A geological disaster early warning device and method

Abstract

The present invention relates to the field of geological disaster early warning technology, and in particular to a geological disaster early warning device and method. The present invention proposes the following scheme: by obtaining the rate of change of the gravity field gradient of the target area, multiple candidate connected units are screened out from all cavity units, and the connectivity is dynamically evaluated using the pressure wave propagation time variation coefficient and the dominant propagation direction in combination with the pressure wave characteristic parameters collected by a three-axis pressure sensor; adjacent cavity units are merged through an iterative operation to achieve efficient identification of connected areas; the calculated connectivity is used to trigger early warning information; the present application adopts a dynamic iteration mechanism and pressure wave characteristic parameter analysis, avoiding the unit-by-unit detection in traditional methods, significantly improving the calculation efficiency and accuracy of the early warning system, and is particularly suitable for early warning and risk assessment of disasters in complex geological environments.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster early warning technology, and in particular to a geological disaster early warning device and method. Background Art

[0002] Existing geological disaster early warning technologies primarily rely on the individual detection and connectivity analysis of cavity units to determine potential geological disaster risks. However, traditional methods often suffer from high computational complexity and low efficiency. Because each cavity unit needs to be analyzed individually, this method is time-consuming and easily limited by data volume and processing power, resulting in a slow response speed, especially in complex geological environments. Therefore, there is an urgent need for a more efficient and accurate geological disaster early warning method that can dynamically and accurately identify connected areas and trigger warning signals while reducing computational complexity.

[0003] The existing technology has the following problems: it is difficult to deal with situations where the geological structure is complex, the cavity units are stacked or blocked, resulting in the inability to effectively identify potential connected areas in the area. To solve the above problems, this application designs a geological disaster early warning device and method. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and provide a geological disaster early warning device and method. By obtaining the rate of change of the gravity field gradient in the target area, multiple candidate connected units are screened out from all cavity units. Combined with the pressure wave characteristic parameters collected by the three-way pressure sensor, the connectivity is dynamically evaluated using the pressure wave propagation time variation coefficient and the dominant propagation direction. Adjacent cavity units are merged through iterative operations to achieve efficient identification of connected areas. The calculated connectivity is used to trigger early warning information. This application adopts a dynamic iteration mechanism and pressure wave characteristic parameter analysis to avoid the unit-by-unit detection in traditional methods, significantly improving the computational efficiency and accuracy of the early warning system, and is particularly suitable for early warning and risk assessment of disasters in complex geological environments.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A geological disaster early warning method is applied to a geological disaster early warning device, wherein the device includes a low-frequency gravimeter and multiple sets of three-axis pressure sensors deployed in a target area. The method includes:

[0007] Acquiring a rate of change of a gravity field gradient of a target area, wherein the target area includes a plurality of cavity units, and a pressure waveguide range of the three-axis pressure sensor is larger than an area range of the cavity units;

[0008] determining a plurality of cavity units as candidate connected units from all cavity units according to the rate of change of the gravity field gradient;

[0009] Perform an iterative operation to collect the pressure wave characteristic parameters of the current corresponding candidate connected unit. If the propagation time variation coefficient of the pressure wave characteristic parameters is less than a preset time threshold, merge the adjacent units along the dominant propagation direction and replace the current corresponding candidate connected unit until the propagation time variation coefficient of the pressure wave characteristic parameters is greater than or equal to the preset time threshold, and output the current connected area;

[0010] The connectivity of the current connected area is calculated to trigger early warning information according to the connectivity.

[0011] According to the rate of change of the gravity field gradient, a plurality of cavity units are determined from all cavity units as candidate connected units, including:

[0012] Calculate the rate of change of the gravity field gradient corresponding to each cavity unit and extract its absolute value as the unit deformation quantification value;

[0013] Based on a preset deformation threshold interval, screening cavity units whose deformation quantization values ​​are within the deformation threshold interval to generate an initial candidate set;

[0014] Perform spatial clustering analysis on the cavity units in the initial candidate set, merge the cavity units that meet the clustering conditions, and obtain candidate connected units.

[0015] The clustering conditions include the Euclidean distance between cavity units and the directional consistency of the gravity field gradient change rate.

[0016] The collecting of pressure wave characteristic parameters of the currently corresponding candidate connected unit includes:

[0017] In the coverage area of ​​the candidate connectivity unit, the three-axis pressure sensor with the largest signal strength is selected as the main sensor;

[0018] Taking the master sensor as the center, multiple three-way pressure sensors are determined as slave sensors to form a monitoring group according to the pressure waveguide coverage range;

[0019] Controlling the main sensor to emit an excitation pressure wave of a preset frequency and synchronously collecting pressure wave signals received by the monitoring group in a specified area, wherein the specified area includes the cavity unit inside the candidate connected unit and the cavity unit adjacent to the candidate connected unit:

[0020] The pressure wave signal is processed to calculate pressure wave characteristic parameters.

[0021] Processing the pressure wave signal to calculate pressure wave characteristic parameters includes:

[0022] performing a time-frequency domain joint noise reduction process on the pressure wave signal;

[0023] Perform envelope analysis on the noise-reduced signal to identify the starting point of the first wave of each slave sensor signal. Using the master sensor's emission moment as time zero, calculate the pressure wave propagation time and generate a propagation time sequence containing n slave sensors.

[0024] Calculating the coefficient of variation of propagation time and the dominant propagation direction according to the propagation time series;

[0025] The propagation time variation coefficient and the dominant propagation direction are used as pressure wave characteristic parameters.

