Underground cavern partition stability early warning method based on micro-seismic monitoring

Abstract

The invention provides an underground cavern partition stability early warning method based on micro-seismic monitoring. The method comprises the following steps: performing model segmentation on three-dimensional model data of an underground cavern by using a geological condition segmentation rule; performing gradient analysis of longitudinal rock stratum strength and axial fracture density on the N sub-models, and establishing rock stratum transition characteristic data; after the space coordinates of the preset protection object are read, the surrounding rock types of the N sub-models are dynamically recognized based on the rock stratum transition feature data, matching mapping is conducted on the recognition result and the space coordinates, and a surrounding rock risk zone of the preset protection object is configured; data acquisition is executed in real time by using a micro-seismic monitoring network; and inputting the geological parameter set into the corresponding N sub-models, and executing early warning identification according to the surrounding rock risk division. According to the invention, the technical problem that the stability risk of the underground cavern is difficult to accurately identify in the prior art can be solved, and the technical effect of improving the stability risk identification efficiency is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of partition early warning, in particular to an underground cavern partition stability early warning method based on microseismic monitoring. BACKGROUND

[0002] With the continuous expansion of underground engineering construction scale and the increasing complexity of underground cavern structure, the safety monitoring and risk prevention and control demand of underground space are becoming more and more urgent. In large mines, tunnels and underground storage facilities, the state of surrounding rock is affected by many factors such as geological conditions, rock structure and construction disturbance, and its stability is directly related to the safety of engineering and personnel life and property. Therefore, how to carry out real-time and fine stability analysis on underground caverns and realize timely early warning of potential risks has become a key technical problem to ensure the safety of underground engineering and personnel life and property.

[0003] At present, the existing underground cavern stability monitoring method mainly relies on manual inspection, single-point sensor data acquisition or static risk assessment based on experience. These methods usually have the defects of limited monitoring coverage, data update lag, difficulty in capturing small changes in rock layers and poor adaptability to complex geological conditions. For example, single-point measurement cannot reflect the regional crack propagation trend and is easily affected by subjective factors, resulting in insufficient accuracy and timeliness of early warning information.

[0004] In summary, the existing technology has the technical problem that due to the single monitoring means, insufficient data fusion capability and lack of dynamic analysis method, the stability risk of underground caverns is difficult to identify and manage in a timely and accurate manner, which further affects the construction safety, personnel protection and long-term operation reliability of underground engineering. SUMMARY

[0005] The purpose of the present application is to provide an underground cavern partition stability early warning method based on microseismic monitoring, which solves the technical problem in the prior art that due to the single monitoring means, insufficient data fusion capability and lack of dynamic analysis method, the stability risk of underground caverns is difficult to identify and manage in a timely and accurate manner, which further affects the construction safety, personnel protection and long-term operation reliability of underground engineering.

[0006] According to one purpose of the present application, the present application provides an underground cavern partition stability early warning method based on microseismic monitoring, comprising the following steps: Obtain three-dimensional model data of the underground cavern, perform model segmentation on the three-dimensional model data by using a geological condition segmentation rule, and construct N sub-models, wherein the geological condition segmentation rule is a segmentation rule that dynamically adjusts the cutting interval according to the gradient change characteristics of the rock layer; Gradient analysis is performed on the N sub-models in the longitudinal rock strength and axial crack density, and rock transition characteristic data containing the change rate of adjacent rock properties and the spatial distribution of the transition area are established; After reading the spatial coordinates of the preset protection object, the surrounding rock types of the N sub-models are dynamically identified based on the rock transition characteristic data, and the identification results are matched and mapped with the spatial coordinates to configure the surrounding rock risk zoning of the preset protection object; The microseismic monitoring network laid in the underground cavern is used to perform real-time data acquisition, and a geological parameter set is established, including microseismic energy release rate, hypocenter location distribution, and stress wave attenuation characteristics; The geological parameter set is input into the corresponding N sub-models, and early warning identification is performed according to the surrounding rock risk zoning to establish an early warning signal.

