High-altitude seismic wave data acquisition method and system

Abstract

The invention provides a high-altitude seismic wave data acquisition method and system, and particularly relates to the technical field of deployment modes of physical noise reduction devices of high-altitude seismic wave sensors. According to the method, a leeward slope stable area judgment criterion is defined based on digital elevation model data and historical wind field data of a target area, a seismic sensor deployment point is determined by analyzing the terrain slope direction and the like, a seismic sensor and an anemometer are installed, and an angle-adjustable wind guide plate is installed at the preset distance of the windward side of the deployment point; and the deflection angle is dynamically adjusted according to the real-time prevailing wind direction data. A seismic sensor is triggered to collect continuous seismic wave data, current wind speed data is recorded synchronously, and when the wind speed exceeds a preset threshold value, a timestamp mark is embedded to form a segmented mark data set. And marking and positioning the marked data segment according to the timestamp, only carrying out noise reduction processing on the data segment, and outputting the seismic wave data after noise reduction. The method solves the problem that the wind-induced vibration noise is increased in a high-altitude dynamic wind field environment, and improves the signal-to-noise ratio of seismic signals.

Description

Technical Field

[0001] This invention relates to the technical field of deployment methods for physical noise reduction devices for high-altitude seismic wave sensors, and more specifically, to a method and system for acquiring high-altitude seismic wave data. Background Technology

[0002] Earthquake monitoring in high-altitude areas is crucial for geological exploration and disaster early warning. However, these areas are prone to strong winds (e.g., daily average wind speeds >15 m / s on plateaus) and have dramatic topographic relief, causing earthquake sensors to be continuously interfered with by vibration and noise from strong winds during data acquisition. Especially in complex slope terrain, dynamic changes in the wind field exacerbate the noise intensity, causing the signal-to-noise ratio of high-frequency seismic signals to drop sharply by more than 40%, severely restricting the acquisition of effective data.

[0003] Current solutions mainly fall into two categories:

[0004] (1) Physical protection measures: such as installing fixed windproof covers or burying sensors, but because the wind direction at high altitudes changes with the terrain in real time, static structures cannot adapt and adjust themselves, resulting in insufficient wind protection efficiency.

[0005] (2) Software noise reduction method: digital filtering algorithm is used to process the acquired data, but it requires high-frequency sampling and complex calculations, which is difficult to implement in real time in low-temperature and low-power devices, and it is easy to filter out the real signal.

[0006] Neither of the above two methods has been able to effectively balance the technical contradiction between windproof performance and equipment adaptability.

[0007] In summary, the technical problem of how to solve the decrease in the signal-to-noise ratio of seismic signals due to increased wind-induced vibration noise in high-altitude dynamic wind field environments is an urgent issue that needs to be addressed. Summary of the Invention

[0008] The main objective of this invention is to provide a method and system for acquiring seismic wave data at high altitudes, so as to at least solve the technical problem of the decrease in the signal-to-noise ratio of seismic signals caused by the increase of wind-induced vibration noise in dynamic wind field environments at high altitudes. Through a dynamic adaptive physical deployment mechanism, the vibration noise caused by dynamic wind field changes is suppressed from the source, and the signal-to-noise ratio of seismic signals is significantly improved.

[0009] To achieve the above objectives, the present invention provides a method and system for high-altitude seismic wave data acquisition.

[0010] In a first aspect, the present invention provides a method for acquiring seismic wave data at high altitudes, the method comprising:

[0011] Based on digital elevation model data and historical wind field data of the target area, a criterion for determining the stability zone of the leeward slope is defined.

[0012] Based on the leeward slope stability zone determination criteria, the terrain aspect, obstacle distribution, and wind shadow attenuation coefficient are analyzed to determine the deployment points of the seismic sensors, and the seismic sensors and anemometers are installed at the deployment points.

[0013] An angle-adjustable wind guide plate is installed at a preset distance on the windward side of the deployment point. The current wind speed data and real-time prevailing wind direction data are obtained in real time through the anemometer. The deflection angle of the angle-adjustable wind guide plate is dynamically adjusted according to the real-time prevailing wind direction data.

[0014] The earthquake sensor is triggered to acquire continuous seismic wave data, and the current wind speed data is recorded synchronously. When the current wind speed data exceeds a preset threshold, a timestamp is embedded in the continuous seismic wave data to form a segmented marked dataset.

[0015] The labeled data segment in the segmented labeled dataset is located based on the timestamp marker, and noise reduction processing is performed only on the labeled data segment to output the noise-reduced seismic wave data; wherein, the labeled data segment is generated when the current wind speed data exceeds a preset threshold.

[0016] Specifically, the criteria for determining the stability zone of the leeward slope, based on digital elevation model data and historical wind field data of the target area, include:

[0017] Calculate the wind direction incident angle at each coordinate point in the target area;

[0018] Based on the data from the digital elevation model, leeward slope areas with a slope greater than 20% are extracted;

[0019] Input the wind direction incident angle and the slope data of the leeward slope area into the leeward slope stability function, and output the leeward slope stability zone determination criteria.

[0020] Specifically, the step of analyzing the terrain aspect, obstacle distribution, and wind shadow attenuation coefficient based on the leeward slope stability zone determination criteria to determine the deployment points of the seismic sensors, and installing the seismic sensors and anemometers at the deployment points, includes:

[0021] Identify areas in the leeward slope stability zone where the obstacle density is less than 5 obstacles / km².

[0022] The wind shadow attenuation coefficient is calculated based on the height and spacing of obstacles in the stable leeward slope zone.

[0023] Coordinate points with a wind shadow attenuation coefficient greater than 0.7 and a slope variation standard deviation less than 10% were selected as deployment points, and the seismic sensor and anemometer were installed there.

[0024] Specifically, the installation of an angle-adjustable wind guide plate at a preset distance upwind of the deployment point, obtaining real-time wind speed and prevailing wind direction data via the anemometer, and dynamically adjusting the deflection angle of the angle-adjustable wind guide plate based on the real-time prevailing wind direction data, includes:

[0025] A lightweight aluminum alloy wind guide plate, physically connected to the anemometer, is installed on the windward side of the deployment point.

[0026] Acquire the real-time prevailing wind direction data collected by the anemometer;

[0027] Based on the real-time prevailing wind direction data, query the wind direction-angle mapping table and adjust the deflection angle to the 30°-60° range corresponding to the mapping table.

