A geophysical network multi-dimensional data visualization analysis method and system

Abstract

The application discloses a kind of geophysical network multidimensional data visualization analysis method and system, specifically related to physical station data visualization technical field;The application establishes the correlation mechanism of earthquake, geomagnetic, gravity data by creating earthquake number, integrates the scattered multi-source data into structured earthquake data package and abnormal data package, forms complete earthquake data archives, then through sub-time zone division, historical benchmark comparison and other methods, the vertical component of magnetic field, horizontal component, magnetic declination and gravity acceleration and other parameters are converted into quantifiable vertical performance value, horizontal performance value and other indexes, and a closed quadrilateral is constructed to intuitively present the relationship of multidimensional data, solve the problem that the prior art cannot form complete earthquake data archives, thereby restricting the depth of earthquake precursor signal mining.

Description

Technical Field

[0001] This invention relates to the field of geophysical observatory data visualization technology, and more specifically, to a method and system for multidimensional data visualization and analysis of geophysical observatories. Background Technology

[0002] Geophysical networks are a crucial infrastructure for acquiring geophysical field information, continuously collecting various types of data, including seismic, geomagnetic, and gravity data. This data is of great significance for studying the Earth's internal structure, monitoring and early warning of geological hazards, and resource exploration.

[0003] However, existing technologies for processing geophysical network data still have the following shortcomings:

[0004] On the one hand, various geophysical data are often stored in a scattered and heterogeneous form, lacking a unified data association mechanism. This makes it difficult to achieve efficient integration and linkage analysis of multi-source data such as earthquakes, geomagnetism, and gravity, and it is impossible to form a complete earthquake data archive, which restricts the in-depth mining of earthquake precursor signals.

[0005] On the other hand, data processing often remains at the level of simple analysis in a single dimension, lacking the ability to comprehensively quantify and visualize multidimensional data. Traditional methods are unable to transform the dynamic changes of parameters such as the vertical component, horizontal component, magnetic declination, and gravitational acceleration of the magnetic field into intuitive and interpretable feature information. This makes it difficult for researchers to quickly identify abnormal patterns in the data and to effectively compare the correlation between real-time monitoring data and historical abnormal data, which greatly affects the accuracy and timeliness of early warning of geological disasters such as earthquakes.

[0006] To address this, a method and system for multidimensional data visualization and analysis of geophysical networks are proposed. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for multidimensional data visualization and analysis of geophysical networks.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A method for visualizing and analyzing multidimensional data from geophysical networks, comprising:

[0010] Multidimensional data construction: Real-time acquisition of seismic data at the time of an earthquake, as well as geomagnetic and gravity data prior to the earthquake, followed by preprocessing and association with the earthquake, geomagnetic, and gravity data through a created earthquake number, and construction of an earthquake data package; geomagnetic data includes the vertical component, horizontal component, and magnetic declination of the magnetic field; gravity data includes gravitational acceleration;

[0011] Multidimensional data processing: The seismic data packets are parsed to determine the latitude and longitude coordinates of the epicenter. A spatial analysis area with a radius of kilometers centered on the epicenter is delineated. Geomagnetic and gravity data within a predetermined time interval before the earthquake are extracted from the seismic data packets. After comprehensive processing, anomaly data packets are constructed and stored in the database in association with the earthquake number and the seismic data packets. The anomaly data packets include vertical, horizontal, angular, and gravity values, as well as a constructed closed quadrilateral.

[0012] Visual presentation: After extracting abnormal data packets in the corresponding spatial analysis area, staff trigger an evaluation signal and monitor the geomagnetic and gravity data in the corresponding spatial analysis area in real time. The data packets are then correlated with the abnormal data packets, and a correlation analysis report is generated and pushed to the staff.

[0013] Specifically, the step of extracting geomagnetic and gravity data of the spatial analysis area within a predetermined time interval before the earthquake from the earthquake data packet and performing comprehensive processing includes:

[0014] The set time interval is divided into sub-time zones according to a preset division ratio. The vertical magnetic field components corresponding to different time points in each sub-time zone are identified. The highest and lowest values ​​of the vertical magnetic field components in each sub-time zone are extracted, and the difference is calculated to obtain the vertical change of each sub-time zone. The vertical change of each sub-time zone is compared with the historical average vertical change pre-stored in the spatial analysis area. The average of the calculated ratios is taken to obtain the vertical performance value within the set time interval.

[0015] Identify the horizontal magnetic field components corresponding to different time points in each sub-time zone, extract the highest and lowest values ​​of the horizontal magnetic field components in each sub-time zone, and calculate the difference to obtain the horizontal change in each sub-time zone; calculate the ratio between the horizontal change in each sub-time zone and the historical average change in the spatial analysis area, and obtain the mean value of each calculated ratio to obtain the horizontal performance value within the set time interval.

