Underground fault activity monitoring method based on distributed optical fibers

Abstract

The invention discloses an underground fault activity monitoring method based on distributed optical fibers, and the method comprises the following steps: carrying out the vertical drilling of a to-be-monitored underground region, arranging an optical fiber information collection module in a drilled hole, and collecting fault activity optical fiber signals through the optical fiber information collection module; the collected fault active optical fiber signals are input to an optical fiber signal noise processing module, interference signals are removed, and real fault active optical fiber signals are obtained; and inputting a real fault activity optical fiber signal into an early warning module based on a fault activity deep learning model, continuously updating and learning, grading the fault activity, and carrying out early warning according to different grades. The fault activity monitoring data obtained by the method is high in precision, can accurately reflect the law of the fault under the condition of weak activity, can be used for long-term effective observation in an underground deep complex environment, improves the underground fault activity monitoring precision, and carries out early warning according to the fault activity level.

Description

Technical Field

[0001] The present invention relates to the technical field of underground fault activity research, and in particular to an underground fault activity monitoring method based on distributed optical fiber. Background Art

[0002] With the rapid development of research on underground fault activity, there are more and more new theories and methods for underground fault activity research, and most of them have been applied to actual projects. However, with the continuous advancement of science and technology and the improvement of research methods, the update speed of existing theories and technical methods related to underground fault activity research is slow, and there are limitations in the application and interpretation of some projects.

[0003] In some actual projects, due to the existence of underground buildings, such as large underground caverns, the study of fault activity in deep underground environments cannot accurately reflect the laws of faults under weak activity due to the lack of accuracy of research instruments. Conventional research instruments and methods are difficult to use for long-term and effective observations in complex deep underground environments, and monitoring data may often be disturbed by other factors such as construction disturbances and underground magnetic fields, which increases the difficulty of fault monitoring. With the development of advanced sensors and the advancement of computer technology, there is an urgent need to update advanced research methods, conduct comprehensive analysis through simultaneous observations with multiple means and multiple technologies, and conduct in-depth research on the process and laws of fault activity, so as to provide data support for further research on fault activity.

[0004] In addition, the results of on-site measurements can best reflect the actual situation. However, due to the differences in the operation and construction methods of the instruments by the on-site data collection personnel, and the different construction difficulties in different geological environments, the acquired data is also closely related to the accuracy of the instruments. There will also be differences in the subsequent analysis of the data. Therefore, the conclusions obtained from the analysis are slightly different from the actual situation, and there are still deficiencies in reflecting the real situation.

[0005] In order to solve the above-mentioned problems related to the study of fault activity, some scholars have also provided some solutions. For example, patent CN202211241819.8 provides an automatic detection method and device for underground faults based on distributed optical fiber acoustic wave sensing, which can be applied to the fields of underground fault detection technology and earthquake analysis technology to determine the location of underground faults. Patent CN202210788830.X provides a deformation measurement and early warning system and method for deep buried tunnels crossing active faults, which mainly solves the problems of poor timeliness of manual measurement of tunnel monitoring and measurement and non-standard safety evaluation systems, and is effectively applied to on-site construction.

[0006] At present, the existing technologies are mainly concentrated on the research of surface fault monitoring methods. For underground faults, the main research is currently focused on non-contact monitoring. There are problems such as insufficient instrument accuracy, errors in monitoring methods, and the influence of other factors such as construction disturbances, which lead to insufficient accuracy of the collected data and cannot accurately reflect the laws of underground faults under weak activity conditions. Therefore, how to effectively solve the problem of underground fault activity monitoring and its accuracy, and provide an effective early warning method, is an issue that needs to be urgently resolved by technical personnel in this field. Summary of the invention

[0007] The purpose of the present invention is to provide a method for monitoring underground fault activity based on distributed optical fiber to address the shortcomings of existing technical means for monitoring underground fault activity. This method can improve the accuracy of underground fault activity monitoring and provide danger warnings based on the fault monitoring situation.

