Metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion

Abstract

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of metamorphic P-T-t path reconstruction, and more particularly, to a metamorphic P-T-t path reconstruction method and system based on multi-source data fusion. BACKGROUND

[0002] The pressure-temperature-time (P-T-t) path reconstruction of metamorphic rocks is a core technical means for understanding the history of crustal evolution, and its research results are widely used in the fields of orogenic belt tectonic process analysis, ore-forming environment diagnosis, and deep earth process inversion.

[0003] Traditional metamorphic P-T-t research usually relies on two technical paths; one is the traditional geothermobarometry method based on the chemical composition of single minerals. Although this method is widely used, it has significant limitations: its applicability is strongly dependent on specific mineral assemblages, and in many rocks that have undergone anatexis, the products are often retrograde metamorphic, making it difficult to effectively capture the peak metamorphic conditions. At the same time, this method highly depends on manual selection of mineral pairs and manual input of composition data, which is highly subjective and prone to systematic bias in geological interpretation due to errors in mineral balance interpretation or improper selection of activity models.

[0004] The other path combines whole-rock composition and thermodynamic phase equilibrium simulation to define metamorphic conditions through P-T cross section calculation, which is theoretically more accurate. However, this path still faces serious challenges in practice: the electron probe point analysis it relies on cannot fully represent the composition zoning and heterogeneity that exists universally within minerals, resulting in unavoidable statistical errors. Although electron probe face scanning technology can qualitatively display the composition distribution, traditional images cannot be directly quantitatively interpreted, and the composition differences between minerals can easily interfere with each other, causing subtle zoning information to be obscured or ignored. In addition, the operation of thermodynamic simulation software is complex, and the quality and reliability of its results are highly dependent on the operator's professional knowledge and experience, making it difficult to standardize the process and objectify the results.

[0005] More problematic is that both of the above traditional technical paths have a common core defect: mineral chemical composition analysis, whole-rock composition acquisition, temperature-pressure calculation, and geochronology determination are usually performed independently; this fragmentation of the data chain not only increases the complexity of the work, but also causes information distortion and precision loss in the data transmission and integration process, making the final constructed P-T-t path difficult to truly and continuously reflect the evolution history of the rock.

[0006] Therefore, how to design a metamorphic rock P-T-t trajectory reconstruction method based on multi-source data fusion, which can fuse multi-source data, has high automation degree, can minimize human intervention and experience dependence, and realize accurate and efficient reconstruction of metamorphic rock P-T-t trajectory is a problem that a person skilled in the art needs to solve. SUMMARY

[0007] Therefore, the present application provides a metamorphic rock P-T-t trajectory reconstruction method based on multi-source data fusion, aiming to solve the key technical problems of strong subjectivity, complicated process, data fragmentation in different technical links, and large error and poor repeatability caused by dependence on artificial experience in the existing metamorphic rock P-T-t trajectory reconstruction method, so as to obtain more accurate and reliable rock metamorphic evolution history information.

[0008] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0009] In the first aspect, the present application provides a metamorphic rock P-T-t trajectory reconstruction method based on multi-source data fusion, comprising the following steps:

[0010] S1, obtaining electronic probe micro-area mapping data and internal standard sample data of metamorphic rock samples, and performing multi-level data correction;

[0011] S2, mineral phase identification and chemical composition quantitative interpretation are performed on the corrected micro-area mapping data to generate mineral classification mapping, element concentration distribution map and whole rock major component data set;

[0012] S3, based on the whole rock major component data set and the mineral classification mapping, thermodynamic phase equilibrium simulation is performed to generate a P-T apparent section map; and based on the element concentration distribution map and the multi-phase equilibrium constraint, a P-T trajectory is extracted from the P-T apparent section map;

[0013] S4, micro-area in-situ geochronology analysis is performed on the selected minerals in the mineral classification mapping to obtain geochronology data;

[0014] S5, the P-T trajectory and the geochronology data are coupled in time and space to construct a P-T-t three-dimensional trajectory model of metamorphic rock, and a visualized reconstruction report is output.