[0016] Specifically, the step of extracting geomagnetic and gravity data of the spatial analysis area within a predetermined time interval before the earthquake from the earthquake data packet and performing comprehensive processing includes:

[0017] Identify the magnetic declination at different time points in each sub-time zone, calculate the average value of each group of magnetic declinations in each sub-time zone, and determine the mean value of the declination in each sub-time zone. Calculate the mean value of the declination in each sub-time zone using the standard deviation formula, determine the angle offset value within the set time interval, and calculate the ratio between the angle offset value and the historical average angle offset value pre-stored in the spatial analysis area to obtain the angle performance value within the set time interval.

[0018] Extract the gravitational acceleration at each time point within the set time interval, and calculate the ratio of the gravitational acceleration at each time point to the historical average gravitational acceleration pre-stored in the spatial analysis area. Take the average of the calculated ratios to obtain the gravitational performance value within the set time interval.

[0019] Specifically, the process of constructing the abnormal data packet is as follows:

[0020] Starting from the origin of the Cartesian coordinate system, four rays extend at equal angles in the plane, representing the vertical, horizontal, angular, and gravitational values, respectively.

[0021] The vertical, horizontal, angular, and gravitational values ​​are used as the extension lengths of four rays, extending from the origin along the ray direction. The endpoints of each ray are connected sequentially to construct a closed quadrilateral. The vertical, horizontal, angular, and gravitational values ​​within a set time interval, along with the constructed closed quadrilateral, are integrated to construct an abnormal data packet.

[0022] Specifically, the triggering of the evaluation signaling and the real-time monitoring of geomagnetic and gravity data in the corresponding spatial analysis area, and the correlation processing with abnormal data packets, specifically involves:

[0023] After triggering the evaluation signal, the geomagnetic and gravity data of the corresponding spatial analysis area are monitored in real time until the cumulative monitoring time reaches the set time interval and then the monitoring stops. The geomagnetic and gravity data within the monitoring time interval are analyzed to construct a real-time data packet, which includes vertical performance values, horizontal performance values, angular performance values, gravity performance values, and a constructed closed quadrilateral.

[0024] The closed quadrilaterals within the real-time data packets are extracted as evaluation graphics, and the closed quadrilaterals of each group of abnormal data packets within the corresponding spatial analysis area are extracted as matching graphics.

[0025] Extract the vertical, horizontal, angular, and gravitational values ​​from the real-time data packets. Analyze the vertical variation, horizontal variation, mean skew angle, and gravitational acceleration of each time zone from these values.

[0026] Plot the vertical changes of each sub-time zone to the corresponding numerical points in the coordinate system, and connect adjacent numerical points to construct a vertical polyline; similarly, plot the horizontal changes of each sub-time zone, the mean angular deviation of each sub-time zone, and the gravitational acceleration at each time point, and construct horizontal polylines, angular polylines, and gravity polylines.

[0027] The vertical, horizontal, angular, and gravity lines corresponding to the real-time data packets are integrated into a real-time line packet.

[0028] Extract the vertical, horizontal, angular, and gravity values ​​of each group of abnormal data packets within the corresponding spatial analysis area. Similarly, construct vertical, horizontal, angular, and gravity polylines. Integrate the vertical, horizontal, angular, and gravity polylines corresponding to the abnormal data packets into an abnormal polyline packet.

[0029] Specifically, the process of generating the correlation analysis report is as follows:

[0030] The evaluation graph and the matching graph are processed together to obtain the graph anomaly overlap index between the evaluation graph and each set of matching graphs; the real-time polyline package and the abnormal polyline package are processed together to obtain the numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package.

[0031] Extract the graphical anomaly overlap index and numerical anomaly overlap index Ej obtained from the analysis of real-time data packets and abnormal data packets, and fill them into a pre-built report template to generate a correlation analysis report.

[0032] Specifically, the obtained graph anomaly overlap index between the evaluated graph and each set of matching graphs is as follows:

[0033] Determine the center point of the evaluation graphic and each set of matching graphics. Align the center of the evaluation graphic with the center point of each set of matching graphics. After alignment, obtain the overlapping area between the evaluation graphic and each set of matching graphics. Calculate the ratio between the obtained overlapping area of ​​each set and the graphic area of ​​the corresponding matching graphic. Subtract the integer from the calculated ratio and take the absolute value to obtain the matching surface value.

