculating the rock mass in-situ stress level of each lithology section, and determining the rock mass strain energy release rate of each lithology section based on the rock mass in-situ stress level;

[0079] The amount of crack formation and the direction of crack propagation on the structural surface are determined based on the rock mass strain energy release rate.

[0080] In an optional embodiment, calculating the rock mass in-situ stress level of each lithology segment, and determining the rock mass strain energy release rate of each lithology segment based on the rock mass in-situ stress level includes:

[0081]

[0082]

[0083] In the above formula, F represents rock stress, t represents deviatoric stress, p represents hydrostatic pressure, p0 represents the critical value of hydrostatic pressure, k represents rock cohesion, α represents rock friction angle, μ represents rock Poisson's ratio, G represents rock strain energy release rate, K1 represents stress intensity factor when shear failure occurs, and K2 represents stress intensity factor when tensile failure occurs.

[0084] Among them, k represents the cohesion of the rock mass, which is related to the lithology of the rock mass.

[0085] Step S2: acquiring TBM mechanical data, and calculating the rock mass destructiveness of the TBM mechanical data to rock masses of different lithology sections.

[0086] It should be noted that one of the elements of the assessment of rock mass integrity is the assessment of rock mass cracks, which include cracks existing in the rock mass itself and cracks caused by impact during TBM excavation. Therefore, in the present invention, it is necessary to calculate through mechanical data such as TBM rotation speed and excavation speed, so as to assess the degree of damage to rock masses of different lithology sections caused by TBM during excavation, so as to facilitate the subsequent identification of adverse geology in the deep shaft model.

[0087] In an optional embodiment, calculating the rock mass destructiveness of the TBM mechanical data for rock masses of different lithology sections includes the following steps:

[0088] Calculate the impact strength generated by the TBM mechanical data, and determine the dynamic stress to which the rock mass of each lithology section is subjected under the impact strength and the dynamic strain under the dynamic stress;

[0089] constructing a dynamic stress-dynamic strain curve, and analyzing the dynamic stress-dynamic strain curve to determine the dynamic compressive strength;

[0090] Based on the dynamic compressive strength, a crack propagation simulation is performed to determine the crack propagation trend of each lithology section under TBM mechanical data.

[0091] It should be noted that irreversible cracks are generated in the rock mass under the impact force of the TBM, and the generation and expansion of the cracks show different trends as the impact force changes. Therefore, in an embodiment of the present invention, the dynamic stress on the rock mass and the dynamic strain of the rock mass under the dynamic stress are used to predict the generation of cracks in the rock mass and the trend of crack expansion.

[0092] In an optional embodiment, calculating the impact strength generated by the TBM mechanical data includes:

[0093]

[0094] In the above formula, E represents the impact strength, f represents the cutterhead thrust, T represents the cutterhead torque, D represents the main machine diameter, and r represents the TBM speed.

[0095] Step S3: integrating the rock mass destructibility with the deep shaft model to generate a deep shaft failure model, analyzing the deep shaft failure model, and determining adverse geological features during TBM excavation.

[0096] It should be noted that after the destructive analysis of the TBM mechanical data on the rock mass of different lithology sections is carried out, it is necessary to apply it to the deep shaft model. Through the destructive analysis and fusion of the rock mass of different lithology sections in the deep shaft model, a deep shaft failure model can be generated, that is, the destructiveness of the rock mass of different lithology sections when subjected to TBM excavation is integrated into the original deep shaft model. The obtained deep shaft failure model can characterize the possibility of TBM mechanical damage during deep shaft excavation, thereby determining the adverse geological characteristics that may exist during TBM excavation.

[0097] Specifically, the purpose of this step is to determine the adverse geological characteristics during the TBM excavation process. In this embodiment, the fusion of rock mass destructiveness and deep shaft model is based on the rock mass lithology, and the destructiveness of TBM to the rock mass of the lithology is marked on the rock mass of the corresponding lithology in the deep shaft model, so as to facilitate the subsequent analysis of the deep shaft destruction model. The deep shaft destruction model can be used to determine the cracks, crack expansion trends and rock strength generated in the deep shaft during the TBM excavation process. The cracks, crack expansion trends and rock strength generated are all characteristic manifestations of adverse geology.

