Data processing system of dense array and device thereof

Abstract

The embodiment of the invention discloses a data processing system of a dense array and a device thereof. The data processing system of the dense array comprises an equipment unit which comprises an array and a mobile communication device, and the array is composed of seismometers of different models; the acquisition unit is used for providing a gateway for data synchronization and information transmission of the mobile communication equipment and providing a format conversion tool for ensuring that data is transmitted according to a unified format; the data unit is used for providing unified data access, data management and index management for multi-source heterogeneous data, and providing data whole-process management and access such as data acquisition, management, archiving storage, query access and the like; the software function unit is used for carrying out standardization processing on the data in the data unit and carrying out task management on array layout in the equipment unit; and the application unit provides a plurality of platform products according to the functions of the data unit and the software function unit.

Description

Technical Field

[0001] This specification relates to the field of geophysics, and in particular to a data processing system and apparatus for a dense array of stations. Background Technology

[0002] In recent years, with the increasing requirements for earthquake monitoring accuracy and the growing demand for disaster prevention and mitigation, dense array observation technology of different scales has been increasingly widely used in seismological research, urban active fault detection, oil and gas resource exploration and other fields.

[0003] The arrays are distributed across different scales. Regional-scale dense arrays, with spacing typically around tens of kilometers, are mostly used for large-scale crustal and mantle structure imaging, seismic activity analysis, and deep seismogenic environment detection. Short-period dense arrays, with spacing at the kilometer level or even smaller, are primarily used for shallow surface structure imaging, active fault detection, and resource exploration. Observations from dense arrays at different scales provide a relatively economical and reliable technical means for probing the fine structure of subsurface media and monitoring seismic activity, and have very promising application prospects.

[0004] However, the expansion of seismic arrays and the surge in data volume have posed significant challenges to traditional data acquisition and processing methods. On the one hand, the lack of unified standards for the acquisition, storage, management, and sharing of massive waveform data leads to low data acquisition efficiency, as well as low efficiency and utilization rates in subsequent data processing. On the other hand, different institutions employ various acquisition and processing procedures. For example, basic processing procedures such as data continuity statistics, data quality assessment, and data preprocessing lack unified standards, making it extremely difficult to further process observational data to develop different data products and obtain relevant research results.

[0005] In other words, current methods for handling dense array data lack controllability, suffer from inconsistent data processing standards, and make it difficult to trace results. Therefore, a system for standardizing the processing of data from dense array data is needed. Summary of the Invention

[0006] This specification provides a data processing system and apparatus for dense arrays to solve the following technical problem: the need for a system for standardizing the data of dense arrays.

[0007] To solve the above-mentioned technical problems, one or more embodiments of this specification are implemented as follows:

[0008] In a first aspect, embodiments of this specification provide a data processing system for a dense seismic array, comprising: an equipment unit including a seismic array and mobile communication equipment, the seismic array being composed of seismometers of different models; an acquisition unit providing a gateway for data synchronization and information transmission of the mobile communication equipment, and providing a format conversion tool to ensure data is transmitted in a unified format; a data unit providing unified data access, data management, and index management for multi-source heterogeneous data, and providing full-process data management and access including data acquisition, governance, archiving and storage, and query access; a software function unit performing standardized processing on the data in the data unit, and performing task management on the deployment of the seismic array in the equipment unit; and an application unit providing multiple platform products based on the functions of the data unit and the software function unit, so that users can access the system based on the platform products.

[0009] In a second aspect, one or more embodiments of this specification provide a data processing device for a dense array of data stations, comprising: a data receiving module for receiving data uploaded by the array; a data storage module for providing unified data access, data management, and index management for multi-source heterogeneous data, and providing full-process data management and access including data acquisition, governance, archiving, storage, and query access; a data processing module for standardizing the data in the data unit to generate standardized format data; and a quality assessment module for assessing the continuity and noise level of the data to perform data quality assessment.