[0034] To evaluate the corresponding side lengths of the graphic and each set of matching graphics, the formula is used. Calculate the matching edge value Y; where and These represent the lengths of the sides corresponding to the evaluated and matched figures, respectively; i is the side length number.

[0035] Extract the calculated matching face value and matching edge value between the evaluation graphic and the matching graphic, and multiply them by the preset face value weight coefficient and edge value weight coefficient respectively. Then sum them to obtain the graphic abnormality overlap index between the evaluation graphic and each set of matching graphics.

[0036] Specifically, the numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package is obtained as follows:

[0037] For each pair of corresponding lines in the real-time line package and the abnormal line package, count the number of inflection points that are successfully matched between the two corresponding lines, and denote it as the inflection point coincidence value E1.

[0038] Obtain the slope of each segment in the two broken lines, and apply the formula... Perform the calculation; subtract the integer from the calculation result and take the absolute value to obtain the coincidence value of the slopes of the two broken lines, E2; where This represents the slope of the g-th segment of the polyline within the real-time polyline package. The slope of the g-th segment of the polyline within the abnormal polyline package; () represents a sign function; the result is 1 when the signs are the same and -1 when the signs are opposite.

[0039] The inflection point coincidence value E1 and slope coincidence value E2 calculated for the two corresponding lines in the real-time line package and the abnormal line package are normalized according to the formula. Calculate the numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package; where E1 and E2 are the weighting coefficients corresponding to the inflection point coincidence value and the slope coincidence value, respectively, and Ez is the similarity index of the two corresponding lines in the real-time line package and the abnormal line package. The preset weighting coefficients are the similarity indices of different lines in the real-time line package and the abnormal line package, where z is the number of the different line similarity indices.

[0040] A multi-dimensional data visualization and analysis system for geophysical networks includes:

[0041] The data acquisition module is used to collect earthquake data during an earthquake and geomagnetic and gravity data before the earthquake occurs in real time. After preprocessing, the data is associated with earthquake, geomagnetic, and gravity data through the created earthquake number, and an earthquake data package is constructed.

[0042] The data processing module is used to parse earthquake data packets, determine the latitude and longitude coordinates of the epicenter from the earthquake data, delineate a spatial analysis area with the epicenter as the center and a radius of kilometers, extract geomagnetic and gravity data of the spatial analysis area within a set time interval before the earthquake from the earthquake data packets, and after comprehensive processing, construct anomaly data packets and store them in the database in association with the earthquake number and earthquake data packets.

[0043] The visualization assessment module is used to trigger assessment signals and monitor the geomagnetic and gravity data of the corresponding spatial analysis area in real time after the staff extracts the abnormal data packets in the corresponding spatial analysis area. It also performs correlation processing between the data packets and the abnormal data packets, generates a correlation analysis report, and pushes it to the staff.

[0044] The technical effects and advantages of this invention are as follows:

[0045] (1) By creating earthquake numbers, a correlation mechanism for earthquake, geomagnetic, and gravity data is established, and scattered multi-source data is integrated into structured earthquake data packets and anomalous data packets to form a complete earthquake data archive. Then, through sub-time zone division, historical benchmark comparison, and other methods, parameters such as the vertical component, horizontal component, magnetic declination, and gravitational acceleration of the magnetic field are transformed into quantifiable vertical and horizontal performance values ​​and other indicators. A closed quadrilateral is constructed to intuitively present the multidimensional data relationship, which solves the problem that existing technologies cannot form a complete earthquake data archive, thus restricting the in-depth mining of earthquake precursor signals.

[0046] (2) By monitoring the geomagnetic and gravity data of the spatial analysis area in real time after the staff triggers the assessment signal, a real-time data packet is constructed, and it is correlated with the abnormal data packet to generate a correlation analysis report, thereby realizing dynamic monitoring and efficient decision support. This solves the problem that the existing technology cannot effectively compare the correlation between real-time monitoring data and historical abnormal data, which greatly affects the accuracy and timeliness of early warning of geological disasters such as earthquakes.

[0047] (3) By using the graphical anomaly overlap index and the numerical anomaly overlap index, the correlation between real-time monitoring data and historical anomaly data is reflected from different perspectives, enabling rapid comparison between real-time data and historical anomaly data, and providing intuitive decision-making basis for earthquake early warning and geological research. Attached Figure Description

[0048] Figure 1 This is a flowchart of a multi-dimensional data visualization and analysis method for geophysical network according to the present invention;

[0049] Figure 2 This is a schematic diagram of a multi-dimensional data visualization and analysis system for geophysical networks according to the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0051] like Figure 1 As shown, a method for visualizing and analyzing multidimensional data from geophysical networks includes:

[0052] Multidimensional data construction: Data acquisition equipment is deployed at various monitoring stations of the geophysical network to ensure real-time acquisition of seismic data at the time of an earthquake, as well as geomagnetic and gravity data prior to the earthquake. After preprocessing, the data is associated with the earthquake, geomagnetic, and gravity data through a created earthquake number, and an earthquake data package is constructed. The earthquake data includes magnitude, focal depth, and earthquake waveform; the geomagnetic data includes the vertical component, horizontal component, and magnetic declination of the magnetic field; and the gravity data includes gravitational acceleration.

