Deposition path process fingerprint decoding method and system based on complex source-sink system

Abstract

The invention discloses a deposition path process fingerprint decoding method and system based on a complex source-sink system, and relates to the technical field of tectonic sedimentology and data modeling crossing. Target strata geochemical characteristic data of a research area are collected and arranged, and a large deposition geochemical data set of the research area is established; and establishing standard stratum characteristics and sedimentary geochemical characteristics thereof in combination with a typical stratum profile of the basin. According to the method, the multi-source supply and re-cycle contribution proportion on the geologic source-sink system deposition path is quantitatively recovered through establishment of target layer series multi-dimensional scaling analysis, homogenization degree analysis, fingerprint decoding parameter PCA data statistics and the like, the defects that a traditional source-sink analysis method depends on prior end member selection and is easily influenced by personal errors are overcome, and the method is suitable for large-scale analysis of the geologic source-sink system deposition path. The inverse Monte Carlo model is utilized to carry out iterative simulation for multiple times, the simulation effect is gradually optimized, the accuracy and reliability of the deposition path and process description result are ensured, and a more scientific and accurate technical means is provided for geological research and resource exploration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tectonic sedimentology and data modeling, in particular to a sedimentary path process fingerprint decoding method and system based on a complex source-sink system. BACKGROUND

[0002] In the complex geological history period, the sedimentary system will undergo various complex evolution processes, and in the field of geological research and resource exploration, accurate understanding of the sedimentary path and source supply is crucial for evaluating mineral resource potential and optimizing exploration strategy. Through the sedimentary path process fingerprint decoding method, the source and transport path of sediments in different periods can be quantitatively tracked.

[0003] In the prior art, the sedimentary path process fingerprint decoding is more dependent on prior end member selection, and the end member selection often needs to be judged artificially, which is easily affected by human error, resulting in inaccurate source-sink analysis results. Therefore, how to combine multi-dimensional scale analysis, homogenization degree analysis and fingerprint decoding parameter PCA data statistical method to quantitatively restore the contribution ratio of multiple source supply and re-rotation in the sedimentary path of the geological source-sink system, use the inverse Monte Carlo model for multiple iteration simulation, and gradually optimize the simulation effect to effectively overcome the defects of the traditional method and ensure the accuracy and reliability of the sedimentary path and process description result is the problem to be solved by the present application. Therefore, the present application proposes a sedimentary path process fingerprint decoding method and system based on a complex source-sink system. SUMMARY

[0004] The present application aims to provide a sedimentary path process fingerprint decoding method and system based on a complex source-sink system to solve the problems raised in the background art.

[0005] To solve the above technical problems, the technical solution adopted by the present application is: In a first aspect, the sedimentary path process fingerprint decoding method based on a complex source-sink system comprises the following steps: Step one: Collect and organize the geochemical characteristic data of the target layer system in the study area, and establish a sedimentary geochemical big data set of the study area; Step two: Combine the typical stratigraphic section of the basin to establish the standard stratigraphic characteristics and their sedimentary geochemical characteristics, and analyze the regional sedimentary facies distribution characteristics; Step three: Based on the multi-dimensional scale analysis (MDS) of element geochemistry, the homogenization degree (P%) of the sedimentary process in the source-sink system is identified; Step four: Based on the comparison of the standard stratigraphic sedimentary geochemical characteristics and the source area characteristics on the sedimentary path, the sedimentary process fingerprint decoding parameters are constructed; Step five: on the basis of building the sedimentary process fingerprint decoding parameter, based on (Principal component analysis) PCA data algorithm, build key fingerprint decoding parameter set, reveal potential source area quantitative source-sink supply ratio and cyclicity; Step six: combined with regional sedimentary facies distribution characteristics, sedimentary process homogenization degree value and sedimentary process fingerprint decoding parameter, through the iteration simulation of inverse Monte Carlo model, the sedimentary path and process in source-sink system are quantitatively described.