[0098] Step S4, acquiring real-time TBM excavation data, and based on the real-time TBM excavation data, using the adverse geological characteristics during the TBM excavation process to identify adverse geological conditions on the deep shaft model, to obtain the adverse geological type, adverse geological location, and adverse geological scale.

[0099] It should be noted that after determining the adverse geological features that may exist during the TBM excavation process, it is necessary to apply them to the detection of actual deep shafts, wherein the real-time TBM excavation data includes real-time TBM mechanical data, rock mass data at the excavation site, and rock slag data at the excavation site. Based on the real-time TBM excavation data, the adverse geological features during the TBM excavation process are used to identify the adverse geology of the deep shaft model, and the type, location, and scale of the adverse geology existing in the deep shaft during the TBM excavation process can be determined.

[0100] The method for identifying adverse geology based on deep vertical shaft TBM excavation provided by the present invention can make adaptive adjustments to the adverse geology identification of deep vertical shafts using TBM excavation under complex terrain conditions, thereby overcoming the problem that existing adverse geology identification methods are unable to accurately identify adverse geology in deep vertical shafts using TBM excavation under complex geological conditions.

[0101] Example 2

[0102] Figure 2 A schematic diagram of the structure of a system for identifying adverse geological conditions based on deep shaft TBM excavation provided in Example 2 of the present invention is shown in FIG. Figure 2As shown, a bad geological identification system based on deep shaft TBM excavation includes:

[0103] A model building module, which is used to divide the deep shaft into lithology sections according to geological characteristics based on geological exploration data, analyze the structural surface geometric characteristics and rock stress level of the rock mass in different lithology sections, and build a deep shaft model based on the analysis results;

[0104] A destructive analysis module, which is used to obtain TBM mechanical data and calculate the rock destructiveness of the TBM mechanical data to rock masses of different lithology sections;

[0105] A feature acquisition module, wherein the feature acquisition module is used to merge the rock mass destructiveness with the deep shaft model to generate a deep shaft failure model, analyze the deep shaft failure model, and determine adverse geological features during TBM excavation;

[0106] An identification module is used to obtain real-time TBM excavation data, and based on the real-time TBM excavation data, use the adverse geological characteristics during the TBM excavation process to identify the adverse geological conditions of the deep shaft model to obtain the adverse geological type, adverse geological location and adverse geological scale.

[0107] In an optional embodiment, the model building module includes:

[0108] A stratigraphic division unit, the stratigraphic division unit is used to calculate the geological exploration data using a Walsh transformation method to obtain a stratigraphic stratification basis, and to perform stratigraphic division on the deep vertical well based on the stratigraphic stratification basis;

[0109] An extraction unit, the extraction unit is used to extract mineral elements from the deep vertical well after the stratum is divided, to obtain a plurality of mineral elements;

[0110] A weight calculation unit, wherein the weight calculation unit is used to perform weight calculation on a plurality of mineral elements to obtain weights of the plurality of mineral elements;

[0111] A lithology segment division unit is used to determine the lithology of each stratum based on the weights of the multiple mineral elements, and to divide the deep vertical well after the stratum division into lithology segments according to the lithology of each stratum.

[0112] Example 3

[0113] Figure 3 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention is shown in FIG. Figure 3 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the computer device can be one or more. Figure 3A processor 21 is taken as an example; the processor 21, the memory 22, the input device 23 and the output device 24 in the electronic device can be connected by a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0114] The memory 22 is a computer-readable storage medium that can be used to store software programs, computer executable programs, and modules. The processor 21 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 22, that is, to implement a method for identifying adverse geology based on deep shaft TBM excavation in Example 1.