[0015] Preferably, the S1 comprises:

[0016] The multi-element X-ray intensity mapping and the BSE or SEI image used for mineral phase identification and positioning are synchronously collected by an electron probe microanalyzer;

[0017] 10-20 representative points are selected in each mineral phase for high-precision single-point analysis, and the oxide weight percentage data after ZAF correction is used as internal standard sample data;

[0018] Based on the internal standard sample data, the multi-element X-ray intensity spectrum is sequentially subjected to dead time correction, terrain correction, time drift correction, mixed pixel correction and standard sample position correction to obtain corrected micro-area spectrum data.

[0019] Preferably, the S2 comprises:

[0020] S21, using a multi-scale convolutional neural network based on an attention mechanism to identify the mineral phase of the corrected micro-area spectrum data, and generating a mineral classification spectrum reflecting the distribution of each mineral phase;

[0021] S22, based on the internal standard sample data, the X-ray intensity in the micro-area spectrum is quantitatively converted into oxide concentration by an empirical correction algorithm, and an element concentration distribution map is generated;

[0022] S23, fusing the mineral classification spectrum and the element concentration distribution map, and calculating and outputting the micro-area scale whole rock major component data set through weighted average and density correction.

[0023] Preferably, the S21 comprises:

[0024] The encoder extracts features from the input corrected micro-area spectrum data through multi-level convolutional layers, and outputs feature maps containing mineral features of different scales;

[0025] The feature map is processed through a channel and spatial attention module, and a weighted feature map is generated by calculating the channel and spatial weight to highlight the key mineral area;

[0026] The decoder performs upsampling and feature fusion on the weighted feature map through a transposed convolutional layer, and outputs a mineral category probability map with the same size as the input spectrum;

[0027] Combined with the classification output layer, the mineral category probability map is subjected to pixel-by-pixel argmax operation to generate a mineral classification spectrum.

[0028] Preferably, in the S22, the empirical alpha factor correction algorithm is expressed as:

[0029]

[0030] Wherein, C i represents the weight percent concentration of element i, k i represents the correction coefficient, I i,sample represents the characteristic X-ray intensity of element i in the sample, I i,standard represents the characteristic X-ray intensity of element i in the standard sample.

[0031] Preferably, the S23 comprises:

[0032] The mineral classification map is pixel-level spatially registered with the element concentration distribution map to establish a correspondence between the mineral phase category and the chemical composition;

[0033] According to the mineral classification map, the area percentage of each mineral phase in the micro-area range is counted;

[0034] From the element concentration distribution map, the chemical composition data of all pixel points in each mineral phase region are extracted, and the average value thereof is calculated as the representative chemical composition of the mineral phase;

[0035] The major element of the whole rock is calculated by the area-weighted average algorithm, and the volume correction is performed on the area-weighted average calculation result in combination with the density value of each mineral phase, and the micro-area scale whole rock major element data set is output.

[0036] Preferably, the S3 comprises:

[0037] S31, based on the whole rock major element data set, selecting a thermodynamic database and defining a solid solution model, setting a pressure-temperature simulation range and a step;

[0038] S32, using the Gibbs free energy minimization algorithm, the stable mineral assemblage at each P-T point is preliminarily solved by linear programming, and then the phase composition and proportion are refined by a nonlinear iterative optimization algorithm to generate a P-T cross section view;

[0039] S33, based on the mineral composition zoning structure or composition gradient information provided by the element concentration distribution map, in combination with the multi-phase equilibrium reaction relationship, the stability domain boundary change of a specific mineral assemblage is tracked in the P-T cross section view, and a metamorphic P-T trajectory is extracted.

[0040] Preferably, the S4 comprises:

[0041] S41, identifying the zircon and monazite dating mineral region in the mineral classification map;

[0042] S42, using a laser ablation system to perform micro-area in-situ ablation under the condition of a spot diameter of 10-30 μm, and correcting the instrument drift based on the combination of external and internal standards;

[0043] S43, collecting U, Th, and Pb isotope signals, calculating U-Pb age by using isotope dilution algorithm to obtain geochronology data.

[0044] Preferably, in the S5, the P-T-t three-dimensional trajectory model of metamorphic rock comprises:

[0045] Based on the geochronology data, the P-T trajectory is provided with an absolute time scale, a continuous function relationship of temperature-pressure-time is established by an interpolation algorithm, and the whole path evolution process of rock metamorphism is reconstructed in a three-dimensional coordinate system.