[0006] The further improvement of the technical scheme of the application is that the step one specifically comprises: The range of the research area and the target layer system are determined, the geographical boundary (latitude and longitude range) and the geological structure unit of the research area are analyzed, and the depth range and the stratigraphic age framework of the target layer system (sandstone group, shale section) are determined, the domestic and foreign public databases, geological exploration reports and journal literatures are retrieved, the major elements, trace elements, rare earth elements and heavy mineral data of the target layer system of the research area are batch grabbed, the fields are aligned according to the unified well name, layer system and depth coordinates, the missing and out-of-tolerance data are removed, the instrument drift is corrected by using the ISO 17025 reference value, the metadata table is established, and the standardized storage of the original data is carried out; The data in the warehouse are subjected to three-level quality inspection: in the first step, the major element discrete value is screened out with the relative deviation of the repeated sample being less than or equal to 5%, in the second step, the contaminated sample is removed with the rare earth element chondrite standardization abnormal coefficient being greater than 3σ, and in the third step, the weathering recirculation influence is tested by using the heavy mineral stability coefficient, the qualified sample is marked, and the quality report is generated; The qualified sample data are coupled according to the depth sequence and the spatial grid, the major trace element matrix, the rare earth element vector and the heavy mineral combination fingerprint table are constructed, the NoSQL database is stored, the three-level index of well-layer-depth and the spatial R-Tree index are established, the element-mineral-chronological field is subjected to second-level joint query, and the expandable and updatable sedimentary geochemical big data set of the research area is formed.

[0007] The further improvement of the technical scheme of the application is that the step two specifically comprises: According to the geological background and the target layer system of the research area, the representative basin typical stratigraphic section is selected as the research object, the stratigraphic age framework is constrained by high-precision chronology test, the sedimentary sequence order is divided in combination with sedimentary cycle analysis, the standard stratigraphic feature library is established, the lithology description, paleontological combination, sedimentary structure and logging response characteristics are covered, the comparable stratigraphic division template is formed, the geochemical element data of the key horizon in the section are synchronously extracted, and the stratigraphic-geochemical correlation matrix is constructed. Based on the established standard stratigraphic characteristics library, geochemical facies analysis is carried out on the target layer system, and the heavy mineral assemblage of ZTR index and garnet / zircon ratio is further verified to verify the source direction, Q type clustering is used to convert geochemical parameters into quantifiable sedimentary facies marks, and an element-mineral-sedimentary facies conversion model is established; With the standard stratigraphic characteristics and sedimentary facies marks as constraints, the data of multiple drilling wells and seismic profiles in the basin are integrated to construct a three-dimensional space model of sedimentary facies, and the kriging interpolation method is used to grid the discrete well point data to generate a continuous sedimentary facies distribution map, combined with the regional tectonic evolution history, the relationship between the sedimentary facies migration path and the basin filling process is analyzed, and a high-resolution regional sedimentary facies distribution characteristic set is formed.

[0008] The further improvement of the technical scheme of the present application is that the step three specifically comprises: Based on the target layer system of the research area, representative samples of the source area and the sink area are collected, whole rock major and trace element analysis is carried out, the data covering the whole process of sediment from source to sink is ensured, and the original geochemical data is standardized to eliminate the influence of sample particle size and mineral composition difference, Z-score standardization method is used to unify the dimension, and a standardized data set containing source-sink sample geochemical parameters is constructed; With the standardized geochemical data as input, non-metric multidimensional scaling analysis algorithm is used, based on Euclidean distance matrix, high-dimensional geochemical data is reduced to 2-3 dimensional space, and the distribution scatter diagram of source-sink samples is generated, and the goodness of fit is evaluated through stress value evaluation model, and then the reconstruction degree of the original source signal by the sedimentary process is quantified by calculating the Euclidean distance variation coefficient of the source-sink sample in the multidimensional scaling space, the greater the distance variation coefficient, the stronger the homogenization effect; Based on the multidimensional scaling analysis result, the expansion ratio of the distribution range of the sink area sample in the multidimensional scaling space relative to the source area is defined as the homogenization degree (P%), that is, the ratio of the standard deviation of the Euclidean distance between the source-sink sample pairs to the average Euclidean distance.

