Method and device for monitoring volcanic eruption events based on real-time seismic amplitude measurements

Abstract

The application provides a volcano eruption event monitoring method and device based on real-time seismic amplitude measurement, which comprises the following steps: obtaining key information and seismic waveform data of a target station; performing standardization preprocessing on the seismic waveform data through signal processing parameters, and converting the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence; configuring an RSAM numerical threshold according to a monitoring alarm parameter, and counting the duration of the RSAM numerical value in the RSAM data sequence exceeding the RSAM numerical threshold; and generating target event alarm information in the case that the duration exceeds a time threshold. The method and device can effectively distinguish real volcano events from environmental interference, have high reliability, do not need to rely on a large amount of high-quality labeled data, have high algorithm efficiency and low algorithm cost.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and apparatus for monitoring volcanic eruption events based on real-time seismic amplitude measurement. Background Technology

[0002] In related technologies, methods for identifying volcanic ionospheric anomalies are commonly used to achieve efficient monitoring of volcanic eruptions. Alternatively, deep learning models can be used to fuse HRRS (High-Resolution Remote Sensing Optical Image) data and SAR (Synthetic Aperture Radar) image data to achieve accurate volcanic eruption early warning. However, these methods rely on indirect response signals of volcanic activity (the impact of eruptions on the ionosphere and changes in landforms). The confidence level of such indirect correlations is usually lower than that of monitoring direct physical signals such as seismic waves. Anomalies may originate from other non-volcanic factors, leading to unclear causal relationships and a high risk of false alarms. Furthermore, using deep learning models for volcanic activity prediction requires massive amounts of high-quality labeled data for training. Volcanic eruptions are rare, and obtaining labeled data is extremely difficult. Model training is costly and time-consuming, and it is difficult to generalize to volcanoes in different regions, resulting in low reliability and timeliness in monitoring volcanic eruptions. Summary of the Invention

[0003] This invention provides a method and device for monitoring volcanic eruption events based on real-time seismic amplitude measurement. This addresses the shortcomings of existing technologies for monitoring volcanic activity, which rely on indirect response signals from volcanic activity, have a high risk of false alarms, and require massive amounts of high-quality labeled data for model training, resulting in high training costs and long cycles, leading to low reliability and timeliness in volcanic activity monitoring.

[0004] This invention provides a method for monitoring volcanic eruption events based on real-time seismic amplitude measurement, comprising: Acquire key information and seismic waveform data of the target station; the key information includes signal processing parameters and monitoring and alarm parameters, and different target stations are associated with different volcanic information. The seismic waveform data is standardized and preprocessed using the signal processing parameters, and the processed seismic waveform data is converted into a real-time seismic amplitude measurement (RSAM) data sequence, which includes multiple consecutive time-series RSAM values. Configure RSAM value thresholds according to monitoring alarm parameters, and count the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds. If the duration exceeds the time threshold, generate target event alarm information; wherein, different monitoring alarm parameters correspond to different RSAM value thresholds.

[0005] According to the present invention, a method for monitoring volcanic eruption events based on real-time seismic amplitude measurement is provided, wherein the key information further includes station parameters; The seismic waveform data was obtained through the following steps: Based on the station parameters and seismic data standard protocol, the seismic waveform data is received from the SeisComp system using an incremental appending mechanism.

[0006] According to the present invention, a method for monitoring volcanic eruption events based on real-time seismic amplitude measurement is provided, wherein the signal processing parameters include a bandpass filter frequency range; The standardization preprocessing of the seismic waveform data using the signal processing parameters includes: The earthquake waveform data is converted into ground velocity data according to the specified format. The ground motion velocity data is filtered according to the bandpass filter frequency range, and abnormal data in the filtered data is removed.

[0007] According to the present invention, a method for monitoring volcanic eruption events based on real-time seismic amplitude measurement is provided, wherein the monitoring and alarm parameters include observation window parameters; The configuration of RSAM numerical thresholds based on monitoring alarm parameters includes: Determine the median corresponding to the observation window parameters; The RSAM numerical threshold is calculated using the following formula: T R =M+n·S; Among them, T R Here, is the RSAM value, M is the median, S is the standard deviation corresponding to the observation window parameter, and n is the coefficient, where n is an integer.