[0007] Further, model segmentation is performed on the three-dimensional model data using geological condition segmentation rules to construct N sub-models, including the following steps: The three-dimensional model data is gridded to establish a regular three-dimensional grid; After defining the longitudinal gradient and axial gradient, a gradient field is constructed based on the rock gradient change characteristics in the regular three-dimensional grid; The cutting interval constraint is configured according to the local gradient amplitude of the gradient field, and adaptive cutting of the three-dimensional model data is performed using the cutting interval constraint to establish N sub-models, where N is an integer greater than 1.

[0008] Further, gradient analysis is performed on the N sub-models in the longitudinal rock strength and axial crack density, including the following steps: Gradient analysis is performed on the N sub-models in the longitudinal rock strength and axial crack density within the model to establish a model combined gradient field; Based on the model combined gradient field, the change rate of adjacent rock properties is calculated along the rock normal and axial direction in each sub-model, and the transition area is identified based on the change rate of adjacent rock properties and the gradient amplitude to establish rock transition characteristic data.

[0009] Further, the central difference method or weighted average method is used to calculate the longitudinal gradient and axial gradient to suppress the influence of local measurement noise on the gradient field.

[0010] Further, the early warning signal is established, including the following steps: A set of emergency disposal schemes is established, and a matching feature set mapped with the set of emergency disposal schemes is configured; Adaptation analysis is performed on the matching feature set according to the early warning signal to establish an adaptation analysis result; The scheme mapping call is performed from the set of emergency disposal schemes according to the adaptation analysis result to perform early warning processing.

[0011] Further, the step of establishing the adaptive analysis result further comprises the following steps: determining whether the adaptive value of the adaptive analysis result is lower than a preset adaptive threshold value; if the adaptive value of the adaptive analysis result is lower than the preset adaptive threshold value, generating an enhanced early warning factor; after the early warning signal is enhanced according to the enhanced early warning factor, executing early warning reporting.

[0012] Further, the set of emergency treatment schemes comprises a ventilation adjustment scheme, a support reinforcement scheme, a personnel evacuation scheme, and a local surrounding rock reinforcement scheme.

[0013] Further, the microseismic monitoring network comprises a plurality of sensor nodes arranged at different positions of the underground cavern, and the sensor nodes perform real-time data uploading and synchronization through wired or wireless communication.

[0014] Further, the step of establishing the early warning signal further comprises the following steps: after data mapping and packaging are performed on the early warning signal, the N sub-models, the rock stratum transition feature data, and the set of geological parameters, a storage instruction is generated; the packaged result is stored and managed according to the storage instruction, and a unique traceability code is established, and traceability management of the early warning signal is performed according to the unique traceability code.

[0015] Further, the step of storing and managing the packaged result according to the storage instruction comprises the following steps: in a preset period, the stored data is read to construct an abnormal log; the abnormal log is synchronized to the cloud, and a partition stability report of the underground cavern is generated on the cloud.

[0016] The technical scheme of the present application achieves the technical target of underground cavern partition stability early warning and dynamic risk management based on multi-source microseismic monitoring, can capture small changes in rock strata in real time, accurately divide risk areas, dynamically generate early warning signals, and automatically trigger emergency treatment schemes, thereby significantly improving the safety and management efficiency of underground engineering. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0018] Figure 1A flowchart of the early warning method of the embodiment of the present application is shown in the figure. Figure 2 A flowchart of the early warning method of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0019] The technical solutions of the present application will be described below in conjunction with the embodiments, obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0020] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0021] In addition, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited. In addition, the terms "mounting", "connecting", "connecting" should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0022] Embodiment 1 The underground cavern partition stability early warning method based on microseismic monitoring provided by the present application solves the technical problems in the prior art that due to the single monitoring means, insufficient data fusion capability and lack of dynamic analysis method, the stability risk of the underground cavern is difficult to identify and manage in time and accurately, which further affects the construction safety, personnel protection and long-term operation reliability of the underground engineering. The technical target of underground cavern partition stability early warning and dynamic risk management based on multi-source microseismic monitoring is achieved, and the technical effect of being able to capture the slight changes of the rock stratum in real time, accurately divide the risk area, dynamically generate the early warning signal and automatically trigger the emergency disposal scheme is achieved, so that the safety and management efficiency of the underground engineering are significantly improved.