[0008] The present invention is achieved through the following technical solutions:

[0009] The present invention provides a method for monitoring underground fault activity based on distributed optical fiber, comprising the following steps:

[0010] Drilling vertically in the underground area to be monitored, and setting an optical fiber information acquisition module in the borehole to collect optical fiber signals of fault activity through the optical fiber information acquisition module;

[0011] Input the collected fault activity optical fiber signal into the optical fiber signal noise processing module to remove the interference signal and obtain the real fault activity optical fiber signal;

[0012] The real fault activity fiber optic signal is input into the early warning module based on the deep learning model of fault activity, which is continuously updated and learned, and the fault activity is classified into different levels, and early warnings are issued according to different levels.

[0013] As a preferred embodiment of the present invention, the optical fiber information acquisition module uses distributed armored optical fiber to monitor fault activity, and lowers the optical fiber into the borehole through a gravity guide head; the bottom of the gravity guide head is conical, the middle is cylindrical, and the upper part has a connecting end, and the optical fiber is connected to the connecting end on the upper part of the gravity guide head.

[0014] As a preferred solution of the present invention, the optical fiber information acquisition module has multiple optical fiber coupling clamps arranged at intervals on the optical fiber located in the borehole, and the optical fiber coupling clamps include two clamping blocks arranged in pairs, and the optical fiber is clamped between the two clamping blocks.

[0015] As a preferred solution of the present invention, after the optical fiber information acquisition module places the optical fiber in the borehole, the borehole is grout-sealed by a grouting drill rod.

[0016] As a preferred embodiment of the present invention, the optical fiber signal noise processing module uses a filter to perform a dual-channel multiple wavelet decomposition method on the distributed optical fiber monitoring data, wherein the useful signal required for monitoring is decomposed into a low-frequency interval, and the useless noise signal is decomposed into a high-frequency interval. The useful signal is retained and the dual-channel multiple wavelet decomposition method is performed again until a useful signal that meets the noise reduction effect is obtained.

[0017] As a preferred solution of the present invention, the optical fiber signal noise processing module selects an available wavelet basis for processing, and the optional wavelet system is any one of Symlets wavelet, Daubechies wavelet and Biorthogonal wavelet.

[0018] As a preferred solution of the present invention, the optical fiber signal noise processing module adopts a threshold denoising method to retain useful signals, decomposes the optical fiber monitoring signal based on different scales to obtain signals of different frequencies, retains low-frequency signals, selects appropriate thresholds based on the useful signals of fault monitoring, and processes high-frequency signals according to the threshold rules.

[0019] As a preferred solution of the present invention, the determination of the threshold of each layer is obtained based on the strength σ of the useful signal. According to the method of dual-channel multiple wavelet decomposition, after a decomposition layer number a is given, all coefficients of a+1 and higher layers are retained. For the i-th layer, 1≤i≤a, the coefficients with the largest absolute value are retained. i coefficients, N i Determined by the following formula:

[0020] N i =M(a+2-i) α

[0021] In the formula, M and α are both empirical coefficients. By default, M is the length of the coefficient after the first layer of decomposition.

[0022] Construct a sequence in descending order |c i |, then the threshold T m for:

[0023] T m =|c(N i )|.

[0024] As a preferred solution of the present invention, the threshold is calculated, and then the distributed optical fiber monitoring signal is soft-processed according to the following formula:

[0025]

[0026] In the formula, R(W,T m ) represents the soft threshold processing of the signal, and W represents the original signal.

[0027] As a preferred embodiment of the present invention, the warning module based on the deep learning model of fault activity receives the data after noise reduction of the distributed optical fiber monitoring fault, transmits it to the data learning server, and pre-establishes a deep learning model based on the transmitted fault information, and obtains the danger results of fault monitoring identification based on the learned model, classifies the identified danger results into levels, and issues danger warnings based on the classification levels.