[0046] In a second aspect, the present application provides a metamorphic rock P-T-t trajectory reconstruction system based on multi-source data fusion, comprising:

[0047] A data acquisition and correction module is configured to acquire electron probe micro-area mapping data and internal standard sample data of the metamorphic rock sample, and perform multi-level data correction.

[0048] An identification and composition quantitative interpretation module is configured to perform mineral phase identification and chemical composition quantitative interpretation on the corrected micro-area mapping data, and generate mineral classification mapping, element concentration distribution map and whole rock major component data set.

[0049] A P-T trajectory extraction module is configured to perform thermodynamic phase equilibrium simulation based on the whole rock major component data set and the mineral classification mapping, generate a P-T cross section map, and extract a P-T trajectory from the P-T cross section map based on the element concentration distribution map and multi-phase equilibrium constraints.

[0050] An age determination module is configured to perform micro-area in-situ geochronology analysis on selected minerals in the mineral classification mapping to obtain geochronology data.

[0051] A P-T-t coupling modeling module is configured to perform spatio-temporal coupling of the P-T trajectory and the geochronology data, construct a P-T-t three-dimensional trajectory model of the metamorphic rock, and output a visualized reconstruction report.

[0052] According to the above technical solution, compared with the prior art, the present application has the following beneficial effects:

[0053] 1) The method fuses electron probe micro-area analysis, computer vision recognition, thermodynamic simulation and micro-area geochronology to construct a complete strategy from data acquisition, mineral intelligent identification, composition interpretation, phase equilibrium simulation to spatio-temporal coupling, reduces the subjective errors and operation inconsistencies introduced by manual intervention and experience dependence in traditional methods, and improves the reliability and repeatability of data results.

[0054] 2) Based on the multi-scale convolutional neural network with attention mechanism and the empirical alpha factor correction algorithm, accurate mineral phase identification and chemical composition quantitative interpretation of the micro-area mapping are realized, which can effectively capture the micro-area composition variation information such as mineral ring band and reaction edge, and obtain high reliability of whole rock composition data at micro-area scale through weighted fusion and density correction, laying a reliable data foundation for subsequent thermodynamic simulation.

[0055] 3) Deep fusion and three-dimensional dynamic visualization of PTT information were achieved. By coupling high-precision PT trajectory with absolute chronology data in three-dimensional spatiotemporal dimensions, a dynamic PTT model that can reflect the metamorphic evolution process of rocks was constructed, and an integrated visualization report was generated, thereby revealing the time series of metamorphic events, changes in temperature and pressure conditions, and tectonic evolution process more intuitively and comprehensively. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0057] Figure 1 A flowchart of a method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion is provided in an embodiment of the present invention.

[0058] Figure 2 This is a schematic diagram of the mineral phase identification and quantitative interpretation process of chemical composition provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the PT trajectory extraction process provided in an embodiment of the present invention;

[0060] Figure 4 A PT view sectional view provided for an embodiment of the present invention;

[0061] Figure 5 This is a schematic diagram of the micro-area in-situ geochronological analysis process provided in an embodiment of the present invention;

[0062] Figure 6 This is a structural framework diagram of a metamorphic rock PTt trajectory reconstruction system based on multi-source data fusion, provided for an embodiment of the present invention. Detailed Implementation

[0063] 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.

[0064] Example 1;

[0065] like Figure 1 As shown, this embodiment provides a method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion, including the following steps:

[0066] S1, acquiring electron probe microanalysis data and internal standard sample data of the metamorphic rock sample, and performing multi-stage data correction;

[0067] S2, mineral phase identification and chemical composition quantitative interpretation are performed on the corrected microanalysis data to generate mineral classification maps, element concentration distribution maps and whole rock major component data sets;

[0068] S3, based on the whole rock major component data set and the mineral classification map, thermodynamic phase equilibrium simulation is performed to generate a P-T apparent section map; and based on the element concentration distribution map and the multi-phase equilibrium constraint, a P-T trajectory is extracted from the P-T apparent section map;

[0069] S4, micro-area in-situ chronology analysis is performed on selected minerals in the mineral classification map to obtain chronology data;

[0070] S5, the P-T trajectory and the chronology data are coupled in space and time to construct a P-T-t three-dimensional trajectory model of the metamorphic rock, and a visual reconstruction report is output.