[0023] As shown in Figure 1 and Figure 2 The underground cavern partition stability early warning method based on microseismic monitoring provided by the present application specifically comprises the following steps: S1: Obtain three-dimensional model data of an underground cavern, perform model segmentation on the three-dimensional model data by using a geological condition segmentation rule, and construct N sub-models, wherein the geological condition segmentation rule is a segmentation rule for dynamically adjusting a cutting interval according to a rock stratum gradient change characteristic.

[0024] Specifically, the present application further comprises: performing grid processing on the three-dimensional model data to establish regular three-dimensional grid points; after defining a longitudinal gradient and an axial gradient, constructing a gradient field according to a rock stratum gradient change characteristic at the regular three-dimensional grid points; configuring a cutting interval constraint according to a local gradient amplitude of the gradient field, performing adaptive cutting of the three-dimensional model data by using the cutting interval constraint, and establishing N sub-models, wherein N is an integer greater than 1.

[0025] Specifically, obtaining three-dimensional model data of an underground cavern means generating a three-dimensional spatial structure model of the underground cavern by using geological surveying, laser scanning or underground detection technology. The three-dimensional model data contains information such as the geometric shape of the cavern, the rock stratum distribution and the fracture position.

[0026] Subsequently, the three-dimensional model data is subjected to grid processing, and the three-dimensional space is divided into a regular cubic grid network, so that each grid point has a clear spatial coordinate and rock stratum attribute, facilitating subsequent analysis and calculation. By establishing regular three-dimensional grid points, the complex underground structure can be discretized to form a data grid convenient for computer processing.

[0027] Then, the longitudinal gradient and the axial gradient are defined to quantify the rate of change of the stratum attribute in different directions. The longitudinal gradient is calculated along the stacking direction of the stratum, reflecting the change of the stratum strength or density with depth; the axial gradient is calculated along the axis direction of the cavern, reflecting the change of the stratum characteristics along the length direction. A gradient field is constructed on the regular three-dimensional grid points according to the gradient change characteristics of the stratum, mapping the rate of change of the stratum attribute of each grid point to a spatial distribution field, facilitating the identification of the stratum area with rapid or transitional changes, thereby forming a complete stratum gradient field.

[0028] Subsequently, the cutting interval constraint is configured according to the local gradient amplitude of the gradient field, which means that the gradient size is used to adjust the density of model division. A large local gradient amplitude indicates that the stratum attribute changes rapidly, and a more intensive cutting is needed to accurately depict the stratum characteristics; a small local gradient amplitude indicates that the stratum changes gently, and the cutting interval can be appropriately enlarged to reduce the calculation amount. The three-dimensional model data is adaptively cut using the cutting interval constraint, i.e., dynamically generating sub-models according to the speed of stratum change, so that the stratum is relatively uniform inside each sub-model and easy to analyze, thereby establishing N sub-models, where N is an integer greater than 1, ensuring that at least multiple analysis units are divided.

[0029] S2: Gradient analysis of the longitudinal stratum strength and the axial fracture density of the N sub-models is performed to establish stratum transition characteristic data containing the spatial distribution of the adjacent stratum attribute change rate and the transition area.

[0030] Specifically, the application further comprises: performing gradient analysis of the longitudinal stratum strength and the axial fracture density within the N sub-models to establish a model combination gradient field; calculating the adjacent stratum attribute change rate along the stratum normal and the axial direction in each sub-model based on the model combination gradient field, and identifying the transition area based on the adjacent stratum attribute change rate and the gradient amplitude to establish stratum transition characteristic data.

[0031] Specifically, the application further comprises: the calculation of the longitudinal gradient and the axial gradient adopts the central difference method or the weighted average method to suppress the influence of local measurement noise on the gradient field.

[0032] Specifically, the gradient analysis of the longitudinal stratum strength and the axial fracture density within the N sub-models is performed, i.e., the rate of change of the stratum strength with depth direction and the rate of change of the fracture number with the length direction of the cavern are calculated respectively. The longitudinal stratum strength refers to the compressive or bearing capacity of the rock in the vertical direction, and the axial fracture density refers to the number or distribution degree of fractures per unit length. Through analysis, the trend of change in space can be obtained, and the results of all sub-models are integrated to form a model combination gradient field, which can reflect the spatial variation law of the stratum properties within the range of the underground cavern as a whole.