[0053] The data acquisition equipment transmits the acquired data to the data processing center via wired or wireless communication.

[0054] Preprocessing: For data of different formats, data cleaning software tools, such as the Pandas library in Python, are used to perform preprocessing operations such as noise reduction, deduplication, and missing value imputation. For example, abnormal spike signals in seismic wave data are removed by setting a reasonable threshold; and missing values ​​in geomagnetic data are imputed by linear interpolation.

[0055] According to the unified data format specification, use format conversion tools to convert data of different formats into a standard format that the system can recognize, such as converting the common seismic data format SAC to HDF5 format.

[0056] Establish a database management system, design a reasonable data table structure, classify and store the preprocessed data according to data type, time, space and other dimensions, and establish the relationship between data to ensure data integrity and consistency. For example, in the earthquake data table, store information such as the time of occurrence, magnitude and focal depth of the earthquake, and associate it with related data tables such as geomagnetism and gravity through a unique earthquake number.

[0057] Multidimensional data processing: The seismic data packets are parsed to determine the latitude and longitude coordinates of the epicenter. A spatial analysis area with a radius of 300-800 kilometers centered on the epicenter is delineated, and the range is dynamically adjusted according to the magnitude. Geomagnetic and gravity data of the spatial analysis area within a set time interval before the earthquake are extracted from the seismic data packets. After comprehensive processing, anomaly data packets are constructed and stored in the database in association with the earthquake number and the seismic data packets to form a complete seismic data archive.

[0058] Specifically:

[0059] The set time interval is divided into sub-time zones according to a preset division ratio. The vertical magnetic field components corresponding to different time points in each sub-time zone are identified. The highest and lowest values ​​of the vertical magnetic field components in each sub-time zone are extracted, and the difference is calculated to obtain the vertical change of each sub-time zone. The vertical change of each sub-time zone is compared with the historical average vertical change pre-stored in the spatial analysis area. The average of the calculated ratios is taken to obtain the vertical performance value within the set time interval.

[0060] Identify the horizontal magnetic field components corresponding to different time points in each sub-time zone, extract the highest and lowest values ​​of the horizontal magnetic field components in each sub-time zone, and calculate the difference to obtain the horizontal change in each sub-time zone; calculate the ratio between the horizontal change in each sub-time zone and the historical average change in the spatial analysis area, and obtain the mean value of each set of ratios to obtain the horizontal performance value within the set time interval.

[0061] To further explain, the set time interval is divided into sub-time zones according to a preset ratio (such as by day, hour, or sliding window). The highest and lowest values ​​of the vertical magnetic field component of each sub-time zone are extracted and the difference is calculated (i.e., change = highest value - lowest value). The change of each sub-time zone is compared with the historical highest change (to obtain the ratio, which can be understood as "relative fluctuation intensity"). Then, the "magnetic field performance value" is obtained through the mean. This method can normalize the comparison between short-term fluctuations and historical background, eliminate the influence of absolute numerical differences, and more objectively reflect the activity level of the vertical magnetic field within the set time interval.

[0062] After extracting the horizontal magnetic field components at each time point within a set time interval, the mean value of the horizontal components in that interval is first obtained by arithmetic averaging (reflecting the overall strength level of the horizontal magnetic field during that period). Then, the ratio is calculated with the historical highest horizontal magnetic field component of the spatial analysis area (representing the strongest disturbance of the horizontal magnetic field in the area in history). The resulting horizontal performance value is essentially a "normalized index of the current average strength of the horizontal magnetic field relative to historical extreme values". Its core is to eliminate absolute numerical differences by comparing with historical extreme values ​​and highlight the relative activity of the current horizontal magnetic field.

[0063] Identify the magnetic declination at different time points in each sub-time zone, calculate the average value of each group of magnetic declinations in each sub-time zone, and determine the mean value of the declination in each sub-time zone. Calculate the mean value of the declination in each sub-time zone using the standard deviation formula, determine the angle offset value within the set time interval, and calculate the ratio between the angle offset value and the historical average angle offset value pre-stored in the spatial analysis area to obtain the angle performance value within the set time interval.