[0009] The further improvement of the technical scheme of the present application is that the calculation expression of the Euclidean distance variation coefficient is as follows: ; ; ; In the formula: The Euclidean distance variation coefficient is defined as the ratio of the standard deviation of the Euclidean distance between the source-sink sample pairs to the average Euclidean distance; The standard deviation of the Euclidean distance between the source-sink sample pairs, The average value of the Euclidean distance between the source-sink sample pairs, is the Euclidean distance between the i th source area sample and the j th sink area sample, M is the number of source area samples, and N is the number of sink area samples; The calculation expression of the homogenization degree is as follows: ; In the formula, P% is the homogenization degree, indicating the expansion ratio of the distribution range of the sink area sample in the multidimensional scale analysis space relative to the source area; is the distribution range of the sink area sample in the multidimensional scale analysis space, which is determined by calculating the area or volume of the convex hull of the sink area sample, and reflects the dispersion degree of the sink area sample in the multidimensional scale analysis space; is the distribution range of the source area sample in the multidimensional scale analysis space, which is determined by calculating the area or volume of the convex hull of the source area sample, and reflects the concentration degree of the source area sample in the multidimensional scale analysis space.

[0010] The further improvement of the technical scheme of the present application is that the step four specifically comprises: Based on the sedimentary path of the study area, representative samples of the source area outcrop, transport channel and sedimentary center are collected, covering the whole process from the source to the sink, and sedimentology analysis, heavy mineral combination identification, whole rock geochemical test and detrital zircon U-Pb chronology determination are simultaneously carried out, and the original data are standardized: the Z-score method is used to eliminate the dimensional difference, a multi-source data set covering sedimentology, mineralogy, geochemistry and chronology is constructed, and the data comparability and geological significance consistency are ensured; By comparing the multidimensional data of the source area and the sedimentary center samples, the preserved or modified source signal in the sedimentary process is identified, the multidisciplinary evidence is comprehensively considered, the fingerprint parameters sensitive to the transport process are extracted, the heavy mineral stability index, the rare earth element fractionation coefficient and the age spectrum similarity are covered, and a quantifiable sedimentary process decoding index system is formed.

[0011] The further improvement of the technical scheme of the present application is that the step five specifically comprises: The parameters of the heavy mineral stability index, the rare earth element fractionation coefficient and the age spectrum similarity are integrated, the detrital zircon age spectrum and the major and trace element ratio are supplemented, a high-dimensional fingerprint data set is formed, the data is standardized by the Z-score method, the collinearity between parameters is tested by the correlation matrix, redundant variables are removed, and a standardized fingerprint data set is formed; Based on the standardized fingerprint data set, PCA analysis is carried out, the eigenvalues and eigenvectors of the covariance matrix are calculated, the number of principal components is determined, the main control parameters of each principal component are identified through the loading matrix, the parameters with significant load (p<0.05) are screened to construct a key fingerprint decoding parameter set, and the conversion from high-dimensional data to low-dimensional latent variables is realized; A provenance supply ratio quantitative model is established by using the key fingerprint decoding parameter set, each parameter is fitted with the end member value of the known provenance area through multiple linear regression, the contribution proportion of different provenances to the sediment is calculated, and the periodic fluctuation characteristics of the key fingerprint decoding parameter set are identified by combining time series analysis, sedimentary cycle levels including Milankovitch cycle and tectonic cycle are divided, the key fingerprint decoding parameter set is further classified through K-means clustering analysis, the provenance supply mode of different sedimentary stages is distinguished, a three-dimensional correlation framework of parameter-provenance-cycle is constructed, and the dynamic evolution of the provenance area and the coupling mechanism of the source-sink system in the filling process of the sedimentary basin are quantitatively revealed.

