Multi-plate paleolatitude evolution curve analysis method and system based on Monte Carlo simulation

Abstract

The present invention relates to the field of earth science and technology, and more specifically, to a Monte Carlo simulation-based method and system for analyzing multi-plate paleolatitude evolution curves. The method comprises the following steps: 1) establishing a selective plate division scheme for the study area; first dividing the tectonic region, then automatically matching the optimal scheme, and finally automatically and accurately locating the plates; 2) capturing and filtering corresponding plate data; 3) simulating paleolatitude evolution curves; 4) obtaining paleolatitude values ​​of a target age by the user; and 5) analyzing and visualizing plate paleolatitude motion characteristics. The present invention is capable of effectively analyzing multi-plate paleolatitude evolution curves.

Description

Technical Field

[0001] The present invention relates to the field of earth science technology, and in particular to a method and system for analyzing multi-plate paleolatitude evolution curves based on Monte Carlo simulation. Background Art

[0002] Paleogeography is a crucial area of ​​research that studies the Earth's surface in deep time, reconstructing plate boundaries, land-sea distribution, sedimentary environments, topography, and biological and climate changes throughout geological history. Calculating plate paleolatitudes is crucial for reconstructing paleogeographic locations and exploring their impact on resources and the environment. Traditional paleolatitude analysis, relying on geological analysis methods such as paleobiogeography, climate-sensitive sediments, and geological relationship reconstruction, can only provide qualitative or semi-quantitative estimates of plate paleolatitudes. This leads to ambiguity and significant errors in further analysis of geological processes such as plate collision, mountain building, and breakup, hindering accurate understanding of the formation processes and favorable reservoir locations of oil, gas, and mineral resources. Currently, leading paleomagnetic methods are the only effective technology capable of quantitatively constraining plate paleolatitudes. Therefore, establishing a paleolatitude calculation method and reconstruction system based on paleomagnetism is crucial.

[0003] Paleomagnetism is a science that explores issues such as plate tectonics, Earth's rotation, and magnetic field changes by studying the magnetic field record of the Earth's history. By analyzing the residual magnetic field in rocks, paleomagnetists can infer the direction of the Earth's magnetic field when the rocks were formed, and use this to reconstruct key Earth evolution information such as the relative positions of ancient continents and the movement paths of plates. One of the important applications of paleomagnetism is the reconstruction of the paleolatitude of plates. By measuring the paleomagnetic direction preserved in rocks and obtaining paleomagnetic poles, the geographical latitude when the rocks were formed can be further determined, thereby inferring the position and movement trajectory of the plate at a specific period. This is of great significance to the study of plate tectonic evolution, continental drift, and the formation and splitting of supercontinents. Paleomagnetic data can provide the following information:

[0004] a. The magnetic inclination of the rock (the angle between the magnetic field and the horizontal plane) can be used to infer the ancient latitude when the rock was formed;

[0005] b. Combining paleomagnetic data from multiple locations can reconstruct paleolatitude distribution over large regions or even the entire world;

[0006] c. As time changes, the age information of rock samples can be used to reconstruct the movement history of plates.

[0007] Existing paleolatitude reconstruction techniques have the following limitations:

[0008] A. The plate division scheme corresponding to paleolatitude calculation is single and inaccurate: Existing technologies only incorporate the GPlates plate division scheme, which is mainly used for global-scale paleogeographic reconstruction. It is a relatively general definition of plate boundaries and has many contradictions with other division schemes in different regions. If this division scheme continues to be used, it may cause errors in matching the user's desired coordinate points to the plate, which will ultimately directly affect the accuracy of the paleogeographic reconstruction results. Therefore, it is necessary to collect and summarize multiple plate division schemes with high credibility and develop a set of plate division usage standards for regional research to provide researchers with different needs with the best solutions so that their target coordinate points can accurately match the plate polygons, providing the most reliable plate characteristics for subsequent more accurate constraints on the history of paleogeographic evolution.

