Micro-seismic monitoring system based on distributed optical fiber acoustic wave sensing

Abstract

The invention discloses a micro-seismic monitoring system based on distributed optical fiber sound wave sensing, and belongs to the technical field of seismic monitoring. The application potential and advantages of the DAS in seismology are applied to the design of the system. The process of processing the DAS signal by using the seismological method comprises the steps of firstly collecting the DAS signal by using equipment, then obtaining the DAS signal through detection and carrying out preprocessing mean removal and normalization, then carrying out picking operation on a waveform signal, carrying out event positioning after obtaining the picked signal, and finally carrying out magnitude fitting. The process of the system design comprises the following steps: in combination with a system design method, performing demand analysis on functions to be realized by the system, performing function definition, selecting a proper system architecture design according to demands and functions, performing technical model selection on the whole system and the like. And finally, a DAS method is combined with system design, and a micro-seismic monitoring system using the DAS is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake monitoring, and in particular to a microseismic monitoring system based on distributed optical fiber acoustic wave sensing. Background Art

[0002] Earthquakes, as highly destructive natural disasters, pose a serious threat to human life and property. Timely and accurate monitoring of microseismic activity is crucial for earthquake early warning, geological structure research, and safety assessment of engineering structures. Traditional microseismic monitoring methods rely primarily on discretely arranged seismic geophones, which typically only provide vibration information in limited locations, resulting in limited monitoring range and low spatial resolution. Furthermore, traditional monitoring systems are expensive to install and maintain, and struggle to achieve continuous, real-time monitoring of large areas.

[0003] Distributed fiber-optic acoustic sensing (DAS), an emerging sensing technology, offers significant advantages, including distributed sensing, high sensitivity, and long-distance monitoring. Using optical fiber as the sensing medium, this technology detects changes in the optical signal within the fiber to acquire external vibration information. This enables continuous vibration monitoring along the fiber, significantly improving the spatial resolution and coverage of monitoring. However, fully leveraging the advantages of DAS technology in microseismic monitoring requires the development of a suite of efficient and accurate microseismic monitoring software.

[0004] Currently, existing microseismic monitoring software on the market has shortcomings in data processing capabilities, software architecture design, and data storage management. Some software's data processing algorithms are not advanced enough to accurately extract microseismic signals from complex noisy environments; some software's interface design is unfriendly and complex, making it difficult to use; and some software's data storage solutions are incomplete, failing to meet the needs of long-term storage and rapid retrieval of large amounts of seismic data. Therefore, developing a powerful and stable microseismic monitoring system based on distributed fiber optic acoustic sensing is of great practical significance. Summary of the Invention

[0005] The purpose of the present invention is to provide a microseismic monitoring system based on distributed fiber optic acoustic wave sensing, which can fully utilize the advantages of distributed fiber optic acoustic wave sensing technology to achieve real-time monitoring of microseismic events, accurate magnitude detection and efficient data management.

[0006] To achieve the above objectives, the present invention provides a microseismic monitoring system based on distributed fiber optic acoustic wave sensing, which is designed in combination with the Python PYQT5 framework to process and analyze seismic data to determine the earthquake magnitude. Specifically, it includes:

[0007] The data acquisition module acquires the seismic data files generated by the distributed fiber optic acoustic wave sensing system through a real-time file monitoring mechanism and extracts the seismic data therein;

[0008] The data preprocessing module removes the mean and normalizes the seismic data to obtain displayable data;

[0009] The waveform picking and time division module uses the STA / LTA or kurtosis method to pick the starting point of the earthquake waveform and divide the time range of the earthquake event according to the STA / LTA results;

[0010] The positioning module uses the grid_search method to locate the earthquake based on the waveform picking results, and determines the source location and time of the earthquake;

[0011] The magnitude fitting module combines earthquake location information, seismic wave amplitude, frequency, propagation distance and other parameters to fit the earthquake magnitude through a neural network model;

[0012] Data visualization module, based on the Pyplot tool, visually displays pre-processed data, waveform picking results, earthquake location information, and magnitude fitting results;

[0013] The user interaction module uses the PYQT5 framework to build an interactive interface, supporting parameter setting, result query and data export.

[0014] Preferably, the data acquisition module supports monitoring and reading of seismic data files in various formats, including but not limited to common binary format, text format, XML format, etc., and can automatically call corresponding parsing algorithms for data extraction according to different file formats.