[0012] Further improvement of the technical scheme of the present application is that the step six specifically comprises: The regional sedimentary facies distribution characteristics, the sedimentary process homogenization degree value and the sedimentary process fingerprint decoding parameter are integrated to construct an initial source-sink system conceptual model, and the spatial constraint conditions of the sedimentary path and the initial distribution range of the process parameters are defined; With the fingerprint decoding parameter as the constraint condition, the sedimentary process including transportation, sorting and mixing is simulated by randomly sampling the parameter space through the inverse Monte Carlo model, and the residual error between the simulation result and the measured regional sedimentary facies distribution characteristics and the sedimentary process homogenization degree value is calculated, and the parameter distribution of the provenance supply rate, the transportation distance and the sedimentation rate is iteratively adjusted until the residual error converges to a preset convergence threshold; The spatial migration trajectory of the sedimentary material in the optimized inverse Monte Carlo model is extracted, covering the path geometry and branch / convergence points, the mixing efficiency of the sedimentary process is quantified by combining the sedimentary process homogenization degree value, the path rationality is verified by the temporal and spatial variation of the fingerprint decoding parameter, and the source-sink system sedimentary path network and the quantitative solution of the key process parameters are output.

[0013] In the second aspect, the sedimentary path process fingerprint decoding system based on the complex source-sink system is used to realize the sedimentary path process fingerprint decoding method based on the complex source-sink system, and comprises: The data acquisition and integration module is used to collect the geochemical characteristic data of the target layer system in the research area, perform standardization processing and quality inspection, construct a sedimentary geochemical big data set of the research area, and ensure data integrity and standardization; The stratigraphic feature modeling module is used to establish a standard stratigraphic feature library in combination with a typical stratigraphic section of a basin, analyze regional sedimentary facies distribution characteristics, construct a regional sedimentary facies distribution characteristic set, and clearly define stratigraphic age, lithological combination and contact relationship, thereby supporting geochemical facies analysis; The sedimentary process analysis module is used to identify the homogenization degree of the sedimentary process in the source-sink system through multidimensional scale analysis, analyze the sedimentary process reconstruction intensity, reveal the sediment transport and mixing rule, and distinguish between conservative and reconstructed transport paths; The fingerprint decoding parameter module is used for constructing a sedimentary process fingerprint decoding parameter system covering sedimentology, heavy mineral combination, geochemistry and chronology, and realizing quantitative tracking of source signals; The inverse Monte Carlo model simulation module is used for regional sedimentary facies distribution characteristics, sedimentary process homogenization degree values and sedimentary process fingerprint decoding parameters, and uses iterative simulation of an inverse Monte Carlo model to optimize source supply and sedimentary path parameters, quantitatively depict source-sink system dynamic processes, and output source-sink system sedimentary path networks and key process parameter quantification.

[0014] Due to the adoption of the above technical solutions, the present application has the following technical progress compared with the prior art: The present application provides a sedimentary path process fingerprint decoding method and system based on a complex source-sink system, which quantitatively recovers the contribution proportion of multiple source supplies and re-cycling on the sedimentary path of a geological source-sink system by establishing multi-dimensional scale analysis, homogenization degree analysis and PCA data statistics of fingerprint decoding parameters, overcomes the shortcomings of the prior art that the prior art depends on prior end member selection and is easily affected by human errors, uses an inverse Monte Carlo model to perform multiple iterative simulations, gradually optimizes the simulation effect, and ensures the accuracy and reliability of the sedimentary path and process depiction results, and provides a more scientific and accurate technical means for geological research and resource exploration.