[0009] B. Data collection and screening are time-consuming and labor-intensive, and lack data quality standardization: While existing paleolatitude calculation methods provide a storable database, data collection and screening still rely heavily on subjective manual effort and judgment at the technical level. This leads to a high risk of data omissions, inefficient screening, time-consuming and labor-intensive processes, and extremely high labor costs. This results in a lack of data reliability and slow data updates. To improve the reliability and efficiency of data collection and screening, it is necessary to develop algorithms that automatically link and capture relevant data from mainstream paleomagnetic databases. Furthermore, new standardized data quality screening technologies corresponding to the seven paleomagnetic quality assessment criteria should be developed to achieve accurate and efficient paleomagnetic data quality standardization. This improvement in efficient data cleaning methods will provide solid raw data support for the next step of reconstructing an accurate and reliable apparent polar wobble curve.

[0010] C. The methods for plate analysis and paleolatitude calculation are overly simple and inefficient, lacking efficient and rapid statistical analysis of the complex motion of multiple plates: There is a long-overlooked problem in paleolatitude calculation based on paleomagnetic data, namely, the paleomagnetic pole error (A95) and age error (σ) have not been effectively statistically analyzed. Among the existing methods in the field of paleomagnetics, (1) the method for calculating the apparent polar excursion curve only uses the Fisher statistics of the paleomagnetic pole at the average time point, while ignoring the statistics of paleomagnetic pole data with overlapping age error ranges. This results in a lack of continuity in the apparent polar excursion curve, which seriously affects the subsequent analysis of plate paleolatitude-related parameter curves and makes it impossible to restore the evolutionary history of paleogeographic location. (2) The paleolatitude curve calculated using the discontinuous apparent polar excursion curve is also discontinuous, with large data gaps. Subsequent calculations still ignore the errors in paleolatitude and age values, and only use the arithmetic mean of paleolatitude and age as a point data for comparative analysis. This cannot accurately reflect the evolution curve of paleolatitude-related parameters (change rate and paleolatitude difference between adjacent plates), resulting in a serious lack of multi-plate motion characteristics in the geological history and low reliability. In the early days, a team developed a simple and automated paleolatitude curve technology for a single plate based on paleomagnetic poles. However, they did not consider the impact of these two errors (A95 and σ) on the accuracy of paleolatitude calculation using multiple paleomagnetic pole data, nor did they further develop statistically significant analysis methods for multiple plate paleolatitude motion parameters (change rate and paleolatitude difference between adjacent plates). As a result, paleolatitude analysis still lacks an automated, fast, accurate, and efficient method. For example, in the reconstruction of the relative movement of a single plate and its surrounding plates, no method for analyzing the movement rate of a single plate in different time periods, nor any method for calculating the difference in paleolatitudes of multiple plates at the same time point have been developed. Moreover, the impact of errors in chronological and paleomagnetic data on the accuracy of calculation results has not been addressed.

[0011] D. Lack of effective visual expression: The existing method model only displays the results of a single paleolatitude curve, and lacks a visual display of the apparent polar wander curve of the screened paleomagnetic data. In addition, there is a lack of analytical curves showing the paleolatitude change rate and its error of multiple plates and the paleolatitude difference and its error of adjacent plates. This results in an inaccurate expression of the mutual movement mode of multiple plates in the latitudinal direction, and will also lead to inaccurate analysis of subsequent paleogeographic reconstructions applied to climate-biological-resource effects. Summary of the Invention

[0012] The present invention provides a method and system for analyzing the paleolatitude evolution curve of multiple plates based on Monte Carlo simulation, which can better perform the analysis of the paleolatitude evolution curve of multiple plates.

[0013] The method for analyzing the multi-plate paleolatitude evolution curve based on Monte Carlo simulation according to the present invention comprises the following steps:

[0014] 1) Establish a selective plate division scheme for the study area;

[0015] First, the structural area is divided, then the optimal solution is automatically matched, and finally the plate is accurately positioned automatically;

[0016] 2) Capture and filter corresponding sector data;

[0017] 3) Simulate the paleolatitude evolution curve;

[0018] 4) The user obtains the paleolatitude value of the target age;

[0019] 5) Analysis and visualization of plate paleolatitude movement characteristics.