[0071] The method effectively overcomes the defects of strong subjectivity, fragmented process, significant error propagation and the like of traditional methods by integrating electron probe microanalysis, mineral intelligent identification based on deep learning, thermodynamic phase equilibrium simulation and micro-area in-situ chronology, and realizes quantitative, accurate and three-dimensional dynamic reconstruction of the P-T-t trajectory of the metamorphic rock from micro-mineral composition to macro-structural evolution history.

[0072] The following further details each step in the above method;

[0073] In this embodiment S1, electron probe microanalysis data and internal standard sample data of the metamorphic rock sample are acquired, and multi-stage data correction is performed; including:

[0074] The electron probe microanalyzer is used to synchronously collect multi-element X-ray intensity maps and BSE or SEI images for mineral phase identification and positioning; specifically, the collection of multi-element X-ray intensity maps needs to ensure that the count statistics of each element is sufficient, including main elements such as manganese Mn, sodium Na, calcium Ca, potassium K, iron Fe, magnesium Mg, aluminum Al, silicon Si, and titanium Ti, which requires that the peak value count of the main elements is not less than 10 4 , to reduce statistical errors; BSE or SEI images are used to confirm main minerals such as garnet, sillimanite, biotite, muscovite, plagioclase, potassium feldspar, and quartz, which should maintain strict spatial correspondence with the X-ray maps, have consistent pixel resolution, and usually be more than 1024x1024 to ensure the accuracy of subsequent registration and analysis;

[0075] 10-20 representative points in each mineral phase are selected for high-precision single-point analysis, and the oxide weight percentage data obtained after ZAF correction is used as internal standard data; the ZAF correction is a combination of atomic number correction, absorption correction and fluorescence correction; the selection of internal standards should cover all main mineral phases and composition variation intervals, for example, points should be selected from the core to the edge of the garnet to establish a reliable intensity-concentration conversion relationship;

[0076] Based on the internal standard data, the multi-element X-ray intensity spectrum is sequentially corrected for dead time, terrain, time drift, mixed pixel and standard sample position, to obtain corrected micro-area spectrum data;

[0077] The dead time correction uses the formula:

[0078]

[0079] Where, I i represents the measured count rate of element i, represents the corrected count rate of element i, and τ represents the detector dead time;

[0080] Specifically, in the dead time correction, the detector dead time τ needs to be calibrated in advance by a standard sample, usually 100-200 ns, and the signal missing phenomenon under high count rate can be effectively eliminated after correction; the terrain correction is based on the surface topography data obtained by BSE image inversion, and the cosine correction is performed on the X-ray exit angle, which is particularly important for inclined surfaces or rough areas; the time drift correction needs to insert a standard sample every 4-6 hours to monitor the intensity change for long-time testing, and the correction is performed through linear or quadratic function fitting to ensure the time sequence stability of the data; the mixed pixel correction uses image morphology method to identify the mixed area of phase boundary, and substitutes the adjacent pure phase pixel through interpolation to ensure the purity of the composition data; the standard sample position correction calculates the sub-pixel level offset vector and applies spatial transformation by cross-correlating the standard sample point coordinates with the spectrum pixel position, to eliminate the systematic error caused by mechanical displacement.

[0081] As Figure 2 shown, the corrected micro-area spectrum data is subjected to mineral phase recognition and chemical composition quantitative interpretation to generate mineral classification spectrum, element concentration distribution map and whole rock major component data set; including:

[0082] S21, using a multi-scale convolutional neural network based on an attention mechanism to recognize the mineral phase of the corrected micro-area spectrum data, to generate a mineral classification spectrum reflecting the distribution of each mineral phase;

[0083] S22, based on the internal standard data, the X-ray intensity in the micro-area spectrum is quantitatively converted into oxide concentration through an empirical correction algorithm to generate an element concentration distribution map;

[0084] S23, fusing the mineral classification map and the element concentration distribution map, calculating and outputting the micro-area scale whole rock major component dataset through weighted average and density correction.