[0026] Calculating the propagation time variation coefficient and the dominant propagation direction according to the propagation time series includes:

[0027] Calculating the mean and standard deviation of the propagation time series;

[0028] The ratio of the mean and the standard deviation is taken as the coefficient of variation of the travel time;

[0029] Construct a local coordinate system with the master sensor as the origin to obtain the spatial position of each slave sensor;

[0030] Compare the pressure wave propagation time of each slave sensor, connect the spatial positions of the three slave sensors with the shortest propagation time, and generate an initial propagation direction cluster;

[0031] The centroid position of the initial propagation direction cluster is calculated, and the direction vector between the origin and the centroid position is used as the dominant propagation direction.

[0032] The merging of adjacent units along the dominant propagation direction and replacing the currently corresponding candidate connected units includes:

[0033] Taking the geometric center of the current candidate connected unit as the benchmark, obtain the three-layer cavity unit with the closest Euclidean distance in the dominant propagation direction as the candidate unit to be merged;

[0034] Calculating the spatial angle between the candidate unit to be merged and the current candidate connected unit, and calculating the cosine value of the spatial angle and the dominant propagation direction;

[0035] Retain candidate units to be merged whose cosine values ​​are less than a preset cosine threshold;

[0036] The filtered candidate units to be merged are connected to the current candidate connected units to replace the current corresponding candidate connected units.

[0037] Calculating the connectivity of the current connected area to trigger warning information according to the connectivity, including:

[0038] The connectivity is calculated based on the number of adjacent unit layers merged along the dominant propagation direction during the iteration process;

[0039] According to the preset three-level warning threshold interval, when the connectivity falls into the corresponding threshold interval, the corresponding level of warning signal is triggered, wherein the three-level warning threshold interval includes yellow warning, orange warning and red warning. The yellow warning indicates that there is a local connectivity risk, the orange warning indicates that the connectivity area is expanding, and the red warning indicates the formation of a through connectivity channel.

[0040] A geological disaster early warning device, comprising a data acquisition module, a data processing module and an early warning module, wherein:

[0041] The data acquisition module is used to obtain the gravity field gradient change rate and pressure wave characteristic parameters of the target area;

[0042] The data processing module determines a plurality of cavity units as candidate connected units from all cavity units according to the rate of change of the gravity field gradient, and performs an iterative operation to collect pressure wave characteristic parameters of the currently corresponding candidate connected units. If the propagation time variation coefficient of the pressure wave characteristic parameters is less than a preset time threshold, the adjacent units are merged along the dominant propagation direction and replace the currently corresponding candidate connected units until the propagation time variation coefficient of the pressure wave characteristic parameters is greater than or equal to the preset time threshold, and the current connected area is output;

[0043] The warning module is used to calculate the connectivity of the current connected area to trigger warning information according to the connectivity.

[0044] The data processing module includes:

[0045] A gravity field data processing unit is used to calculate the rate of change of the gravity field gradient corresponding to each cavity unit, generate an initial candidate set, and perform spatial clustering analysis on the cavity units in the initial candidate set, merge the cavity units that meet the clustering conditions, and obtain candidate connected units;

[0046] a pressure wave signal processing unit, configured to perform joint time-frequency domain noise reduction processing on the pressure wave signal, perform envelope analysis on the noise-reduced signal, identify the starting point of the first wave of each slave sensor signal, calculate the pressure wave propagation time with the master sensor emission moment as time zero, generate a propagation time sequence comprising n slave sensors, calculate the propagation time variation coefficient and the dominant propagation direction based on the propagation time sequence, and use the propagation time variation coefficient and the dominant propagation direction as pressure wave characteristic parameters;

[0047] Iterative merging of control units, including:

[0048] Taking the geometric center of the current candidate connected unit as the benchmark, obtain the three-layer cavity unit with the closest Euclidean distance in the dominant propagation direction as the candidate unit to be merged;

[0049] Calculating the spatial angle between the candidate unit to be merged and the current candidate connected unit, and calculating the cosine value of the spatial angle and the dominant propagation direction;

[0050] Retain candidate units to be merged whose cosine values ​​are less than a preset cosine threshold;

[0051] The filtered candidate units to be merged are connected to the current candidate connected units to replace the current corresponding candidate connected units.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This application uses a dynamic iteration mechanism based on pressure wave characteristic parameters to achieve efficient and accurate identification of connected areas, reducing the computational complexity of the geological disaster early warning process. Compared with the traditional method of detecting cavity units one by one, this application can avoid tedious one-by-one analysis. It uses pressure wave data collected by a three-dimensional pressure sensor, combined with the rate of change of the gravity field gradient, to dynamically evaluate and merge candidate connected units. This can cope with complex geological structures, stacked or blocked cavity units, and improve the response speed and accuracy of the early warning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0055] Figure 1 This is a flow chart of a geological disaster early warning method according to embodiment 1 of the present invention;

[0056] Figure 2 This is a schematic diagram of the collection and placement of pressure wave characteristic parameters according to Example 1 of the present invention;

[0057] Figure 3 This is a flow chart of calculating pressure wave characteristic parameters according to Example 1 of the present invention;

[0058] Figure 4 This is a schematic diagram of determining the cavity unit in Example 1 of the present invention;

[0059] Figure 5 This is a schematic diagram of iterative determination of candidate connected units in embodiment 1 of the present invention;

[0060] Figure 6 This is a schematic diagram of merging candidate connected units in Example 1 of the present invention. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0062] Example 1

[0063] See also Figure 1 The present invention provides an embodiment of a geological disaster early warning method, which is applied to a geological disaster early warning device. The device includes a low-frequency gravimeter and multiple sets of three-axis pressure sensors deployed in a target area. The method is suitable for solving the problem of difficulty in monitoring the progressive destruction of karst collapse and deep buried caves. The specific steps of the method are as follows:

[0064] S1: obtaining the rate of change of the gravity field gradient of the target area, where the target area includes multiple cavity units;

[0065] In this embodiment, low-frequency gravimeters deployed within the target area acquire real-time gravity field data. Based on this data, the rate of change of the gravity field gradient is calculated. The core objective is to determine the gravity variation trend within the target area, enabling preliminary identification of potential cavities. Because the gradual destruction of karst collapse or deep caves is difficult to detect using traditional monitoring methods, the rate of change of the gravity field gradient can effectively identify potentially dangerous areas.