[0028] Specifically, the process involves triggering the seismic sensor to acquire continuous seismic wave data and simultaneously recording the current wind speed data. When the current wind speed data exceeds a preset threshold, a timestamp is embedded in the continuous seismic wave data to form a segmented labeled dataset, including:

[0029] The seismic sensor is activated to acquire continuous seismic wave data at a sampling rate of 100Hz.

[0030] Synchronously receive the current wind speed data stream transmitted by the anemometer;

[0031] When three consecutive data points in the current wind speed data stream are detected to exceed 15 m / s, a millisecond-level timestamp is embedded in the continuous seismic wave data for the corresponding time period to generate a segmented labeled dataset with a labeled index.

[0032] Specifically, the step of locating the labeled data segment in the segmented labeled dataset based on the timestamp, performing noise reduction processing only on the labeled data segment, and outputting the noise-reduced seismic wave data includes:

[0033] Parse the start time point of the timestamp mark End time ;

[0034] Extract from the segmented label dataset to The marked data segment of the interval;

[0035] The marked data segment is filtered by a moving average with a window length of 0.5 seconds to output noise-reduced seismic wave data.

[0036] Specifically, when three consecutive data points in the current wind speed data stream are detected to exceed 15 m / s, a millisecond-level timestamp is embedded in the continuous seismic wave data for the corresponding time period to generate a segmented labeled dataset with a labeled index, including:

[0037] Based on the sampling time interval of the current wind speed data stream, calculate the decision time window covering three consecutive sampling points;

[0038] When three consecutive wind speed data points within the judgment time window all exceed 15 m / s, the start timestamp T of the valid high wind speed event is output. s With end timestamp T e ;

[0039] The start timestamp T s and end timestamp T e Encoded as a millisecond-level timestamp index;

[0040] Locating the start timestamp T in the continuous seismic wave data s up to the end timestamp T e The corresponding data segment embeds the timestamp index and associates it with the event marker status bit;

[0041] Generate a segmented labeled dataset containing the time stamp index and the event stamp status bit; wherein the event stamp status bit is a preset encoding value 01, used to indicate the valid high wind speed event.

[0042] In a second aspect, the present invention provides a high-altitude seismic wave data acquisition system, wherein the acquisition system applies the acquisition method described in the first aspect, and the acquisition system includes:

[0043] The definition module is used to define the criteria for determining the stability zone of the leeward slope based on digital elevation model data and historical wind field data of the target area;

[0044] The deployment point determination module is connected to the definition module. The deployment point determination module is used to analyze the terrain slope aspect, obstacle distribution and wind shadow attenuation coefficient according to the leeward slope stability zone determination criteria to determine the deployment point of the seismic sensor.

[0045] The equipment installation module is connected to the deployment point determination module, and the equipment installation module is used to install earthquake sensors and anemometers at the deployment point;

[0046] A wind direction control module is connected to the equipment installation module. The wind direction control module is used to install an angle-adjustable wind guide plate at a preset distance on the windward side of the deployment point. The module acquires the current wind speed data and the real-time dominant wind direction data through the anemometer, and dynamically adjusts the deflection angle of the angle-adjustable wind guide plate according to the real-time dominant wind direction data.

[0047] The data acquisition and labeling module is connected to the wind guidance control module. The data acquisition and labeling module is used to trigger the seismic sensor to acquire continuous seismic wave data, synchronously record the current wind speed data, and embed timestamps into the continuous seismic wave data to form a segmented labeled dataset when the current wind speed data exceeds a preset threshold.

[0048] A directional noise reduction processing module is connected to the data acquisition and labeling module. The directional noise reduction processing module is used to locate the labeled data segment in the segmented labeled dataset according to the timestamp mark, perform noise reduction processing only on the labeled data segment, and output the noise-reduced seismic wave data; wherein the labeled data segment is generated by the current wind speed data exceeding a preset threshold.

[0049] Specifically, the definition module includes:

[0050] An incident angle calculation unit is used to calculate the wind direction incident angle at each coordinate point in the target area;

[0051] The slope extraction unit is used to extract leeward slope areas with a slope greater than 20% based on the digital elevation model data.

[0052] The stability function processing unit is connected to the incident angle calculation unit and the slope extraction unit. The stability function processing unit is used to input the wind direction incident angle and the slope data of the leeward slope area into the leeward slope stability function and output the leeward slope stability zone determination criteria.

[0053] Specifically, the deployment point determination module includes:

[0054] A low-density area identification unit is used to identify areas in the leeward slope stable zone where the obstacle density is less than 5 obstacles / km².

[0055] The attenuation coefficient calculation unit is used to calculate the wind shadow attenuation coefficient based on the height and spacing of obstacles in the leeward slope stability zone.

[0056] The deployment point selection unit is connected to the low-density area identification unit and the attenuation coefficient calculation unit. The deployment point selection unit is used to select coordinate points with a wind shadow attenuation coefficient greater than 0.7 and a slope change standard deviation of less than 10% as deployment points.

[0057] This application provides a method and system for high-altitude seismic wave data acquisition. The method first defines a leeward slope stability zone determination criterion based on digital elevation model data and historical wind field data of the target area. Based on this, it analyzes topographic slope aspect, obstacle distribution, and wind shadow attenuation coefficient to determine the deployment points of seismic sensors and install seismic sensors and anemometers. An adjustable-angle wind deflector is installed at a preset distance on the windward side of the deployment point. Real-time wind speed and prevailing wind direction data are acquired using the anemometer, and the deflection angle of the wind deflector is dynamically adjusted. The seismic sensors are triggered to acquire continuous seismic wave data and simultaneously record wind speed data. When the wind speed exceeds a preset threshold, a timestamp is embedded to form a segmented labeled dataset. The labeled data segment is located based on the timestamp, and only that segment is denoised before outputting denoised seismic wave data. This method solves the problem of signal-to-noise ratio degradation caused by wind-induced vibration noise under dynamic wind fields at high altitudes, thus improving the quality of seismic signals. Attached Figure Description

[0058] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0059] Figure 1 This is a flowchart illustrating the high-altitude seismic wave data acquisition method provided in this application.

[0060] Figure 2 This is a connection diagram for the high-altitude seismic wave data acquisition system provided in this application.

[0061] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0064] In this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0065] This application provides a method and system for acquiring seismic wave data at high altitudes. The method first uses digital elevation model data and historical wind field data of the target area to define criteria for determining the stability zone of the leeward slope, thereby determining the deployment points of seismic sensors and installing related equipment. An adjustable wind guide vane is installed on the windward side of the deployment point, and its angle is dynamically adjusted according to the real-time prevailing wind direction. The seismic sensors are triggered to acquire data and record wind speed; data is marked when the wind speed exceeds a threshold. Finally, the marked data segments are located and noise is reduced to suppress vibration noise at its source and improve the signal-to-noise ratio of the seismic signal.

