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

Abstract

The invention relates to the technical field of earth science, in particular to a multi-plate paleolatitude evolution curve analysis method and system based on Monte Carlo simulation, and the method comprises the following steps: 1) building a selective plate division scheme of a research area; construction areas are firstly divided, then an optimal scheme is automatically matched, and finally plates are automatically and accurately positioned; 2) capturing and screening corresponding plate data; 3) simulating a paleo-latitude evolution curve; 4) the user obtains a paleo-latitude value of a target age; and 5) analyzing and visually expressing the palaeolatitude motion characteristics of the plate. According to the method, multi-plate paleolatitude evolution curve analysis can be well carried out.

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 an important aspect of studying various geographical features of the earth's surface in deep time, restoring plate boundaries, land and sea distribution, sedimentary environment, topography, biology and climate change during the geological history. Calculating the paleolatitude of plates is crucial to restore the paleogeographic location and explore the resource and environmental effects. Traditional paleolatitude analysis based on geological analysis methods, including paleobiogeography, climate-sensitive sediments, and geological relationship reconstruction, can only qualitatively or semi-quantitatively calculate the paleolatitude position of plates. This is multi-solution and has a large degree of error for further analysis of geological processes such as plate collision, mountain building or rifting, resulting in an inability to accurately understand the formation process of oil, gas and mineral resources and their favorable storage locations. The currently leading paleomagnetic method is the only effective technology that can quantitatively constrain the paleolatitude of plates. Therefore, it is very necessary to establish a set of paleolatitude calculation methods and reconstruction systems based on paleomagnetism.

[0003] Paleomagnetism is a science that studies the magnetic field records of the Earth's history to explore issues such as plate tectonics, Earth's rotation, and magnetic field changes. 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 paleolatitudes of plates. By measuring the paleomagnetic directions preserved in rocks and obtaining paleomagnetic poles, the geographical latitudes 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 the distribution of paleolatitudes 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] The existing paleolatitude reconstruction techniques have the following limitations:

[0008] A. The plate division scheme corresponding to the paleolatitude calculation is single and inaccurate: the existing technology only embeds one plate division scheme, GPlates, 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 method of division continues to be used, it may cause errors in matching the coordinate points expected by the user 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, develop a set of plate division usage standards for regional research, and provide the best solutions for researchers with different needs, so that their target coordinate points can accurately match the plate polygons, and provide 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: Although the existing paleolatitude calculation method provides a storable database, the collection and screening of data still heavily rely on subjective hands-on and thinking judgment at the human technical level, which has the disadvantages of high risk of data omission, low screening efficiency, time-consuming and labor-intensive, and extremely high labor costs, resulting in a lack of data reliability and slow updates and upgrades. In order to improve the reliability and efficiency of data collection and screening, it is necessary to develop algorithms for automatically linking and capturing relevant data from mainstream paleomagnetic databases, and develop new standardized data quality screening technologies corresponding to the seven standards for paleomagnetic quality identification, so as to achieve accurate and efficient paleomagnetic data quality standardization, and realize efficient data cleaning methods. Improvement can provide solid original data support for the next step of reconstructing an accurate and reliable apparent polar shift curve.

[0010] C. The methods of plate analysis and calculation of paleolatitude are too simple and inefficient, lacking efficient and rapid statistical analysis of the complex movement of multiple plates: There is a long-ignored problem in the calculation of paleolatitude based on paleomagnetic data, that is, 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 of calculating the apparent polar excursion curve first uses only 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, resulting in a lack of continuity in the apparent polar excursion curve, which will seriously affect the subsequent analysis of the plate paleolatitude related parameter curve and make it impossible to restore the evolution history of the paleogeographic location. (2) The paleolatitude curve calculated using the discontinuous apparent pole 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 movement characteristics in the geological history and low reliability. In the early days, a team developed a simple and automated paleolatitude curve technology for calculating a single plate based on paleomagnetic poles. The impact of these two errors (A95 and σ) on the accuracy of calculating paleolatitude using multiple paleomagnetic pole data was not considered, and no further statistically significant analysis methods for paleolatitude movement parameters of multiple plates (change rate and paleolatitude difference between adjacent plates) were developed. 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, there is no method for analyzing the movement rate of a single plate in different time periods, nor is there a method for calculating the difference in paleolatitudes of multiple plates at the same time point. There is also no solution to the problem of the impact of errors in chronological and paleomagnetic data on the accuracy of calculation results.

[0011] D. Lack of effective visual expression: The existing method model only displays the results of a single paleolatitude curve, lacks the visualization of the apparent polar excursion curve of the screened paleomagnetic data statistics, and also lacks the analytical curve display of the paleolatitude change rate and its error of multiple plates and the paleolatitude difference and its error of adjacent plates. This results in the inaccurate expression of the mutual movement mode of multiple plates in terms of latitude, and will also lead to inaccurate analysis of the subsequent paleogeographic reconstruction applied to the climate-biology-resource effects. Summary of the invention

[0012] The present invention provides a method and system for analyzing multi-plate paleolatitude evolution curves based on Monte Carlo simulation, which can better perform multi-plate paleolatitude evolution curve analysis.