[0010] The above-described at least one technical solution adopted in one or more embodiments of this specification can achieve the following beneficial effects: By providing a data processing system for a dense seismic array, comprising: an equipment unit including a seismic array and mobile communication equipment, wherein the seismic array is composed of seismometers of different models; an acquisition unit providing a gateway for data synchronization and information transmission of the mobile communication equipment, and providing a format conversion tool to ensure that data is transmitted in a unified format; a data unit for providing unified data access, data management, and index management for multi-source heterogeneous data, and providing full-process data management and access including data acquisition, governance, archiving and storage, and query access; a software function unit for standardizing the data in the data unit, and for managing the deployment of the seismic array in the equipment unit; and an application unit providing multiple platform products based on the functions of the data unit and the software function unit, so that users can access the system based on the platform products, thereby forming a comprehensive solution integrating task management, data acquisition, data processing, quality assessment, visualization, and data sharing in the task processing of the dense seismic array, ensuring the controllability of task execution, the standardization of data processing, and the traceability of results. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of the architecture of a data processing system for a dense array provided in one or more embodiments of this specification;

[0013] Figure 2 This is a schematic diagram of a data directory structure provided in an embodiment of this specification;

[0014] Figure 3 This is a schematic diagram illustrating a seamless data merging process provided in an embodiment of this specification.

[0015] Figure 4 This is a schematic diagram of a multi-peak HVSR curve provided in the embodiments of this specification;

[0016] Figure 5 A schematic diagram of cross-sectional thermals provided for an embodiment of this specification;

[0017] Figure 6 This is a schematic diagram of the structure of a data processing device for a dense array provided in an embodiment of this specification. Detailed Implementation

[0018] This specification provides a data processing system and apparatus for dense arrays.

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0020] like Figure 1 As shown, Figure 1 This is a schematic diagram of the architecture of a data processing system for a dense array provided in one or more embodiments of this specification. The system includes:

[0021] The equipment unit includes an array of seismometers and mobile communication equipment, wherein the array consists of seismometers of different models.

[0022] The acquisition unit provides a gateway for data synchronization and information transmission of the mobile communication device, and provides a format conversion tool to ensure that data is transmitted in a uniform format;

[0023] The data unit is designed for multi-source heterogeneous data, providing unified data access, data management, and index management, and offering full-process data management and access, including data acquisition, governance, archiving and storage, and query access.

[0024] The software functional unit performs standardized processing on the data in the data unit and performs task management on the array deployment in the equipment unit.

[0025] The application unit provides multiple platform products based on the functions of the data unit and the software function unit, so that users can access the system based on the platform products.

[0026] The equipment unit provides system access devices, which are divided into two categories: one is seismometers of different models, which are combined into an array; the other is mobile devices, mainly mobile phones used by construction workers on site to feed back construction information to the system.

[0027] The acquisition unit provides the data required by the system. It completes the offline import and association of data by configuring the seismograph. The acquisition layer also provides a gateway for data synchronization and information transmission between mobile devices and the system. At the same time, the acquisition layer also provides a format conversion tool to ensure that the data is stored and processed in a unified format.

[0028] The data unit is designed to handle multi-source heterogeneous seismic operational data, including array observation data, mission data, seismic event information data, seismic phase data, instrument responses, and station metadata. It relies on heterogeneous database resources such as relational databases, file systems, and object storage to construct a full lifecycle data resource system. This system provides unified data access, thematic data management, and index management functions, and offers efficient and flexible access to data throughout the entire process, including data acquisition, governance, archiving, storage, and querying.

[0029] The software functional unit performs standardized processing on the data in the data unit and performs task management on the array deployment in the equipment unit; specifically, it provides a collection of all business functions within the system, which includes the most basic data processing services (preprocessing, noise cross-correlation, HVSR calculation of seismic phase picking, seismic correlation, etc.) as well as task management functions for array deployment (site management, task management, task monitoring, and permission management, etc.).

[0030] The application unit, combining data support and business capability support provided by the data layer and software function layer, provides data application-based product services, including: a dense array task management platform, dense array site task software, and a dense array data processing platform. The dense array task management platform is used to plan site deployment, formulate tasks, and group personnel according to the observation area and research objectives, and then decompose specific tasks and assign them to multiple construction teams to complete the array deployment. The dense array site task platform is used by the construction teams to execute the array deployment tasks and record auxiliary data such as construction logs, equipment status, and environmental information. The dense array data processing platform is used to receive the data uploaded by the array after the array deployment is completed, perform multi-dimensional automatic processing and analysis, invert the underground medium structure, identify microseismic events, and quantitatively evaluate data quality.