[0066] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0067] Figure 1 This is a flowchart illustrating the high-altitude seismic wave data acquisition method provided in this application, as shown below. Figure 1 As shown, this embodiment provides a method for acquiring seismic wave data at high altitudes. The method includes:

[0068] S101: Based on digital elevation model data and historical wind field data of the target area, define the criteria for determining the stability zone of the leeward slope.

[0069] Specifically, the criteria for determining the stability zone of the leeward slope, based on digital elevation model data and historical wind field data of the target area, include:

[0070] Calculate the wind direction incident angle at each coordinate point in the target area;

[0071] Based on the data from the digital elevation model, leeward slope areas with a slope greater than 20% are extracted;

[0072] Input the wind direction incident angle and the slope data of the leeward slope area into the leeward slope stability function, and output the leeward slope stability zone determination criteria.

[0073] The specific steps of implementation S101 include:

[0074] 1. Calculation of wind direction and angle of incidence

[0075] 1.1 Input data preparation:

[0076] Obtain 10-meter resolution digital elevation model data for the target area (e.g., Nagqu Prefecture in Tibet Autonomous Region), using the WGS84 coordinate system.

[0077] Extract the historical prevailing wind direction data for the past 5 years recorded by the meteorological stations in the area (the average annual wind direction in the example area is 240°).

[0078] 1.2 Slope Aspect Calculation:

[0079] The slope analysis tool in ArcGIS Pro is used to process digital elevation model data and output the slope angle of each grid point (unit: degrees, 0° is due north, increasing clockwise to 360°).

[0080] 1.3 Calculation of wind direction and angle of incidence:

[0081] 1.3.1 Perform the following for each grid point:

[0082] Wind incidence angle = | Historical average prevailing wind direction - Current grid slope angle |.

[0083] (Example: If the historical prevailing wind direction is 240° and the slope angle of a certain grid is 210°, then the wind incidence angle is 30°).

[0084] 2. Extraction of leeward slope area

[0085] 2.1 Slope Calculation: The standard algorithm in geographic information science (Burrough third-order inverse distance squared weighted difference method) is adopted:

[0086] 2.1.1 Using the target grid as the center, read the elevation values ​​of 9 grids within a 3×3 window.

[0087] 2.1.2 Calculate the east-west elevation change rate: East-west elevation change rate = (weighted sum of elevations of the three right grids - weighted sum of elevations of the three left grids) / (8 × grid resolution).

[0088] 2.1.3 Calculate the north-south elevation change rate: North-south elevation change rate = (weighted sum of elevations of the three lower grids - weighted sum of elevations of the three upper grids) / (8 × grid resolution).

[0089] 2.1.4 Calculate the slope percentage: Slope percentage = 100 × arctan(√(east-west change rate² + north-south change rate²)).

[0090] 2.2 Area Filtering: Extract grids with a slope value greater than 20% (Example: The slope of a certain grid is calculated to be 25.3%).

[0091] 3. Leeward slope stability function treatment

[0092] 3.1 Stability Coefficient Calculation:

[0093] Stability coefficient = cos(wind angle of incidence) × (slope percentage / 100); (Example: wind angle of incidence 30°, slope 25.3%, stability coefficient = cos(30°) × 0.253 ≈ 0.65).

[0094] 3.2 Generation of Judgment Criteria:

[0095] Grids with a stability coefficient ≥ 0.6 are marked as leeward slope stability zones (0.65 in the example meets the criteria).

[0096] Output data: Generate a spatial distribution map of the stable zone on the leeward slope (GeoTIFF format), with a resolution of 10 meters, and each grid cell stores the stability coefficient value.

[0097] This step, through quantifying the interaction between topographic slope aspect and historical wind field (wind direction incident angle calculation) and slope analysis, accurately identifies stable leeward slope areas (stability coefficient ≥ 0.6) less affected by strong winds. In field measurements on the Nagqu Plateau in Tibet Autonomous Region (altitude 4800 meters), the wind speed in the selected area was 42% lower than that on the windward slope, and the power spectral density of wind-induced vibration noise decreased by 15 dB, significantly reducing high wind noise areas by 83%, providing a source-based basis for optimizing the site selection for earthquake sensor deployment to resist wind interference.

[0098] S102: Based on the leeward slope stability zone determination criteria, analyze the terrain aspect, obstacle distribution, and wind shadow attenuation coefficient to determine the deployment points of the seismic sensors, and install the seismic sensors and anemometers at the deployment points.

[0099] Specifically, the step of analyzing the terrain aspect, obstacle distribution, and wind shadow attenuation coefficient based on the leeward slope stability zone determination criteria to determine the deployment points of the seismic sensors, and installing the seismic sensors and anemometers at the deployment points, includes:

[0100] Identify areas in the leeward slope stability zone where the obstacle density is less than 5 obstacles / km².

[0101] The wind shadow attenuation coefficient is calculated based on the height and spacing of obstacles in the stable leeward slope zone.

[0102] Coordinate points with a wind shadow attenuation coefficient greater than 0.7 and a slope variation standard deviation less than 10% were selected as deployment points, and the seismic sensor and anemometer were installed there.

[0103] The specific steps of implementation S102 include:

[0104] 1. Identification of low obstacle density areas

[0105] 1.1 Input data:

[0106] Inherit the leeward slope stability zone determination layer (GeoTIFF format, 10-meter resolution, including grids with stability coefficient Stab≥0.6) output by S101.

[0107] 1.2 Obstacle Density Calculation:

[0108] Use the kernel density estimation algorithm of the geographic information system software QGIS to input the target area obstacle vector data (such as rock and bush locations).

[0109] Set the search radius to 500 meters and output an obstacle density raster layer (unit: obstacles / km²).

[0110] 1.3 Region Filtering:

[0111] Extract grid areas with obstacle density values ​​below 5 per km² (Example: a certain area has a density value of 3.2 per km²).

[0112] 2. Calculation of wind shadow attenuation coefficient

[0113] 2.1 Parameter Acquisition:

[0114] For each raster cell filtered in step 1:

[0115] Extract the obstacle height h (unit: meters, obtained from lidar point cloud data);

[0116] Calculate the average distance d between obstacles (unit: meters, calculated after segmenting obstacles using the Voronoi polygon method).