[0033] Then, the gradient field is combined based on the model to calculate the property change rate of adjacent rock strata in the normal direction and axial direction of the rock strata, that is, to further calculate the property difference between adjacent rock strata, such as strength difference, density difference, or fracture density difference. The normal direction of the rock strata is perpendicular to the rock strata plane, the axial direction of the rock strata is along the main direction of the cavern, and the property change rate of adjacent rock strata represents the parameter change proportion of two adjacent rock strata. Then, in combination with the change rate and the gradient amplitude of the corresponding position, the area where the rock strata property rapidly transitions can be identified, which is referred to as a transition area. Finally, the data of the transition area is sorted into rock strata transition feature data, thereby determining the place where there is a significant difference in geological properties in the cavern.

[0034] The longitudinal gradient and the axial direction are calculated using the central difference method, which takes the parameter values of a position before and after a certain point to approximate the change rate of the point by dividing the difference between the two values by the interval. The longitudinal gradient refers to the change of rock strata strength or density in the depth direction, and the axial direction refers to the change of fracture density or other geological parameters in the direction of the cavern. Compared with the single difference, the central difference method can reduce the calculation bias, and thus is suitable for accurate calculation of the rock strata properties inside the model.

[0035] Meanwhile, the calculation of the longitudinal gradient and the axial direction can also use the weighted average method, which averages the parameter values of multiple adjacent points according to certain weights to estimate the change of a certain point. The weighted average method can smooth out local mutation values, making the calculated gradient more stable. The weight is related to the spatial position or data reliability of the point, for example, the closer the data to the target point, the greater the weight, and the farther the distance, the smaller the weight.

[0036] S3: After reading the spatial coordinates of the preset protection object, the rock strata transition feature data is used to dynamically identify the surrounding rock types of the N sub-models, and the identification results are matched and mapped with the spatial coordinates to configure the surrounding rock risk zoning of the preset protection object.

[0037] Specifically, after reading the spatial coordinates of the preset protection object, the object position that needs to be focused on in the cavern model is determined, such as important ventilation wells, mechanical and electrical equipment, or personnel concentration areas. The spatial coordinates are the specific position parameters of the preset protection object in the three-dimensional model, including the numerical values of X, Y, and Z directions, which can clearly indicate the actual position of the object inside the cavern.

[0038] Then, based on the rock strata transition feature data, the surrounding rock types of the N sub-models are dynamically identified to determine the rock properties around the protection object in different sub-models. The surrounding rock types include hard rock strata, soft rock strata, broken rock strata, etc., and dynamic identification means that the surrounding rock types will be adjusted as the monitoring data is continuously updated. The rock strata transition feature data plays a supporting role, reflecting the changes of rock strata strength, fracture density, and other parameters in space, thereby helping to accurately determine the surrounding rock characteristics of each sub-model.

[0039] Subsequently, the recognition result is matched and mapped with the spatial coordinates, and the determination result of the surrounding rock type is corresponded to the specific position of the protected object. The matching and mapping is a data association process. For example, in a three-dimensional model, if the sub-model of the region where the coordinate of a protected object is located is identified as a broken rock layer, the protected object will be marked as being in a high-risk surrounding rock environment. The abstract geological analysis result can be directly corresponded to the actual spatial position, and the risk identification is more intuitive.

[0040] Finally, the surrounding rock risk zoning of the preset protected object is configured, that is, the risk level and region of the protected object are divided according to the matching result. The risk zoning generally includes low risk, medium risk and high risk, different risk levels will correspond to different protection measures or warning thresholds, and the risk distribution range around each protected object can be clearly marked, so that targeted measures can be taken.

[0041] S4: Real-time data acquisition is performed by using a microseismic monitoring network arranged in the underground cavern, and a geological parameter set is established, the geological parameter set including a microseismic energy release rate, a hypocenter positioning distribution and a stress wave attenuation characteristic.