[0008] According to the present invention, a method for monitoring volcanic eruption events based on real-time seismic amplitude measurement is provided, wherein the target event alarm information includes the event number, the target station number, the duration, and the peak value corresponding to each RSAM in the RSAM data sequence; After generating the target event alarm information, the method further includes: The target event alarm information is structured and encapsulated to obtain encapsulated data; The encapsulated data is sent to the monitoring and early warning model management platform via the API interface, so that the monitoring and early warning model management can determine the target event type by comparing the encapsulated data with at least one of the image monitoring data and gas monitoring data.

[0009] The present invention also provides a volcanic eruption event monitoring device based on real-time seismic amplitude measurement, comprising: The data acquisition module is used to acquire key information and seismic waveform data of the target station; the key information includes signal processing parameters and monitoring alarm parameters, and different target stations are associated with different volcanic information; The calculation module is used to perform standardized preprocessing on the seismic waveform data using the signal processing parameters, and to convert the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence, wherein the RSAM data sequence includes multiple consecutive time-series RSAM values. The statistics module is used to configure RSAM value thresholds according to monitoring alarm parameters, and to count the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds. If the duration exceeds the time threshold, target event alarm information is generated. Different monitoring alarm parameters correspond to different RSAM value thresholds.

[0010] According to the present invention, a volcanic eruption event monitoring device based on real-time seismic amplitude measurement is provided, wherein the target event alarm information includes the event number, the target station number, the duration, and the peak value corresponding to each RSAM in the RSAM data sequence; The device further includes: The data sending module is used to encapsulate the target event alarm information in a structured manner after the target event alarm information is generated, so as to obtain encapsulated data. The encapsulated data is sent to the monitoring and early warning model management platform via the API interface, so that the monitoring and early warning model management can determine the target event type by comparing the encapsulated data with at least one of the image monitoring data and gas monitoring data.

[0011] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the volcanic eruption event monitoring method based on real-time seismic amplitude measurement as described above.

[0012] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the volcanic eruption event monitoring method based on real-time seismic amplitude measurement as described above.

[0013] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the volcanic eruption event monitoring method based on real-time seismic amplitude measurement as described above.

[0014] The present invention provides a method and device for monitoring volcanic eruptions based on real-time seismic amplitude measurement. This method standardizes and preprocesses seismic waveform data using signal processing parameters, converts the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence, configures RSAM value thresholds according to monitoring and alarm parameters, and counts the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value threshold. When the duration exceeds the time threshold, a target event alarm is generated. By designing a dual-condition joint judgment of amplitude exceeding the threshold and duration for volcanic activity time alarms, this method can effectively distinguish between real volcanic events and environmental interference, exhibiting high reliability and not relying on massive amounts of high-quality labeled data. The algorithm is highly efficient and low-cost. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided by the present invention.

[0017] Figure 2 This is the logical design diagram of the volcano RSAM monitoring model provided by the present invention.

[0018] Figure 3 This is a schematic diagram of the volcanic eruption event monitoring device based on real-time seismic amplitude measurement provided by the present invention.

[0019] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] The following is combined Figures 1-3 This invention describes a method and apparatus for monitoring volcanic eruption events based on real-time seismic amplitude measurement.

[0022] Figure 1 This is a flowchart illustrating the volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps: Step 110: Obtain key information and seismic waveform data of the target station; key information includes signal processing parameters and monitoring alarm parameters, and different target stations are associated with different volcanic information.

[0023] In this step, the target station can refer to a seismic monitoring station deployed in the volcano monitoring area, such as a station equipped with a broadband seismometer or a short-period seismometer.

[0024] In this step, in order to adapt to different volcano monitoring needs, users can pre-set configuration parameters, i.e. key information, including signal processing parameters (such as filtering frequency range) used to define data cleaning rules, and monitoring alarm parameters (such as observation window duration and minimum duration threshold) used to set trigger logic.