[0117] 2.2 Coefficient Calculation:

[0118] The wind shadow attenuation coefficient K was calculated using the standard fluid dynamics model.

[0119] K = 1 - 0.5 × (h / d)².

[0120] Parameter description:

[0121] h: Average height of the obstacle (Example: h = 2.5 meters);

[0122] d: Average distance between obstacles (Example: d = 15 meters);

[0123] Example result: K=1-0.5×(2.5 / 15)²≈0.986.

[0124] 3. Deployment site selection and equipment installation

[0125] 3.1 Terrain stability analysis: Calculate the standard deviation of slope variation within a 10×10 grid window (100m×100m). :

[0126] (n=100 grids, σ<10%)

[0127] in, This represents the slope value of the i-th grid cell. This represents the average slope value of all grid cells within the window.

[0128] 3.2 Deployment point conditions:

[0129] Simultaneously satisfy:

[0130] The wind shadow attenuation coefficient K > 0.7 (0.986 in the example is acceptable);

[0131] The standard deviation of slope variation σ is less than 10% (example: σ = 8.3% at a certain point).

[0132] 3.3 Equipment Installation:

[0133] Dig an installation pit 0.5 meters deep at the selected coordinate point (e.g., E92.8° N31.2°);

[0134] Fixed seismic sensor (any model, must meet sensitivity of 0.1mg) and three-cup anemometer (starting wind speed ≤0.5m / s).

[0135] The equipment is powered by a dual power source consisting of solar panels (power ≥10W) and lithium thionyl chloride batteries.

[0136] 4. Output data

[0137] Generate a spatial distribution map of deployment points (Shapefile format), with the following attributes:

[0138] Coordinates (latitude and longitude);

[0139] Wind shadow attenuation coefficient K;

[0140] Slope standard deviation σ;

[0141] Obstacle density value.

[0142] This step quantifies the distribution of obstacles (density <5 obstacles / km²) and terrain stability (slope standard deviation <10%), and calculates the wind shadow attenuation coefficient (K>0.7) using a fluid dynamics model. After selecting a deployment point on the Nagqu Plateau in Tibet Autonomous Region (altitude 4800 meters), field measurements show that the power spectral density of wind-induced vibration noise is reduced by 12dB (compared to non-optimized points), and the signal-to-noise ratio of the sensor signal is improved by 40%, providing an optimized deployment scheme to resist wind interference for high-altitude seismic data acquisition.

[0143] S103: Install an angle-adjustable wind guide plate at a preset distance on the windward side of the deployment point, and obtain the current wind speed data and real-time dominant wind direction data through the anemometer, and dynamically adjust the deflection angle of the angle-adjustable wind guide plate according to the real-time dominant wind direction data.

[0144] Specifically, the installation of an angle-adjustable wind guide plate at a preset distance upwind of the deployment point, obtaining real-time wind speed and prevailing wind direction data via the anemometer, and dynamically adjusting the deflection angle of the angle-adjustable wind guide plate based on the real-time prevailing wind direction data, includes:

[0145] A lightweight aluminum alloy wind guide plate, physically connected to the anemometer, is installed on the windward side of the deployment point.

[0146] Acquire the real-time prevailing wind direction data collected by the anemometer;

[0147] Based on the real-time prevailing wind direction data, query the wind direction-angle mapping table and adjust the deflection angle to the 30°-60° range corresponding to the mapping table.

[0148] The specific steps in step S103 during implementation include:

[0149] 1. Wind guide plate installation

[0150] 1.1 Determining the installation location:

[0151] At the deployment point determined by S102 (e.g., coordinates E92.8° N31.2°), 1.5 meters upwind (preset distance), dig an installation base with a depth of 0.3 meters.

[0152] 1.2 Hardware Connection:

[0153] Install a lightweight aluminum alloy wind guide plate (1.2m × 0.8m) and physically connect it to the anemometer (three-cup anemometer) via a rigid connecting rod, ensuring that the anemometer and the center axis of the wind guide plate are horizontally aligned.

[0154] 2. Real-time acquisition of dominant wind direction data

[0155] 2.1 Data Acquisition:

[0156] The anemometer is activated to collect real-time prevailing wind direction data at a sampling rate of 10 Hz (unit: degrees, 0° is due north, increasing clockwise to 360°).

[0157] 2.2 Data Transmission:

[0158] Wind direction data is transmitted in real time to the embedded controller (STM32F407 chip) via RS485 serial port protocol.

[0159] 3. Dynamic adjustment of deflection angle

[0160] 3.1 Mapping table lookup:

[0161] The controller calls the pre-stored wind direction-angle mapping table (see Table 1) and outputs the target deflection angle based on the real-time prevailing wind direction data:

[0162] Table 1: Wind Direction-Angle Mapping Table (Example)

[0163] Wind direction range (degrees) Deflection angle (degrees) 0°-45° and 315°-360° 30° 46°-135° 45° 136°-225° 60° 226°-314° 30°

[0164] 3.2 Angle adjustment execution:

[0165] The controller drives the stepper motor to rotate, and adjusts the wind guide plate to the target angle (accuracy ±2°) through the gear set. The angle range is strictly limited to 30°-60° (example: adjust to 45° when the wind direction is 120°).

[0166] This step uses a physically connected anemometer to acquire the prevailing wind direction data in real time, and drives a stepper motor to dynamically adjust the aluminum alloy wind guide plate to the 30°-60° range according to a preset mapping table (wind direction range → deflection angle). In actual measurements on the Nagqu Plateau in Tibet Autonomous Region (altitude 4800 meters), this mechanism reduced the power spectral density of wind-induced vibration noise by 18dB (compared to the unadjusted scheme) and improved the signal-to-noise ratio of seismic signals by 35%, effectively solving the problem of vibration and noise interference caused by dynamic wind fields at high altitudes.

[0167] S104: Trigger the seismic sensor to acquire continuous seismic wave data and simultaneously record the current wind speed data; when the current wind speed data exceeds a preset threshold, embed a timestamp marker into the continuous seismic wave data to form a segmented marker dataset.

[0168] Specifically, the process involves triggering the seismic sensor to acquire continuous seismic wave data and simultaneously recording the current wind speed data. When the current wind speed data exceeds a preset threshold, a timestamp is embedded in the continuous seismic wave data to form a segmented labeled dataset, including:

[0169] The seismic sensor is activated to acquire continuous seismic wave data at a sampling rate of 100Hz.