[0020] Preferably, in step 1), specifically:

[0021] 1.1) Divide the world into six tectonic domains based on the study area: Paleo-Asian Ocean, Tethys, North America, Europe, Antarctica, Australia, Africa, and South America, and number the plates in each tectonic domain;

[0022] 1.2) The mainstream models were ranked according to the citation rate of each tectonic domain, and it was determined that the Paleo-Asian Ocean and Tethys tectonic domains matched the HB18 model, the North American and European tectonic domains matched the TH14 model, the Antarctic and Australian tectonic domains matched the MK16 model, and the African and South American tectonic domains matched the MA21 model;

[0023] 1.3) Each plate in each solution is matched with its latitude and longitude range in QGIS and created as a Shapefile (.shp) file loading package. A new R language algorithm is provided, using the geospatial analysis package and the st_read function to read these plate Shapefiles with spatial ranges. Then, if the user enters the desired latitude and longitude coordinates (x, y), the program converts the coordinate point and the plate spatial range into an .sf object and uses the st_contains function to determine which plate polygon the coordinate point falls within. Finally, the program outputs the plate ID to which the coordinate point belongs. If no plate polygon contains the point, a mismatch message is output.

[0024] Preferably, in step 2), specifically:

[0025] 2.1) Obtain data from multiple paleomagnetic databases;

[0026] Use the httr and dplyr packages in R to help send network requests and process data. Use the httr package to send HTTP requests to obtain data from the website database.

[0027] 2.2) Read data;

[0028] In the R language environment, the readxl package is used to read the data from the website database, and then the dplyr package is used to filter the data related to the corresponding target plate polygon. Finally, the write.csv function is used to write the filtered data into a CSV file and store it in a folder.

[0029] 2.3) Parsing data;

[0030] According to the seven criteria for paleomagnetic quality, data that meets at least three of the criteria are selected. In the R language environment, the rowSums function is used to score each row of data, and rows with scores greater than three are selected based on the scores and saved in the filtered_data data frame. Finally, the filtered data is output and the data is saved as a new CSV file.

[0031] Preferably, in step 3), specifically:

[0032] 3.1) Perform a sliding average over a 10 million-year time window; taking into account the chronological error range of the data, calculate the apparent polar motion curve of the target plate;

[0033] First, the filtered paleomagnetic data were used to create a CSV file based on three columns of data: age, longitude, and latitude. In the R language environment, the mean.vector function of the paleomagnetic analysis ape package was used to calculate the Fisher mean of each time window.

[0034] 3.2) Calculation of paleolatitude values;

[0035] The paleomagnetic pole is relative to the researcher's desired coordinate point, and the expected magnetic inclination and its confidence interval are calculated. The corresponding paleolatitude value and its error range ΔΦ are further calculated according to the formula tan I = 2tanλ.

[0036] 3.3) Presentation of the Monte Carlo simulation of paleolatitude evolution curves and calculation of their expected values;

[0037] First, the paleolatitude CSV file is read. Each paleolatitude data includes age and its error value, paleolatitude value and its error value. Then, a Monte Carlo simulation is performed on each data point to generate random paleolatitude values ​​and age values, and their average values ​​are calculated. Finally, the average value and confidence interval of the simulated data are calculated to generate a curve chart of paleolatitude value changes with age and a CSV data file.

[0038] Preferably, in step 4), specifically:

[0039] Based on the acquired paleolatitude evolution curve of the target plate, the researchers defined a get_paleolatitude function, which accepts a target age value target_age as input and returns the average paleolatitude value and its error range corresponding to the age value.

[0040] Preferably, in step 5), specifically:

[0041] 5.1) Analysis and visualization of paleolatitude movement rates;

[0042] First, read a CSV file containing paleolatitude data. Each row of data includes three columns of data: age value and its error range, and three columns of data: paleolatitude value and its error range. Each row of data is arranged from top to bottom in ascending order of age value. Then, the paleolatitude change rate and its error value between each pair of adjacent time data points are calculated. Next, a Monte Carlo simulation is used to generate the distribution of paleolatitude change rate, and the mean and confidence interval of these simulated data are calculated. Finally, a curve of paleolatitude change rate versus age value is plotted and output as CSV, JGP, SVG, and PDF files.