[0015] The present application provides a sedimentary path process fingerprint decoding method and system based on a complex source-sink system, which quantitatively recovers the contribution proportion of multiple source supplies and re-cycling on the sedimentary path of a geological source-sink system by establishing multi-dimensional scale analysis, homogenization degree analysis and PCA data statistics of fingerprint decoding parameters, overcomes the shortcomings of the prior art that the prior art depends on prior end member selection and is easily affected by human errors, uses an inverse Monte Carlo model to perform multiple iterative simulations, gradually optimizes the simulation effect, and ensures the accuracy and reliability of the sedimentary path and process depiction results, and provides a more scientific and accurate technical means for geological research and resource exploration. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0017] Figure 1 The workflow schematic diagram of the sedimentary path process fingerprint decoding method and system based on a complex source-sink system of the present application; Figure 2 The method flowchart of the sedimentary path process fingerprint decoding method based on a complex source-sink system of the present application. DETAILED DESCRIPTION

[0018] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0019] Embodiment 1, as shown in Figure 1 , Figure 2 The present application provides a method for decoding the fingerprint of the sedimentary path process based on a complex source-sink system, which comprises the following steps: Step one: Collect and organize the geochemical characteristics data of the target layer system in the study area, establish the sedimentary geochemical big data set of the study area (including: major and trace elements, rare earth elements and heavy mineral characteristics, etc.), clarify the scope of the study area and the target layer system, analyze the geographical boundary (latitude and longitude range) and geological structure unit of the study area, and determine the depth range and stratigraphic age framework of the target layer system (sandstone group, shale section), retrieve the domestic and foreign public databases (GeoRef, ScienceDirect, CNKI, National Geological Data Library, etc.), geological exploration reports and journal literature, batch capture the major elements (SiO2, Al2O3, etc.), trace elements (Zr, Nb, etc.), rare earth elements (La, Ce, etc.) and heavy minerals (zircon, garnet, etc.) data of the target layer system in the study area, align the fields according to the unified well name, layer system and depth coordinates, eliminate the missing and out-of-tolerance data (delete samples with a missing rate of major elements > 20% or a missing rate of rare earth elements > 30%), correct the instrument drift using ISO 17025 reference values, establish the metadata table, standardize the original data into the database, and conduct three-level quality inspection on the data in the database: first, remove the discrete values of major elements with a relative deviation of repeated samples ≤ 5%, second, remove the contaminated samples with an abnormal coefficient > 3σ after rare earth element chondrite normalization, third, use the heavy mineral stability coefficient to test the influence of weathering and recycling, mark the qualified samples and generate a quality report, ensure that each record in the big data set meets the high-precision source-sink analysis requirements, couple the qualified sample data according to the depth sequence and spatial grid, construct the major and trace element matrix, rare earth element vector and heavy mineral combination fingerprint table, and store them using NoSQL database, establish three-level index of well-layer-depth and spatial R-Tree index, realize second-level joint query of element-mineral-age field, and form an expandable and updatable sedimentary geochemical big data set of the study area, among which, the major and trace element matrix is arranged according to the depth sequence, the row name is well-layer-depth, and the column name is element symbol; the rare earth element vector is standardized by PAAS (Taylor and McLennan, 1985), and the La-Lu distribution pattern data is generated; the heavy mineral combination fingerprint table calculates the relative content (%) of each mineral, and constructs the Q-type cluster analysis input file; Step two: Establish standard stratigraphic characteristics and their sedimentary