[0042] Specifically, the present application further comprises: the microseismic monitoring network comprises a plurality of sensor nodes arranged at different positions of the underground cavern, and the sensor nodes perform real-time data uploading and synchronization through wired or wireless communication.

[0043] Specifically, real-time data acquisition is performed by using a microseismic monitoring network arranged in the underground cavern, that is, a plurality of microseismic sensor nodes are arranged in the underground cavern, and the micro vibrations inside the rock mass can be captured. The microseismic monitoring network is a detection system composed of a plurality of sensors, which are connected through wired or wireless mode, and can generate signals and immediately transmit when the geological stress changes or the rock is broken. Real-time data acquisition means that after being detected, it will be uploaded immediately, and there is no obvious delay, thereby ensuring the continuity and timeliness of monitoring.

[0044] Then, establishing a set of geological parameters means analyzing and organizing all the collected monitoring data to form an important parameter combination that can represent the stability of the underground cavern. Among them, the set of geological parameters includes microseismic energy release rate, hypocenter location distribution, and stress wave attenuation characteristics. The microseismic energy release rate represents the energy released by the rock mass per unit time due to rupture or stress adjustment. The higher the energy release rate, the more frequent the rock mass activity and the poorer the stability. The hypocenter location distribution refers to the distribution of microseismic events in space, including the depth, location, and number of hypocenters. If the hypocenters are concentrated in a certain area, it indicates that there may be potential problems in that area. The stress wave attenuation characteristics refer to the law of intensity decay of seismic waves during propagation, which is related to the structure of the rock layer and the properties of the medium. If the attenuation is fast, it indicates that the rock mass structure is loose or broken.

[0045] Specifically, the microseismic monitoring network includes multiple sensor nodes deployed at different positions in the underground cavern, which can capture the microseismic signals occurring in the underground rock mass, helping to analyze the stability of the rock layer and the stress change. For example, in a mine or underground cavern, when the rock mass releases energy, the microseismic monitoring network can capture the signal and process it in a very short time. The sensor nodes are independent monitoring units deployed at different positions in the underground cavern, each node has the functions of vibration acquisition, signal processing, and communication. The distribution of sensor nodes is determined according to the characteristics of the underground structure and the monitoring target, such as installing nodes at both ends of the cavern, key corners, or areas prone to cracks to ensure comprehensive coverage.

[0046] The sensor nodes are interconnected through wired or wireless communication. Wired communication usually uses optical fiber or cable, which has high stability and low delay; while wireless communication relies on radio waves or relay stations, which is suitable for difficult wiring or temporary monitoring scenarios. For example, in a 1000-meter-long tunnel, the front half of the nodes can be connected by wire to ensure high-speed transmission, and the back half can use wireless communication to improve flexibility.

[0047] Specifically, real-time data upload means that the sensor nodes transmit the collected microseismic signals to the ground control center at the moment of occurrence to ensure that the monitoring results can be immediately analyzed and processed; while data synchronization means that multiple nodes maintain consistency in time and content to achieve more accurate hypocenter location and energy evaluation through comparison and joint calculation. For example, if three sensor nodes collect the same microseismic signal at the same 0.01 seconds and upload it in real time, the hypocenter location can be quickly calculated through the difference in wave speed.

[0048] S5: input the set of geological parameters into the corresponding N sub-models, perform early warning identification according to the surrounding rock risk zoning, and establish early warning signals.

[0049] Specifically, the application further comprises: establishing a set of emergency disposal schemes and configuring a matching feature set mapped with the set of emergency disposal schemes; performing adaptive analysis of the matching feature set according to the early warning signal, and establishing an adaptive analysis result; performing scheme mapping calling from the set of emergency disposal schemes according to the adaptive analysis result, and executing early warning processing.

[0050] Specifically, the application further comprises: the set of emergency disposal schemes comprises a ventilation adjustment scheme, a support reinforcement scheme, a personnel evacuation scheme and a local surrounding rock reinforcement scheme.

[0051] Specifically, the application further comprises: judging whether the adaptive value of the adaptive analysis result is lower than a preset configuration threshold value; if the adaptive value of the adaptive analysis result is lower than the preset configuration threshold value, generating an enhanced early warning factor; and executing early warning after enhancing the early warning signal according to the enhanced early warning factor.