[0085] Further, S21 includes: the encoder extracts features of the input corrected micro-area map data through a multi-level convolutional layer, and outputs a feature map containing different scale mineral features; the feature map is processed through a channel and spatial attention module, and a weighted feature map is generated by calculating channel and spatial weights to highlight key mineral areas; the decoder performs upsampling and feature fusion on the weighted feature map through a transposed convolutional layer, and outputs a mineral category probability map with the same size as the input map; the mineral category probability map is subjected to a pixel-by-pixel argmax operation in combination with a classification output layer to generate a mineral classification map;

[0086] The encoder-decoder network based on the attention mechanism is adopted, multi-scale feature extraction, channel and spatial attention weighting are performed, the recognition ability of key mineral areas is strengthened, pixel-level classification results are generated after upsampling and feature fusion, and finally a high-precision mineral classification map is output;

[0087] Further, the empirical alpha factor correction algorithm in S22 is expressed as:

[0088]

[0089] wherein, C i represents the weight percentage concentration of oxide of element i, k i represents a correction coefficient, I i,sample represents the characteristic X-ray intensity of element i in the sample, I i,standard represents the characteristic X-ray intensity of element i in the standard sample; the empirical alpha factor correction coefficient k i is obtained by linear regression of internal standard sample data, and the determination coefficient R 2 is usually greater than 0.99, ensuring the linear reliability of intensity-concentration conversion; for overlapping peaks or interfering elements, a peak stripping algorithm or a multivariate correction model is required for deconvolution.

[0090] Further, S23 includes:

[0091] The mineral classification map and the element concentration distribution map are pixel-level spatially registered to establish the correspondence between the mineral phase category and the chemical composition; the pixel-level spatial registration adopts mutual information maximization or feature point matching algorithm to ensure that the mineral phase boundary and the composition gradient are strictly aligned;

[0092] According to the mineral classification map, the area percentage of each mineral phase in the micro-area range is counted.

[0093] extracting chemical composition data of all pixel points in each mineral phase region from the element concentration distribution map, and calculating the average value as the representative chemical composition of the mineral phase;

[0094] calculating the major element composition of the whole rock by the area-weighted average algorithm, and combining the density values of the mineral phases to correct the area-weighted average calculation result in volume, and outputting the micro-scale whole rock major element composition dataset;

[0095] The area-weighted average algorithm is represented as:

[0096]

[0097] wherein, represents the calculated concentration of the pth oxide in the whole rock, represents the average concentration of the pth oxide in the qth mineral phase, A q represents the area percentage of the qth mineral phase, and n represents the total number of mineral phases;

[0098] The volume correction is represented as:

[0099]

[0100] wherein, represents the pth oxide concentration in the whole rock after volume correction, and is the density of the qth mineral phase;

[0101] This step completes the whole process from intelligent recognition of mineral phases, accurate quantitative interpretation of chemical composition, to efficient calculation of whole rock composition by deeply integrating computer vision and chemometrics methods. This process not only realizes the automatic and high-precision segmentation of mineral boundaries, but also obtains reliable and micron-level spatial resolution composition information through multi-source data registration and fusion, providing complete input data basis for subsequent thermodynamic simulation and improving the objectivity and repeatability of the whole process.

[0102] As shown in Figure 3 , based on the whole rock major element composition dataset and the mineral classification map, thermodynamic phase equilibrium simulation is performed to generate a P-T cross-sectional view; and based on the element concentration distribution map and the multi-phase equilibrium constraint, a P-T trajectory is extracted from the P-T cross-sectional view; including:

[0103] S31, based on the whole rock major element composition dataset, selecting a thermodynamic database and defining a solid solution model, setting the pressure-temperature simulation range and step; the thermodynamic database needs to be selected according to the rock type, and the solid solution model should cover the key minerals;

[0104] Specifically, based on the whole rock major element dataset, the MnNCKFMASHTO system database is selected, and the specific solid solution models of garnet, melt, muscovite, staurolite, biotite, cordierite, ilmenite and feldspar are activated to build a thermodynamic system close to the real geological conditions; then, the simulation ranges of the system pressure (P) and temperature (T) are set to 4-15 kbar and 650-900℃, respectively;

[0105] S32, the Gibbs free energy minimization algorithm is used to preliminarily solve the stable mineral assemblage at each P-T point by linear programming, and then the phase composition and proportion are refined by a nonlinear iterative optimization algorithm to generate a P-T apparent section; the initial solution of the Gibbs free energy minimization is obtained by linear programming, and the phase composition is refined by Newton iteration or simulated annealing algorithm in the nonlinear optimization, wherein the initial temperature of the simulated annealing is set to 1000K, the cooling rate is 0.95, and the iteration number is >10 4 to ensure convergence to the global optimum;