[0015] Preferably, the preset intervals in the data preprocessing module can be customized by the user in the parameter setting window of the user interaction module to meet different display and analysis requirements.

[0016] Preferably, in the waveform picking and time division module, the length of the short time window and the long time window of the STA / LTA method and the preset threshold, and the preset kurtosis threshold of the kurtosis method can be dynamically adjusted in the user interaction module to adapt to the monitoring needs of different geological environments and earthquake characteristics.

[0017] Preferably, in the positioning module, parameters such as the range of the grid area containing the possible earthquake source location, the density of the grid points, etc. can be set in the user interaction module so as to be flexibly adjusted according to different monitoring areas and accuracy requirements.

[0018] Preferably, the structure (number of layers, number of neurons), training parameters (learning rate, number of iterations), etc. of the neural network model in the magnitude fitting module can be modified and optimized in the user interaction module to improve the accuracy of magnitude fitting.

[0019] Preferably, the data visualization module can perform operations such as scaling, translating, and rotating the displayed graphics according to the user's operations in the user interaction module, and can also switch the display of different types of graphics to provide a more comprehensive and detailed data analysis perspective.

[0020] Preferably, the user interaction module provides data saving and exporting functions, allowing users to save processed seismic data, analysis results, visualization graphics, etc. to a local storage device. The saving formats include but are not limited to common image formats, text formats, database formats, etc.

[0021] Preferably, the user interaction module has a multi-language support function, and the user can select different languages ​​on the interface to operate, thereby improving the versatility and ease of use of the software.

[0022] Preferably, the software system also includes a data storage module, which uses database technology (MySQL, SQLite, etc.) to store and manage the acquired seismic data, intermediate results in the processing process, final analysis results, etc., and supports data backup, recovery, query and statistics operations.

[0023] Preferably, the data storage module can perform hierarchical storage according to the importance and frequency of use of data, storing frequently used data in high-speed storage devices and storing historical data in large-capacity storage devices to improve data storage efficiency and access speed.

[0024] Preferably, the software system has a real-time early warning function. When an earthquake event is detected and the magnitude exceeds a preset early warning threshold, early warning information is promptly sent to the user through interface prompts, sound alarms, etc. of the user interaction module.

[0025] Preferably, the software system has remote control and management functions, and users can remotely access the software system through a network connection to perform operations such as parameter setting, data query, and system monitoring, thereby realizing remote management of earthquake monitoring work.

[0026] Therefore, the present invention adopts a microseismic monitoring system based on distributed optical fiber acoustic wave sensing with the above structure, which has the following beneficial effects:

[0027] (1) The present invention can acquire seismic data generated by a distributed fiber optic acoustic wave sensing system in real time and perform effective preprocessing.

[0028] (2) The present invention uses advanced algorithms to accurately pick up earthquake waveforms, divide the time range of earthquake events, and locate earthquakes.

[0029] (3) The present invention accurately fits the magnitude of an earthquake based on earthquake location information and other relevant information.

[0030] (4) The present invention provides a user-friendly interface to facilitate users to view, analyze and manage data.

[0031] (5) The present invention realizes reliable storage and rapid retrieval of seismic data, facilitating long-term preservation and subsequent research of the data.

[0032] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a data processing flow chart;

[0034] Figure 2 Schematic diagram of the software main interface; (a) is the data display window; (b) is the parameter setting window; (c) is the drawing window; (d) is the data table window;

[0035] Figure 3 Schematic diagram of the main interface from another perspective; (a) is the data display window; (b) is the parameter setting window; (c) is the drawing window; (d) is the data table window;

[0036] Figure 4 Schematic diagram of the intermediate steps of data processing;

[0037] Figure 5 This is a schematic diagram of magnitude fitting or positioning results. DETAILED DESCRIPTION

[0038] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0039] Unless otherwise defined, the technical or scientific terms used in the present invention shall have the usual meanings understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "include" or "comprise" mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0040] Example

[0041] The present invention provides a microseismic monitoring system based on distributed optical fiber acoustic wave sensing, comprising:

[0042] The data acquisition module acquires the seismic data files generated by the distributed fiber optic acoustic wave sensing system through a real-time file monitoring mechanism and extracts the seismic data therein;

[0043] The data preprocessing module removes the mean and normalizes the seismic data to obtain displayable data;

[0044] The waveform picking and time division module uses the STA / LTA or kurtosis method to pick the starting point of the earthquake waveform and divide the time range of the earthquake event according to the STA / LTA results;

[0045] The positioning module uses the grid_search method to locate the earthquake based on the waveform picking results, and determines the source location and time of the earthquake;

[0046] The magnitude fitting module combines earthquake location information, seismic wave amplitude, frequency, propagation distance and other parameters to fit the earthquake magnitude through a neural network model;

[0047] Data visualization module, based on the Pyplot tool, visually displays pre-processed data, waveform picking results, earthquake location information, and magnitude fitting results;

[0048] The user interaction module uses the PYQT5 framework to build an interactive interface, supporting parameter setting, result query and data export.