[0064] To clarify, the angular performance value is calculated by the ratio of the sub-time zone mean standard deviation of magnetic declination (ΔD) (angular offset value) to the historical highest angular offset value, which essentially reflects the degree of dispersion of magnetic declination and its relative relationship with historical extreme fluctuations.

[0065] Extract the gravitational acceleration at each time point within the set time interval, and calculate the ratio of the gravitational acceleration at each time point to the historical average gravitational acceleration pre-stored in the spatial analysis area. Take the average of the calculated ratios to obtain the gravitational performance value within the set time interval.

[0066] Additional explanation: Extract the gravitational acceleration at each time point within a set time interval, compare it with the historical average gravitational acceleration (calculate the ratio), and then obtain the "gravity performance value" through the average value, which reflects the overall degree of deviation of the current gravitational field from the historical average state;

[0067] Starting from the origin of the Cartesian coordinate system (0, 0), four rays extend at equal angles in the plane, representing the vertical, horizontal, angular, and gravitational values, respectively.

[0068] The vertical, horizontal, angular, and gravitational values ​​are used as the extension lengths of four rays, extending from the origin along the ray direction. The endpoints of each ray are connected sequentially to construct a closed quadrilateral. The vertical, horizontal, angular, and gravitational values ​​within a set time interval, along with the constructed closed quadrilateral, are integrated to construct an abnormal data packet.

[0069] In addition, this visualization method can quickly determine the degree of performance of each parameter, providing an intuitive reference for the comprehensive analysis of earthquake precursors;

[0070] Visual presentation: After extracting abnormal data packets in the corresponding spatial analysis area, the staff triggers the evaluation signal and monitors the geomagnetic and gravity data in the corresponding spatial analysis area in real time, and performs correlation processing between the data packets and the abnormal data packets. After processing, a correlation analysis report is generated and pushed to the staff.

[0071] Specifically:

[0072] After triggering the evaluation signal, the geomagnetic and gravity data of the corresponding spatial analysis area are monitored in real time until the cumulative monitoring time reaches the set time interval and then the monitoring stops. The geomagnetic and gravity data within the monitoring time interval are analyzed to construct a real-time data packet, which includes vertical performance values, horizontal performance values, angular performance values, gravity performance values, and a constructed closed quadrilateral.

[0073] The closed quadrilaterals within the real-time data packets are extracted as evaluation graphics, and the closed quadrilaterals of each group of abnormal data packets within the corresponding spatial analysis area are extracted as matching graphics.

[0074] Determine the center point of the evaluation graphic and each set of matching graphics. Align the center of the evaluation graphic with the center point of each set of matching graphics. After alignment, obtain the overlapping area between the evaluation graphic and each set of matching graphics. Calculate the ratio between the obtained overlapping area of ​​each set and the graphic area of ​​the corresponding matching graphic. Subtract the integer from the calculated ratio and take the absolute value to obtain the matching surface value.

[0075] To evaluate the corresponding side lengths of the graphic and each set of matching graphics, the formula is used. Calculate the matching edge value Y; the smaller the result, the more consistent the proportions of the side lengths (e.g., if a side of the real-time quadrilateral is 2.0, and the corresponding side of the abnormal quadrilateral is 2.2, the relative error is 0.09, indicating a higher degree of matching); where and These represent the lengths of the sides corresponding to the evaluated and matched figures, respectively; i is the side length number.

[0076] Extract the calculated matching face value and matching edge value between the evaluation graphic and the matching graphic, and multiply them by the preset face value weight coefficient and edge value weight coefficient respectively. Then sum them to obtain the graphic abnormality overlap index between the evaluation graphic and each set of matching graphics.

[0077] To elaborate further, the analysis process simultaneously focuses on the spatial morphological overlap of the graphics (matching face values ​​to reflect the overall contour similarity) and the proportional consistency of each dimension (matching boundary values ​​to reflect whether the relative change trends of the four components of vertical, horizontal, angle, and gravity are consistent). The two are then integrated into a "graphic anomaly overlap index" through weighting coefficients. This multi-dimensional integration method considers both the overall shape of the graphics and the proportional relationship of each key indicator, enabling a more comprehensive identification of real-time data that is highly similar to historical anomaly patterns and reducing the misjudgments that may be caused by single-dimensional analysis.

[0078] Extract the vertical, horizontal, angular, and gravitational values ​​from the real-time data packets. Analyze the vertical variation, horizontal variation, mean skew angle, and gravitational acceleration of each time zone from these values.