[0106] S33, based on the mineral composition zoning structure or composition gradient information provided by the element concentration distribution diagram, and combined with the multi-phase equilibrium reaction relationship, the stability domain boundary change of a specific mineral assemblage is tracked in the P-T apparent section to extract the metamorphic P-T trajectory;

[0107] As Figure 4 shown, the stability domain of the peak mineral assemblage Grt (Garnet)-Ky (Kyanite)-Bt (Biotite)-Kfs (Potassium Feldspar)-Pl (Plagioclase)-Melt (Melt)-Ilm (Ilmenite)-Qz (Quartz) corresponds to a high-pressure and high-temperature environment. By accurately tracking the intersection area of the garnet composition zoning calcium almandine (X Grs ) and magnesium almandine (X Prp ) isopleths, combined with the X Mg isopleths in biotite and the change trend of calcium feldspar (An) and sodium feldspar (Ab) components in plagioclase, the peak metamorphic conditions can be jointly limited to about 11 kbar and 810℃, which reflects that the metamorphic rock sample has experienced metamorphism on a scale of the middle and lower crust;

[0108] With the decrease of temperature and pressure conditions, the mineral assemblage changes to Grt (Garnet)-Sill (Sillimanite)-Bt (Biotite)-Kfs (Potassium Feldspar)-Pl (Plagioclase)-Melt (Melt)-Ilm (Ilmenite)-Qz (Quartz), indicating the beginning of the retrograde metamorphic stage, and showing that the garnet manganese almandine (X Sps ) isopleths extend to low pressure direction, combined with the XMg The decrease of the values of the components and the decrease of the An component in the plagioclase, the changes of the component gradients jointly constrain the P-T conditions of the retrograde stage to be about 7.8 kbar and 760 ℃; the trajectory reveals that the metamorphic rock sample experienced a near-isothermal decompression retrograde process after the peak stage, reflecting the dynamic background of tectonic uplift and crustal exhumation;

[0109] In this step, the P-T cross section of the study area is constructed by the Gibbs free energy minimization algorithm and the multiphase equilibrium constraint, and further combined with the spatial variation information such as mineral composition zoning, the continuous P-T trajectory reflecting the metamorphic evolution history is extracted; the leap from static component data to dynamic temperature and pressure condition inversion is realized, which provides a reliable model basis for the thermodynamic reconstruction of the metamorphic process of rocks.

[0110] As shown in Figure 5 The micro-area in-situ chronology analysis of the selected mineral in the mineral classification map is performed to obtain chronology data; including:

[0111] S41, identifying zircon and monazite dating mineral regions in the mineral classification map; the dating mineral identification needs to be combined with the mineral classification map and the BSE / CL image, and the regions without cracks and inclusions are preferred;

[0112] S42, micro-area in-situ ablation is performed under the condition of a light spot diameter of 10-30 μm by using a laser ablation system, and the instrument drift is corrected based on the combination of external and internal standards; the laser ablation needs to optimize the energy density and repetition frequency, the internal standard can be selected 43 Ca or 49 Ti, and the external standard adopts international standards such as 91500, GJ-1, and the standard sample is inserted once every 10 points for correction.

[0113] S43, U, Th, and Pb isotope signals are collected, U-Pb age is calculated by using isotope dilution algorithm, and chronology data is obtained; the isotope data needs to be corrected for ordinary lead and interference deduction, the age calculation adopts Isoplot or similar algorithm, and the weighted average age needs to report MSWD and 95% confidence interval;

[0114] In this step, the zircon and monazite dating mineral regions are accurately located by comprehensively analyzing the mineral classification map and the BSE / CL image, the micro-area in-situ sampling is performed by using the laser ablation system with optimized parameters, and the instrument drift and signal interference are eliminated by combining internal and external standard correction; finally, the U-Pb age is calculated by using the isotope dilution method, and the chronology data with high precision and high reliability is obtained by ordinary lead correction, interference deduction and statistical verification, which provides absolute time constraint for the P-T-t trajectory.