[0049] Figure 1 、 Figure 4 、 Figure 5The system's data processing flow is presented in detail, including the main steps of seismic data acquisition, data preprocessing, waveform picking, time range division, earthquake location, and magnitude fitting. Through this flow chart, users can clearly understand how the system processes and analyzes seismic data.

[0050] Figure 2 、 Figure 3 Shows the main interface layout of the system, including Figure 2 (a) and Figure 3 (a) is the data display window, Figure 2 (b) and Figure 3 (b) is the parameter setting window, Figure 2 (c) and Figure 3 (c) is the drawing window, Figure 2 (d) and Figure 3 (d) is the data table window. Users can perform various operations through the main interface, including data import, parameter setting, drawing display, etc.

[0051] Specifically, the magnitude detection method of the present invention comprises the following steps:

[0052] S1. Seismic Data Acquisition: Through a real-time file monitoring mechanism, the software continuously monitors the seismic data files generated by the distributed fiber optic acoustic sensing system. When a new file is generated, the software immediately reads the data from the file and imports it into the system for subsequent processing. This real-time data acquisition ensures the software can respond promptly to microseismic events and provides the data foundation for accurate magnitude detection.

[0053] S2. Data preprocessing involves removing the mean and normalizing the acquired seismic data. De-averaging eliminates the DC component in the data, concentrating the data closer to zero to facilitate subsequent signal processing. Specifically, this involves calculating the mean of the data sequence and then subtracting it from each data point. Normalization maps the data range to a fixed interval, typically [0, 1]. This improves data comparability and stability, while also facilitating convergence and computational efficiency in subsequent algorithms.

[0054] The pre-processed data can be visualized, allowing users to intuitively observe the characteristics of seismic signals.

[0055] S3. Seismic waveform picking and time range division: seismic waveform picking is performed using the STA / LTA (Short-TermAverage / Long-TermAverage) or kurtosis method in seismology.

[0056] STA / LTA method: This method detects the starting point of the seismic waveform based on the difference in energy changes between short-term and long-term seismic signals. The specific steps are as follows:

[0057] Defines the length of the short time window (STA) and the long time window (LTA).

[0058] Calculate the average energy of seismic data in short time windows and long time windows respectively.

[0059] The ratio of the two was calculated, namely the STA / LTA ratio.

[0060] When the STA / LTA ratio exceeds the preset threshold, it is determined to be the starting point of the earthquake waveform.

[0061] Kurtosis method: Kurtosis is a statistic that describes the distribution of data. For seismic signals, the kurtosis value changes significantly when an earthquake occurs. Therefore, the starting point of the seismic waveform can be detected by calculating the kurtosis value of seismic data. The specific method is:

[0062] Calculate the kurtosis value of seismic data.

[0063] When the kurtosis value exceeds the preset kurtosis threshold, it is determined to be the starting point of the earthquake waveform.

[0064] The time range of an earthquake event is divided based on the STA / LTA data. The moment when the STA / LTA ratio exceeds the preset threshold is used as the start time of the earthquake event. When the STA / LTA ratio falls below the preset end threshold again, the moment is used as the end time of the earthquake event, thus determining the time range of the earthquake event.

[0065] S4. Earthquake location: Based on the waveform picking operation results, the grid_search method is used to locate the earthquake. The specific steps are as follows:

[0066] Set a grid area containing possible earthquake source locations. The size and resolution of the grid area can be adjusted according to the scope and accuracy requirements of the actual monitoring area.

[0067] For each grid point within the grid area, the theoretical propagation time for a seismic wave to propagate from that grid point to each distributed fiber optic acoustic sensor node is calculated. The calculation of the theoretical propagation time requires consideration of the propagation velocity of the seismic wave in the medium, which can be estimated based on local geological conditions.

[0068] Subtract the theoretical propagation time from the data file to get the calculated theoretical onset time. The theoretical onset time is calculated differently depending on the waveform.