[0028] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0029] The present invention vertically drills a hole in the underground area to be monitored, and sets an optical fiber information acquisition module in the hole to acquire optical fiber signals of fault activity through distributed optical fibers; then the acquired optical fiber signals of fault activity are input into an optical fiber signal noise processing module to remove interference signals and obtain true optical fiber signals of fault activity; the signals are then input into an early warning module based on a deep learning model of fault activity, which is continuously updated and learned, and the fault activity is graded, and early warnings are issued according to different grades; the fault activity monitoring data acquired by the method has high accuracy, can accurately reflect the laws of faults under weak activity conditions, can be used for long-term and effective observations in complex deep underground environments, and the accuracy of underground fault activity monitoring is improved; an early warning module based on a deep learning model of fault activity is used to identify dangerous results and grade classifications, and dangerous warnings are issued according to the classification grades. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] 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:

[0031] Figure 1 It is a flow chart of the underground fault activity monitoring method based on distributed optical fiber in the present invention;

[0032] Figure 2 It is a schematic diagram of the gravity guide head in the present invention;

[0033] Figure 3 It is a schematic diagram of the connection between the gravity guide head and the optical fiber in the present invention;

[0034] Figure 4 It is a schematic diagram of the optical fiber coupling card head in the present invention;

[0035] Figure 5 It is a schematic diagram of optical fiber signal decomposition in the present invention.

[0036] Marks and corresponding parts names in the attached drawings:

[0037] 101 - first clamping block, 102 - second clamping block, 103 - nut, 104 - screw, 105 - groove, 201 - bottom, 202 - middle, 203 - connection end, 2031 - arc-shaped cavity, 2032 - flexible material, 204 - distributed armored optical fiber. DETAILED DESCRIPTION

[0038] 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.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0040] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features.

[0041] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0042] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists, A and B exist at the same time, and B exists. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0043] In the embodiments of the present application, the same reference numerals represent the same components, and for the sake of brevity, detailed descriptions of the same components are omitted in different embodiments. It should be understood that the thickness, length, width and other dimensions of various components in the embodiments of the present application shown in the drawings, as well as the overall thickness, length, width and other dimensions of the integrated device are only exemplary descriptions and should not constitute any limitation to the present application.

[0044] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces), unless otherwise clearly and specifically defined.

[0045] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0046] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0047] In the study of fault activity in deep-earth environments, the existing research instruments are not accurate enough, resulting in insufficient accuracy in the collected data to accurately reflect the laws of faults under weak activity conditions. Conventional research instruments and methods are difficult to use for long-term effective observations in complex deep underground environments, and monitoring data may often be interfered with by construction disturbances, underground magnetic fields and other factors, which increases the difficulty of fault monitoring.

[0048] In view of this, the applicant has invented a method for monitoring underground fault activity through in-depth research. This method collects underground fault activity signals through distributed optical fibers and removes noise from the collected optical fiber signals. At the same time, a deep learning model pre-established based on the existing fault monitoring information is used to obtain the dangerous results of fault monitoring identification and issue early warnings.

[0049] Please refer to Figures 1 to 5 , a method for monitoring underground fault activity based on distributed optical fiber provided in an embodiment of the present application comprises the following steps:

[0050] Drilling vertically in the underground area to be monitored, and setting an optical fiber information acquisition module in the borehole to collect optical fiber signals of fault activity through the optical fiber information acquisition module;

[0051] Input the collected fault activity optical fiber signal into the optical fiber signal noise processing module to remove the interference signal and obtain the real fault activity optical fiber signal;

[0052] The real fault activity fiber optic signal is input into the early warning module based on the deep learning model of fault activity, which is continuously updated and learned, and the fault activity is classified into different levels, and early warnings are issued according to different levels.