[0052] Specifically, the application further comprises: after data mapping and packaging of the early warning signal, the N sub-models, the rock stratum transition feature data and the set of geological parameters, generating a storage instruction; storing and managing the packaging result according to the storage instruction, and establishing a unique traceability code, and performing traceability management of the early warning signal according to the unique traceability code.

[0053] Specifically, the application further comprises: reading the storage data in a preset period to construct an abnormal log; synchronizing the abnormal log to the cloud, and generating a partition stability report of the underground cavern in the cloud.

[0054] Specifically, establishing a set of emergency disposal schemes and configuring a matching feature set mapped with the set of emergency disposal schemes means that an emergency plan that can be used when a risk occurs in an underground cavern is prepared in advance, such as ventilation adjustment, personnel evacuation or local reinforcement. The matching feature set is the trigger condition or applicable scene corresponding to the emergency disposal scheme, such as triggering the evacuation scheme when the energy release rate of a certain area exceeds 15 joules per minute, or triggering the reinforcement scheme when the seismic source is concentrated near a certain equipment. By establishing a mapping relationship, automatic correspondence between risk features and emergency measures can be realized.

[0055] Specifically, the set of emergency disposal schemes refers to a series of measures prepared in advance to deal with possible emergencies, including different types of solutions for rapid action in disaster or abnormal state. The set of emergency disposal schemes comprises a ventilation adjustment scheme, a support reinforcement scheme, a personnel evacuation scheme and a local surrounding rock reinforcement scheme. The ventilation adjustment scheme refers to a measure of changing the air volume and direction of the mine ventilation system to dilute harmful gases, reduce temperature or improve air quality. For example, when the gas concentration rises to 2%, the ventilation unit is automatically started or the air door is adjusted according to the scheme set to ensure air circulation and safety.

[0056] Next, the support reinforcement scheme refers to measures to improve the stability of surrounding rock by increasing or strengthening the support structure in the underground roadway. The support method may include anchor rods, steel frames, or sprayed concrete. When the deformation rate of the roadway is monitored to be above 10 mm per hour, the support reinforcement scheme can be immediately implemented to avoid further collapse.

[0057] Then, the personnel evacuation scheme refers to measures to quickly and orderly evacuate underground workers to a safe location when danger occurs or risk increases. Its content usually includes the planning of evacuation routes, the scheduling of personnel in batches, and the designation of safety zones. For example, when the gas concentration exceeds 3% or the stress wave signal is abnormally concentrated, the system will preferentially implement the evacuation scheme.

[0058] Finally, the local surrounding rock reinforcement scheme refers to measures to reinforce specific areas of rock mass to prevent local collapse or crack propagation. For example, if the rock mass crack propagation rate in a certain area increases from 1 mm per hour to 5 mm per hour, concrete spraying or anchor reinforcement is needed to ensure stability in the local area.

[0059] The adaptation value of the adaptation analysis result is compared with the preset configuration threshold value. The adaptation value is a numerical indicator of the matching degree between the emergency treatment scheme and the early warning signal, while the preset configuration threshold value is a critical standard set by the system in advance to determine whether the current matching result is reliable enough. For example, in the disaster monitoring of an underground mine, the adaptation value can be quantified as a percentage. If the result is 60% and the threshold value is set to 70%, it means that the current matching effect is insufficient.

[0060] Next, if the adaptation value of the adaptation analysis result is lower than the preset configuration threshold value, an enhanced early warning factor will be generated. The enhanced early warning factor is a correction parameter used to improve the sensitivity and weight of the original early warning signal to avoid false negatives or false positives caused by insufficient matching. For example, if the adaptation value is low when the mine gas is abnormal, the enhanced early warning factor will increase the sensitivity to gas concentration changes, thereby improving the accuracy of risk identification.

[0061] Then, the early warning signal is enhanced according to the enhanced early warning factor, and the early warning is executed. Enhancement means amplification or optimization based on the original signal, so that the final warning information is more likely to attract the attention of the operator and can trigger more stringent protective measures. For example, the original gas concentration signal shows 1.8%, but after enhancement, the system will alarm when the standard exceeds 2.0%, so that personnel can take measures in advance.