[0170] Synchronously receive the current wind speed data stream transmitted by the anemometer;

[0171] When three consecutive data points in the current wind speed data stream are detected to exceed 15 m / s, a millisecond-level timestamp is embedded in the continuous seismic wave data of the corresponding time period to generate a segmented labeled dataset with a labeled index. Specifically, this step includes: calculating a judgment time window covering three consecutive sampling points based on the sampling time interval of the current wind speed data stream; and outputting the start timestamp T of the valid high wind speed event when all three consecutive wind speed data points exceed 15 m / s within the judgment time window. s With end timestamp T e ; the start timestamp T s and end timestamp T e Encoded as a millisecond-level time-stamped index; locating the starting timestamp T in the continuous seismic wave data. s up to the end timestamp T e The corresponding data segment is embedded with the time stamp index and associated with the event stamp status bit; a segmented stamped dataset containing the time stamp index and the event stamp status bit is generated; wherein, the event stamp status bit is a preset encoding value 01, used to indicate the valid high wind speed event.

[0172] The specific steps in step S104 during implementation include:

[0173] 1. Seismic wave data acquisition initiated

[0174] 1.1 Device Initialization:

[0175] Start the seismic sensor (wideband velocity type, frequency response range 0.01-100Hz) installed at the deployment point, set the sampling rate to 100Hz, and collect continuous seismic wave data (time series format, unit: m / s).

[0176] 1.2 Data storage format:

[0177] The data is stored in the MiniSEED standard format, with each data block containing 4096 sampling points (duration 40.96 seconds).

[0178] 2. Synchronous reception of wind speed data stream

[0179] 2.1 Data transmission protocol:

[0180] The current wind speed data stream transmitted by the three-cup anemometer is acquired in real time via RS485 serial port (sampling rate 10Hz, unit: m / s).

[0181] 2.2 Time Alignment Mechanism:

[0182] The seismic sensor and anemometer clocks are synchronized using PTP (Precise Time Protocol) to ensure that the timestamp error is less than 1 millisecond.

[0183] 3. High wind speed event detection and tagging

[0184] 3.1 Calculation of the decision time window:

[0185] Based on the wind speed data stream sampling interval Δt = 0.1 seconds (10Hz), the decision time window covering three consecutive sampling points is calculated: T_window = 3 × Δt = 0.3 seconds.

[0186] 3.2 Continuous wind speed threshold determination:

[0187] Centered on the current time point t, wind speed data within the time window [t-0.15 seconds, t+0.15 seconds] is collected.

[0188] If three consecutive wind speed values ​​inside the window (e.g., t-0.1s, t, t+0.1s) are all greater than 15m / s, a high wind speed event is triggered.

[0189] 3.3 Event Timestamp Generation:

[0190] Start timestamp T s The time within the event window when the first wind speed point is greater than 15 m / s (e.g., t-0.1s).

[0191] End timestamp T e The time when the last wind speed point in the event window is greater than 15 m / s (e.g., t+0.1s).

[0192] 4. Generation of time stamp index

[0193] 4.1 Index Encoding Rules:

[0194] T s and T e Encoded as a 64-bit millisecond-level timestamp index:

[0195] Index_TS=(T s <<32)|T e

[0196] in:

[0197] Ts: Start timestamp (Unix time in milliseconds, such as 1717593605123);

[0198] T e : End timestamp (Unix time in milliseconds, such as 1717593605423).

[0199] 4.2 Status bit binding:

[0200] The associated event is marked with the status bit Flag_01 (fixed binary value 01, indicating a valid high wind speed event).

[0201] 5. Seismic wave data labeling

[0202] 5.1 Data Segment Location:

[0203] In continuous seismic wave data, the time range [T] is extracted. s ,T e The corresponding data segment (example: T) s =5123ms to T e =5423ms).

[0204] 5.2 Index Embedding:

[0205] Insert a 16-byte marker field at the beginning of the data segment: [start timestamp T] s (8 bytes)][End timestamp T e (8 bytes) [Status bit 01 (1 byte)].

[0206] 6. Generation of segmented labeled datasets

[0207] Output a segmented dataset with labeled indexes. The data structure is: [Label Header][Seismic Wave Data Segment][Checksum]; where the label header contains Index_TS and Flag_01. The seismic wave data segment contains the original waveform sampled at 100Hz (length = (T...). e -T s (100 points). Checksum: CRC32 check value.

[0208] This step involves precisely and synchronously acquiring seismic wave data (100Hz) and wind speed data (10Hz). When three consecutive wind speed points >15m / s are detected (with a time window of 0.3 seconds), a millisecond-level time stamp index (including start and end timestamps T) is embedded in the corresponding seismic wave data segment. s / T e (and status bit 01), generating a segmented labeled dataset. In field measurements on the Nagqu Plateau (4800 meters above sea level) in the Tibet Autonomous Region, this mechanism achieved a 95% accuracy rate in labeling wind noise events, reduced the computational load of subsequent noise reduction processing by 70%, and effectively improved the signal-to-noise ratio of high-altitude seismic signals by 35%.

[0209] S105: Locate the labeled data segment in the segmented labeled dataset according to the timestamp mark, perform noise reduction processing only on the labeled data segment, and output the noise-reduced seismic wave data.

[0210] The marked data segment is generated when the current wind speed data exceeds a preset threshold.

[0211] Specifically, the step of locating the labeled data segment in the segmented labeled dataset based on the timestamp, performing noise reduction processing only on the labeled data segment, and outputting the noise-reduced seismic wave data includes:

[0212] Parse the start time point of the timestamp mark End time ;

[0213] Extract from the segmented label dataset to The marked data segment of the interval;

[0214] The marked data segment is filtered by a moving average with a window length of 0.5 seconds to output noise-reduced seismic wave data.

[0215] The specific steps in step S105 during implementation include:

[0216] 1. Timestamp parsing

[0217] 1.1 Input data source:

[0218] Receive the segmented labeled dataset (MiniSEED format) generated by S104, which contains a millisecond-level time stamp index.

[0219] 1.2 Time Point Extraction:

[0220] Read the 16-byte tag field from the tag header:

[0221] Start time point The first 8 bytes store the Unix timestamp (millisecond value, such as 1717593605123).

[0222] End time The last 8 bytes store the Unix timestamp (millisecond value, such as 1717593605423).

[0223] 1.3 Time Conversion:

[0224] Convert Unix timestamps to UTC time format (example: =2024-06-05 08:00:05.123, =08:00:05.423).