geochemical characteristics based on typical stratigraphic sections of the basin, analyze regional sedimentary facies distribution characteristics, and select representative basin typical stratigraphic sections as the research object according to the geological background and target layer series of the study area. The selection principles of the section are: covering typical stratigraphic outcrops in different tectonic units of the basin (such as depression, slope, uplift), preferentially selecting sections with complete lithology exposure, clear contact relationship, and abundant paleontological fossils (such as basin edge outcrops, key wells), integrating geological outcrops, drilling cores and logging data to determine stratigraphic age, lithologic combination and contact relationship, and constraining stratigraphic age framework through high-precision chronology testing, combining sedimentary cycle analysis to divide sedimentary sequence levels, establishing a standard stratigraphic characteristic library covering lithology description (color, particle size, composition), paleontological combination, sedimentary structure (bedding, ripple mark) and logging response characteristics (GR, RT curve shape), forming a comparable stratigraphic division template, simultaneously extracting geochemical element data (major, trace, rare earth elements) of key stratigraphic levels in the section, constructing a stratigraphic-geochemical correlation matrix, based on the established standard stratigraphic characteristic library, conducting geochemical facies analysis of the target layer series, quantifying weathering intensity of the source area through major element ratios, using rare earth element distribution patterns and trace element ratios to identify sediment source types, i.e. continental island arc and passive continental margin, and further verifying the source direction combined with ZTR index and garnet / zircon ratio of heavy mineral assemblage, using Q-type clustering to convert geochemical parameters into quantifiable sedimentary facies markers, establishing an element-mineral-sedimentary facies conversion model, using standard stratigraphic characteristics and sedimentary facies markers as constraints, integrating basin multi-well and seismic section data to construct a three-dimensional sedimentary facies model, and using Kriging interpolation method to grid discrete well point data to generate continuous sedimentary facies distribution map, depicting the spatial distribution of key facies belts including delta, coastal and deepwater fan, combining with regional tectonic evolution history, analyzing the relationship between sedimentary facies migration path and basin filling process, and forming a high-resolution regional sedimentary facies distribution characteristic set; Step three: Based on the multivariate scaling analysis (MDS) of elemental geochemistry, the homogenization degree (P%) of the sedimentary process in the source-sink system is identified. Based on the target layer system of the study area, representative samples of the source area (outcrop of provenance area, river sediment) and the sink area (sedimentary center of basin) are collected for whole rock major and trace element analysis to ensure that the data cover the whole process of sediment from source to sink. The original geochemical data are standardized to eliminate the influence of sample particle size and mineral composition difference. Z-score standardization method is used to unify the dimension to construct a standardized dataset containing geochemical parameters of source-sink samples. The non-metric multidimensional scaling algorithm is used to reduce the high-dimensional geochemical data to 2-3 dimensional space based on the Euclidean distance matrix to generate the distribution scatter plot of source-sink samples. The stress value evaluation model is used to fit the goodness-of-fit (Stress < 0.15, indicating that the dimension reduction result retains > 85% of the original data variation), ensuring that the dimension reduction result retains the main variation characteristics of the original data. In the multidimensional scaling analysis space, the source area samples show an aggregated distribution, reflecting the uniformity of the chemical composition of the provenance area. The sink area samples show a dispersed trend due to transportation, sorting and mixing. The distance variation coefficient of the source-sink sample pairs in the multidimensional scaling analysis space is