[0025] It should be noted that this implementation supports both independent monitoring of a single seismic station and parallel monitoring of multiple seismic stations of the same type distributed in the same volcanic area or different areas. That is, it adopts a one-to-one station configuration and service docking scheme, supports flexible configuration in multi-station monitoring scenarios, and supports concurrent processing and horizontal expansion capabilities of multiple stations. Each station can be configured with independent key information according to its geological background and equipment environment.

[0026] In this embodiment, a real-time streaming data access mechanism can be used to establish a persistent network connection with an earthquake data center (such as the SeisComp system or the IRIS data center), thereby achieving real-time data synchronization by receiving the latest waveform data packets (usually in MiniSEED format) collected by the stations.

[0027] Step 120: Perform standardization preprocessing on the seismic waveform data using signal processing parameters, and convert the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence, which includes multiple consecutive time-series RSAM values.

[0028] In this step, standardization preprocessing is the process of converting the raw seismic waveform data into a uniform physical quantity (ground velocity, unit μm / s) and filtering out noise.

[0029] In this step, by reducing the dimensionality of the high-frequency sampled waveform data into amplitude time series data that is easier to analyze for trend analysis, RSAM (Real-time Seismic Amplitude Measurement) data sequence can be obtained. This process is used to eliminate differences in instrument response, thereby extracting the core indicators that reflect the strength of volcanic activity energy.

[0030] Specifically, the seismic waveform data is first corrected to ground velocity physical quantities based on the instrument response files of the stations. Then, preset signal processing parameters (0.5Hz-5.0Hz bandpass filtering) are applied to filter out high-frequency wind noise and low-frequency tidal interference, while retaining the characteristic signals of volcanic activity. Next, the filtered continuous waveform is segmented according to preset time windows (the window length can be configured according to user needs, for example, one minute is a calculation unit). For each minute data segment, the absolute value of its amplitude is first taken, and then the average value is calculated to obtain the RSAM value of the current energy intensity. As time goes by, these values ​​are continuously generated, and finally an RSAM data sequence reflecting the trend of volcanic activity is obtained.

[0031] This embodiment can also have an adaptive sampling mechanism, which dynamically adjusts the sampling rate according to the characteristics of volcanic activity, balancing data accuracy and computational efficiency.

[0032] Specifically, in the process of converting seismic waveform data into RSAM data, instead of using a fixed sampling frequency, the sampling density (i.e., the size of the time window) for calculating RSAM values ​​is intelligently switched according to the current monitored volcanic activity status (such as a quiet period or an active period). This ensures that rapidly changing volcanic activity characteristics can be captured while maximizing the saving of computing resources and storage space.

[0033] In this step, regardless of the type of seismograph (such as broadband or short-period) or the original data format (SEED, MiniSEED, etc.) used at the front end, the processed RSAM data will be encapsulated into a data stream format with a completely consistent structure. For example, RSAM data sequences contain fields such as standard timestamps, amplitude values ​​in uniform physical units, and mass identifiers, eliminating the barriers between heterogeneous data sources.

[0034] Step 130: Configure RSAM value thresholds according to monitoring alarm parameters, and count the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds. If the duration exceeds the time threshold, generate target event alarm information. Different monitoring alarm parameters correspond to different RSAM value thresholds.

[0035] In this step, the target event alarm information is a set of metadata used to describe the details of a potential volcanic activity event. The target event alarm information includes one or more of the following: event number, target station number, duration, and peak values ​​corresponding to each RSAM in the RSAM data sequence.

[0036] In this step, statistical methods are used to dynamically generate RSAM numerical thresholds based on historical data, or a preset fixed threshold is used as the RSAM numerical threshold for the current detection task.

[0037] In this step, the RSAM numerical threshold is dynamically updated with the data in the observation window. When the background of volcanic activity changes (for example, from a calm state with extremely low background noise to a trembling state with frequent magma activity), the algorithm can automatically sense this volcanic activity state and raise or lower the threshold accordingly, so as to maintain reasonable alarm sensitivity in different activity stages.