[0062] According to the results of the adaptive analysis, the scheme mapping call is performed from the emergency treatment scheme set, and the warning processing is performed, which means that the optimal or most suitable emergency scheme is selected according to the adaptive results and automatically called, and then the related measures are executed in the cavern. The scheme mapping call means that the abstract analysis results are converted into specific operations, such as triggering an alarm, starting a personnel evacuation process, or issuing reinforcement construction instructions. The warning processing is the implementation link of the entire emergency response, which ensures that the risk can be controlled in time.

[0063] On the other hand, data mapping and packaging of the warning signal, N sub-models, rock stratum transition characteristic data, and geological parameter set means that data of different sources are organized into a whole through association rules, and a corresponding relationship is established. The warning signal represents the alarm information generated by the monitoring system when detecting potential risks, the N sub-models are the structured models obtained after the underground cavern area is segmented, the rock stratum transition characteristic data describes the parameter characteristics of the rock stratum when it transitions from one stable state to another, and the geological parameter set includes key indicators such as microseismic energy release rate, hypocenter location distribution, and stress wave attenuation characteristics. After data packaging is completed, a storage instruction is generated to guide the storage operation. The storage instruction specifies the location, format, and method of data storage.

[0064] Reading stored data within a preset period means automatically extracting saved data at a fixed time interval set in advance. The preset period is usually determined according to actual monitoring needs, such as 1 hour, 1 day, or 1 week. The read data includes warning signals, sub-models, rock stratum characteristics, and geological parameters. Building an abnormal log means filtering and organizing the read data, and recording the parts that reflect unstable signs or deviate from the normal range, which can clearly show the time, location, and cause of the anomaly.

[0065] Synchronizing the abnormal log to the cloud means transmitting the abnormal records generated locally to the remote cloud platform through the network, ensuring that the data can be centrally stored and shared. The advantage of the cloud is strong computing power, large storage space, and support for multiple users to access simultaneously. Synchronization ensures that abnormal information generated at different monitoring points and different times can be uniformly summarized in a central system.

[0066] Generating a partition stability report for the underground cavern in the cloud means using the computing resources and analysis models of the cloud platform to classify and comprehensively evaluate the abnormal log, and giving stability conclusions for different regions. The partition stability report usually divides the entire underground cavern into several regions, and each region is marked as safe, controllable risk, or high-risk state. For example, in a case where a cavern is divided into 5 regions, there may be 3 stable regions, 1 region with slight abnormalities, and 1 region showing high risk.

[0067] Establishing a unique traceability code means generating an unrepeatable identification code for each piece of packaged stored data, which is used for quick positioning and retrieval in the future. Traceability management is to use this unique traceability code to link early warning signals with their corresponding sub-models, feature data and geological parameters, ensuring that the source and formation process of the data can be traced back at any time.

[0068] In summary, the underground cavern partition stability early warning method based on microseismic monitoring provided by the present application has the following technical effects: by achieving the technical target of underground cavern partition stability early warning and dynamic risk management based on multi-source microseismic monitoring, it can capture small changes in rock layers in real time, accurately divide risk areas, dynamically generate early warning signals and automatically trigger emergency disposal schemes, thereby significantly improving the safety and management efficiency of underground engineering.

[0069] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for early warning of zonal stability of underground caverns based on microseismic monitoring, characterized in that, Includes the following steps: S1. Obtain the three-dimensional model data of the underground cavern, and use the geological condition segmentation rules to segment the three-dimensional model data to construct N sub-models. The geological condition segmentation rules are segmentation rules that dynamically adjust the cutting spacing according to the characteristics of rock layer gradient changes. S2. Perform gradient analysis on the longitudinal rock layer strength and axial fracture density of N sub-models to establish rock layer transition characteristic data that includes the rate of change of adjacent rock layer properties and the spatial distribution of the transition region. S3. After reading the spatial coordinates of the preset protected object, dynamically identify the surrounding rock types of N sub-models based on the rock stratum transition feature data, match and map the identification results with the spatial coordinates, and configure the surrounding rock risk zoning of the preset protected object. S4. Real-time data acquisition is performed using a microseismic monitoring network deployed in underground caverns to establish a set of geological parameters, which includes microseismic energy release rate, source location distribution, and stress wave attenuation characteristics. S5. Input the set of geological parameters into the corresponding N sub-models, perform early warning identification based on the surrounding rock risk zoning, and establish an early warning signal.