[0031] This invention provides a data processing system for a dense seismic array, comprising: an equipment unit including a seismic array and mobile communication equipment, wherein the seismic array consists of seismometers of different models; an acquisition unit providing a gateway for data synchronization and information transmission of the mobile communication equipment, and providing format conversion tools to ensure data is transmitted in a unified format; a data unit providing unified data access, data management, and index management for multi-source heterogeneous data, and providing full-process data management and access including data acquisition, governance, archiving, storage, and query access; a software function unit performing standardized processing of the data in the data unit and task management of the seismic array deployment in the equipment unit; and an application unit providing multiple platform products based on the functions of the data unit and the software function unit, enabling users to access the system based on these platform products. This forms a comprehensive solution integrating task management, data acquisition, data processing, quality assessment, visualization, and data sharing within the dense seismic array task processing, ensuring the controllability of task execution, the standardization of data processing, and the traceability of results.

[0032] Furthermore, during the deployment of the array, the administrator can receive instructions through the dense array task management platform to create construction teams with task types in the system. The acquisition unit collects the array information after deployment so that the administrator can monitor and review the status of the task.

[0033] For example, administrators can intuitively plan site deployment / deployment tasks in various ways, quickly assign tasks by block or user, and monitor the task progress of each team in real time (pending approval, completed, etc.). The platform achieves full lifecycle management from task allocation to data aggregation through functions such as task planning and user access control.

[0034] Through the dense array mission management platform, administrators can distinguish the status of equipment (including seismometers and mobile communication equipment) in the map view using different icons (specifically, green icon for in use; gray icon for removed). Clicking on the icon allows users to view equipment details (such as latitude and longitude, equipment number, and deployment personnel).

[0035] Administrators can manage construction teams within the system. This includes creating teams, entering team names (e.g., "First Deployment Team," "Northwest Inspection Group"), and specifying the team leader and member list (selected from existing users). Each team can be bound to specific task type permissions (e.g., deployment only or all tasks) to ensure clear responsibilities. The system also allows for member information maintenance, supporting team information editing, member addition / deletion, and status changes (enabling / disabling). The system records each team's historical task execution records for performance evaluation and resource optimization.

[0036] Furthermore, administrators can manage and analyze task statistics. Administrators can select a target block in the visual interface or choose a specific line number / area via a drop-down menu, fill in basic task information, and simultaneously assign all tasks to be allocated within that area (such as all site deployments within a block) to a designated construction team with one click. Multiple modes are supported, including "assignment by whole block," "assignment by line number," and "assignment by construction team." For example, odd-numbered lines in block A can be assigned to team one, and even-numbered lines to team two, improving work collaboration efficiency. After allocation, the task status is updated to "assigned," and a notification is simultaneously pushed to the mobile device.

[0037] The system automatically summarizes the overall completion status of various tasks (line finding, station deployment, inspection, station removal), including the total number of tasks, the number completed, the number in progress, the number not yet started, and the completion rate. It supports filtering and statistics by time range (day / week / month), task type, or block, generating bar charts, line charts, or pie charts to intuitively reflect the overall project progress. The status distribution of various tasks is marked on the map with different colors and icons, specifically including:

[0038] Deployment task: Yellow triangle (to be deployed) → Green square (already deployed) → Gray cross (deployment error);

[0039] Station removal task: Red inverted triangle (to be removed) → Gray dot (already retrieved).

[0040] It also supports layer on / off control, allowing users to view the distribution of a specific type of task individually. Clicking on any task will bring up a details card displaying the task number, executor, start / end time, device number, and approval status.

[0041] Furthermore, administrators can monitor and review the status of tasks. Deployed array information can be uploaded manually or automatically by the array itself. For example, a "Construction Team Task Dashboard" can be provided, displaying key indicators such as the total number of tasks for each team, the number completed, the completion rate, the average time spent per task, and the number of abnormal tasks in real time. Additionally, task execution reports for individual teams can be generated, including task distribution heatmaps, daily completion trend charts, and summaries of common problems (such as equipment failure frequency and the number of times coordinate deviations exceed limits).

[0042] After submitting site deployment tasks on the app, the tasks enter a "pending review" status, where designated reviewers (such as the technical lead or project manager) conduct online reviews. Reviewers can view all information on the task details page (forms, photos, tracks, coordinates) in the system and choose "Approve" or "Reject." If rejected, a specific reason must be provided, and the system automatically returns the task to the original construction worker and marks it as "requires rectification." After rectification, the task can be resubmitted, forming a closed-loop management system.