[0079] Plot the vertical changes of each sub-time zone to the corresponding numerical points in the coordinate system, and connect adjacent numerical points to construct a vertical polyline; similarly, plot the horizontal changes of each sub-time zone, the mean angular deviation of each sub-time zone, and the gravitational acceleration at each time point, and construct horizontal polylines, angular polylines, and gravity polylines.

[0080] The vertical, horizontal, angular, and gravity lines corresponding to the real-time data packets are integrated into a real-time line packet.

[0081] Extract the vertical, horizontal, angular, and gravity values ​​of each group of abnormal data packets within the corresponding spatial analysis area. Similarly, construct vertical, horizontal, angular, and gravity polylines. Integrate the vertical, horizontal, angular, and gravity polylines corresponding to the abnormal data packets into an abnormal polyline packet.

[0082] For each pair of corresponding lines in the real-time line package and the abnormal line package, count the number of inflection points that are successfully matched between the two corresponding lines, and record it as the inflection point coincidence value E1. The inflection point represents the point in the line where the slope changes from positive to negative or from negative to positive (such as the time point when it changes from rising to falling), reflecting the frequency of trend changes.

[0083] Obtain the slope of each segment in the two broken lines, and apply the formula... Perform the calculation; the closer the result is to 1, the higher the synchronicity of the upward / downward trend (e.g., when the real-time line rises, the abnormal line also rises); subtract the integer from the calculation result and take the absolute value to obtain the slope coincidence value E2 of the two lines; where This represents the slope of the g-th segment of the polyline within the real-time polyline package. The slope of the g-th segment of the polyline within the abnormal polyline package; () represents a sign function; the result is 1 when the signs are the same and -1 when the signs are opposite.

[0084] The inflection point coincidence value E1 and slope coincidence value E2 calculated for the two corresponding lines in the real-time line package and the abnormal line package are normalized according to the formula. Calculate the numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package; where E1 and E2 are the weighting coefficients corresponding to the inflection point coincidence value and the slope coincidence value, respectively, and Ez is the similarity index of the two corresponding lines in the real-time line package and the abnormal line package. The preset weighting coefficients are the similarity indices of different lines in the real-time line package and the abnormal line package, and z is the number of the different line similarity indices;

[0085] In addition, by focusing on dynamic trend characteristics, multi-dimensional weighted fusion, and standardized quantification, the similarity between real-time geophysical field changes and historical earthquake anomaly patterns was accurately identified, providing strong support for dynamic monitoring and scientific early warning of earthquake precursors and effectively improving the accuracy and reliability of anomaly identification.

[0086] Extract the graphical anomaly overlap index and numerical anomaly overlap index Ej obtained from the analysis of real-time data packets and abnormal data packets, and fill them into a pre-built report template to generate a correlation analysis report; Example 2

[0087] Please see Figure 2 As shown, based on Embodiment 1 of this application, a method for visualizing and analyzing multi-dimensional data of geophysical networks is provided. Embodiment 2 of this application proposes a system for visualizing and analyzing multi-dimensional data of geophysical networks. Embodiment 2 is merely a preferred embodiment of Embodiment 1, and its implementation will not affect the individual implementation of Embodiment 1.

[0088] Specifically, the difference in the multi-dimensional data visualization and analysis system for geophysical networks provided in Embodiment 2 of this application lies in that it includes:

[0089] The data acquisition module is used to collect earthquake data during an earthquake and geomagnetic and gravity data before the earthquake occurs in real time. After preprocessing, the data is associated with earthquake, geomagnetic, and gravity data through the created earthquake number, and an earthquake data package is constructed.

[0090] The data processing module is used to parse earthquake data packets, determine the latitude and longitude coordinates of the epicenter from the earthquake data, delineate a spatial analysis area with the epicenter as the center and a radius of kilometers, extract geomagnetic and gravity data of the spatial analysis area within a set time interval before the earthquake from the earthquake data packets, and after comprehensive processing, construct anomaly data packets and store them in the database in association with the earthquake number and earthquake data packets.

[0091] The visualization assessment module is used to trigger assessment signals and monitor the geomagnetic and gravity data of the corresponding spatial analysis area in real time after the staff extracts the abnormal data packets in the corresponding spatial analysis area. It also performs correlation processing between the data packets and the abnormal data packets, generates a correlation analysis report, and pushes it to the staff.