[0066] S2: According to the rate of change of the gravity field gradient, multiple cavity units are determined from all cavity units as candidate connected units;

[0067] In this embodiment, candidate connected cells are screened from multiple cavity cells within the target area based on the calculated rate of change of the gravity field gradient. To improve the accuracy of connected cell screening, the deformation of each cavity cell is screened based on a preset deformation threshold. Among a large number of cavity cells, those that are closely related to gravity field changes and potentially associated with disaster risk are identified, providing efficient preliminary identification for subsequent early warnings.

[0068] S3: Collect the pressure wave characteristic parameters of the corresponding candidate connected unit;

[0069] In this embodiment, multiple sets of three-dimensional pressure sensors are used to monitor the selected candidate connected cells in real time and collect the pressure wave characteristic parameters therein. The three-dimensional pressure sensors can simultaneously collect pressure waves from multiple directions, obtaining more comprehensive and accurate data.

[0070] S4: If the coefficient of variation of the propagation time of the pressure wave characteristic parameter is less than the preset time threshold, the adjacent units are merged along the dominant propagation direction and the corresponding candidate connected units are replaced;

[0071] In this embodiment, when the coefficient of variation of the propagation time of the collected pressure wave characteristic parameters is lower than a preset threshold, it indicates that there is a high degree of consistency and connectivity between the cavity units in the region. Based on this judgment, adjacent candidate connected units are merged along the dominant propagation direction to replace the current unit. This step utilizes the propagation law of pressure wave characteristics to optimize the efficiency of merging candidate connected units, making the identification of connected areas more accurate. Compared with traditional unit-by-unit judgment, this merging method based on the dominant propagation direction can greatly improve efficiency and avoid overly complex calculation and analysis processes.

[0072] S5: performing an iterative operation until the coefficient of variation of the propagation time of the pressure wave characteristic parameter is greater than or equal to a preset time threshold, and outputting the current connected area;

[0073] In this embodiment, if the coefficient of variation of the propagation time of the pressure wave characteristic parameter remains below the preset time threshold during each iteration, cells are merged again until the coefficient of variation reaches the preset time threshold. This iterative mechanism ensures that the ultimately identified connected regions are more consistent with actual geological characteristics, avoiding excessive or insufficient identification of connected regions.

[0074] S6: Calculate the connectivity of the current connected area to trigger warning information according to the connectivity;

[0075] In this embodiment, the severity of potential risks is assessed by calculating the connectivity of the currently connected area. A higher connectivity indicates a more severe geological hazard. Based on the calculated connectivity and combined with the three preset warning thresholds (yellow warning, orange warning, and red warning), different levels of warning signals are triggered.

[0076] The present application provides a geological disaster early warning method, which efficiently identifies the connected areas in the target area through a dynamic iterative mechanism, and triggers the early warning signal according to the connectivity. In the prior art, the geological disaster early warning system usually needs to perform connectivity detection on each cavity unit. This method is not only computationally complex but also inefficient. In this traditional detection method, analyzing the connectivity of each cavity unit one by one not only requires a large amount of computing resources, but is also limited by the data processing speed. Especially in geological disaster early warning scenarios that require real-time response, this method is particularly insufficient and cannot effectively handle the concurrent calculation of large amounts of data. It is easy to cause the response time to be too long, making it difficult to meet actual early warning needs.

[0077] In this embodiment, a dynamic iteration mechanism based on pressure wave characteristic parameters is adopted, which significantly improves the recognition efficiency of connected areas while reducing the computational complexity. Specifically, the characteristic of the three-dimensional pressure sensor covering multiple cavity units is utilized, and the target area is preliminarily screened in combination with the rate of change of the gravity field gradient, and candidate connected units are selected, and the connectivity is judged by the coefficient of variation of the propagation time of the pressure wave. The candidate area can be dynamically adjusted, and adjacent units can be merged based on the dominant propagation direction, thereby avoiding the tedious steps of unit-by-unit detection in traditional methods. This not only greatly reduces the amount of calculation, but also ensures more accurate and efficient identification of connected areas. The present application can process large amounts of data in real time, ensuring timely and accurate early warnings in complex geological disaster scenarios, and avoiding the delays and high computational costs of traditional solutions.

[0078] Furthermore, combined with the design that the waveguide range of the sensor is larger than the area of ​​the cavity unit, the solution of this application can extract cross-unit regional features between multiple cavity units, thereby improving the overall detection capability. In specific applications, the sensor can cover a wider range of areas and perform dynamic regional identification, ensuring efficient and accurate disaster warnings even in complex, stacked geological structures. Compared with the traditional one-by-one traversal method, this application has significantly improved execution efficiency, accuracy, and real-time performance, can effectively support early warning and precise response to geological disasters, and solves many problems of existing technologies in efficiently identifying connected areas.