[0043] 5.2) Analysis and visualization of paleolatitude differences between adjacent plates;

[0044] First, read the CSV files containing paleolatitude data of two adjacent plates. Each row of data includes three columns of data: age value and its error range, and three columns of data: paleolatitude value and its error range. Each row of data is arranged from top to bottom in order of age value. Then, automatically analyze the two rows of data with the same age value in the two files. Calculate the paleolatitude difference and its error value between the data points with the same time in the two files. Then, perform Monte Carlo simulation and calculate the paleolatitude difference and its error value using the matched data. Finally, draw a curve of the paleolatitude difference changing with age value and output it as CSV, JGP, SVG and PDF files.

[0045] The present invention provides a multi-plate paleolatitude evolution curve analysis system based on Monte Carlo simulation, which adopts the above-mentioned multi-plate paleolatitude evolution curve analysis method based on Monte Carlo simulation.

[0046] The beneficial effects of the present invention are as follows:

[0047] (1) It provides the best plate division scheme for users conducting research in different regions around the world. Users can achieve accurate and fast automatic matching of plate polygons based on the input target coordinate points, effectively solving the problem that the plate division scheme before the existing paleolatitude calculation is too general, time-consuming and inaccurate.

[0048] (2) Provide a systematic approach to quickly link to the world's mainstream paleomagnetic database platform, quickly and accurately capture the latest and most complete paleomagnetic data for the target plate, and achieve the effect of quickly updating data at any time. The innovative design standardization and automated algorithm for distinguishing the quality of paleomagnetic data achieves the purpose of rapid and accurate data standardization and cleaning, effectively solving the defects of existing methods of data acquisition and screening, such as time-consuming and labor-intensive, low accuracy, and slow update, and provides the most authentic and reliable original data for the next step of reconstructing the apparent polar motion curve.

[0049] (3) The innovatively introduced sliding average method based on a 10 million-year time window (taking into account the range of chronological errors) provides researchers with the fastest and most accurate automatic calculation and generation of plate apparent polar motion curves. This can effectively solve the shortcoming of existing technologies that ignore the chronological error information of paleomagnetic data, resulting in inaccurate average results. At the same time, it can make up for the problem of incomplete apparent polar motion curves caused by the lack of sliding average in existing technologies, thereby achieving the goal of accurately and efficiently reconstructing the true apparent polar motion curve, laying a solid analytical data foundation for the next step of paleolatitude analysis.

[0050] (4) The present invention uses a Monte Carlo simulation method to fit the paleolatitude evolution curve of each paleomagnetic pole-converted paleolatitude value (including errors) with the age value (including errors), thereby automatically generating a paleolatitude curve based on statistical analysis. This new simulation technology, combined with the innovative Fisher calculation method, overcomes the shortcomings of the original model that ignores the analysis of age value errors and leads to inaccurate paleolatitude evolution curves, achieving the goal of more accurate, faster, and more convenient paleolatitude curve calculation. For example, if a user wants to calculate the paleolatitude evolution curve of 200-100Ma through a series of paleomagnetic data of the North China Plate, the existing technology can only manually calculate the average age value of each paleomagnetic data to calculate the paleolatitude value and its error, and then draw the evolution curve; if the age error of the paleomagnetic data of the North China Plate used is large, the paleolatitude value and its error range calculated by simple arithmetic average will lack accuracy; if the Monte Carlo simulation of the present invention is used, the optimal evolution curve can be fitted through statistical principles, achieving reasonable and accurate calculations, and also providing key theoretical support for the next step of conducting plate paleolatitude change rate and paleolatitude difference analysis.

[0051] (5) The present invention uses Monte Carlo simulation to analyze the characteristic curve of plate paleolatitude motion—the plate paleolatitude change rate and the paleolatitude difference between adjacent plates. This completely compensates for the shortcomings of existing methods that simply calculate the evolution curve using the arithmetic mean of paleolatitude data (ignoring the error range), resulting in inaccurate data and time-consuming and labor-intensive processes. In particular, for the analysis of plate kinematic characteristics with relatively small amounts of data, the traditional methods may not be able to restore the true kinematic characteristics of the plate using scattered data points. However, using the Monte Carlo simulation of the present invention, statistical analysis can be performed based on the original data, quickly, automatically, and accurately restoring the plate kinematic laws.