[0043] In the system, the data unit is used to provide unified index management for multi-source heterogeneous seismic operational data, including: dividing the received observation data according to the number of records, and constructing an index structure for each observation data. The index structure includes: the start time, start position, file size of the observation data, and the timestamp of the start time of each data record, data position, and sampling rate; determining the metadata of the index structure; storing the data position, index file position, and metadata in the database, and constructing a two-level partition of the observation data according to date and station position.

[0044] The observation data received by the data unit may be unstructured. In this case, before constructing the index structure of the observation data, the data unit is also used to: read and parse the file type of the observation data, and perform data integration according to the file type of the observation data. Specifically, this includes: for seismic phase files, reading them line by line, parsing them according to different business attributes, and constructing data objects from the parsed data; for tabular files, splitting each field in each row of data using regular expressions, and constructing different data objects from the split fields; for XML files, parsing the file into a DOM tree or SAX event stream, or constructing data objects by obtaining the tag names, attribute values, and text content of elements in the XML file.

[0045] In other words, data units provide for the integration of unstructured data. Specifically, this includes:

[0046] For parsing business text files, it provides the ability to parse seismic phase text files, including: seismic phase files: CSF, PHA, PHASE; instrument responses: DATALESS, RESP; it supports reading line by line, can parse according to different business attributes, and construct data objects from the parsed data.

[0047] For parsing tabular files, this feature provides parsing capabilities for both CSV and Excel files. It supports converting data from tabular files into data objects (such as arrays, dictionaries, tables, etc.), reading each row of data, splitting each field in each row using regular expressions or business logic, and constructing different data objects from the split fields.

[0048] For JSON file parsing, this provides the ability to read data from JSON files. Key implementation parameters include specifying the path or name of the input file and the save method. Common JSON file formats include seismic phases and earthquake directories.

[0049] For XML file parsing, it provides the ability to parse XML files, supporting the parsing of files into a DOM tree or SAX event stream. It can output the entire XML file content or extract data from the XML by obtaining element tag names, attribute values, and text content. Common XML file formats include STATIONXML.

[0050] For parsing seismic observation data files. This involves parsing a fixed seismic observation data file, breaking down its contents into record lines for processing.

[0051] Furthermore, the data unit archives the observation data using the following directory structure, such as... Figure 2 As shown, Figure 2 This diagram illustrates a data directory structure provided in an embodiment of this specification. In this diagram, data in the directory is organized by year, network, site, and channel. This tree structure simplifies data archiving. A whole year can be moved to external storage, such as file storage.

[0052] In this way, data is stored in the channel directory. A file is created for the sensor location of each device for each day of the year. The filename follows the format $net.$sta.$loc.$cha.$year.$yday.data, where:

[0053] Net: Taiwan network code, such as 'II';

[0054] Sta: Station code, such as "BFO";

[0055] Loc: Location code of the acoustic device, for example, '00';

[0056] Cha: Channel code, such as 'BHZ';

[0057] Year: Historical years;

[0058] Yday: January 1st of each year, the day that begins with "000".

[0059] Furthermore, in this process, the data unit is also used for data integration, specifically including: determining whether the acquired observation data meets the merging conditions; if so, merging the observation data, wherein the merging conditions include: the type of observation data supports merging, the data header of the previous observation data is compatible with the data header of the observation data to be merged, and the end event of the previous observation data is equal to the start time of the observation data to be merged. After merging, the data header of the previous observation data is updated to reflect the new block size, and the updated previous observation data is stored.

[0060] The platform can create large, contiguous data blocks by reducing the number of data blocks. The advantage of large blocks is that the block headers occupy less disk space. Additionally, finding a specific timestamp is faster because fewer block headers need to be read. (See below.) Figure 3 As shown, Figure 3 This is a schematic diagram illustrating a seamless data merging process provided in an embodiment of this specification. Figure 3 A new plugin package has arrived. In Option A, the merge fails and a new data block is created. Option B: The merge succeeds. In the latter case, the new data is appended to the existing data block, and the original block header is updated to reflect the new block size.