[0115] The embodiment S5, the P-T track is coupled with the chronological data in space-time, a P-T-t three-dimensional track model of metamorphic rock is constructed, and a visual reconstruction report is output;

[0116] The P-T-t three-dimensional track model of the metamorphic rock includes: providing an absolute time scale for the P-T track based on the chronological data, establishing a continuous function relationship of temperature-pressure-time through an interpolation algorithm, and reconstructing the whole path evolution process of rock metamorphism in a three-dimensional coordinate system;

[0117] Further, the visual reconstruction report is output; the report integrates the P-T view section, the P-T-t three-dimensional track curve, the mineral composition distribution thermogram and the age data in the form of a chart, and contains a textual explanation of the key metamorphic stage, the temperature and pressure condition and the duration.

[0118] The P-T-t track reconstruction method of the metamorphic rock based on multi-source data fusion provided by the embodiment effectively fuses multi-source heterogeneous data and multi-disciplinary strategies, breaks through the technical bottleneck in traditional reconstruction, and realizes high-precision quantitative description of the P-T-t track of metamorphism; the reconstruction result exhibits significant advantages in the space-time continuity of the temperature and pressure path and the analysis of the dynamic process, and provides a reliable data model and an empirical basis for deepening the research on the coupling relationship between metamorphism and tectonic evolution.

[0119] Embodiment 2;

[0120] As shown in Figure 6 The embodiment provides a P-T-t track reconstruction system of metamorphic rock based on multi-source data fusion, which comprises:

[0121] The data acquisition and correction module is used for acquiring the electron probe micro-area mapping data and internal standard sample data of the metamorphic rock sample, and performing multi-level data correction;

[0122] The identification and composition quantitative interpretation module is used for identifying the mineral phase and quantitatively interpreting the chemical composition of the corrected micro-area mapping data, generating a mineral classification map, an element concentration distribution map and a whole rock major component data set;

[0123] The P-T track extraction module is used for performing thermodynamic phase equilibrium simulation based on the whole rock major component data set and the mineral classification map, generating a P-T view section, and extracting a P-T track from the P-T view section based on the element concentration distribution map and the multi-phase equilibrium constraint;

[0124] The dating module is used for performing micro-area in-situ chronological analysis on the selected minerals in the mineral classification map to obtain chronological data;

[0125] A P-T-t coupling modeling module is configured to couple the P-T path with the chronology data, build a P-T-t three-dimensional path model of metamorphic rocks, and output a visual reconstruction report.

[0126] The system, through multi-source data fusion and multi-technology method cooperation, builds an integrated platform from micro-area data collection, mineral intelligent identification, thermodynamic simulation to chronology analysis, realizes automatic reconstruction and three-dimensional visual presentation of the P-T-t path of metamorphic rocks, and significantly improves the accuracy of data processing and the reliability of geological interpretation.

[0127] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts are described in the method part.

[0128] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion, characterized in that, Includes the following steps: S1. Obtain electron probe microanalysis data and internal standard data of metamorphic rock samples, and perform multi-level data correction; S2. Mineral phase identification and quantitative interpretation of chemical composition are performed on the corrected micro-area map data to generate mineral classification map, element concentration distribution map and whole rock major component dataset; S3. Based on the whole-rock major component dataset and mineral classification map, perform thermodynamic phase equilibrium simulation to generate PT profile; and based on the element concentration distribution map and multiphase equilibrium constraints, extract the PT trajectory from the PT profile. S4. Perform micro-area in-situ geochronological analysis on the selected minerals in the mineral classification map to obtain geochronological data; S5. Couple the PT trajectory with the geochronological data in time and space to construct a three-dimensional PT trajectory model of the metamorphic rock and output a visual reconstruction report.

2. The method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion according to claim 1, characterized in that, S1 includes: Multi-element X-ray intensity spectra and BSE or SEI images for mineral phase identification and localization were acquired simultaneously using an electron probe microanalyzer. High-precision single-point analysis was performed on 10-20 representative sites in each mineral phase, and the oxide weight percentage data was exported after ZAF correction as internal standard data. Based on the internal standard data, the multi-element X-ray intensity spectrum is sequentially corrected for dead time, topography, time drift, mixed pixel, and standard position to obtain the corrected micro-area spectrum data.

3. The method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion according to claim 1, characterized in that, S2 includes: S21. A multi-scale convolutional neural network based on an attention mechanism is used to identify mineral phases in the corrected micro-area map data, generating a mineral classification map that reflects the distribution of each mineral phase. S22. Based on internal standard sample data, the X-ray intensity in the micro-area spectrum is quantitatively converted into oxide concentration using an empirical correction algorithm to generate an elemental concentration distribution map. S23. Integrate the mineral classification map and element concentration distribution map, and calculate and output the whole rock principal component dataset at the micro-area scale after weighted averaging and density correction.