[0052] (6) The method of the present invention provides a new visual display, providing researchers with vector paleolatitude visualization files that can be easily downloaded and edited, which can effectively solve the problem of inaccurate paleolatitude evolution curve analysis and visualization expression in existing systems.

[0053] In summary, the paleolatitude evolution curve analysis method and model of the present invention have developed an intelligent, standardized, and digital plate division scheme model and coordinate point matching method, and quickly capture and filter data as paleomagnetic data are continuously updated. Monte Carlo simulation is used to deeply analyze and calculate the paleolatitude-related evolution curves of multiple plates, and finally the curve is visualized to achieve a more standard, accurate, fast and efficient paleolatitude evolution analysis and visualization display. It can quantitatively constrain the paleolatitude position evolution history of multiple plates, and be directly applied to paleoclimate-paleobiological evolution analysis related to paleogeographic latitudinal position. It can also analyze the influence of the paleolatitude position of oil and gas plates (basins) in different periods on the formation of oil and gas "source-reservoir-cap", directly serving oil and gas exploration prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Flowchart of a method for analyzing multi-plate paleolatitude evolution curves based on Monte Carlo simulation in an embodiment;

[0055] Figure 2 The following is an example of the paleolatitude evolution curve of the main plates of the Meso-Asian Ocean-Tethys tectonic domain over time and the climate-coal seam resource efficiency diagram (Ma is one million years ago);

[0056] Figure 3 Schematic diagram of the evolution of the latitudinal motion rate of the North China Plate in different time periods based on Monte Carlo simulation in the embodiment. DETAILED DESCRIPTION

[0057] In order to further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and embodiments. It should be understood that the embodiments are merely for explaining the present invention and are not intended to limit the present invention.

[0058] Example

[0059] like Figure 1 As shown, this embodiment provides a multi-plate paleolatitude evolution curve analysis method based on Monte Carlo simulation, which includes the following steps:

[0060] 1) Establish a selective plate division scheme for the study area;

[0061] In step 1), in order to accurately utilize the accurate division scheme of each area, this embodiment establishes a new digital method: first divide the structural area, then automatically match the best scheme, and finally automatically and accurately locate the plate. This is different from the prior art's general method of directly matching a global plate division scheme.

[0062] Specifically:

[0063] 1.1) The world is divided into six tectonic domains according to the study area: Paleo-Asia, Tethys, North America, Europe, Antarctica, Australia, Africa, and South America. Each tectonic domain plate is uniquely numbered (ID).

[0064] 1.2) Based on the Google Scholar model citation records, the mainstream division scheme models published in the past 10 years were ranked according to the citation rate of each tectonic domain. It was determined that the Paleo-Asian Ocean and Tethys tectonic domains match the HB18 model, the North American and European tectonic domains match the TH14 model, the Antarctic and Australian tectonic domains match the MK16 model, and the African and South American tectonic domains match the MA21 model.

[0065] 1.3) Each plate of each solution is matched with the latitude and longitude range information in QGIS, and a loading package is created for the Shapefile (.shp) format (already converted to the WGS84 coordinate system). A new R language algorithm program is provided, using the geospatial analysis package (sf package) and the st_read function to read these plate (closed Polygon) Shapefile files with spatial ranges. For example, for the spatial range of a certain plot, the coordinate points of the plate Polygon (x1, y1, x2, y2...x n ,y n ); the file contains multiple such plate polygons; then, if the user enters the desired longitude and latitude coordinates (x, y), the program converts the coordinates and the plate spatial range into sf objects, and uses the st_contains function to determine which plate polygon the coordinate point is within; finally, the plate ID to which the coordinate point belongs is output. If no plate polygon contains the point, a mismatch message is output.

[0066] 2) Capture and filter corresponding sector data;

[0067] In step 2), specifically:

[0068] 2.1) Obtain data from multiple paleomagnetic databases (such as MagIC and GPMDB);

[0069] Use the httr and dplyr packages in R to help send network requests and process data. Use the httr package to send HTTP requests to obtain data from the website database.