[0079] The specific steps of S2 are as follows:

[0080] S2.1: Calculate the rate of change of the gravity field gradient corresponding to each cavity unit and extract its absolute value as the unit deformation quantification value;

[0081] Specifically, the rate of change of the gravity field gradient of each cavity unit is calculated in order to quantify the gravity changes experienced by each cavity unit in the target area. Since geological hazard warning relies on changes in the gravity field in the region, especially for areas where cavities or underground spaces may exist, changes in the gravity field can often reflect potential risk areas. Therefore, by calculating the rate of change of the gravity field gradient corresponding to each cavity unit, it is possible to determine whether the cavity is changing and whether there is a potential risk of collapse or deformation. The rate of change of the gravity field gradient of each cavity unit provides a quantitative representation of the geological activity in the area, and by extracting the absolute value of the rate of change, it is possible to eliminate the influence of positive and negative changes and focus on the magnitude of the change.

[0082] In this embodiment, the calculation of the rate of change of the gravity field gradient is performed by differential processing of multi-point gravity data within the target area and calibration with a spatial coordinate system, ensuring spatial consistency and accuracy of the gradient rate of change. This effectively distinguishes areas with significant changes, providing reliable data support for further screening and identifying cavity units with the highest likelihood of connectivity.

[0083] S2.2: Based on a preset deformation threshold interval, screening cavity units whose deformation quantization values ​​are within the deformation threshold interval to generate an initial candidate set;

[0084] Specifically, in practical applications, the gravity field changes of most cavity units do not pose a significant risk of geological hazards. Only those with large deformation amplitudes deserve further attention. The specific deformation threshold range can be determined by referring to relevant literature.

[0085] In this embodiment, the deformation quantification value of each cavity unit is compared with a preset threshold range to select cavity units within the threshold range. The preset deformation threshold range is determined based on historical geological data, regional geological characteristics, and possible disaster types. Typically, the threshold range is adaptively adjusted based on actual needs.

[0086] S2.3: Performing spatial clustering analysis on the cavity units in the initial candidate set, merging the cavity units that meet the clustering conditions to obtain candidate connected units, wherein the clustering conditions include the Euclidean distance between the cavity units and the directional consistency of the rate of change of the gravity field gradient;

[0087] Specifically, Euclidean distance is used to measure the spatial proximity between cavity units. This is because the connectivity of geological hazards is not only related to the deformation changes of individual cavity units, but also closely related to the physical environment of the surrounding area. By calculating the Euclidean distance between cavity units, it is possible to determine which cavity units are spatially close and, therefore, to consider them as belonging to the same connected area. Furthermore, the directional consistency of the rate of change of the gravity field gradient is used to ensure that the clustered cavity units have similar geological characteristics. The directionality of gravity field changes often reflects the underlying geological structural characteristics, such as the direction of rock layer changes and the orientation of underground cavities. By analyzing the directional consistency of the rate of change of the gravity field gradient of cavity units, it is possible to further confirm which cavity units belong to the same geological body and which cavity units are spatially related and interconnected in geological activities. Combining these two criteria for cluster analysis ensures that the clustered connected units are more meaningful and accurately reflect the spatial distribution of potential geological hazard risk areas.

[0088] Furthermore, because the clustered candidate connected units represent connected areas in a larger range, the number of units that need to be processed in subsequent analysis can be reduced, avoiding the high computational complexity of unit-by-unit detection.

[0089] See also Figure 2 , a schematic diagram of the placement of pressure wave characteristic parameters collected in an embodiment of the present invention, a main sensor is used to send an excitation pressure wave of a preset frequency, the solid arrow represents the sender, slave sensor 1, slave sensor 2, and slave sensor 3 constitute a monitoring group for receiving pressure wave signals, the dotted arrow represents the receiver, the candidate connection unit acts as the sender to send pressure waves, and the receiver receives pressure wave signals from the candidate connection unit, the adjacent cavity unit 1, and the adjacent cavity unit 2. The specific steps of S3 are as follows:

[0090] S3.1: Within the coverage area of ​​the candidate connectivity unit, select the three-axis pressure sensor with the largest signal strength as the primary sensor;

[0091] Specifically, the three-dimensional pressure sensor with the highest signal strength is selected as the main sensor to ensure that the signal acquisition in the monitoring area has the best sensitivity and accuracy. The three-dimensional pressure sensor can collect pressure wave information in three directions simultaneously. Compared with traditional one-way sensors, the three-dimensional pressure sensor can fully capture the propagation characteristics of pressure waves from different directions, thereby improving the integrity of data acquisition. The sensor with the highest signal strength in the coverage area of ​​the candidate connectivity unit is selected as the main sensor to ensure that the strongest signal source can provide the most reliable benchmark for the entire monitoring process. A strong signal source helps reduce interference from environmental noise and ensures that the transmission and reception quality of the signal is in the best state in complex geological environments, thereby improving the reliability and accuracy of subsequent pressure wave signals.

[0092] In this embodiment, primary sensors are selected based on the signal strength within their respective areas for dynamic monitoring. The sensor's reception capabilities are evaluated based on the actual conditions of each area, and real-time feedback is used to determine which sensor provides the strongest signal. This allows for dynamic adjustment of the acquisition plan to any geological changes or complex environments, maximizing signal quality.

[0093] S3.2: With the master sensor as the center, determine multiple three-axis pressure sensors as slave sensors to form a monitoring group based on the pressure waveguide coverage range of the master sensor;

[0094] Specifically, by selecting a master sensor as the center and determining multiple slave sensors as the monitoring group, the purpose is to ensure that comprehensive pressure wave propagation data can be obtained during the actual monitoring process. The master sensor is the monitoring center, and based on the coverage of its pressure waveguide, three-way pressure sensors at corresponding positions are selected as slave sensors to ensure that all relevant pressure wave signals in the entire monitoring area can be fully collected. The selected slave sensors will be reasonably allocated based on their relative position to the master sensor and the coverage of the pressure waveguide. This configuration method effectively ensures that the sensors can cover all monitoring areas and can adjust the sensor coverage according to different geological conditions and environmental requirements.