[0225] 2. Extraction of marked data segments

[0226] 2.1 Data Location:

[0227] In the segmented labeled dataset, time intervals are extracted [ , The corresponding continuous seismic wave data segment:

[0228] Data length calculation:

[0229] Number of data points = ( - ) × sampling rate;

[0230] (Sampling rate 100Hz, example:) - =300 milliseconds → Number of data points = 30).

[0231] 2.2 Data Extraction:

[0232] Accurately copy from the raw seismic wave data stream to Data for a given time period (Example: containing 30 sampling points).

[0233] 3. Moving average filtering for noise reduction

[0234] 3.1 Filtering algorithm execution:

[0235] Apply a moving average filter with a window size of 0.5 seconds (standard signal processing algorithm) to the marked data segment:

[0236] 3.1.1 Set the sliding window width W = 50 points (0.5 seconds × 100Hz);

[0237] 3.1.2 For each target point n, calculate the mean within the window:

[0238] y[n]=(x[n-24]+x[n-23]+...+x[n]+...+x[n+25]) / 50;

[0239] Parameter description:

[0240] x[n]: Original seismic wave data points;

[0241] y[n]: Filtered output;

[0242] The value of n ranges from 25 to (number of data points - 25).

[0243] 3.2 Boundary processing: The first and last 25 points are supplemented with data using the mirror extension method.

[0244] 4. Noise reduction data output

[0245] 4.1 Data Structure:

[0246] Output denoised seismic wave data segments (time interval [ , (The format should adhere to the MiniSEED standard.)

[0247] 4.2 Data Association: Retain the original tag header information (including timestamp index and status bit 01).

[0248] This step accurately locates the seismic wave data segment corresponding to the high wind speed event by parsing the time stamp index. to The data segment was denoised using a moving average filter with a window length of 0.5 seconds. In actual measurements on the Nagqu Plateau in Tibet Autonomous Region (altitude 4800 meters), the computational load was reduced by 70% and the signal-to-noise ratio was improved by 35% compared to the global filtering scheme, effectively preserving the integrity of the original seismic signal in non-wind-noise areas.

[0249] This embodiment provides a method for acquiring seismic wave data at high altitudes. Based on digital elevation model data and historical wind field data of the target area, the method defines a criterion for determining the stability zone of the leeward slope. Based on this criterion, it analyzes the terrain aspect, obstacle distribution, and wind shadow attenuation coefficient to accurately determine the deployment points of seismic sensors, and then installs the seismic sensors and anemometers. An adjustable-angle wind deflector is installed at a preset distance on the windward side of the deployment point. The anemometer acquires real-time data on current wind speed and prevailing wind direction, and the deflection angle of the wind deflector is dynamically adjusted. The seismic sensors are triggered to acquire continuous seismic wave data and simultaneously record wind speed data. When the wind speed exceeds a preset threshold, a timestamp is embedded to form a segmented labeled dataset. The labeled data segment is located based on the timestamp, and noise reduction processing is performed only on that segment before outputting the denoised seismic wave data. This method effectively solves the problem of decreased signal-to-noise ratio (SNR) of seismic signals due to wind-induced vibration noise under dynamic wind fields at high altitudes, achieving noise suppression and improved SNR from the source.

[0250] Figure 2 This application provides a connection diagram for a high-altitude seismic wave data acquisition system, as shown below. Figure 2 As shown, this is the high-altitude seismic wave data acquisition system provided in this embodiment. This system applies... Figure 1 The method for acquiring high-altitude seismic wave data described in the embodiment includes an acquisition system comprising:

[0251] The definition module is used to define the criteria for determining the stability zone of the leeward slope based on digital elevation model data and historical wind field data of the target area;

[0252] The deployment point determination module is connected to the definition module. The deployment point determination module is used to analyze the terrain slope aspect, obstacle distribution and wind shadow attenuation coefficient according to the leeward slope stability zone determination criteria to determine the deployment point of the seismic sensor.

[0253] The equipment installation module is connected to the deployment point determination module, and the equipment installation module is used to install earthquake sensors and anemometers at the deployment point;

[0254] A wind direction control module is connected to the equipment installation module. The wind direction control module is used to install an angle-adjustable wind guide plate at a preset distance on the windward side of the deployment point. The module acquires the current wind speed data and the real-time dominant wind direction data through the anemometer, and dynamically adjusts the deflection angle of the angle-adjustable wind guide plate according to the real-time dominant wind direction data.

[0255] The data acquisition and labeling module is connected to the wind guidance control module. The data acquisition and labeling module is used to trigger the seismic sensor to acquire continuous seismic wave data, synchronously record the current wind speed data, and embed timestamps into the continuous seismic wave data to form a segmented labeled dataset when the current wind speed data exceeds a preset threshold.

[0256] A directional noise reduction processing module is connected to the data acquisition and labeling module. The directional noise reduction processing module is used to locate the labeled data segment in the segmented labeled dataset according to the timestamp mark, perform noise reduction processing only on the labeled data segment, and output the noise-reduced seismic wave data; wherein the labeled data segment is generated by the current wind speed data exceeding a preset threshold.

[0257] Specifically, the definition module includes:

[0258] An incident angle calculation unit is used to calculate the wind direction incident angle at each coordinate point in the target area;

[0259] The slope extraction unit is used to extract leeward slope areas with a slope greater than 20% based on the digital elevation model data.

[0260] The stability function processing unit is connected to the incident angle calculation unit and the slope extraction unit. The stability function processing unit is used to input the wind direction incident angle and the slope data of the leeward slope area into the leeward slope stability function and output the leeward slope stability zone determination criteria.

[0261] Specifically, the deployment point determination module includes:

[0262] A low-density area identification unit is used to identify areas in the leeward slope stable zone where the obstacle density is less than 5 obstacles / km².

[0263] The attenuation coefficient calculation unit is used to calculate the wind shadow attenuation coefficient based on the height and spacing of obstacles in the leeward slope stability zone.

[0264] The deployment point selection unit is connected to the low-density area identification unit and the attenuation coefficient calculation unit. The deployment point selection unit is used to select coordinate points with a wind shadow attenuation coefficient greater than 0.7 and a slope change standard deviation of less than 10% as deployment points.

[0265] In practice, the high-altitude seismic wave data acquisition system provided in this embodiment specifically includes:

[0266] I. System Hardware Architecture and Connection Relationships

[0267] 1. Define the module

[0268] 1.1 Hardware Components:

[0269] Industrial-grade embedded processor (ARM Cortex-A53 architecture, 1.2GHz).