[0061] Furthermore, if a data packet containing two different days of data arrives, the data is split according to the date limit, if possible. The first part is appended to the existing data file. For the second part, a new day file is created, containing a new header file and data blocks. This method ensures that the sample is stored in the correct data file, thus increasing access time. At this point, only data packet types that provide pruning operations support splitting data blocks. Received plugin packets may contain overlapping time spans. If the data packet type supports this, the data will be pruned to create a seamless data stream.

[0062] An index structure is established to optimize waveform data query performance and support rapid retrieval and access to seismic observation data. The system can generate index information for a waveform data file based on the file's index. By using the index position at each location recorded in the index, waveform data can be read quickly, and basic information about the waveform data segment can be quickly understood based on the index header information. A multi-dimensional joint indexing strategy is adopted to achieve efficient continuous data querying. Specifically, continuous waveform data files are divided into different time windows by data records through a sharded index (divided by time window) to form index files. Each index file includes the metadata of the data file and the precise start timestamp and sampling rate parameters of each data record, forming a tree-like index structure. At the same time, a spatiotemporal hybrid retrieval architecture is used to transform metadata such as "station code, channel type, sampling rate" into a keyword dictionary, optimizing the query path.

[0063] In this system, the software functional unit performs standardization processing on the data in the data unit, including: preprocessing the observation data, the preprocessing including: removing the mean, normalizing, removing linear trends, bandpass filtering, and deskewing operations.

[0064] Mean reduction involves subtracting the mean of each dimension from the three-way subtraction of the input data, centering each dimension to 0. The aim is to reduce the number of data features that the model is not interested in, thereby reducing the risk of overfitting.

[0065] Normalization includes normalization using a ternary method towards the global maximum. The aim is to reduce the dimensionality of data features that the model is not concerned with, thereby reducing the risk of overfitting and accelerating model convergence.

[0066] De-linearization involves removing linear trends from the data, allowing analysis to focus on the fluctuations in trending data. The goal is also to reduce the dimensionality of data features that the model is not interested in, thus reducing the risk of overfitting.

[0067] Bandpass filtering includes: applying a bandpass filter to the three-way data, setting a target frequency range, and removing high-frequency noise and low-frequency interference. It also involves smoothing the signal using digital filtering algorithms (such as the Butterworth filter) while preserving effective frequency components.

[0068] Deskewing involves performing a deskewing operation independently on each dimension (X / Y / Z axis) of the triangular data. This is done by fitting a linear trend to the data (e.g., using least squares), calculating and subtracting the trend term, causing the data to fluctuate around a zero mean. For example, for time series data, this involves fitting its linear components.

[0069] Furthermore, in preprocessing, the default length of the noisy cross-correlation data is one day; if the length is less than one day, it is padded with zeros. If the length is greater than one day, the waveform is truncated to one day. Subsequently, the waveforms for each day are low-pass and high-pass filtered, and then sampled or downsampled. Sampling is faster but only allows sampling by integer factors, while downsampling supports any factor and can be used with different sampling rate station configurations.

[0070] In this system, the software functional unit standardizes the data in the data unit in the following way: once all waveforms are loaded into memory, all waveform segments (their default duration is 30 minutes, which is configurable) are cyclically calculated according to different stations, different components, and different filters.

[0071] Specifically, this includes: accepting user-defined parameters for the duration and station information of waveforms included in the observation data (the duration, array location, and parameters to be processed can be customized, etc.), and processing most of the observation data according to the customized duration and station information as follows:

[0072] For stations with the same station, different components, and different filters, the following iterative calculation method is used to generate standardized relevant data. And store:

[0073]

[0074] Wherein, R, T, E and N represent earthquake waveforms of different components, R represents the radial component pointing in the direction of the hypocenter; T represents the tangential component, which is perpendicular to the radial direction and in the horizontal plane; E represents the eastward component; N represents the northward component; Az is the azimuth angle; X and Y are any one of R, T, E and N, and X*(f) is the complex conjugate of X(f).

[0075] The system supports the use of a reference function (REF) defined by an absolute or relative time range. This reference function represents the standard cross-correlation function for a pair of stations. Additionally, correlation functions are superimposed for each day or several days according to a set superposition step size. The correlation coefficient of the resulting cross-correlation functions is used to indicate whether something has occurred beneath, around, or on a particular sensor.