[0095] S3.3: Control the main sensor to emit an excitation pressure wave of a preset frequency, and synchronously collect pressure wave signals received by the monitoring group in a specified area, wherein the specified area includes the cavity unit inside the candidate connected unit and the cavity unit adjacent to the candidate connected unit:

[0096] Specifically, the primary sensor is controlled to emit an excitation pressure wave of a preset frequency, while simultaneously collecting the pressure wave signals received by the monitoring group sensors. This ensures consistent signal transmission and synchronized monitoring. By emitting an excitation wave of a specific frequency, the propagation characteristics of the excitation wave are consistent throughout the entire monitoring area, making the propagation time and characteristics of the pressure wave signal comparable across space. The choice of frequency needs to be adjusted based on the geological characteristics of the area and the actual conditions of pressure wave propagation.

[0097] Furthermore, synchronously collecting signals from the monitoring group's sensors ensures consistent time bases for pressure wave signals received from different sensors, enabling accurate time comparison and data analysis. The selection of a defined region, encompassing the cavity cells within the candidate connected cells and their adjacent areas, ensures signal coverage of the target area and its surroundings, avoiding incomplete data due to missing local areas.

[0098] S3.4: Processing the pressure wave signal to calculate pressure wave characteristic parameters;

[0099] Specifically, calculating the characteristic parameters of the pressure wave helps further analyze the connectivity of the cavity units. By analyzing the propagation time of the pressure wave, we can infer the propagation speed of the pressure wave in different cavity units and thus determine the interconnectivity between the cavity units. If the propagation time of the pressure wave varies significantly between different units, it indicates that there may be a problem in the connectivity between these cavity units.

[0100] See also Figure 3 , the flow chart of calculating the characteristic parameters of the pressure wave according to the embodiment of the present invention, the specific steps of S3.4 are as follows:

[0101] S3.4.1: Performing a time-frequency domain joint noise reduction process on the pressure wave signal;

[0102] Specifically, joint time-frequency domain noise reduction is designed to effectively remove the effects of environmental noise, interference from the equipment itself, and other unnecessary disturbances during signal propagation during signal acquisition. Pressure wave signals typically contain low-frequency and high-frequency components. The low-frequency components may be affected by ground vibration and equipment noise, while the high-frequency components may be overwhelmed by the sensor's own noise and reflected signals. To improve signal quality, it is necessary to filter out these noises through time-frequency analysis combined with noise reduction techniques. During the joint time-frequency domain noise reduction process, the signal is analyzed jointly in the time and frequency domains, and an appropriate window is selected to decompose the signal, allowing the time and frequency characteristics of the signal to be processed separately. This method can eliminate noise from different sources while retaining the main characteristics of the signal.

[0103] S3.4.2: Perform envelope analysis on the noise-reduced signal to identify the starting point of the first wave of each slave sensor signal. Using the master sensor emission moment as time zero, calculate the pressure wave propagation time to generate a propagation time series consisting of n slave sensors.

[0104] Specifically, envelope analysis is used to extract the main waveform features of the noise-reduced signal, especially the signal envelope, which can effectively demonstrate the intensity variation trend of the signal. By identifying the starting point of the first wave of the signal, that is, the moment when the pressure wave is emitted from the master sensor and reaches each slave sensor, the propagation time of each pressure wave can be accurately measured. This process ensures the temporal consistency and comparability of all subsequent measurements by ensuring that the moment of emission from the master sensor is the time zero. The accuracy of the propagation time calculation is a prerequisite for analyzing the propagation characteristics of the pressure wave. Accurate propagation time helps to reveal the connectivity between cavity units.

[0105] In this embodiment, the envelope analysis method first smoothes the noise-reduced signal to extract the main variation trend of the signal. This trend is then used to determine the starting point of each pressure wave received by the slave sensor. By accurately measuring these starting points, the propagation time of the pressure wave between different cavity units can be calculated, generating a propagation time series encompassing multiple slave sensors.

[0106] S3.4.3: Calculate the coefficient of variation of propagation time and the dominant propagation direction based on the propagation time series;

[0107] Specifically, the coefficient of variation of propagation time is calculated based on the propagation time series in order to evaluate the consistency of the propagation of pressure waves between different cavity units. If the variability of the propagation time is large, it means that there may be obstacles in the connection between the cavity units. Otherwise, it means that there are connections between the cavity units. In practical applications, geological disaster warnings need to rely on this coefficient of variation to quickly judge potential dangerous areas, because a low coefficient of variation usually means that there are unstable geological changes such as ruptures and collapses in the area, resulting in connections between the two cavity units. At the same time, the calculation of the dominant propagation direction is to further analyze the propagation characteristics of the pressure wave in space and help determine the main propagation path of the pressure wave between the cavity units. By comparing the propagation times of multiple slave sensors, the paths with the shortest propagation times are found, and the directions of these paths are calculated to determine a dominant propagation direction.

[0108] In this embodiment, the calculation method of the coefficient of variation of propagation time and the dominant propagation direction is based on analysis of the spatial position and the time difference of pressure wave propagation.