[0053] In the present application, a vertical hole is drilled in the underground area to be monitored, and an optical fiber information acquisition module is arranged in the borehole. The optical fiber signal of fault activity is collected through distributed optical fiber. Since the distributed optical fiber is used to obtain the fault activity signal, the collected fault activity data has high accuracy and can accurately reflect the weak activity of the fault; the collected fault activity optical fiber signal is input into the optical fiber signal noise processing module to remove the interference signal, and the real fault activity optical fiber signal is obtained, thereby further improving the accuracy of the required fault activity data; the signal after noise reduction processing is then input into the early warning module based on the deep learning model of fault activity, which is continuously updated and learned, and the fault activity is graded, and early warnings are issued according to different grades; the underground fault activity monitoring method improves the accuracy of underground fault activity monitoring, and can be used for long-term and effective observation in complex deep underground environments.

[0054] According to some embodiments of the present application, the optical fiber information acquisition module uses distributed armored optical fiber to monitor fault activity, and lowers the optical fiber into the borehole through a gravity guide head; the bottom of the gravity guide head is conical, the middle is cylindrical, and the upper part has a connecting end, and the optical fiber is connected to the connecting end on the upper part of the gravity guide head.

[0055] The distributed optical fiber monitors fault activity through vertical drilling. The optical fiber can be a strain sensing optical fiber armored with steel strands. A gravity guide is used when lowering the optical fiber. Since the gravity guide has a certain weight, when the optical fiber is driven to be lowered into the vertical borehole, the optical fiber can be in a straight state in the borehole, thereby preventing the optical fiber from bending in the borehole and affecting the accuracy of the fault activity monitoring data.

[0056] Specifically, the bottom 201 of the gravity guide head adopts a metal cone shape, which is used to clear obstacles encountered in the borehole during the falling process; the middle part 202 of the gravity guide head is cylindrical and mainly uses its own gravity to bring the optical fiber into the vertical borehole; the upper part of the gravity guide head has a connection end 203, which is used to connect with the distributed armored optical fiber 204. The top of the connection end 203 has an arc-shaped cavity 2031, and the two ends of the arc-shaped cavity 2031 pass through the two opposite sides of the connection end 203. The mouth of the arc-shaped cavity 2031 is grooved upward, and the shape of the groove is semicircular. After the distributed armored optical fiber 204 passes through the arc-shaped cavity 2031, it fits the groove. A flexible material 2032 is used in the middle of the arc-shaped cavity 2031 for buffering to protect the optical fiber.

[0057] According to some embodiments of the present application, the optical fiber information acquisition module has a plurality of optical fiber coupling clamps arranged at intervals on the optical fiber located in the borehole, and the optical fiber coupling clamps include two clamping blocks arranged in pairs, and the optical fiber is clamped between the two clamping blocks.

[0058] Since the distribution of underground faults can be roughly understood through vertical drilling, the fiber coupling clamps can be arranged at intervals in the area where the underground faults are distributed. By setting the fiber coupling clamps, the good coupling between the optical fiber and the surrounding rock and soil can be maintained, and the fiber entanglement and knotting during the placement process can be avoided. At the same time, when the optical fiber segment arranged between two adjacent optical fiber coupling clamps is squeezed by the rock and soil, it will not affect the monitoring accuracy of other optical fiber segments. It is particularly suitable for monitoring the activity of multiple underground faults in different formation depths.

[0059] Specifically, the optical fiber coupling chuck includes a first clamping block 101 and a second clamping block 102 arranged in pairs, the optical fiber is located between the first clamping block 101 and the second clamping block 102, and the first clamping block 101 and the second clamping block 102 are brought close to each other by a fastening assembly, so that the optical fiber coupling chuck is fixedly installed on the distributed armored optical fiber. The fastening assembly includes a screw 104 and a nut 103, wherein one end of the screw 104 is fixedly connected to the first clamping block 101 or the second clamping block 102, and the nut 103 is sleeved on the screw 104, and the first clamping block 101 and the second clamping block 102 can be pushed close to each other by rotating the nut 103.