[0038] For example, a sliding window mechanism (such as the most recent hour) can be used to calculate the statistical characteristics (median and standard deviation) of RSAM data within the window corresponding to the monitoring alarm parameters in real time, and recalculate the current alarm limit accordingly to obtain the corresponding RSAM value threshold.

[0039] In this embodiment, the volcanic eruption activity detected by the current station is considered a valid event only if there are consecutive RSAM values ​​in the RSAM data sequence that exceed the RSAM value threshold (amplitude intensity is sufficient) and the total duration corresponding to the consecutive time sequence exceeds the preset time threshold (duration duration is sufficient). That is, the generated target event alarm information is used to indicate that the volcano has erupted.

[0040] For example, based on the observation window (e.g., the past hour) set in the monitoring alarm parameters, the historical RSAM sequence within this window is statistically analyzed to calculate the RSAM value threshold (e.g., 300 μm / s), and the latest RSAM value is monitored in real time. Suppose that the RSAM value at the current time t0 rises to 500 μm / s (exceeding the threshold of 300 μm / s), the start of the potential event is recorded from t0. Subsequently, the subsequent RSAM values ​​are continuously tracked, and it is found that the high amplitude state continues until time t1 before falling back below the threshold, and the time difference between t1 and t0 is 45 seconds. Since 45 seconds exceeds the minimum duration threshold (e.g., 30 seconds) set in the monitoring alarm parameters, it is determined that this is a real volcanic eruption or significant activity event, and then standardized alarm information containing event ID, triggering station (CBS), start and end time (t0, t1, t1-t0), duration (45s), and peak amplitude is generated, i.e., target event alarm information.

[0041] This embodiment supports parameterized configuration of the entire process from data acquisition and processing to event detection, including station parameters, filtering parameters, observation window parameters, threshold parameters, etc., to achieve rapid adaptation and accurate monitoring of different volcanoes.

[0042] The volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided in this invention preprocesses seismic waveform data using signal processing parameters, converts the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence, configures an RSAM value threshold according to monitoring and alarm parameters, and counts the duration for which the RSAM value in the RSAM data sequence exceeds the RSAM value threshold. When the duration exceeds the time threshold, a target event alarm is generated. By designing a dual-condition joint judgment of amplitude exceeding the threshold and duration for volcanic activity time alarm, it can effectively distinguish between real volcanic events and environmental interference, has high reliability, does not require massive amounts of high-quality labeled data, and has high algorithm efficiency and low algorithm cost.

[0043] In some embodiments, key information also includes station parameters; seismic waveform data is obtained by the following steps: receiving seismic waveform data from the SeisComp system using an incremental appending mechanism based on the station parameters and seismic data standard protocols.

[0044] In this embodiment, station parameters are used to uniquely identify and locate a specific data source in the distributed seismic monitoring network, including the data center host address (Host, such as IRIS), network code, station code (Station, required), location code, and channel type (Channel, default is BHZ).

[0045] In this embodiment, the seismic data standard protocol can be SEED (Standard for the Exchange of Earthquake Data) or MiniSEED protocol, which are used to shield the underlying hardware differences.

[0046] In this embodiment, the incremental appending mechanism is a real-time streaming data acquisition strategy. By continuously monitoring or polling the data source, it only receives waveform data segments newly generated since the last acquisition time and splices them to the end of the existing data sequence in chronological order.

[0047] Specifically, when the system (the subject of this application) connects to the SeisComp seismic data management platform located at monitoring center A, it first establishes a network connection with the SeisComp server according to preset station parameters (e.g., Host: 10.10.1.5, Network: CB, Station: CBS, Channel: BHZ). After the connection is established, the real-time data stream subscription service is started according to the MiniSEED protocol specification. Assuming that the current system has processed data up to time point T, it requests the latest data packet after time T from SeisComp. When the SeisComp system acquires a new seismic waveform, it will immediately package it and send it to this system via the network. After receiving the incremental data packet, this system does not overwrite the entire file, but directly appends it to the end of the waveform buffer in memory to form a continuous real-time seismic waveform stream, thus obtaining the seismic waveform data.