[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 divide the construction area, then automatically match the best solution, and finally automatically and accurately locate the plate;

[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 paleo-latitudinal movement characteristics.

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

[0021] 1.1) Divide the world into six tectonic domains according to 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 division scheme models were sorted 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 of each solution is matched with the latitude and longitude range information in QGIS, and a loading package is made into a file in the Shapefile (.shp) format. A new R language algorithm program is provided, and the geospatial analysis package is used to use the st_read function to read these plate Shapefile files with spatial ranges; then, if the user enters the expected longitude and latitude coordinate points (x, y), the program converts the coordinate points and the plate spatial range into sf objects, and uses the st_contains function to determine which plate polygon the coordinate point is in; finally, the plate ID to which the coordinate point belongs is output, and if no plate polygon contains the point, a mismatch information 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, select data that meet at least three of the criteria; use the rowSums function in the R language environment to score each row of data, filter out rows with scores greater than 3, and save them in the filtered_data data frame; finally, output the filtered data and choose to save the data as a new CSV file.

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

[0032] 3.1) Take a sliding average over a 10 million year time window and calculate the apparent polar excursion curve of the target plate taking into account the age error range of the data;

[0033] First, the selected paleomagnetic data were used to create a CSV file based on three columns of data information: 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 expected magnetic inclination angle and its confidence interval are calculated by placing the paleomagnetic pole relative to the researcher's expected coordinate point, and 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 curve and calculation of its expected value;

[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 values ​​changing 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 rate;

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

[0043] 5.2) Analysis and visualization of the difference in paleolatitude between adjacent plates;

[0044] First, read the CSV files containing paleolatitude data of two adjacent plates. Each row of data includes three columns of age value and its error range and three columns of paleolatitude value and its error range. Each row of data is arranged from top to bottom in order of 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 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 realize accurate and fast automatic matching of plate polygons based on the input target coordinate points, which effectively solves the problem that the existing plate division scheme before paleolatitude calculation is too general, time-consuming and inaccurate.

[0048] (2) Provide a systematic way to quickly link to the global mainstream paleomagnetic database platform, so as to 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 automatic algorithm for distinguishing the quality of paleomagnetic data can achieve the purpose of fast and accurate data standardization and cleaning, effectively solving the defects of the existing methods of data acquisition and screening, which are time-consuming and labor-intensive, low in accuracy, and slow in updating, and provide the most authentic and reliable original data for the next step of reconstruction of the apparent polar shift curve.

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

[0050] (4) The present invention uses the Monte Carlo simulation method to fit the paleolatitude evolution curve of each paleomagnetic pole converted paleolatitude value (including error) and age value (including error), and realizes the automatic generation of paleolatitude curve based on statistical analysis. This new simulation technology is innovatively combined with the Fisher calculation method to make up for the shortcomings of the original model that ignores the error analysis of age values ​​and leads to inaccurate paleolatitude evolution curve, so as to achieve the purpose 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 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 the Monte Carlo simulation method to analyze the plate paleolatitude motion characteristic curve - the plate paleolatitude change rate and the paleolatitude difference of adjacent plates, which completely makes up for the defects of the existing method that uses the arithmetic mean of paleolatitude data (ignoring the error range) to simply calculate its evolution curve, resulting in inaccurate data and time-consuming and labor-intensive. In particular, for some plate kinematic characteristic analysis with a small amount of data, sporadic data points may not be able to restore the true kinematic characteristics of the plate using traditional methods, but using the Monte Carlo simulation of the present invention, statistical analysis can be performed based on the original data, and the plate kinematic law can be quickly, automatically and accurately restored.

[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 evolution history of the paleolatitude positions of multiple plates, and is directly applied to paleoclimate-paleobiological evolution analysis related to paleogeographic latitudinal positions. It can also analyze the influence of the paleolatitude positions of oil and gas-bearing plates (basins) at different periods on the formation of oil and gas "source-reservoir-cap", and directly serve oil and gas exploration prediction. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0055] Figure 2 This is 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 movement 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 in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[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 general method of directly matching a global plate division scheme in the prior art.

[0062] Specifically:

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

[0064] 1.2) Based on the citation records of Google Scholar models, the mainstream division models published in the past 10 years 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.

[0065] 1.3) Each plate of each solution is matched with the latitude and longitude range information in QGIS, and a loading package is made into a Shapefile (.shp) format file (already converted to WGS84 coordinate system). A new algorithm program in R language is provided, and the geospatial analysis package (sf package) is used to use the st_read function to read these plates (closed Polygons) with spatial ranges. For example, the spatial range of a certain plot of land is loaded with 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 inputs the desired longitude and latitude coordinates (x, y), the program converts the coordinates and the plate space range into sf objects, and uses the st_contains function to determine which plate polygon the coordinate point is in; finally, the plate ID to which the coordinate point belongs is output, and 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, 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 in 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, filter the data that meet at least three criteria (R>3); use the rowSums function in the R language environment to score each row of data, filter out rows with scores greater than 3, and save them in the filtered_data data frame; finally, output the filtered data and choose to save the data as a new CSV file.