[0092] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0093] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0094] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0095] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0097] 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; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0099] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for visualizing and analyzing multidimensional data from geophysical networks, characterized in that, include: Multidimensional data construction: Real-time acquisition of seismic data at the time of an earthquake, as well as geomagnetic and gravity data prior to the earthquake, followed by preprocessing and association with the earthquake, geomagnetic, and gravity data through a created earthquake number, and construction of an earthquake data package; geomagnetic data includes the vertical component, horizontal component, and magnetic declination of the magnetic field; gravity data includes gravitational acceleration; Multidimensional data processing: The seismic data packets are parsed to determine the latitude and longitude coordinates of the epicenter. A spatial analysis area with a radius of kilometers centered on the epicenter is delineated. Geomagnetic and gravity data within a predetermined time interval before the earthquake are extracted from the seismic data packets. After comprehensive processing, anomaly data packets are constructed and stored in the database in association with the earthquake number and the seismic data packets. The anomaly data packets include vertical, horizontal, angular, and gravity values, as well as a constructed closed quadrilateral. The specific process of constructing the exception data packet is as follows: Starting from the origin of the Cartesian coordinate system, four rays extend at equal angles in the plane, representing the vertical, horizontal, angular, and gravitational values, respectively. The vertical, horizontal, angular, and gravitational values ​​are used as the extension lengths of four rays, extending from the origin along the ray direction. The endpoints of each ray are connected sequentially to construct a closed quadrilateral. The vertical, horizontal, angular, and gravitational values ​​within a set time interval, along with the constructed closed quadrilateral, are integrated to construct an abnormal data packet. Visual presentation: After extracting abnormal data packets in the corresponding spatial analysis area, the staff triggers the evaluation signal and monitors the geomagnetic and gravity data in the corresponding spatial analysis area in real time, and performs correlation processing between the data packets and the abnormal data packets. After processing, a correlation analysis report is generated and pushed to the staff. Specifically: After triggering the evaluation signal, the geomagnetic and gravity data of the corresponding spatial analysis area are monitored in real time until the cumulative monitoring time reaches the set time interval and then the monitoring stops. The geomagnetic and gravity data within the monitoring time interval are analyzed to construct a real-time data packet, which includes vertical performance values, horizontal performance values, angular performance values, gravity performance values, and a constructed closed quadrilateral. The closed quadrilaterals within the real-time data packets are extracted as evaluation graphics, and the closed quadrilaterals of each group of abnormal data packets within the corresponding spatial analysis area are extracted as matching graphics. Extract the vertical, horizontal, angular, and gravitational values ​​from the real-time data packets. Analyze the vertical variation, horizontal variation, mean skew angle, and gravitational acceleration of each time zone from these values. Plot the vertical changes of each sub-time zone to the corresponding numerical points in the coordinate system, and connect adjacent numerical points to construct a vertical polyline; similarly, plot the horizontal changes of each sub-time zone, the mean angular deviation of each sub-time zone, and the gravitational acceleration at each time point, and construct horizontal polylines, angular polylines, and gravity polylines. The vertical, horizontal, angular, and gravity lines corresponding to the real-time data packets are integrated into a real-time line packet. Extract the vertical, horizontal, angular, and gravity values ​​of each group of abnormal data packets within the corresponding spatial analysis area. Similarly, construct vertical, horizontal, angular, and gravity polylines. Integrate the vertical, horizontal, angular, and gravity polylines corresponding to the abnormal data packets into an abnormal polyline packet.

2. The method for multidimensional data visualization and analysis of geophysical networks according to claim 1, characterized in that, The process of extracting geomagnetic and gravity data of the spatial analysis area from the seismic data packet within a predetermined time interval before the earthquake, and then performing comprehensive processing, specifically involves: The set time interval is divided into sub-time zones according to a preset division ratio. The vertical magnetic field components corresponding to different time points in each sub-time zone are identified. The highest and lowest values ​​of the vertical magnetic field components in each sub-time zone are extracted, and the difference is calculated to obtain the vertical change of each sub-time zone. The vertical change of each sub-time zone is compared with the historical average vertical change pre-stored in the spatial analysis area. The average of the calculated ratios is taken to obtain the vertical performance value within the set time interval. Identify the horizontal magnetic field components corresponding to different time points in each sub-time zone, extract the highest and lowest values ​​of the horizontal magnetic field components in each sub-time zone, and calculate the difference to obtain the horizontal change in each sub-time zone; calculate the ratio between the horizontal change in each sub-time zone and the historical average change in the spatial analysis area, and obtain the mean value of each calculated ratio to obtain the horizontal performance value within the set time interval.