[0115] The memory 22 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 22 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 22 may further include a memory remotely arranged relative to the processor 21, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0116] The input device 23 can be used to receive the ID and password input by the user, etc. The output device 24 is used to output the network configuration page.

[0117] Example 4

[0118] Embodiment 4 of the present invention further provides a computer-readable storage medium, wherein the computer-executable instructions, when executed by a computer processor, are used to implement a method for identifying adverse geology based on deep shaft TBM excavation as provided in Embodiment 1.

[0119] An embodiment of the present invention provides a storage medium containing computer executable instructions, and its computer executable instructions are not limited to the method operations provided in Example 1, and can also execute related operations in a method for identifying adverse geology based on deep shaft TBM excavation provided in any embodiment of the present invention.

[0120] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying adverse geology based on deep shaft TBM excavation, characterized in that: The steps include: Based on the geological exploration data, the deep shaft is divided into lithological sections according to geological characteristics, and the structural surface geometric characteristics and rock mass stress level of different lithological sections are analyzed. Based on the analysis results, a deep shaft model is constructed; Obtaining TBM mechanical data, and calculating the rock mass destructiveness of the TBM mechanical data to rock masses of different lithology sections; The rock mass destructibility is integrated with the deep shaft model to generate a deep shaft failure model, and the deep shaft failure model is analyzed to determine adverse geological features during TBM excavation; Acquire real-time TBM excavation data, and based on the real-time TBM excavation data, use the adverse geological characteristics in the TBM excavation process to identify the adverse geological conditions on the deep shaft model, so as to obtain the adverse geological type, adverse geological location and adverse geological scale; Calculating the rock mass destructiveness of the TBM mechanical data to rock masses of different lithology sections includes the following steps: Calculate the impact strength generated by the TBM mechanical data, and determine the dynamic stress to which the rock mass of each lithology section is subjected under the impact strength and the dynamic strain under the dynamic stress; constructing a dynamic stress-dynamic strain curve, and analyzing the dynamic stress-dynamic strain curve to determine the dynamic compressive strength; Based on the dynamic compressive strength, a crack propagation simulation is performed to determine the crack propagation trend of each lithology section under TBM mechanical data; Among them, the deep shaft is divided into lithological sections according to geological characteristics based on geological exploration data, including the following steps: The geological exploration data are calculated using the Walsh transformation method to obtain a stratigraphic stratification basis, and the deep vertical shaft is divided into stratigraphic groups based on the stratigraphic stratification basis; Mineral elements are extracted from the deep vertical shaft after the stratum division to obtain multiple mineral elements; Calculate the weights of multiple mineral elements to obtain the weights of multiple mineral elements; The lithology of each stratum is determined based on the weights of the multiple mineral elements, and the deep vertical well after the stratum division is divided into lithology sections according to the lithology of each stratum.

2. The method for identifying adverse geology based on deep shaft TBM excavation according to claim 1, characterized in that: The Walsh transformation method is used to calculate the geological exploration data to obtain the basis for stratigraphic stratification, which includes the following steps: A. extracting measurement curves including three-directional logging curves, induction logging curves, and neutron logging curves from the geological exploration data, and performing Walsh filtering on the measurement curves to obtain Walsh filtering curves; B. Selecting the initial interface depth of the deep vertical shaft as the starting stratum interface of the deep vertical shaft, moving a step length and calculating the interpolation average value of the logging average value of the logging curve within the step length and the average value of the logging curve within the previous step length, and calculating the weighted average value of the interpolation average value; C. Select any value between 0 and 1 as the threshold value. If the weighted average value is greater than or equal to the threshold value, the interface depth after one step in step B is taken as the deep shaft formation interface. Otherwise, repeat step B. D. Determine the zero-crossing point by performing wavelet transformation on the measurement curve corresponding to the largest weighted average value among the weighted average values ​​in step B; E. Using the logging data on both sides of the deep vertical well stratum interface obtained in step C as the stratum stratification pending points, matching the stratum stratum specific points with the zero-crossing points to obtain stratum stratum points.