[0076] Furthermore, the software functional unit can perform a fast Fourier transform and calculate the power spectrum for each user-defined time window, and use the geometric or arithmetic mean of the power spectra over all time periods to smooth out the effects of random noise. Significantly abnormal spectral ratio curves are manually removed. The power spectrum ratio in the horizontal direction (X / Y) to the vertical direction (Z) is calculated, where the amplitude spectrum of the horizontal component can be calculated from the geometric mean of the east-west component (E) and the north-south component (N).

[0077]

[0078] The peak frequency in the HVSR curve is independent of the wavefield components of the background noise and coincides with the S-wave resonance frequency of the site. Typically, the relationship between the resonance frequency and the sediment thickness is used to convert the peak frequency into the depth of the wave impedance interface below the station, thereby determining the sediment thickness.

[0079]

[0080] Where H represents the thickness of the sedimentary layer, Vs is the average S-wave velocity of the sedimentary layer, and f is the peak frequency of the HVSR spectral ratio curve.

[0081] Furthermore, the software functional unit can also evaluate the data quality of the calculation results. This includes calculating the power spectral density (PSD), the root mean square (RMS) of the signal, or the probability density function (PDF) and continuity, etc.

[0082] This is a commonly used method for estimating the background noise of a radio station. This method obtains the spectrum of a time-domain signal through Fourier transform, squares the spectrum to obtain the power spectrum, and then calculates the power of each frequency segment according to the frequency resolution, thereby estimating the energy distribution of signal and noise interference.

[0083] Calculating the power spectral density (PSD) is a common method for estimating the background noise of a seismic station. This method obtains the spectrum of a time-domain signal through Fourier transform, squares the spectrum to obtain the power spectrum, and then calculates the power at each frequency resolution to estimate the energy distribution of signal and noise interference. It is important to note that the signals acquired by seismographs are real-valued signals, and the power spectrum is bilaterally symmetrical; therefore, only the single-sided spectrum within the real frequency range needs to be considered. Considering that the observation instrument used for the seismic noise test at the station site is a digital seismograph, and the observed physical quantity is ground motion velocity, the PSD value calculated using the total power spectral density formula is the velocity power spectral density.

[0084] The root mean square (RMS) of a signal is a numerical value used to characterize the signal strength. Based on the calculated PSD data, the bandwidth of the octave band is calculated through the upper and lower limits of the octave band. Then, according to the "Technical Requirements for Seismic Station Observation Environment (GB / T 19531.1-2004)", the RMS is calculated from the velocity power spectral density.

[0085] The probability density function (PDF) calculated from a large number of power spectral density (PSD) samples can be used to assess the noise situation at seismic stations. Specifically, it involves analyzing the characteristics of seismic environmental noise by calculating the main frequency bands of the noise signals from each station.

[0086] Based on the continuous waveform data obtained from Kafka, the continuity rate is calculated in one-hour time windows. The calculation formula is: the actual data collected by the station in one hour / the data that the station should receive. After the calculation is completed, the result is stored in the MySQL database.

[0087] In addition, the system supports data download and sharing. Specifically, through the interface or RESTful API, the system supports:

[0088] Provides the ability to query and download event waveform information by earthquake catalog parameters (time, magnitude, location):

[0089] It provides the ability to query and download event-related information based on earthquake catalog parameters (time, magnitude, location);

[0090] The system allows users to query and download the noise cross-correlation superposition results. Parameters include: station pair information (Sta1-Sta2), time range, and signal-to-noise ratio threshold (e.g., ≥ 0.5). The system returns the preprocessed cross-correlation curve image and the superposition result data (txt format).

[0091] The system provides PSD and PDF values ​​for querying statistical waveform data based on criteria such as time, station, and channel. It retrieves matching data by querying a database table. The statistical results include values ​​for the network, station, channel, PSD, and PDF. The system also allows users to download the retrieved data as an Excel spreadsheet.

[0092] The system allows users to query basic station information by station type, station code, and other criteria, including: station network code, station code, channel information, and station type. The system also allows users to download the retrieved data as an Excel spreadsheet.

[0093] It provides functions for querying and downloading HVSR spectral ratio curves, enabling researchers to quickly obtain HVSR curves, peak frequencies, and amplitude information for specific stations.