[0270] Digital elevation model data storage unit (eMMC flash memory, capacity 64GB);

[0271] Historical wind field database unit (SD card interface, supports FAT32 format).

[0272] 1.2 Physical Connections:

[0273] The processor connects to the data storage unit via the PCIe bus;

[0274] The processor connects to the database unit via the SPI interface.

[0275] 1.3 Workflow:

[0276] The processor calls the Burrough third-order inverse distance squared weight difference algorithm to calculate the slope (see S101 embodiment for steps), runs the wind direction incident angle calculation program (code logic: θ=|β-α|), and outputs the leeward slope stability zone determination layer to the deployment point determination module.

[0277] 2. Deployment point determination module

[0278] 2.1 Hardware Components:

[0279] Spatial analysis coprocessor (FPGA chip);

[0280] Obstacle LiDAR scanning unit (905nm wavelength, scanning accuracy ±0.1m).

[0281] 2.2 Physical Connections:

[0282] The FPGA is connected to the UART interface of the LiDAR via GPIO pins;

[0283] The processor sends stable region layer data to the FPGA via the AXI bus.

[0284] 2.3 Workflow:

[0285] FPGA execution:

[0286] Obstacle density calculation: Kernel density estimation algorithm is used to process lidar point cloud data;

[0287] Wind shadow attenuation coefficient calculation: Real-time solution K=1-0.5×(h / d)² (h is the obstacle height, d is the average spacing);

[0288] Deployment point output: Filter coordinates with K>0.7 and slope standard deviation<10% (RS232 output to equipment installation module).

[0289] 3. Equipment Installation Module

[0290] 3.1 Hardware Components:

[0291] Robotic arm control unit (stepper motor driven, positioning accuracy ±2cm);

[0292] Sensor mounting platform (made of carbon fiber, with a load-bearing capacity of 5kg).

[0293] 3.2 Physical Connections:

[0294] The control unit receives the deployment point coordinates via the CAN bus;

[0295] The platform is equipped with a seismic sensor (velocity type, frequency response 0.01-100Hz) and an anemometer (ultrasonic type, range 0-60m / s).

[0296] 3.1 Workflow:

[0297] The robotic arm drills a 0.5-meter-deep mounting hole at the deployment point, fixes the sensor, and connects the power supply (12V DC).

[0298] 4. Wind guidance control module

[0299] 4.1 Hardware Components:

[0300] Lightweight aluminum alloy wind deflector (1.2m × 0.8m, 2mm thick);

[0301] Stepper motor (torque 34.3mN·m);

[0302] Wind direction-angle mapping table storage chip (EEPROM, 256 bytes).

[0303] 4.2 Physical Connections:

[0304] The anemometer outputs real-time dominant wind direction data to the motor controller via RS485;

[0305] The controller queries the mapping table to drive the motor to rotate (angle feedback accuracy ±1°).

[0306] 4.3 Workflow:

[0307] Real-time wind direction data → Mapping table query (e.g., 240° → 45°) → Adjust the motor guide plate to the 30°-60° range.

[0308] 5. Data Acquisition and Labeling Module

[0309] 5.1 Hardware Components:

[0310] Seismic data acquisition card (24-bit ADC, 100Hz sampling rate);

[0311] Time synchronization unit (GPS / BeiDou dual-mode, timing accuracy ±1μs).

[0312] 5.2 Physical Connections:

[0313] The acquisition card connects to the seismic sensor via an LVDS interface;

[0314] The synchronization unit aligns the anemometer clock via the PTP protocol.

[0315] 5.3 Workflow:

[0316] Detect three consecutive wind speed points >15m / s (time window 0.3 seconds) → generate millisecond-level time stamp index → ​​write to MiniSEED data header.

[0317] 6. Directional noise reduction processing module

[0318] 6.1 Hardware Components:

[0319] Digital signal processor (1 GFLOPS of floating-point operations);

[0320] Dedicated circuit for moving average filtering (window length adjustable in 0.5 seconds).

[0321] 6.2 Physical Connections:

[0322] The DSP reads the marked data segment through the DMA channel;

[0323] The filtering circuit outputs noise-reduced data to the Gigabit Ethernet interface.

[0324] 6.3 Workflow:

[0325] Parsing time stamps - → Extract data segment → 50-point moving average filter (boundary mirror extension) → Output noise-reduced seismic wave.

[0326] II. System Linkage Effect

[0327] This system was deployed on the Nagqu Plateau (4800 meters above sea level) in the Tibet Autonomous Region, and actual measurements showed:

[0328] The defined stable zone output by the module reduces the wind speed at the installation point by 42% (compared to the windward slope).

[0329] The wind guidance control module dynamically adjusts (30°-60°) to reduce wind noise by 18dB;

[0330] The directional noise reduction module only processes the marked segment (accounting for 12% of the total data stream), reducing the computational load by 70% and improving the signal-to-noise ratio by 35%.

[0331] The modules achieve data closure through standardized interfaces (RS485 / CAN / LVDS) to solve the problem of vibration and noise interference caused by dynamic wind fields at high altitudes.

[0332] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0333] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for acquiring seismic wave data at high altitudes, characterized in that, The method includes: Based on digital elevation model data and historical wind field data of the target area, a criterion for determining the stability zone of the leeward slope is defined. Based on the leeward slope stability zone determination criteria, the terrain aspect, obstacle distribution, and wind shadow attenuation coefficient are analyzed to determine the deployment points of the seismic sensors, and the seismic sensors and anemometers are installed at the deployment points. An angle-adjustable wind guide plate is installed at a preset distance on the windward side of the deployment point. The current wind speed data and real-time prevailing wind direction data are obtained in real time through the anemometer. The deflection angle of the angle-adjustable wind guide plate is dynamically adjusted according to the real-time prevailing wind direction data. The earthquake sensor is triggered to acquire continuous seismic wave data, and the current wind speed data is recorded synchronously. When the current wind speed data exceeds a preset threshold, a timestamp is embedded in the continuous seismic wave data to form a segmented marked dataset. The labeled data segment in the segmented labeled dataset is located based on the timestamp marker, and noise reduction processing is performed only on the labeled data segment to output the noise-reduced seismic wave data; wherein, the labeled data segment is generated when the current wind speed data exceeds a preset threshold.

2. The method for acquiring seismic wave data at high altitudes according to claim 1, characterized in that, The criteria for determining the stability zone of the leeward slope, based on digital elevation model data and historical wind field data of the target area, include: Calculate the wind direction incident angle at each coordinate point in the target area; Based on the data from the digital elevation model, leeward slope areas with a slope greater than 20% are extracted; Input the wind direction incident angle and the slope data of the leeward slope area into the leeward slope stability function, and output the leeward slope stability zone determination criteria.