[0069] The energy of the theoretical earthquake occurrence time is superimposed, and the one with the largest energy within the same time is the actual earthquake occurrence time.

[0070] S5. Magnitude fitting: Perform magnitude fitting based on earthquake location information and other information to determine the actual magnitude of the earthquake. The specific process is as follows:

[0071] Collect other information related to the earthquake, including the amplitude, frequency, and propagation distance of the seismic waves. This information can be extracted from seismic data or obtained through other means.

[0072] A magnitude fitting model was established, using earthquake location information and other collected information as input parameters. The magnitude fitting model was constructed using a neural network approach. An empirical formula was also used to calculate magnitude.

[0073] A set of data is used to train the Kp and Ks parameters in the magnitude fitting model. After obtaining the parameters, the input parameters are substituted into the magnitude fitting model for calculation to obtain the actual magnitude of the earthquake.

[0074] The testing and optimization of the system are as follows:

[0075] 1. Test plan

[0076] After the system development is completed, comprehensive testing is required to ensure that the system's functions and performance meet the requirements. The test plan includes the following aspects:

[0077] Functional testing: Verify whether the various functional modules of the system are working properly, and test functions such as data acquisition, data processing, waveform picking, positioning, magnitude fitting, drawing display and data storage.

[0078] Performance testing: Test the system's data processing speed, response time, and memory usage to ensure that the system can process large amounts of seismic data within a reasonable time.

[0079] Accuracy test: The accuracy of the system is evaluated by simulating earthquake events of different types and intensities and comparing the magnitude results calculated by the system with the actual values.

[0080] Compatibility testing: Test the system's compatibility under different operating systems and hardware environments to ensure that the system can operate normally.

[0081] 2. Optimization strategy

[0082] Based on the test results, the system is optimized. The optimization strategy includes the following aspects:

[0083] Algorithm optimization: Optimize the data processing algorithm and magnitude fitting model to improve the efficiency and accuracy of the algorithm. Use parallel computing technology to accelerate the data processing process and use more complex machine learning models to improve the accuracy of magnitude fitting.

[0084] Code optimization: Optimize the system code to reduce code redundancy and complexity, and improve code readability and maintainability. Use more efficient data structures and algorithms, and optimize function calls and loop structures.

[0085] Interface optimization: Optimize the system interface to improve its aesthetics and usability. Adjust the interface layout, add interactive prompts, and enhance the user experience.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A microseismic monitoring system based on distributed optical fiber acoustic wave sensing, characterized in that: include: The data acquisition module acquires the seismic data files generated by the distributed fiber optic acoustic wave sensing system through a real-time file monitoring mechanism and extracts the seismic data therein; The data preprocessing module removes the mean and normalizes the seismic data to obtain displayable data; The waveform picking and time division module uses the STA / LTA or kurtosis method to pick the starting point of the earthquake waveform and divide the time range of the earthquake event according to the STA / LTA results; The positioning module uses the grid_search method to locate the earthquake based on the waveform picking results, and determines the source location and time of the earthquake; The magnitude fitting module combines earthquake location information, seismic wave amplitude, frequency, propagation distance and other parameters to fit the earthquake magnitude through a neural network model; Data visualization module, based on the Pyplot tool, visually displays pre-processed data, waveform picking results, earthquake location information, and magnitude fitting results; The user interaction module uses the PYQT5 framework to build an interactive interface, supporting parameter setting, result query and data export.

2. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: The data acquisition module supports monitoring and parsing of seismic data files in binary, text or XML formats.

3. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: The normalized preset interval of the data preprocessing module is customized through the user interaction module.

4. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: In the waveform picking and time division module, the window length and threshold of STA / LTA and the kurtosis threshold of kurtosis adopt a dynamic adjustment strategy.

5. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: The range and density of the grid area in the positioning module can be set in the user interaction module according to monitoring requirements.

6. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: The neural network model structure and training parameters of the magnitude fitting module are optimized through the user interaction module.

7. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: The data visualization module supports scaling, translation, and switching display of different types of graphics.

8. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: The user interaction module provides data saving and exporting functions, and supports picture, text, and database formats.

9. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: It also includes a data storage module that uses MySQL or SQLite database to store seismic data, intermediate results and analysis results, and supports data backup and recovery.

10. The microseismic monitoring system based on distributed fiber optic acoustic wave sensing according to claim 1, characterized in that: When the magnitude exceeds the preset threshold, an early warning will be issued through an interface prompt or sound alarm.

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