[0109] S3.4.4: The coefficient of variation of the propagation time and the dominant propagation direction are used as characteristic parameters of the pressure wave;

[0110] The specific steps of S3.4.3 are as follows:

[0111] S3.4.3.1: Calculate the mean and standard deviation of the propagation time series;

[0112] Specifically, the mean and standard deviation of the propagation time series are calculated to quantify the propagation characteristics of the pressure wave between different sensors. The propagation time series reflects the propagation delay of the pressure wave from the master sensor to each slave sensor, while the mean and standard deviation provide the central tendency and dispersion of the propagation delay. The mean represents the average propagation time of the entire sequence, which can be used to understand the general trend of pressure wave propagation in a region. The standard deviation reveals the degree of fluctuation in the propagation time, reflecting possible regional differences or inconsistencies in the pressure wave propagation process. If the standard deviation is large, it means that the propagation time is highly unstable, which may indicate the presence of physical obstacles in the area; if the standard deviation is small, it means that the propagation characteristics of the pressure wave in the area are relatively consistent, there are fewer obstacles, and there is a risk of collapse.

[0113] In this embodiment, propagation time series calculations are performed by processing collected response data from the sensors. Real-time computation techniques are used to determine the propagation time of each pressure wave, and the spatial average and standard deviation are calculated. This approach provides general characteristics of pressure wave propagation throughout the monitored area, providing a basis for subsequent connectivity analysis. The calculation of standard deviations allows for better identification of areas with unstable propagation characteristics when assessing connectivity, thereby ensuring the system's ability to promptly detect potential geological risks.

[0114] S3.4.3.2: The ratio of the mean to the standard deviation shall be used as the coefficient of variation of the travel time.

[0115] Specifically, the coefficient of variation is a commonly used statistic to measure the degree of data dispersion. It calculates the ratio of the standard deviation to the mean to obtain a relative quantitative indicator used to represent the inconsistency of propagation time. In geological disaster warning, the size of the coefficient of variation can help assess the connectivity within the target area. A low coefficient of variation indicates high connectivity within the area, and there may be unstable connections between multiple cavity units; while a high coefficient of variation indicates that the propagation of pressure waves is difficult, and there is usually no connection between multiple cavity units, and the risk of collapse is low.

[0116] S3.4.3.3: Construct a local coordinate system with the master sensor as the origin and obtain the spatial position of each slave sensor;

[0117] Specifically, a local coordinate system is constructed with the master sensor as the origin to uniformly manage and analyze the spatial positions of slave sensors. This system provides a standardized spatial framework for subsequent analysis, accurately locating the relative position of each slave sensor in three-dimensional space and ensuring that data from different spatial locations can be compared within the same reference frame. By establishing the master sensor as the origin, the spatial positions of other slave sensors can be calculated, providing clear spatial information for subsequent propagation direction analysis and geological feature identification.

[0118] In this embodiment, by precisely measuring the spatial coordinates of each sensor and combining them with pressure wave propagation data, we can better understand and predict the propagation paths of pressure waves in different geological structures. The technical effect of establishing a local coordinate system is to standardize spatial data, ensuring that subsequent analysis is not affected by changes in the geographic coordinate system or geological structure, thereby ensuring the consistency and accuracy of data analysis.

[0119] S3.4.3.4: Compare the pressure wave propagation time of each slave sensor and connect the spatial positions of the three slave sensors with the shortest propagation time to generate an initial propagation direction cluster;

[0120] Specifically, by comparing the propagation times of different sensors, the sensor with the shortest propagation time is identified, and the primary direction of the pressure wave propagation is inferred. The three slave sensors with the shortest propagation times typically indicate that the cavity units corresponding to the regions of these sensors are relatively close to the candidate cavity units, and the pressure wave propagation path is relatively direct. By connecting the spatial positions of these three sensors, an initial cluster of propagation directions can be generated.

[0121] In this embodiment, because pressure waves usually propagate along the most direct and shortest path, by initially selecting the sensor with the shortest propagation time, the main propagation path of the pressure wave can be effectively captured, thereby providing strong support for subsequent geological connectivity analysis.

[0122] S3.4.3.5: Calculate the centroid position of the initial propagation direction cluster, and use the direction vector between the origin and the centroid position as the dominant propagation direction;

[0123] Specifically, the centroid of the initial propagation direction cluster is calculated, and the direction vector between this centroid and the origin is used as the dominant propagation direction. This accurately determines the primary propagation path of the pressure wave within the geological region. The centroid is a spatial point that is a weighted average of the spatial positions of the sensors. By calculating the centroid, the geometric center of the propagation direction is determined. Using the direction vector between the origin and the centroid as the dominant propagation direction is crucial for accurately determining the prevailing direction of pressure wave propagation and avoiding misjudgments of propagation directions due to instability in individual sensor data.

[0124] The specific steps of S4 are as follows:

[0125] S4.1: Based on the geometric center of the current candidate connected unit, obtain the three-layer cavity unit with the closest Euclidean distance in the dominant propagation direction as the candidate unit to be merged;

[0126] S4.2: Calculate the spatial angle between the candidate unit to be merged and the current candidate connected unit, and calculate the cosine value of the spatial angle and the dominant propagation direction;

[0127] S4.3: retain candidate units to be merged whose cosine values ​​are less than a pre-set cosine threshold;

[0128] S4.4: Connect the selected candidate unit to be merged with the current candidate connected unit to replace the current corresponding candidate connected unit;

[0129] The specific steps of S5 are as follows:

[0130] S5.1: Calculate the connectivity based on the number of adjacent unit layers merged along the dominant propagation direction during the iteration process;

[0131] S5.2: According to the preset three-level warning threshold interval, when the connectivity falls into the corresponding threshold interval, the corresponding level of warning signal is triggered, wherein the three-level warning threshold interval includes yellow warning, orange warning and red warning. The yellow warning indicates that there is a local connectivity risk, the orange warning indicates that the connectivity area is expanding, and the red warning indicates the formation of a through connectivity channel.