3. The method for identifying adverse geology based on deep shaft TBM excavation according to claim 2, characterized in that: The analysis of structural surface geometric characteristics and rock mass in-situ stress levels of rock masses in different lithology sections includes the following steps: Calculating the rock mass in-situ stress level of each lithology section, and determining the rock mass strain energy release rate of each lithology section based on the rock mass in-situ stress level; The amount of crack formation and the direction of crack propagation on the structural surface are determined based on the rock mass strain energy release rate.

4. The method for identifying adverse geology based on deep shaft TBM excavation according to claim 3 is characterized in that: Calculating the rock mass in-situ stress level of each lithology segment and determining the rock mass strain energy release rate of each lithology segment based on the rock mass in-situ stress level includes: In the above formula, F represents rock stress, t represents deviatoric stress, p represents hydrostatic pressure, p0 represents the critical value of hydrostatic pressure, k represents rock cohesion, α represents rock friction angle, μ represents rock Poisson's ratio, G represents rock strain energy release rate, K1 represents stress intensity factor when shear failure occurs, and K2 represents stress intensity factor when tensile failure occurs.

5. The method for identifying adverse geology based on deep shaft TBM excavation according to claim 1, characterized in that: The impact strength generated by calculating the TBM mechanical data includes: In the above formula, E represents the impact strength, f represents the cutterhead thrust, T represents the cutterhead torque, D represents the main machine diameter, and r represents the TBM speed.

6. A system for identifying adverse geology based on deep shaft TBM excavation, characterized in that: include: A model building module, which is used to divide the deep shaft into lithology sections according to geological characteristics based on geological exploration data, analyze the structural surface geometric characteristics and rock stress level of the rock mass in different lithology sections, and build a deep shaft model based on the analysis results; A destructive analysis module, which is used to obtain TBM mechanical data and calculate the rock destructiveness of the TBM mechanical data to rock masses of different lithology sections; A feature acquisition module, wherein the feature acquisition module is used to merge the rock mass destructiveness with the deep shaft model to generate a deep shaft failure model, analyze the deep shaft failure model, and determine adverse geological features during TBM excavation; An identification module, the identification module is used to obtain real-time TBM excavation data, and based on the real-time TBM excavation data, use the unfavorable geological characteristics in the TBM excavation process to identify the unfavorable geological conditions of the deep shaft model, so as to obtain the unfavorable geological type, unfavorable geological location and unfavorable geological scale; Wherein, the destructive analysis module includes: An impact strength calculation unit, used to calculate the impact strength generated by the TBM mechanical data, and determine the dynamic stress to which the rock mass of each lithology section is subjected under the impact strength and the dynamic strain under the dynamic stress; A dynamic compressive analysis unit, used to construct a dynamic stress-dynamic strain curve, and analyze the dynamic stress-dynamic strain curve to determine the dynamic compressive strength; The expansion trend analysis unit is used to simulate the crack expansion based on the dynamic compressive strength and determine the crack expansion trend of each lithology section under TBM mechanical data.

7. The system for identifying adverse geology based on deep shaft TBM excavation according to claim 6, characterized in that: The model building module includes: A stratigraphic division unit, the stratigraphic division unit is used to calculate the geological exploration data using a Walsh transformation method to obtain a stratigraphic stratification basis, and to perform stratigraphic division on the deep vertical well based on the stratigraphic stratification basis; An extraction unit, the extraction unit is used to extract mineral elements from the deep vertical well after the stratum is divided, to obtain a plurality of mineral elements; A weight calculation unit, wherein the weight calculation unit is used to perform weight calculation on a plurality of mineral elements to obtain weights of the plurality of mineral elements; A lithology segment division unit is used to determine the lithology of each stratum based on the weights of the multiple mineral elements, and to divide the deep vertical well after the stratum division into lithology segments according to the lithology of each stratum.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for identifying adverse geology based on deep shaft TBM excavation as described in any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a method for identifying adverse geology based on deep shaft TBM excavation as described in any one of claims 1 to 5 is implemented.

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