[0094] In addition, this system includes a visualization module that supports the visualization of dense array data processing products and thematic data related to earthquake events, as well as the comprehensive visualization of data quality products and data services. Specifically, this includes functions such as site information visualization, noise visualization, earthquake event visualization, HVSR spectral ratio curve inversion visualization, and data quality visualization.

[0095] In site information visualization, the web interface integrates offline GIS map services (such as Tianditu), supporting the loading of array site coordinates (WGS84), survey area boundaries (polygon / line number division), and terrain data. Map layer overlay is supported, allowing users to freely switch between layers such as site distribution maps, survey area division maps, and topographic maps, with interactive operations such as zooming, panning, and area selection. Sites can be labeled, with different statuses (completed / incomplete) distinguished by color or icon, and users can click to view site details (number, equipment information, task status).

[0096] In noise visualization, certain station paths in specific areas have special observational significance because they pass through key points of specific geological structures. By selecting certain station paths using maps or lists and defining them as the same observation group, the superimposed waveforms of the stations along the path can be viewed, revealing their unique observational significance.

[0097] The earthquake event visualization provides earthquake event management, allowing users to view earthquake event information including: event occurrence time, location, and statistics recorded at stations. It also provides the ability to query and statistically analyze events based on different criteria.

[0098] In the visualization of HVSR spectral ratio curve inversion, the HSVR spectral ratio curve is plotted based on the HSVR calculation results, using a logarithmic coordinate graph (horizontal axis for frequency, vertical axis for amplitude), with peak frequency and amplitude labeled. Where: Peak frequency: corresponds to the resonant frequency of the subsurface medium, reflecting the thickness or velocity structure of the sedimentary layer. Amplitude variation: The higher the amplitude, the more significant the amplification effect at that frequency (e.g., the amplification of seismic motion by soft soil layers). Multi-peak characteristics: Complex geological structures (such as multi-layered sedimentation) may lead to multi-peak HVSR curves. Figure 4 As shown, Figure 4 This is a schematic diagram of a multi-peak HVSR curve provided in an embodiment of this specification. Furthermore, the formation thickness distribution can be calculated based on the HVSR curve spectral ratio interpolation method and displayed as a profile heatmap (e.g., S-wave velocity variation with depth). Manual adjustment of inversion parameters (e.g., frequency band range, resolution) is supported, and results can be previewed in real time. Figure 5 As shown, Figure 5 This is a schematic diagram of cross-sectional thermals provided for an embodiment of this specification.

[0099] The data quality visualization features include the ability to generate PSD and PSDS plots by instrument, channel, and start time. This function retrieves the required results from the database and directly uses ECharts' line charts to generate PSD and PSDS plots. It also mentions the ability to generate PDF plots by instrument, channel, and start time. This function retrieves the required results from the database and directly uses ECharts' 2D heatmap raster plots to generate PDF plots. This facilitates real-time monitoring of the data quality of the instrument array, allowing for the improvement or removal of substandard arrays.

[0100] Secondly, based on the same idea, one or more embodiments of this specification also provide a device corresponding to the above system, such as... Figure 6 As shown.

[0101] Figure 6 This is a schematic diagram of the structure of a data processing device for a dense array provided in one or more embodiments of this specification. The device includes:

[0102] Data receiving module 601 receives data uploaded by the array of stations;

[0103] The data storage module 603 is designed for multi-source heterogeneous data, providing unified data access, data management, and index management, and providing full-process data management and access, including data acquisition, governance, archiving and storage, and query access.

[0104] The data processing module 605 performs standardization processing on the data in the data unit to generate standardized format data;

[0105] The quality assessment module 607 assesses the continuity and noise level of the data to perform a data quality assessment.

[0106] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0107] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0108] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A data processing system for a dense array of radio stations, comprising: The equipment unit includes an array of seismometers and mobile communication equipment, wherein the array consists of seismometers of different models. The acquisition unit provides a gateway for data synchronization and information transmission of the mobile communication device, and provides a format conversion tool to ensure that data is transmitted in a uniform format; The data unit is designed for multi-source heterogeneous data, providing unified data access, data management, and index management, and offering full-process data management and access, including data acquisition, governance, archiving and storage, and query access. The software functional unit performs standardized processing on the data in the data unit and performs task management on the array deployment in the equipment unit. The application unit provides multiple platform products based on the functions of the data unit and the software function unit, so that users can access the system based on the platform products.