[0070] 2.2) Read data;

[0071] In the R language environment, the readxl package is used to read the data from the website database (in Excel format), and then the dplyr package is used to filter the data related to the corresponding target plate polygon. Finally, the write.csv function is used to write the filtered data into a CSV file and store it in a folder.

[0072] 2.3) Parsing data;

[0073] According to the seven criteria for paleomagnetic quality, data that meets at least three criteria (R>3) are selected. In the R language environment, the rowSums function is used to score each row of data, and rows with scores greater than 3 are selected based on the score and saved in the filtered_data data frame. Finally, the filtered data is output and the data is saved as a new CSV file.

[0074] 3) Simulate the paleolatitude evolution curve;

[0075] In step 3), specifically:

[0076] 3.1) Taking a 10 million-year (My) time window as the sliding average and considering the age error range of the data, calculate the apparent polar motion curve of the target plate;

[0077] First, the filtered paleomagnetic data were used to create a CSV file based on three columns of data: age, longitude, and latitude. In the R language environment, the mean.vector function of the paleomagnetic analysis ape package was used to calculate the Fisher mean of each time window.

[0078] 3.2) Calculation of paleolatitude values;

[0079] The paleomagnetic pole (including longitude and latitude and confidence interval A95) is relative to the researcher's desired coordinate point (i.e., reference point), and the expected magnetic inclination and its confidence interval (a95) are calculated. The corresponding paleolatitude value and its error range ΔΦ are further calculated according to the formula tan I = 2tanλ.

[0080] 3.3) Presentation of the Monte Carlo simulation of paleolatitude evolution curves and calculation of their expected values;

[0081] First, read the paleolatitude CSV file. Each paleolatitude data includes age (Age) and its error value (Age_min, Age_max), paleolatitude value (Paleolatitude) and its error value (Paleolatitude_min, Paleolatitude_max); then perform Monte Carlo simulation on each data point to generate random paleolatitude values ​​and age values, and calculate their average value; finally, calculate the average value and confidence interval of the simulated data to generate a curve chart of paleolatitude value changing with age (jpg, svg and pdf formats) and a CSV data file.

[0082] 4) The user obtains the paleolatitude value of the target age;

[0083] In step 4), specifically:

[0084] Based on the acquired paleolatitude evolution curve of the target plate, the researchers defined a get_paleolatitude function, which accepts a target age value target_age as input (taking 120Ma as an example) and returns the average paleolatitude value and its error range corresponding to the age value.

[0085] 5) Analysis and visualization of plate paleolatitude movement characteristics.

[0086] In step 5), specifically:

[0087] 5.1) Analysis and visualization of paleolatitude movement rates;

[0088] First, read a CSV file containing paleolatitude data. Each row of data includes three columns of data: age value and its error range (minimum age value, maximum age value) and paleolatitude value and its error range (minimum paleolatitude value, maximum paleolatitude value). Each row of data is arranged from top to bottom in ascending order of age value. Then, the paleolatitude change rate and its error value between each pair of adjacent time data points are calculated. Next, a Monte Carlo simulation is used to generate the distribution of paleolatitude change rate, and the mean and confidence interval of these simulated data are calculated. Finally, a curve of paleolatitude change rate versus age value is plotted and output as CSV, JGP, SVG, and PDF files.

[0089] 5.2) Analysis and visualization of paleolatitude differences between adjacent plates;

[0090] First, read the CSV files containing paleolatitude data of two adjacent plates. Each row of data includes three columns of data: age value and its error range (minimum age value, maximum age value) and three columns of data: paleolatitude value and its error range (minimum paleolatitude value, maximum paleolatitude value). Each row of data is arranged from top to bottom according to the age value from small to large. Then, automatically analyze the two rows of data with the same age value in the two files. Calculate the paleolatitude difference and its error value between the two files with the same time data points. Then, perform Monte Carlo simulation and use the matched data to calculate the paleolatitude difference and its error value. Finally, draw a curve of the paleolatitude difference changing with the age value and output it as CSV, JGP, SVG and PDF files.

[0091] The present invention provides a multi-plate paleolatitude evolution curve analysis system based on Monte Carlo simulation, which adopts the above-mentioned multi-plate paleolatitude evolution curve analysis method based on Monte Carlo simulation.