[0060] Furthermore, there is one screw 104 on each side, and the first clamping block 101 is a fixed rectangular iron block. The first clamping block 101 is welded and fixed to one end of the screw 104. A groove 105 is opened in the middle of the first clamping block 101 in the shape of a semicircular arc for fitting the optical fiber. The second clamping block 102 is a movable rectangular iron block that can slide along the screw 104. At the same time, a groove is also opened in the middle of the second clamping block 102 in the shape of a semicircular arc for fitting the optical fiber. The nut 103 is used to fix the second clamping block 102, and the optical fiber passes through the groove. The nut 103 is continuously tightened, and the second clamping block 102 is pushed to continuously move toward the first clamping block 101 until the grooves on both sides fit the optical fiber and maintain a certain tightness.

[0061] According to some embodiments of the present application, after the optical fiber information acquisition module places the optical fiber in the borehole, the borehole is grout-sealed through the grouting drill rod. By grouting and sealing the hole after the optical fiber is placed, the grouting solidified body can be tightly combined with the rock and soil, thereby achieving good coupling between the optical fiber and the rock and soil. Specifically, during grouting, the grouting drill rod is lowered to the bottom of the borehole, and the cement slurry should be grouted from the bottom of the hole. While grouting, the grouting drill rod is lifted, and the lifting of the grouting drill rod is kept at a uniform and slow speed to achieve full hole closure.

[0062] According to some embodiments of the present application, the optical fiber signal noise processing module uses a filter to perform a dual-channel multiple wavelet decomposition method on the distributed optical fiber monitoring data, wherein the useful signal required for monitoring is decomposed into a low-frequency range, and the useless noise signal is decomposed into a high-frequency range. The useful signal is retained and the dual-channel multiple wavelet decomposition method is performed again until a useful signal with a better noise reduction effect is obtained.

[0063] According to some embodiments of the present application, the optical fiber signal noise processing module selects an available wavelet basis for processing, and the optional wavelet system is any one of Symlets wavelet, Daubechies wavelet and Biorthogonal wavelet. Among them, Symlets wavelet is a symmetric wavelet basis, referred to as sym wavelet; Daubechies wavelet is a Daubechies wavelet, referred to as db wavelet; Biorthogonal wavelet is a biorthogonal wavelet, referred to as bior wavelet.

[0064] According to some embodiments of the present application, the fiber signal noise processing module uses a threshold denoising method to retain useful signals, decomposes the fiber monitoring signal based on different scales, obtains signals of different frequencies, retains low-frequency signals, selects appropriate thresholds based on the useful signals of fault monitoring, and processes high-frequency signals based on threshold rules. The fiber signal noise processing module first performs wavelet decomposition on the fiber monitoring data, and then performs threshold denoising, retains the effective signal of the fiber monitoring fault, removes the noise signal, and further improves the accuracy of the fault signal.

[0065] According to some embodiments of the present application, the determination of the threshold of each layer is mainly based on the strength σ of the useful signal. According to the method of dual-channel multiple wavelet decomposition, after a decomposition layer number a is given, all coefficients of a+1 and higher layers are retained. For the i-th layer, 1≤i≤a, the coefficients with the largest absolute value are retained. i coefficients, N i Determined by the following formula:

[0066] N i =M(a+2-i) α

[0067] In the formula, M and α are both empirical coefficients. By default, M is the length of the coefficient after the first layer of decomposition.

[0068] Construct a sequence in descending order |c i |, then the threshold T m for:

[0069] T m =|c(N i )|.

[0070] According to some embodiments of the present application, the threshold is calculated, and then the distributed optical fiber monitoring signal is soft-processed according to the following formula:

[0071]

[0072] In the formula, R(W,T m ) represents the soft threshold processing of the signal, and W represents the original signal.

[0073] According to some embodiments of the present application, the early warning module based on the deep learning model of fault activity receives the data after noise reduction of the distributed optical fiber monitoring fault, transmits it to the data learning server, and pre-establishes a deep learning model based on the transmitted fault information, and obtains the danger results of fault monitoring identification based on the learned model, classifies the identified danger results into levels, and issues danger warnings based on the classification levels.