calculated to quantify the degree of modification of the original provenance signal in the sedimentary process. The greater the distance variation coefficient, the stronger the homogenization. Based on the multidimensional scaling analysis result, the expansion ratio of the distribution range of the sink area samples in the multidimensional scaling analysis space relative to the source area is defined as the homogenization degree (P%), which is the ratio of the standard deviation of the Euclidean distance between the source-sink sample pairs to the average Euclidean distance. The higher the P% value, the stronger the mixing and sorting of the sediments in the transportation process, and the stronger the homogenization and modification of the original provenance signal. In addition, the calculation expression of the Euclidean distance variation coefficient is as follows: ; ; ; In the formula: is the Euclidean distance variation coefficient, defined as the ratio of the standard deviation of the Euclidean distance between the source-sink sample pairs to the average Euclidean distance; is the standard deviation of the Euclidean distance between the source-sink sample pairs, is the average value of the Euclidean distance between the source-sink sample pairs, is the Euclidean distance between the i th source area sample and the j th sink area sample, M is the number of source area samples, and N is the number of sink area samples; = 0, indicating that the Euclidean distance between all source-sink sample pairs is completely the same, i.e. no variation, when tends to When the variation is large, it indicates that the Euclidean distance between the source and sink sample pairs varies greatly; when it is small... The value indicates that the Euclidean distance between source-sink sample pairs is relatively concentrated, the homogenization effect is weak, and the larger value indicates that the homogenization effect is weak. The value indicates that the Euclidean distance between source-sink sample pairs varies greatly, and the homogenization effect is strong; The expression for calculating the degree of homogenization is as follows: ; In the formula: P% represents the degree of homogenization, indicating the expansion ratio of the distribution range of the sink sample in the multidimensional scale analysis space relative to the source region; The distribution range of the sink sample in the multidimensional scale analysis space is determined by calculating the area or volume of the convex hull of the sink sample, reflecting the degree of dispersion of the sink sample in the multidimensional scale analysis space. The distribution range of the source region sample in the multidimensional scale analysis space is determined by calculating the area or volume of the convex hull of the source region sample, reflecting the concentration degree of the source region sample in the multidimensional scale analysis space. When P% = 0%, it indicates that the distribution range of the sink region sample is exactly the same as that of the source region sample, that is, there is no homogenization effect. When P% approaches a certain value... When the P% value is greater than that of the source region sample, it indicates that the homogenization effect is very strong; a smaller P% value indicates that the distribution range of the sink region sample is relatively small and the homogenization effect is weak, while a larger P% value indicates that the distribution range of the sink region sample is relatively large and the homogenization effect is strong. Step 4: Based on the comparison of standard stratigraphic geochemical characteristics and source area characteristics along the sedimentary path, construct fingerprint decoding parameters for the sedimentary process (sedimentology, heavy mineral assemblage, geochemistry, chronology, etc.). Step 5: Based on the construction of the fingerprint decoding parameters of the deposition process, and using the PCA data statistical algorithm based on Principal Component Analysis, construct a set of key fingerprint decoding parameters to reveal the quantitative source-sink supply ratio and cyclicity of potential source areas; Step Six: Combining regional sedimentary facies distribution characteristics, homogenization degree of sedimentary process, and fingerprint decoding parameters of sedimentary process, quantitatively characterize the sedimentary paths and processes in the source-sink system through iterative simulation using the inverse Monte Carlo model.