[0092] Based on the developed Monte Carlo simulation-based multi-plate paleolatitude evolution curve analysis method and model, paleolatitude evolution curves were calculated and visualized for the Permian North China Block, which is extremely rich in coal resources, and its surrounding major plates. The specific research process and results are as follows:

[0093] 1. It is clear that the North China and surrounding plates belong to the Paleo-Asian Ocean-Tethys tectonic domain, so the Paleo-Asian Ocean-Tethys tectonic domain plate division scheme of HB18 is selected.

[0094] 2. Linking MagIC and GPMDB paleomagnetic databases, capturing the latest paleomagnetic data of the main plates of the Paleo-Asian Ocean-Tethys tectonic domain, and automatically screening the data with R>3 using the discrimination criteria of the invented model, thus realizing the establishment of the latest and most comprehensive paleomagnetic data table for the Permian period of the Paleo-Asian Ocean-Tethys tectonic domain.

[0095] 3. Using a 10 million-year sliding window and the Fisher averaging method, the Permian apparent polar excursion curve of each plate is reconstructed quickly and accurately.

[0096] 4. Input the reference point and convert the apparent polar motion curve into the paleolatitude curve ( Figure 2 ), Monte Carlo simulation was used to analyze the paleo-latitudinal movement rate of each plate (e.g. North China Plate, Figure 3 Part a in the middle is a Monte Carlo simulation of 10,000 data capture and analysis diagrams). For example, it was determined that the latitudinal movement rate of the North China Plate in the Permian period showed multiple accelerations ( Figure 3 Part b in the middle shows the median value and error of the continuous change of latitude rate in multiple stages; Figure 3 The middle part is the Monte Carlo statistical analysis of the difference in paleolatitudes in different time periods in North China), which may explain the direct impact of the North China block's residence time in different climatic zones on its coal seam development.

[0097] 5. The visualization of multi-plate paleolatitude evolution curves with age and paleolatitude values, as well as the export of vector files, have enabled the research case to be successfully used in the analysis of tectonic backgrounds such as oil and gas basins and paleoclimate environments.

[0098] The paleolatitude evolution process of the North China Plate in the Permian reconstructed using this method, combined with the division of paleoclimate zones, profoundly explains the distribution pattern of coal resources in the early Permian period and provides reliable theoretical basis data for coal mine exploration and research in North China.

[0099] The above is a schematic description of the present invention and its embodiments, which is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs a structure and embodiment similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A multi-plate paleolatitude evolution curve analysis method based on Monte Carlo simulation, characterized by: The following steps are involved: 1) Establish a selective plate division scheme for the study area; First, the structural area is divided, then the optimal solution is automatically matched, and finally the plate is accurately positioned automatically; In step 1), specifically: 1.1) Divide the world into six tectonic domains based on the study area: Paleo-Asian Ocean, Tethys, North America, Europe, Antarctica, Australia, Africa, and South America, and number the plates in each tectonic domain; 1.2) The mainstream models were ranked according to the citation rate of each tectonic domain, and it was determined that the Paleo-Asian Ocean and Tethys tectonic domains matched the HB18 model, the North American and European tectonic domains matched the TH14 model, the Antarctic and Australian tectonic domains matched the MK16 model, and the African and South American tectonic domains matched the MA21 model; 1.3) Each plate in each solution is mapped to its latitude and longitude range in QGIS, and then converted into a Shapefile (.shp) file loading package. A new R language algorithm is provided, using the geospatial analysis package and the st_read function to read these spatially bounded plate Shapefiles. Next, when the user enters the desired latitude and longitude coordinates (x, y), the program converts the coordinate point and the plate spatial range into an .sf object, and uses the st_contains function to determine which plate polygon the coordinate point falls within. Finally, the program outputs the plate ID to which the coordinate point belongs. If no plate polygon contains the point, a mismatch message is output. 2) Capture and filter corresponding sector data; 3) Simulate the paleolatitude evolution curve; 4) The user obtains the paleolatitude value of the target age; 5) Analysis and visualization of plate paleolatitude movement characteristics.