[0074] 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 monitoring underground fault activity based on distributed optical fiber, characterized in that: The following steps are involved: Drilling vertically in the underground area to be monitored, and setting an optical fiber information acquisition module in the borehole to collect optical fiber signals of fault activity through the optical fiber information acquisition module; Input the collected fault activity optical fiber signal into the optical fiber signal noise processing module to remove the interference signal and obtain the real fault activity optical fiber signal; The real fault activity fiber optic signal is input into the early warning module based on the deep learning model of fault activity, which is continuously updated and learned, and the fault activity is classified into different levels, and early warnings are issued according to different levels.

2. The underground fault activity monitoring method based on distributed optical fiber according to claim 1 is characterized in that: The optical fiber information acquisition module uses distributed armored optical fiber to monitor fault activity and lowers the optical fiber into the borehole through a gravity guide head; the bottom of the gravity guide head is conical, the middle is cylindrical, and the upper part has a connecting end, and the optical fiber is connected to the connecting end at the upper part of the gravity guide head.

3. The underground fault activity monitoring method based on distributed optical fiber according to claim 2 is characterized in that: The optical fiber information acquisition module has a plurality of optical fiber coupling clamps arranged at intervals on the optical fiber located in the borehole. The optical fiber coupling clamps include two clamping blocks arranged in pairs, and the optical fiber is clamped between the two clamping blocks.

4. The method for monitoring underground fault activity based on distributed optical fiber according to claim 1, characterized in that: After the optical fiber information acquisition module places the optical fiber in the borehole, the borehole is grout-sealed through the grouting drill rod.

5. The method for monitoring underground fault activity based on distributed optical fiber according to claim 1, characterized in that: The optical fiber signal noise processing module uses a filter to perform a dual-channel multiple wavelet decomposition method on the distributed optical fiber monitoring data, wherein the useful signal required for monitoring is decomposed into a low-frequency interval, and the useless noise signal is decomposed into a high-frequency interval. The useful signal is retained and the dual-channel multiple wavelet decomposition method is performed again until a useful signal that meets the noise reduction effect is obtained.

6. The underground fault activity monitoring method based on distributed optical fiber according to claim 5 is characterized in that: The optical fiber signal noise processing module selects an available wavelet basis for processing, and the optional wavelet system is any one of Symlets wavelet, Daubechies wavelet and Biorthogonal wavelet.

7. The method for monitoring underground fault activity based on distributed optical fiber according to claim 5, characterized in that: The optical fiber signal noise processing module adopts a threshold denoising method to retain useful signals, decomposes the optical fiber monitoring signal based on different scales, obtains signals of different frequencies, retains low-frequency signals, selects appropriate thresholds based on the useful signals of fault monitoring, and processes high-frequency signals according to the threshold rules.

8. The method for monitoring underground fault activity based on distributed optical fiber according to claim 7, characterized in that: The determination of the threshold of each layer is based on the strength σ of the useful signal. According to the dual-channel multiple wavelet decomposition method, after a decomposition layer number a is given, all coefficients of a+1 and higher layers are retained. For the i-th layer, 1≤i≤a, the N with the largest absolute value are retained. i coefficients, N i Determined by the following formula: N i =M(a+2-i) α In the formula, M and α are both empirical coefficients. By default, M is the length of the coefficient after the first layer of decomposition. Construct a sequence in descending order |c i |, then the threshold T m for: T m =|c(N i )|。 9. The method for monitoring underground fault activity based on distributed optical fiber according to claim 8, characterized in that: The resulting threshold is calculated and then the distributed optical fiber monitoring signal is soft-processed according to the following formula: In the formula, R(W,T m ) represents the soft threshold processing of the signal, and W represents the original signal.

10. The underground fault activity monitoring method based on distributed optical fiber according to claim 1, characterized in that: The warning module based on the deep learning model of fault activity receives the data after noise reduction of the distributed optical fiber monitoring fault, transmits it to the data learning server, and pre-establishes a deep learning model based on the transmitted fault information, and obtains the danger results of fault monitoring identification based on the learned model, classifies the identified danger results into levels, and issues danger warnings based on the classification levels.

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