[0074] 3) Simulate the paleolatitude evolution curve;

[0075] In step 3), specifically:

[0076] 3.1) Take a sliding average over a 10 million year (My) time window; consider the age error range of the data and calculate the apparent polar excursion curve of the target plate;

[0077] First, the selected paleomagnetic data were used to create a CSV file based on three columns of data information: 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 expected 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 curve and calculation of its expected value;

[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 values; finally, calculate the average value and confidence interval of the simulated data to generate a curve chart of paleolatitude values ​​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 paleo-latitudinal movement characteristics.

[0086] In step 5), specifically:

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

[0088] First, read the 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 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 in order of age value from small to large. Then, calculate the paleolatitude change rate and its error value between each pair of adjacent time data points. Then, use Monte Carlo simulation to generate the distribution of paleolatitude change rate, and calculate the mean and confidence interval of these simulated data. Finally, draw the curve of paleolatitude change rate versus age value and output it as CSV, JGP, SVG and PDF files.

[0089] 5.2) Analysis and visualization of the difference in paleolatitude 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 in order of 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 the curve of paleolatitude difference changing with 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] According to the developed Monte Carlo simulation-based multi-plate paleolatitude evolution curve analysis method and model, paleolatitude evolution curves of the Permian North China Block, which is extremely rich in coal resources, and its surrounding major plates were calculated and visualized. 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. Link MagIC and GPMDB paleomagnetic databases to capture the latest paleomagnetic data of the main plates of the Paleo-Asian Ocean-Tethys tectonic domain, and use the judgment criteria of the invented model to automatically screen the data R>3, thus realizing the establishment of the latest and most complete 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 average method, the apparent polar excursion curve of each plate in the Permian period is reconstructed quickly and accurately.

[0096] 4. Input the reference point and convert the apparent polar motion curve into the paleolatitude curve ( Figure 2 ), using Monte Carlo simulation to analyze the paleo-latitudinal movement rate of each plate (such as the North China Plate, Figure 3 Part a in the middle is a Monte Carlo simulation of 10,000 data capture analysis diagrams). For example, it was determined that the latitudinal movement rate of the North China Plate in the Permian Plate 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 of paleolatitudes in different time periods in North China), which may explain the direct influence of the residence time of the North China block in different climatic zones on the development of its coal seams.

[0097] 5. The visualization of multi-plate paleolatitude evolution curves with age and paleolatitude values ​​and the export of vector files have made the research case 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 period reconstructed using this method, combined with the division of paleoclimate zones, profoundly explains the distribution pattern of coal seam resources in the early Permian period and provides reliable theoretical basis data for coal mine exploration and research in North China.

[0099] The present invention and its embodiments are described schematically above, and the description 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 it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope 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 divide the construction area, then automatically match the best solution, and finally automatically and accurately locate the plate; 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 paleo-latitudinal movement characteristics.

2. The method for analyzing the multi-plate paleolatitude evolution curve based on Monte Carlo simulation according to claim 1, characterized in that: In step 1), specifically: 1.1) Divide the world into six tectonic domains according to 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 division scheme models were sorted 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 of each solution is matched with the latitude and longitude range information in QGIS, and a loading package is made into a file in the Shapefile (.shp) format. A new R language algorithm program is provided, and the geospatial analysis package is used to use the st_read function to read these plate Shapefile files with spatial ranges; then, if the user enters the expected longitude and latitude coordinate points (x, y), the program converts the coordinate points and the plate spatial range into sf objects, and uses the st_contains function to determine which plate polygon the coordinate point is in; finally, the plate ID to which the coordinate point belongs is output, and if no plate polygon contains the point, a mismatch information is output.

3. The method for analyzing the multi-plate paleolatitude evolution curve based on Monte Carlo simulation according to claim 2 is 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, select data that meet at least three of the criteria; use the rowSums function in the R language environment to score each row of data, filter out rows with scores greater than 3, and save them in the filtered_data data frame; finally, output the filtered data and choose to save the data as a new CSV 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 3), specifically: 3.1) Take a sliding average over a 10 million year time window and calculate the apparent polar excursion curve of the target plate taking into account the age error range of the data; First, the selected paleomagnetic data were used to create a CSV file based on three columns of data information: 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 expected 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 curve and calculation of its expected value; 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 values ​​changing with age and a CSV data file.

5. The method for analyzing multi-plate paleolatitude evolution curves based on Monte Carlo simulation according to claim 4 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.

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

7. 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 1-6.

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