[0132] See also Figure 4 , a schematic diagram of cavity unit determination in an embodiment of the present invention divides the target area into 10×10 units, a total of 100 units, each unit is a small square, and the cavity units are marked as gray blocks, and the white blocks represent obstacle units. It can be understood that the definition of obstacle units refers to those areas where pressure waves cannot be effectively propagated or cannot participate in connectivity analysis. Specifically, obstacle units are usually caused by geological structures or external factors (such as the physical properties of rock formations, impermeable obstacles, etc.). The pressure waves in these areas cannot pass through or the propagation time is abnormal, and usually there is no need to consider them. However, in practice, obstacle units are not necessarily complete obstacles, and pressure waves may still pass through. Therefore, the introduction of obstacle units in specific processing helps to accurately identify areas where potential connectivity changes actually exist. It should be noted that the target area in this embodiment is only a schematic diagram for ease of understanding. In actual applications, there will be more obstacle units and cavity units.

[0133] See also Figure 5 , a schematic diagram of iterative judgment of candidate connected units in an embodiment of the present invention, assuming that there are candidate connected unit 1 and candidate connected unit 2, the propagation time variation coefficients of the two candidate connected units are judged in turn, the propagation time variation coefficient of candidate connected unit 1 is less than the time threshold, so it is necessary to merge along the dominant propagation direction of candidate connected unit 1, and the propagation time variation coefficient of candidate connected unit 2 is greater than the time threshold, so there is no need to iteratively process candidate connected unit 2, but directly output the current area of ​​candidate connected unit 2 as the connected area.

[0134] See also Figure 6 , a schematic diagram of merging candidate connected units according to an embodiment of the present invention, Figure 5 The judgment results are merged, and candidate unit 1 to be merged, candidate unit 2 to be merged, and candidate unit 3 to be merged are determined according to the dominant propagation direction.

[0135] Example 2

[0136] The present invention provides an embodiment: a geological disaster early warning device, the device comprising a data acquisition module, a data processing module and an early warning module, wherein:

[0137] The data acquisition module is used to obtain the gravity field gradient change rate and pressure wave characteristic parameters of the target area;

[0138] The data processing module determines a plurality of cavity units as candidate connected units from all cavity units according to the rate of change of the gravity field gradient, and performs an iterative operation to collect pressure wave characteristic parameters of the currently corresponding candidate connected units. If the propagation time variation coefficient of the pressure wave characteristic parameters is less than a preset time threshold, the adjacent units are merged along the dominant propagation direction and replace the currently corresponding candidate connected units until the propagation time variation coefficient of the pressure wave characteristic parameters is greater than or equal to the preset time threshold, and the current connected area is output;

[0139] The warning module is used to calculate the connectivity of the current connected area to trigger warning information according to the connectivity.

[0140] The data processing module includes:

[0141] A gravity field data processing unit is used to calculate the rate of change of the gravity field gradient corresponding to each cavity unit, generate an initial candidate set, and perform spatial clustering analysis on the cavity units in the initial candidate set, merge the cavity units that meet the clustering conditions, and obtain candidate connected units;

[0142] a pressure wave signal processing unit, configured to perform joint time-frequency domain noise reduction processing on the pressure wave signal, perform envelope analysis on the noise-reduced signal, identify the starting point of the first wave of each slave sensor signal, calculate the pressure wave propagation time with the master sensor emission moment as time zero, generate a propagation time sequence comprising n slave sensors, calculate the propagation time variation coefficient and the dominant propagation direction based on the propagation time sequence, and use the propagation time variation coefficient and the dominant propagation direction as pressure wave characteristic parameters;

[0143] Iterative merging of control units, including:

[0144] Taking the geometric center of the current candidate connected unit as the benchmark, obtain the three-layer cavity unit with the closest Euclidean distance in the dominant propagation direction as the candidate unit to be merged;

[0145] Calculating the spatial angle between the candidate unit to be merged and the current candidate connected unit, and calculating the cosine value of the spatial angle and the dominant propagation direction;

[0146] Retain candidate units to be merged whose cosine values ​​are less than a preset cosine threshold;

[0147] The filtered candidate units to be merged are connected to the current candidate connected units to replace the current corresponding candidate connected units.

[0148] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A geological disaster early warning method, applied to a geological disaster early warning device, the device comprising a low-frequency gravimeter and multiple sets of three-axis pressure sensors deployed in a target area, characterized in that: The method comprises: Acquiring a rate of change of a gravity field gradient of a target area, wherein the target area includes a plurality of cavity units, and a pressure waveguide range of the three-axis pressure sensor is larger than an area range of the cavity units; determining a plurality of cavity units as candidate connected units from all cavity units according to the rate of change of the gravity field gradient; Perform an iterative operation to collect the pressure wave characteristic parameters of the current corresponding candidate connected unit. If the propagation time variation coefficient of the pressure wave characteristic parameters is less than a preset time threshold, merge the adjacent units along the dominant propagation direction and replace the current corresponding candidate connected unit until the propagation time variation coefficient of the pressure wave characteristic parameters is greater than or equal to the preset time threshold, and output the current connected area; Calculating the connectivity of the current connected area to trigger early warning information according to the connectivity; The collecting of pressure wave characteristic parameters of the currently corresponding candidate connected unit includes: In the coverage area of ​​the candidate connectivity unit, the three-axis pressure sensor with the largest signal strength is selected as the main sensor; Taking the master sensor as the center, multiple three-way pressure sensors are determined as slave sensors to form a monitoring group according to the pressure waveguide coverage range; Controlling the main sensor to emit an excitation pressure wave of a preset frequency and synchronously collecting pressure wave signals received by the monitoring group in a specified area, wherein the specified area includes the cavity unit inside the candidate connected unit and the cavity unit adjacent to the candidate connected unit: The pressure wave signal is processed to calculate pressure wave characteristic parameters.