2. The system as claimed in claim 1, wherein, The multiple platform products include: The dense array mission management platform is used to plan the deployment of stations, formulate tasks, and group personnel according to the observation area and scientific research objectives. It also decomposes specific tasks and assigns them to multiple construction teams to complete the array deployment. The dense array site task platform is used by the construction team to execute the array deployment task and record auxiliary data such as construction logs, equipment status, and environmental information. The dense array data processing platform is used to receive the data uploaded by the array after the array is deployed, carry out multi-dimensional automatic processing and analysis, invert the underground medium structure, identify microseismic events, and quantitatively evaluate the data quality.

3. In the system as described in claim 2, the dense array task management platform receives instructions from the administrator to create construction teams containing task types in the system, and the acquisition unit collects the array information after deployment so that the administrator can monitor and review the status of the task.

4. The system as described in claim 2, wherein, The dense array data processing platform performs multi-dimensional automatic processing and analysis, and quantitatively evaluates data quality, including: Multi-dimensional automatic processing and analysis are carried out, including: providing a customizable data processing flow, processing the observed data according to the custom data processing flow, and generating standardized format data; The quantitative assessment of data quality includes: calculating the continuity rate and noise level of the observed data to assess the data quality.

5. The system as claimed in claim 1, wherein, The data unit is used to provide unified index management for multi-source heterogeneous seismic operational data, including: The received observation data is divided according to the number of records, and an index structure is built for each observation data. The index structure includes: the start time, start position, file size of the observation data, and the timestamp of the start time, data position, and sampling rate of each data record. Determine the metadata of the index structure; The data location, index file location, and metadata are stored in the database, and the observation data are divided into two-level partitions according to date and station location.

6. The system of claim 5, wherein, When the observed data is unstructured data, the data unit, before constructing the index structure of the observed data, is also used for: Read and parse the file type of the observation data, and perform data integration according to the file type of the observation data, specifically including: For the seismic phase file, it is read line by line, parsed according to different business attributes, and data objects are constructed from the parsed data; For table files, regular expressions are used to split each field in each row of data, and the split fields are then used to construct different data objects. For XML files, the file can be parsed into a DOM tree or SAX event stream, or a data object can be constructed by obtaining the tag names, attribute values, and text content of elements in the XML file; For a fixed seismic observation data file, read the header information and data information of the seismic observation data file to construct a data object.

7. The system as claimed in claim 1, wherein, The data unit is also used for: Determine whether the acquired observation data meets the merging conditions. If it does, merge the observation data. The merging conditions include: The type of observation data supports merging, and the header of the previous observation data can be compatible with the header of the observation data to be merged, and the end time of the previous observation data is equal to the start time of the observation data to be merged. After merging, the header of the previous observation is updated to reflect the new block size, and the updated previous observation is stored.

8. The system of claim 1, wherein, The software functional unit performs standardization processing on the data in the data unit, including: preprocessing the observation data, the preprocessing including: removing the mean, normalizing, removing linear trends, bandpass filtering, and deskewing operations.

9. The standardized data processing module as described in claim 1, wherein, The software functional unit performs standardization processing on the data in the data unit, including: The system accepts user-defined parameters for the duration and station information of waveforms included in the observation data, and processes most of the observation data according to the defined duration and station information as follows: For stations with the same station, different components, and different filters, the following iterative calculation method is used to generate standardized relevant data. And store: Where R, T, E, and N represent earthquake waveforms with different components, R represents the radial component pointing towards the source direction; T represents the tangential component, which is perpendicular to the radial direction and lies in the horizontal plane; E represents the eastward component; N represents the northward component; Az is the azimuth angle; X and Y are any one of R, T, E, and N, and X*(f) is the complex conjugate of X(f).

10. A data processing device for a dense array of stations, comprising: The data receiving module receives data uploaded by the array of stations; The data storage module is designed for multi-source heterogeneous data, providing unified data access, data management, and index management, and offering full-process data management and access, including data acquisition, governance, archiving and storage, and query access. The data processing module performs standardization processing on the data in the data unit to generate standardized format data; The quality assessment module evaluates the continuity and noise level of the data to perform a data quality assessment.

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