2. The method for early warning of zonal stability of underground caverns based on microseismic monitoring according to claim 1, characterized in that, The three-dimensional model data is segmented using geological condition segmentation rules to construct N sub-models, including the following steps: S101. Perform gridding processing on the three-dimensional model data to establish regular three-dimensional grid points; S102. After defining the longitudinal gradient and axial gradient, a gradient field is constructed on the regular three-dimensional grid points according to the characteristics of rock layer gradient changes. S103. Configure the cutting spacing constraint according to the local gradient magnitude of the gradient field, and use the cutting spacing constraint to perform adaptive cutting of the three-dimensional model data to establish N sub-models, where N is an integer greater than 1.

3. The method for early warning of zonal stability of underground caverns based on microseismic monitoring according to claim 1, characterized in that, Gradient analysis of longitudinal rock layer strength and axial fracture density is performed on N sub-models, including the following steps: S201. Perform gradient analysis on the longitudinal rock layer strength and axial fracture density within the N sub-models to establish a combined gradient field for the models. S202. Based on the gradient field of the model combination, calculate the rate of change of adjacent rock layer properties along the rock layer normal and axial direction in each sub-model, and identify the transition region based on the rate of change of adjacent rock layer properties and gradient amplitude, and establish rock layer transition feature data.

4. The method for early warning of zonal stability of underground caverns based on microseismic monitoring according to claim 3, characterized in that, The longitudinal gradient and axial gradient are calculated using the central difference method or the weighted average method to suppress the influence of local measurement noise on the gradient field.

5. The method for early warning of underground cavern zonal stability based on microseismic monitoring according to claim 1, characterized in that, In S5, establishing an early warning signal includes the following steps: S501. Establish a set of emergency response plans and configure a matching feature set that maps to the set of emergency response plans; S502. Perform adaptation analysis on the matching feature set based on the warning signal, and establish adaptation analysis results; S503. Based on the adaptation analysis results, the solution mapping is invoked from the emergency response solution set, and the early warning processing is executed.

6. The method for early warning of zonal stability of underground caverns based on microseismic monitoring according to claim 5, characterized in that, In S502, establishing the adaptation analysis results also includes the following steps: S5021. Determine whether the adaptation value of the adaptation analysis result is lower than the preset configuration threshold. S5022. If the adaptation value of the adaptation analysis result is lower than the preset configuration threshold, an enhanced early warning factor is generated. S5023. After enhancing the warning signal according to the enhanced warning factor, the warning is issued.

7. The method for early warning of zonal stability of underground caverns based on microseismic monitoring according to claim 5, characterized in that, In S501, the set of emergency response plans includes ventilation adjustment plan, support and reinforcement plan, personnel evacuation plan, and local surrounding rock reinforcement plan.

8. The method for early warning of zonal stability of underground caverns based on microseismic monitoring according to claim 1, characterized in that, In S4, the microseismic monitoring network includes multiple sensor nodes deployed at different locations in the underground cavern. The sensor nodes perform real-time data uploading and synchronization through wired or wireless communication.

9. The method for early warning of zonal stability of underground caverns based on microseismic monitoring according to claim 5, characterized in that, In S5, establishing an early warning signal also includes the following steps: S504. After mapping and packaging the warning signal, the N sub-models, the rock stratum transition characteristic data, and the geological parameter set, a storage instruction is generated. S505. Store and manage the packaging results according to the storage instruction, establish a unique traceability code, and perform traceability management of the early warning signal according to the unique traceability code.

10. The method for early warning of zonal stability of underground caverns based on microseismic monitoring according to claim 9, characterized in that, The packaging results are stored and managed according to the storage instructions, including the following steps: S5051. Read stored data within a preset period and construct an exception log; S5052. Synchronize the abnormal logs to the cloud and generate a zone stability report for the underground cavern in the cloud.

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