2. A geological disaster early warning method according to claim 1, characterized in that: According to the rate of change of the gravity field gradient, a plurality of cavity units are determined from all cavity units as candidate connected units, including: Calculate the rate of change of the gravity field gradient corresponding to each cavity unit and extract its absolute value as the unit deformation quantification value; Based on a preset deformation threshold interval, screening cavity units whose deformation quantization values ​​are within the deformation threshold interval to generate an initial candidate set; Perform spatial clustering analysis on the cavity units in the initial candidate set, merge the cavity units that meet the clustering conditions, and obtain candidate connected units.

3. A geological disaster early warning method according to claim 2, characterized in that: The clustering conditions include the Euclidean distance between cavity units and the directional consistency of the gravity field gradient change rate.

4. A geological disaster early warning method according to claim 1, characterized in that: Processing the pressure wave signal to calculate pressure wave characteristic parameters includes: performing a time-frequency domain joint noise reduction process on the pressure wave signal; Perform envelope analysis on the noise-reduced signal to identify the starting point of the first wave of each slave sensor signal. Using the master sensor's emission moment as time zero, calculate the pressure wave propagation time and generate a propagation time sequence containing n slave sensors. Calculating the coefficient of variation of propagation time and the dominant propagation direction according to the propagation time series; The propagation time variation coefficient and the dominant propagation direction are used as pressure wave characteristic parameters.

5. A geological disaster early warning method according to claim 4, characterized in that: Calculating the propagation time variation coefficient and the dominant propagation direction according to the propagation time series includes: Calculating the mean and standard deviation of the propagation time series; The ratio of the mean and the standard deviation is taken as the coefficient of variation of the travel time; Construct a local coordinate system with the master sensor as the origin to obtain the spatial position of each slave sensor; Compare the pressure wave propagation time of each slave sensor, connect the spatial positions of the three slave sensors with the shortest propagation time, and generate an initial propagation direction cluster; The centroid position of the initial propagation direction cluster is calculated, and the direction vector between the origin and the centroid position is used as the dominant propagation direction.

6. A geological disaster early warning method according to claim 1, characterized in that: The merging of adjacent units along the dominant propagation direction and replacing the currently corresponding candidate connected units includes: Taking the geometric center of the current candidate connected unit as the benchmark, obtain the three-layer cavity unit with the closest Euclidean distance in the dominant propagation direction as the candidate unit to be merged; Calculating the spatial angle between the candidate unit to be merged and the current candidate connected unit, and calculating the cosine value of the spatial angle and the dominant propagation direction; Retain candidate units to be merged whose cosine values ​​are less than a preset cosine threshold; The filtered candidate units to be merged are connected to the current candidate connected units to replace the current corresponding candidate connected units.

7. A geological disaster early warning method according to claim 1, characterized in that: Calculating the connectivity of the current connected area to trigger warning information according to the connectivity, including: The connectivity is calculated based on the number of adjacent unit layers merged along the dominant propagation direction during the iteration process; According to the preset three-level warning threshold interval, when the connectivity falls into the corresponding threshold interval, the corresponding level of warning signal is triggered, wherein the three-level warning threshold interval includes yellow warning, orange warning and red warning. The yellow warning indicates that there is a local connectivity risk, the orange warning indicates that the connectivity area is expanding, and the red warning indicates the formation of a through connectivity channel.

8. A geological disaster early warning device, used to implement a geological disaster early warning method according to any one of claims 1 to 7, characterized in that: The device includes a data acquisition module, a data processing module and an early warning module, wherein: The data acquisition module is used to obtain the gravity field gradient change rate and pressure wave characteristic parameters of the target area; The data processing module determines a plurality of cavity units as candidate connected units from all cavity units according to the rate of change of the gravity field gradient, and performs an iterative operation to collect pressure wave characteristic parameters of the currently corresponding candidate connected units. If the propagation time variation coefficient of the pressure wave characteristic parameters is less than a preset time threshold, the adjacent units are merged along the dominant propagation direction and replace the currently corresponding candidate connected units until the propagation time variation coefficient of the pressure wave characteristic parameters is greater than or equal to the preset time threshold, and the current connected area is output; The warning module is used to calculate the connectivity of the current connected area to trigger warning information according to the connectivity.

9. A geological disaster early warning device according to claim 8, characterized in that: The data processing module includes: A gravity field data processing unit is used to calculate the rate of change of the gravity field gradient corresponding to each cavity unit, generate an initial candidate set, and perform spatial clustering analysis on the cavity units in the initial candidate set, merge the cavity units that meet the clustering conditions, and obtain candidate connected units; a pressure wave signal processing unit, configured to perform joint time-frequency domain noise reduction processing on the pressure wave signal, perform envelope analysis on the noise-reduced signal, identify the starting point of the first wave of each slave sensor signal, calculate the pressure wave propagation time with the master sensor emission moment as time zero, generate a propagation time sequence comprising n slave sensors, calculate the propagation time variation coefficient and the dominant propagation direction based on the propagation time sequence, and use the propagation time variation coefficient and the dominant propagation direction as pressure wave characteristic parameters; Iterative merging of control units, including: Taking the geometric center of the current candidate connected unit as the benchmark, obtain the three-layer cavity unit with the closest Euclidean distance in the dominant propagation direction as the candidate unit to be merged; Calculating the spatial angle between the candidate unit to be merged and the current candidate connected unit, and calculating the cosine value of the spatial angle and the dominant propagation direction; Retain candidate units to be merged whose cosine values ​​are less than a preset cosine threshold; The filtered candidate units to be merged are connected to the current candidate connected units to replace the current corresponding candidate connected units.

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