3. The method for multidimensional data visualization and analysis of geophysical network according to claim 2, characterized in that, The process of extracting geomagnetic and gravity data of the spatial analysis area from the seismic data packet within a predetermined time interval before the earthquake, and then performing comprehensive processing, specifically involves: Identify the magnetic declination at different time points in each sub-time zone, calculate the average value of each group of magnetic declinations in each sub-time zone, and determine the mean value of the declination in each sub-time zone. Calculate the mean value of the declination in each sub-time zone using the standard deviation formula, determine the angle offset value within the set time interval, and calculate the ratio between the angle offset value and the historical average angle offset value pre-stored in the spatial analysis area to obtain the angle performance value within the set time interval. Extract the gravitational acceleration at each time point within the set time interval, and calculate the ratio of the gravitational acceleration at each time point to the historical average gravitational acceleration pre-stored in the spatial analysis area. Take the average of the calculated ratios to obtain the gravitational performance value within the set time interval.

4. The method for multidimensional data visualization and analysis of geophysical network according to claim 1, characterized in that, The specific process for generating the correlation analysis report is as follows: The evaluation graph and the matching graph are processed together to obtain the graph anomaly overlap index between the evaluation graph and each set of matching graphs; the real-time polyline package and the abnormal polyline package are processed together to obtain the numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package. Extract the graphical anomaly overlap index and numerical anomaly overlap index Ej obtained from the analysis of real-time data packets and abnormal data packets, and fill them into a pre-built report template to generate a correlation analysis report.

5. The method for multidimensional data visualization and analysis of geophysical networks according to claim 4, characterized in that, The obtained graph anomaly overlap index between the evaluated graph and each set of matching graphs is specifically as follows: Determine the center point of the evaluation graphic and each set of matching graphics. Align the center of the evaluation graphic with the center point of each set of matching graphics. After alignment, obtain the overlapping area between the evaluation graphic and each set of matching graphics. Calculate the ratio between the obtained overlapping area of ​​each set and the graphic area of ​​the corresponding matching graphic. Subtract the integer from the calculated ratio and take the absolute value to obtain the matching surface value. To evaluate the corresponding side lengths of the graphic and each set of matching graphics, the formula is used. Calculate the matching edge value Y; where and These represent the lengths of the sides corresponding to the evaluated and matched figures, respectively; i is the side length number. Extract the calculated matching face value and matching edge value between the evaluation graphic and the matching graphic, and multiply them by the preset face value weight coefficient and edge value weight coefficient respectively. Then sum them to obtain the graphic abnormality overlap index between the evaluation graphic and each set of matching graphics.

6. The method for multidimensional data visualization and analysis of geophysical network according to claim 4, characterized in that, The numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package is obtained as follows: For each pair of corresponding lines in the real-time line package and the abnormal line package, count the number of inflection points that are successfully matched between the two corresponding lines, and denote it as the inflection point coincidence value E1. Obtain the slope of each segment in the two broken lines, and apply the formula... Perform the calculation; subtract the integer from the calculation result and take the absolute value to obtain the coincidence value of the slopes of the two broken lines, E2; where This represents the slope of the g-th segment of the polyline within the real-time polyline package. The slope of the g-th segment of the polyline within the abnormal polyline package; This is a sign function; it is 1 when the signs are the same and -1 when the signs are opposite. The inflection point coincidence value E1 and slope coincidence value E2 calculated for the two corresponding lines in the real-time line package and the abnormal line package are normalized according to the formula. Calculate the numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package; where E1 and E2 are the weighting coefficients corresponding to the inflection point coincidence value and the slope coincidence value, respectively, and Ez is the similarity index of the two corresponding lines in the real-time line package and the abnormal line package. The preset weighting coefficients are the similarity indices of different lines in the real-time line package and the abnormal line package, where z is the number of the different line similarity indices.

7. A multi-dimensional data visualization and analysis system for geophysical networks, applied to the multi-dimensional data visualization and analysis method for geophysical networks as described in any one of claims 1-6, characterized in that, include: The data acquisition module is used to collect earthquake data during an earthquake and geomagnetic and gravity data before the earthquake occurs in real time. After preprocessing, the data is associated with earthquake, geomagnetic, and gravity data through the created earthquake number, and an earthquake data package is constructed. The data processing module is used to parse earthquake data packets, determine the latitude and longitude coordinates of the epicenter from the earthquake data, delineate a spatial analysis area with the epicenter as the center and a radius of kilometers, extract geomagnetic and gravity data of the spatial analysis area within a set time interval before the earthquake from the earthquake data packets, and after comprehensive processing, construct anomaly data packets and store them in the database in association with the earthquake number and earthquake data packets. The visualization assessment module is used to trigger assessment signals and monitor the geomagnetic and gravity data of the corresponding spatial analysis area in real time after the staff extracts the abnormal data packets in the corresponding spatial analysis area. It also performs correlation processing between the data packets and the abnormal data packets, generates a correlation analysis report, and pushes it to the staff.

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