[0048] The volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided in this invention receives seismic waveform data from the SeisComp system using an incremental appending mechanism through station parameters and seismic data standard protocols. This reduces data transmission overhead and ensures real-time performance, supports real-time event response, and enables uninterrupted automatic monitoring with low latency throughout the entire process from data acquisition to event alarm.

[0049] In some embodiments, the signal processing parameters include a bandpass filter frequency range; standardizing the seismic waveform data using the signal processing parameters includes: converting the seismic waveform data into a format that corresponds to ground velocity data; filtering the ground velocity data according to the bandpass filter frequency range; and removing outlier data from the filtered data.

[0050] In this embodiment, the original digital count values ​​are first converted to physical dimensions using the instrument response, that is, the seismic waveform data is converted into ground velocity data. Then, the ground velocity data is filtered in the frequency domain according to the bandpass filter frequency range to effectively suppress high-frequency noise and low-frequency interference. Finally, the filtered data is cleaned to identify and remove invalid segments caused by equipment failure or transmission errors, so as to obtain the processed seismic waveform data and ensure the reliability of subsequent analysis data.

[0051] For example, for a specific volcano monitoring station, the system first sets the bandpass filter frequency range to f1Hz-f2Hz; then it reads the seismic waveform data and queries the instrument response sensitivity constant of the station's seismometer, converting it into ground velocity data with clear physical meaning through deconvolution; next, the system applies a bandpass filter of f1Hz-f2Hz to process the ground velocity data, thereby filtering out long-term tidal signals and ocean wave background noise with frequencies below f1Hz, while cutting off near-field anthropogenic noise with frequencies above f2Hz, retaining only the tremor signal frequency band reflecting the characteristics of volcanic magma activity; finally, the system performs a quality scan on the filtered data, marking non-numerical or physically impossible extreme values ​​as anomalies and directly removing them, obtaining the processed seismic waveform data.

[0052] The volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided in this invention establishes a unified instrument response correction mechanism to convert raw digital signals from different devices and sources into standardized physical quantities. Through a configurable bandpass filter, it eliminates the influence of device differences and environmental noise, ensuring the direct comparability of data from multiple stations and eliminating device differences and environmental noise.

[0053] In some embodiments, the monitoring alarm parameters include observation window parameters; configuring the RSAM numerical threshold according to the monitoring alarm parameters includes: determining the median corresponding to the observation window parameters; and calculating the RSAM numerical threshold using the following formula: T R =M+n·S; Among them, T R Here, represents the RSAM value, M is the median, S is the standard deviation corresponding to the observation window parameter, and n is the coefficient, where n is an integer.

[0054] In this embodiment, the observation window parameter is used to define the historical data backtracking range; for example, the observation window parameter is set to 3600 seconds.

[0055] In this embodiment, n can be set according to user needs; for example, n can be 3 or -3.

[0056] Specifically, the system sets the observation window parameter to 3600 seconds and the coefficient n=±3; the system extracts the RSAM historical data sequence within 3600 seconds prior to the current moment in real time. Assuming this sequence contains 60 data points (1 point per minute), the system first sorts these 60 values, finds the median (e.g., M=50 μm / s); then, the system calculates the statistic of the dispersion of these 60 values, obtaining the standard deviation (e.g., S=10 μm / s); finally, the standard deviation is substituted into the RSAM value threshold calculation formula to obtain the current RSAM value threshold (e.g., T). R=80 μm / s), meaning that at the current background noise level, only when a new RSAM monitoring value exceeds 80 μm / s will it be considered a potential anomaly.

[0057] The volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided in this invention uses a statistical learning method and an algorithm based on the median and standard deviation of RSAM data under a dynamic observation window to automatically adjust and optimize the RSAM numerical threshold according to the volcanic activity state.

[0058] In some embodiments, the target event alarm information includes the event number, the target station number, the duration, and the peak value corresponding to each RSAM in the RSAM data sequence. After generating the target event alarm information, the volcanic eruption event monitoring method based on real-time seismic amplitude measurement further includes: structurally encapsulating the target event alarm information to obtain encapsulated data; and sending the encapsulated data to the monitoring and early warning model management platform through an API interface, so that the monitoring and early warning model management can determine the target event type by comparing at least one of image monitoring data and gas monitoring data with the encapsulated data.