2. The method for analyzing multi-plate paleolatitude evolution curves based on Monte Carlo simulation according to claim 1, characterized in that: In step 2), specifically: 2.1) Obtain data from multiple paleomagnetic databases; Use the httr and dplyr packages in R to help send network requests and process data. Use the httr package to send HTTP requests to obtain data from the website database. 2.2) Read data; In the R language environment, the readxl package is used to read the data from the website database, and then the dplyr package is used to filter the data related to the corresponding target plate polygon. Finally, the write.csv function is used to write the filtered data into a CSV file and store it in a folder. 2.3) Parsing data; According to the seven criteria for paleomagnetic quality, data that meets at least three of the criteria are selected. In the R language environment, the rowSums function is used to score each row of data, and rows with scores greater than three are selected based on the scores and saved in the filtered_data data frame. Finally, the filtered data is output and the data is saved as a new CSV file.

3. The method for analyzing multi-plate paleolatitude evolution curves based on Monte Carlo simulation according to claim 2, characterized in that: In step 3), specifically: 3.1) Perform a sliding average over a 10 million-year time window; taking into account the chronological error range of the data, calculate the apparent polar motion curve of the target plate; First, the filtered paleomagnetic data were used to create a CSV file based on three columns of data: age, longitude, and latitude. In the R language environment, the mean.vector function of the paleomagnetic analysis ape package was used to calculate the Fisher mean of each time window. 3.2) Calculation of paleolatitude values; The paleomagnetic pole is relative to the researcher's desired coordinate point, and the expected magnetic inclination and its confidence interval are calculated. The corresponding paleolatitude value and its error range ΔΦ are further calculated according to the formula tan I = 2tanλ. 3.3) Presentation of the Monte Carlo simulation of paleolatitude evolution curves and calculation of their expected values; First, the paleolatitude CSV file is read. Each paleolatitude data includes age and its error value, paleolatitude value and its error value. Then, a Monte Carlo simulation is performed on each data point to generate random paleolatitude values ​​and age values, and their average values ​​are calculated. Finally, the average value and confidence interval of the simulated data are calculated to generate a curve chart of paleolatitude value changes with age and a CSV data file.

4. The method for analyzing multi-plate paleolatitude evolution curves based on Monte Carlo simulation according to claim 3 is characterized in that: In step 4), specifically: Based on the acquired paleolatitude evolution curve of the target plate, the researchers defined a get_paleolatitude function, which accepts a target age value target_age as input and returns the average paleolatitude value and its error range corresponding to the age value.

5. The method for analyzing multi-plate paleolatitude evolution curves based on Monte Carlo simulation according to claim 4, characterized in that: In step 5), specifically: 5.1) Analysis and visualization of paleolatitude movement rates; First, read a CSV file containing paleolatitude data. Each row of data includes three columns of data: age value and its error range, and three columns of data: paleolatitude value and its error range. Each row of data is arranged from top to bottom in ascending order of age value. Then, the paleolatitude change rate and its error value between each pair of adjacent time data points are calculated. Next, a Monte Carlo simulation is used to generate the distribution of paleolatitude change rate, and the mean and confidence interval of these simulated data are calculated. Finally, a curve of paleolatitude change rate versus age value is plotted and output as CSV, JGP, SVG, and PDF files. 5.2) Analysis and visualization of paleolatitude differences between adjacent plates; First, read the CSV files containing paleolatitude data from two adjacent plates. Each row of data includes three columns of data: age value and its error range, and three columns of data: paleolatitude value and its error range. Each row of data is arranged from top to bottom in ascending order of age value. Then, automatically analyze the two rows of data with the same age value in the two files. Calculate the paleolatitude difference and its error between the two files with the same time data points; then, perform Monte Carlo simulation and use the matching data to calculate the paleolatitude difference and its error; finally, plot the paleolatitude difference versus age value and output it as CSV, JGP, SVG and PDF files.

6. A multi-plate paleolatitude evolution curve analysis system based on Monte Carlo simulation, characterized by: It adopts the multi-plate paleolatitude evolution curve analysis method based on Monte Carlo simulation as described in any one of claims 1 to 5.

Images