4. The method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion according to claim 3, characterized in that, S21 includes: The encoder extracts features from the input corrected micro-area map data through multiple convolutional layers, and outputs a feature map containing mineral features at different scales. The feature map is processed by the channel and spatial attention module. By calculating the channel and spatial weights, a weighted feature map is generated to highlight key mineral areas. The decoder upsamples and fuses the weighted feature map through a transposed convolutional layer, and outputs a mineral category probability map with the same size as the input map. By combining the classification output layer, pixel-by-pixel argmax operation is performed on the mineral category probability map to generate a mineral classification map.

5. The method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion according to claim 3, characterized in that, In S22, the empirical α-factor correction algorithm is expressed as follows: Among them, C i k represents the weight percentage concentration of the oxide of element i. i I represents the correction factor. i,sample I is represented by the characteristic X-ray intensity of element i in the sample. i,standard It is represented by the characteristic X-ray intensity of element i in the standard sample.

6. The method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion according to claim 3, characterized in that, S23 includes: The mineral classification map and the elemental concentration distribution map are spatially registered at the pixel level to establish the correspondence between mineral phase categories and chemical compositions; Based on the mineral classification map, the percentage of area of ​​each mineral phase within the micro-region is calculated; The chemical composition data of all pixels in each mineral phase region are extracted from the element concentration distribution map, and the average value is calculated as the representative chemical composition of the mineral phase. The principal components of the whole rock are calculated using an area-weighted average algorithm. The results are then volume-corrected by combining the density values ​​of each mineral phase, and a dataset of principal components of the whole rock at the micro-area scale is output.

7. The method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion according to claim 1, characterized in that, S3 includes: S31. Based on the whole rock major component dataset, select a thermodynamic database and define a solid solution model, and set the pressure-temperature simulation range and step size; S32. Using the Gibbs free energy minimization algorithm, the stable mineral assemblage at each PT point is initially solved by linear programming, and then the composition and proportion of each phase are refined by nonlinear iterative optimization algorithm to generate PT view profile. S33. Based on the mineral composition zonation structure or composition gradient information provided by the element concentration distribution map, and combined with the multiphase equilibrium reaction relationship, track the stability domain boundary changes of a specific mineral assemblage in the PT view profile to extract the metamorphic PT trajectory.

8. The method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion according to claim 1, characterized in that, S4 includes: S41. Identify the zircon and monazite dating mineral regions in the mineral classification map; S42. Micro-area in-situ ablation is performed using a laser ablation system with a spot diameter of 10-30 μm, and instrument drift is corrected based on a combination of external and internal standards. S43. Collect U, Th, and Pb isotope signals, calculate U-Pb age using the isotope dilution algorithm, and obtain chronological data.

9. The method for reconstructing the PTT trajectory of metamorphic rocks based on multi-source data fusion according to claim 1, characterized in that, In S5, constructing the PTt three-dimensional trajectory model of metamorphic rocks includes: Based on the chronological data, an absolute time scale is provided for the PT trajectory. A continuous functional relationship of temperature-pressure-time is established through an interpolation algorithm, and the entire path evolution process of rock metamorphism is reconstructed in a three-dimensional coordinate system.

10. A metamorphic rock PTt trajectory reconstruction system based on multi-source data fusion, characterized in that, include: Data acquisition and correction module: used to acquire electron probe microanalytical data and internal standard data of metamorphic rock samples, and to perform multi-level data correction; The identification and quantitative interpretation module is used to identify mineral phases and quantitatively interpret chemical components in the corrected micro-area map data, generating mineral classification maps, elemental concentration distribution maps, and whole-rock major component datasets. PT trajectory extraction module: used to perform thermodynamic phase equilibrium simulation based on the whole rock major component dataset and mineral classification map to generate PT view profile; and to extract PT trajectory from the PT view profile based on the element concentration distribution map and multiphase equilibrium constraints; Chronology module: used to perform micro-area in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; PTt Coupled Modeling Module: This module is used to spatiotemporally couple the PT trajectory with geochronological data to construct a three-dimensional PTt trajectory model of metamorphic rocks and output a visual reconstruction report.

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