[0059] In this embodiment, the target event alarm information is transformed into a standardized output that can be used by the business, that is, the data is encapsulated, and the event data is pushed to the monitoring and early warning model management platform through API interface or message queue. Relevant experts can view the event details, waveforms and parameters on the monitoring and early warning model management platform and make comprehensive decisions based on multi-source data such as image monitoring and gas monitoring.

[0060] For example, when the system detects an abnormal vibration at station A that meets the triggering conditions, it generates a unique event number, records the triggering station number, calculates the duration of the event from trigger to end, scans the RSAM sequence within that time period, locks the maximum amplitude as the peak value, and assembles the above information into a standard JSON data packet. The system calls the API interface of the monitoring and early warning model management platform and sends the JSON packet out via an HTTP POST request. After receiving the JSON packet, the monitoring and early warning model management platform immediately displays a pop-up warning on the large screen and automatically retrieves the high-definition visible light monitoring image and sulfur dioxide (SO2) gas concentration curve for that area during that time period. If experts find through comparison that the RSAM shows vibration, but the monitoring image shows no signs of eruption at the crater and the gas concentration has not increased, they can comprehensively determine that the event is "underground magma disturbance" rather than "surface eruption," thus completing a precise judgment through human-machine collaboration.

[0061] The volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided in this invention encapsulates the target event alarm information in a structured manner and sends the encapsulated data to the monitoring and early warning model management platform through an API interface. This supports the visual configuration and unified scheduling of the monitoring and early warning model management platform. The monitoring and early warning model management platform determines the type of the target event by combining at least one of image monitoring data and gas monitoring data with the encapsulated data, further improving the reliability and accuracy of volcanic eruption event monitoring results.

[0062] Figure 2 This is the logical design diagram of the volcano RSAM monitoring model provided by the present invention. Figure 2 In the illustrated embodiment, the design diagram includes the following process: (1) Configure monitoring for the stations; specifically, configure monitoring for stations 1 to 4; according to the configuration, start a timed task to continuously call the RSAM waveform monitoring program (or monitoring program).

[0063] The monitoring configuration parameters include several options: original waveform data source, volcano binding for this station, alarm status, filtering parameters, sensor calibration parameters, RSAM generation parameters, monitoring configuration: monitoring frequency band, and monitoring configuration: monitoring waveform indicators.

[0064] (2) Send the seismic waveform data and related configuration parameters to the RSAM waveform monitoring program. The program mainly performs the following steps: ① Seismic waveform data processing service; ② RSAM waveform data generation service; ③ RSAM waveform monitoring and alarm service; and finally generates RSAM waveform data and alarm events (for RSAM data sequences and target event alarm information respectively).

[0065] (3) The RSAM waveform monitoring program sends RSAM waveform data and alarm events to each station.

[0066] The aforementioned monitoring program is encapsulated as a Python service, which is uniformly scheduled and managed by the platform and supports horizontal scaling.

[0067] The volcanic eruption event monitoring device based on real-time seismic amplitude measurement provided by the present invention will be described below. The volcanic eruption event monitoring device based on real-time seismic amplitude measurement described below can be referred to in correspondence with the volcanic eruption event monitoring method based on real-time seismic amplitude measurement described above.

[0068] Figure 3 This is a schematic diagram of the volcanic eruption event monitoring device based on real-time seismic amplitude measurement provided by the present invention, as shown below. Figure 3 As shown, the device includes: a data acquisition module 310, a calculation module 320, and a statistics module 330.

[0069] The data acquisition module 310 is used to acquire key information and seismic waveform data of the target station; the key information includes signal processing parameters and monitoring alarm parameters, and different target stations are associated with different volcanic information; The calculation module 320 is used to perform standardized preprocessing on the seismic waveform data through signal processing parameters, and to convert the processed seismic waveform data into a real-time seismic amplitude measurement RSAM data sequence, which includes multiple consecutive time-series RSAM values. The statistics module 330 is used to configure RSAM value thresholds according to monitoring alarm parameters, and to count the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds. When the duration exceeds the time threshold, target event alarm information is generated. Different monitoring alarm parameters correspond to different RSAM value thresholds.