3. The method for acquiring seismic wave data at high altitudes according to claim 1, characterized in that, The step of analyzing the terrain aspect, obstacle distribution, and wind shadow attenuation coefficient based on the leeward slope stability zone determination criteria to determine the deployment points of the seismic sensors, and installing the seismic sensors and anemometers at the deployment points, includes: Identify areas in the leeward slope stability zone where the obstacle density is less than 5 obstacles / km². The wind shadow attenuation coefficient is calculated based on the height and spacing of obstacles in the stable leeward slope zone. Coordinate points with a wind shadow attenuation coefficient greater than 0.7 and a slope variation standard deviation less than 10% were selected as deployment points, and the seismic sensor and anemometer were installed there.

4. The method for acquiring seismic wave data at high altitudes according to claim 1, characterized in that, The installation of an angle-adjustable wind guide plate at a preset distance on the windward side of the deployment point, and the acquisition of current wind speed data and real-time prevailing wind direction data through the anemometer, and the dynamic adjustment of the deflection angle of the angle-adjustable wind guide plate based on the real-time prevailing wind direction data, includes: A lightweight aluminum alloy wind guide plate, physically connected to the anemometer, is installed on the windward side of the deployment point. Acquire the real-time prevailing wind direction data collected by the anemometer; Based on the real-time prevailing wind direction data, query the wind direction-angle mapping table and adjust the deflection angle to the 30°-60° range corresponding to the mapping table.

5. The method for acquiring seismic wave data at high altitudes according to claim 1, characterized in that, The earthquake sensor is triggered to acquire continuous seismic wave data, and the current wind speed data is recorded simultaneously. When the current wind speed data exceeds a preset threshold, a timestamp is embedded in the continuous seismic wave data to form a segmented labeled dataset, including: The seismic sensor is activated to acquire continuous seismic wave data at a sampling rate of 100Hz. Synchronously receive the current wind speed data stream transmitted by the anemometer; When three consecutive data points in the current wind speed data stream are detected to exceed 15 m / s, a millisecond-level timestamp is embedded in the continuous seismic wave data for the corresponding time period to generate a segmented labeled dataset with a labeled index.

6. The method for acquiring seismic wave data at high altitudes according to claim 1, characterized in that, The step of locating the labeled data segment in the segmented labeled dataset based on the timestamp, performing noise reduction processing only on the labeled data segment, and outputting the noise-reduced seismic wave data includes: Parse the start time point of the timestamp mark and End time; Extract from the segmented label dataset to The marked data segment of the interval; The marked data segment is filtered by a moving average with a window length of 0.5 seconds to output noise-reduced seismic wave data.

7. The method for acquiring seismic wave data at high altitudes according to claim 5, characterized in that, When three consecutive data points in the current wind speed data stream are detected to exceed 15 m / s, a millisecond-level timestamp is embedded in the continuous seismic wave data for the corresponding time period to generate a segmented labeled dataset with a labeled index, including: Based on the sampling time interval of the current wind speed data stream, calculate the decision time window covering three consecutive sampling points; When three consecutive wind speed data points within the judgment time window all exceed 15 m / s, the start timestamp T of the valid high wind speed event is output. s With end timestamp T e ; The start timestamp T s and end timestamp T e Encoded as a millisecond-level timestamp index; Locating the start timestamp T in the continuous seismic wave data s up to the end timestamp T e The corresponding data segment embeds the timestamp index and associates it with the event marker status bit; Generate a segmented labeled dataset containing the time stamp index and the event stamp status bit; wherein the event stamp status bit is a preset encoding value 01, used to indicate the valid high wind speed event.

8. A system for acquiring seismic wave data at high altitudes, characterized in that, The acquisition system employs the acquisition method according to any one of claims 1-7, and the acquisition system comprises: The definition module is used to define the criteria for determining the stability zone of the leeward slope based on digital elevation model data and historical wind field data of the target area; The deployment point determination module is connected to the definition module. The deployment point determination module is used to analyze the terrain slope aspect, obstacle distribution and wind shadow attenuation coefficient according to the leeward slope stability zone determination criteria to determine the deployment point of the seismic sensor. The equipment installation module is connected to the deployment point determination module, and the equipment installation module is used to install earthquake sensors and anemometers at the deployment point; A wind direction control module is connected to the equipment installation module. The wind direction control module is used to install an angle-adjustable wind guide plate at a preset distance on the windward side of the deployment point. The module acquires the current wind speed data and the real-time dominant wind direction data through the anemometer, and dynamically adjusts the deflection angle of the angle-adjustable wind guide plate according to the real-time dominant wind direction data. The data acquisition and labeling module is connected to the wind guidance control module. The data acquisition and labeling module is used to trigger the seismic sensor to acquire continuous seismic wave data, synchronously record the current wind speed data, and embed timestamps into the continuous seismic wave data to form a segmented labeled dataset when the current wind speed data exceeds a preset threshold. A directional noise reduction processing module is connected to the data acquisition and labeling module. The directional noise reduction processing module is used to locate the labeled data segment in the segmented labeled dataset according to the timestamp mark, perform noise reduction processing only on the labeled data segment, and output the noise-reduced seismic wave data; wherein the labeled data segment is generated by the current wind speed data exceeding a preset threshold.

9. The system according to claim 8, characterized in that, The definition module includes: An incident angle calculation unit is used to calculate the wind direction incident angle at each coordinate point in the target area; The slope extraction unit is used to extract leeward slope areas with a slope greater than 20% based on the digital elevation model data. The stability function processing unit is connected to the incident angle calculation unit and the slope extraction unit. The stability function processing unit is used to input the wind direction incident angle and the slope data of the leeward slope area into the leeward slope stability function and output the leeward slope stability zone determination criteria.

10. The system according to claim 8, characterized in that, The deployment point determination module includes: A low-density area identification unit is used to identify areas in the leeward slope stable zone where the obstacle density is less than 5 obstacles / km². The attenuation coefficient calculation unit is used to calculate the wind shadow attenuation coefficient based on the height and spacing of obstacles in the leeward slope stability zone. The deployment point selection unit is connected to the low-density area identification unit and the attenuation coefficient calculation unit. The deployment point selection unit is used to select coordinate points with a wind shadow attenuation coefficient greater than 0.7 and a slope change standard deviation of less than 10% as deployment points.

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