[0070] It should be noted that each of the above functional modules can be deployed, upgraded and maintained independently. The system has high cohesion and low coupling characteristics, and the failure of a single module will not affect the operation of the overall system.

[0071] The volcanic eruption event monitoring device based on real-time seismic amplitude measurement provided by this invention performs standardized preprocessing of seismic waveform data through signal processing parameters, and converts the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence. Then, it configures RSAM value thresholds according to monitoring and alarm parameters, and counts the duration of RSAM values ​​exceeding the RSAM value threshold in the RSAM data sequence. When the duration exceeds the time threshold, target event alarm information is generated. By designing a dual-condition joint judgment of amplitude exceeding the threshold and duration for volcanic activity time alarm, it can effectively distinguish between real volcanic events and environmental interference, with high reliability, no need to rely on massive amounts of high-quality labeled data, high algorithm efficiency, and low algorithm cost.

[0072] In some embodiments, the volcanic eruption event monitoring device based on real-time seismic amplitude measurement further includes a data transmission module 340.

[0073] The data sending module 340 is used to structurally encapsulate the target event alarm information after generating it, to obtain encapsulated data. The encapsulated data is sent to the monitoring and early warning model management platform via the API interface, so that the monitoring and early warning model management can determine the type of target event by comparing at least one of the image monitoring data and gas monitoring data with the encapsulated data.

[0074] The volcanic eruption event monitoring device based on real-time seismic amplitude measurement provided in this invention encapsulates target event alarm information in a structured manner and sends the encapsulated data to the monitoring and early warning model management platform via an API interface. This supports the visual configuration and unified scheduling of the monitoring and early warning model management platform. The monitoring and early warning model management platform determines the type of target event by combining at least one of image monitoring data and gas monitoring data with the encapsulated data, further improving the reliability and accuracy of volcanic eruption event monitoring results.

[0075] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440. The processor 410, communication interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a volcanic eruption event monitoring method based on real-time seismic amplitude measurement. This method includes: acquiring key information and seismic waveform data from target stations; the key information includes signal processing parameters and monitoring alarm parameters, with different target stations associated with different volcanic information; standardizing and preprocessing the seismic waveform data using the signal processing parameters, and converting the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence, which includes multiple consecutive time-series RSAM values; configuring RSAM value thresholds according to the monitoring alarm parameters, and counting the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds; generating target event alarm information when the duration exceeds the time threshold; wherein different monitoring alarm parameters correspond to different RSAM value thresholds.

[0076] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided by the above methods. The method includes: acquiring key information and seismic waveform data of a target station; the key information includes signal processing parameters and monitoring alarm parameters, with different target stations being associated with different volcanic information; performing standardized preprocessing on the seismic waveform data using the signal processing parameters, and converting the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence, the RSAM data sequence including multiple consecutive time-series RSAM values; configuring RSAM value thresholds according to the monitoring alarm parameters, and counting the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds; and generating target event alarm information when the duration exceeds the time threshold; wherein different monitoring alarm parameters correspond to different RSAM value thresholds.

[0078] In another aspect, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the volcanic eruption event monitoring method based on real-time seismic amplitude measurement provided by the above methods. The method includes: acquiring key information and seismic waveform data of the target station; the key information includes signal processing parameters and monitoring alarm parameters, and different target stations are associated with different volcanic information; performing standardized preprocessing on the seismic waveform data through the signal processing parameters, and converting the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence, the RSAM data sequence including multiple consecutive time-series RSAM values; configuring RSAM value thresholds according to the monitoring alarm parameters, and counting the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds, and generating target event alarm information when the duration exceeds the time threshold; wherein, different monitoring alarm parameters correspond to different RSAM value thresholds.

[0079] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0080] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring volcanic eruption events based on real-time seismic amplitude measurement, characterized in that, include: Acquire key information and seismic waveform data of the target station; The key information includes signal processing parameters and monitoring alarm parameters, with different target stations being associated with different volcanic information; The seismic waveform data is standardized and preprocessed using the signal processing parameters, and the processed seismic waveform data is converted into a real-time seismic amplitude measurement (RSAM) data sequence, which includes multiple consecutive time-series RSAM values. Configure RSAM value thresholds according to monitoring alarm parameters, and count the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds. If the duration exceeds the time threshold, generate target event alarm information; wherein, different monitoring alarm parameters correspond to different RSAM value thresholds.

2. The method for monitoring volcanic eruption events based on real-time seismic amplitude measurement according to claim 1, characterized in that, The key information also includes station parameters; The seismic waveform data was obtained through the following steps: Based on the station parameters and seismic data standard protocol, the seismic waveform data is received from the SeisComp system using an incremental appending mechanism.

3. The method for monitoring volcanic eruption events based on real-time seismic amplitude measurement according to claim 1, characterized in that, The signal processing parameters include the bandpass filter frequency range; The standardization preprocessing of the seismic waveform data using the signal processing parameters includes: The earthquake waveform data is converted into ground velocity data according to the specified format. The ground motion velocity data is filtered according to the bandpass filter frequency range, and abnormal data in the filtered data is removed.

4. The method for monitoring volcanic eruption events based on real-time seismic amplitude measurement according to claim 1, characterized in that, The monitoring and alarm parameters include observation window parameters; The configuration of RSAM numerical thresholds based on monitoring alarm parameters includes: Determine the median corresponding to the observation window parameters; The RSAM numerical threshold is calculated using the following formula: T R =M+n·S; Among them, T R Here, is the RSAM value, M is the median, S is the standard deviation corresponding to the observation window parameter, and n is the coefficient, where n is an integer.

5. The method for monitoring volcanic eruption events based on real-time seismic amplitude measurement according to claim 1, characterized in that, The target event alarm information includes the event number, the target station number, the duration, and the peak value corresponding to each RSAM in the RSAM data sequence; After generating the target event alarm information, the method further includes: The target event alarm information is structured and encapsulated to obtain encapsulated data; The encapsulated data is sent to the monitoring and early warning model management platform via the API interface, so that the monitoring and early warning model management can determine the target event type by comparing at least one of the image monitoring data and gas monitoring data with the encapsulated data.

6. A volcanic eruption event monitoring device based on real-time seismic amplitude measurement, characterized in that, include: The data acquisition module is used to acquire key information and seismic waveform data of the target station; The key information includes signal processing parameters and monitoring alarm parameters, with different target stations being associated with different volcanic information; The calculation module is used to perform standardized preprocessing on the seismic waveform data using the signal processing parameters, and to convert the processed seismic waveform data into a real-time seismic amplitude measurement (RSAM) data sequence, wherein the RSAM data sequence includes multiple consecutive time-series RSAM values. The statistics module is used to configure RSAM value thresholds according to monitoring alarm parameters, and to count the duration for which RSAM values ​​in the RSAM data sequence exceed the RSAM value thresholds. If the duration exceeds the time threshold, target event alarm information is generated. Different monitoring alarm parameters correspond to different RSAM value thresholds.

7. The volcanic eruption event monitoring device based on real-time seismic amplitude measurement according to claim 6, characterized in that, The target event alarm information includes the event number, the target station number, the duration, and the peak value corresponding to each RSAM in the RSAM data sequence; The device further includes: The data sending module is used to encapsulate the target event alarm information in a structured manner after the target event alarm information is generated, so as to obtain encapsulated data. The encapsulated data is sent to the monitoring and early warning model management platform via the API interface, so that the monitoring and early warning model management can determine the target event type by comparing at least one of the image monitoring data and gas monitoring data with the encapsulated data.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the volcanic eruption event monitoring method based on real-time seismic amplitude measurement as described in any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the volcanic eruption event monitoring method based on real-time seismic amplitude measurement as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the volcanic eruption event monitoring method based on real-time seismic amplitude measurement as described in any one of claims 1 to 5.

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