[0020] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: Step four specifically includes: Based on the sedimentary pathway of the study area, representative samples were collected from the outcrop of the source area, the transport pathway (river / coast) and the sedimentary center (basin), covering the whole process from source to sink. Simultaneous sedimentological analysis (lithology, sedimentary structure, grain size), heavy mineral assemblage identification (ZTR index, mineral stability classification), whole rock geochemical testing (major / minor / rare earth elements) and detrital zircon U-Pb dating were carried out, and the original data were standardized: the Z-score method was used to eliminate dimensional differences, and a multi-source data set covering sedimentology, mineralogy, geochemistry and chronology was constructed to ensure data comparability and consistency with geological significance. By comparing the multi-dimensional data of the source area and the sedimentary center samples, the preserved or modified source signal in the sedimentary process was identified. In sedimentology, the influence of transport distance on grain size sorting and sedimentary structure type was analyzed. In mineralogy, the enrichment / loss of heavy mineral stability (garnet, zircon, etc.) during transport was evaluated. In geochemistry, the chemical weathering and mixing effects were quantified using discriminant diagrams (Th-Co-Zr diagram) and element ratios (Eu / Eu*, La / Co, etc.). In chronology, the spatiotemporal variation of sediment sources was determined by comparing detrital zircon age spectra. By integrating multi-disciplinary evidence, sensitive fingerprint parameters were extracted, including heavy mineral stability index, rare earth element fractionation coefficient and age spectrum similarity, to form a quantifiable sedimentary process decoding index system. The calculation expression of the heavy mineral stability index is as follows: ; In the formula: is the heavy mineral stability index, m is the total number of heavy mineral species (such as zircon, garnet, tourmaline, etc.), is the stability weight of the kth mineral, which is classified according to the anti-weathering and transport ability of the mineral. Super-stable minerals (zircon, tourmaline) , stable minerals (garnet, anatase) , unstable minerals (hornblende, pyroxene) , is the content percentage of the kth mineral, The theoretical range of is: When the sample only contains unstable minerals, When the sample only contains super-stable minerals, With the increase of transport distance, unstable minerals (such as hornblende) are gradually lost, and super-stable minerals (such as zircon) are relatively enriched, increases; chemical weathering increases, unstable minerals decompose, and stable minerals remain, increases; the source changes. If the source area itself is mainly composed of stable minerals (such as recycled sediments), is high; on the contrary, if the source contains a large amount of volcanic clasts (unstable minerals), Low; The calculation expression of the fractionation coefficient of rare earth elements is as follows: ; The ratio of light rare earth elements (La) to heavy rare earth elements (Yb) in the sample is normalized by the Post-Archean Australian Shale (PAAS) standard: ; In the formula: is the fractionation coefficient of rare earth elements, is the measured value of the sample, (McLennan, 1989); is the reference value of PAAS normalization, reflecting the average rare earth distribution pattern of the upper crust; in actual sedimentary rocks, is usually between 0.5 and 2.0; chemical weathering, light rare earth elements (La) are more easily leached than heavy rare earth elements (Yb), resulting in a decrease in light rare earth elements (La) in the sample, (negative anomaly), under extreme weathering conditions (such as laterization), it can be as low as 0.3-0.5; source mixing, if the sediment is mixed with basic igneous rocks (enriched in light rare earth elements), (positive anomaly), if mixed with carbonate rocks (relatively enriched in heavy rare earth elements), ; transport and sorting, in long-distance transport, the degree of fractionation of rare earth elements decreases, tending to 1 (close to the PAAS distribution); The calculation expression of the similarity of age spectra is as follows: ; ; In the formula: is the similarity of age spectra, D is the Kolmogorov-Smirnov (K-S) test statistic, , is the cumulative distribution function (CDF) of the two groups of detrital zircon ages, representing the age distribution of the source area and the sample at the sedimentary center, respectively; x is the age variable (unit: Ma); is the maximum value of all age points; , , : the two age distributions are exactly the same (D=0), : the two age distributions have no overlap (D=1); source stability, if the sediment source supply is stable (such as single-source long-term supply), close to 1, if the tectonic activity in the source area is frequent (such as orogenic uplift), leading to multiple periods of erosion, Decrease; transport distance, short distance transport, the age spectrum of the sedimentary center is similar to the source area, High, long distance transport, possible mixing of other sources or age screening, Decrease; sample size, too few zircon particles (e.g., <30) can cause statistical bias in age distribution, Reliability decreases; Step five specifically includes: Integrate the parameters of heavy mineral stability index, rare earth element fractionation coefficient and age spectrum similarity, supplement the detrital zircon age spectrum and major trace element ratio, form a high-dimensional fingerprint data set, use Z-score method for standardization processing, eliminate the dimensional difference and the unevenness of data distribution, make each parameter on the same scale for analysis, and through the correlation matrix test the collinearity between parameters, eliminate redundant variables, form a standardized fingerprint data set, based on the standardized fingerprint data set, carry out PCA analysis, calculate the eigenvalue and eigenvector of the covariance matrix, determine the number of principal components (retain the first n principal components with cumulative variance contribution rate >80%), identify the main control parameters of each principal component through the loading matrix: parameters with absolute value of loading >0.7 are considered as the key control factors of the principal component, select parameters with significant loading (p<0.05) to construct the key fingerprint decoding parameter set, realize the conversion from high-dimensional data to low-dimensional latent variables, use the key fingerprint decoding parameter set to establish the source supply ratio quantitative model, through multiple linear regression, fit each parameter with the end member value of the known source area, calculate the contribution proportion of different sources to the sediment, and combine time series analysis to identify the periodic fluctuation characteristics of the key fingerprint decoding parameter set, divide the sedimentary cycle level including the Milankovitch cycle and the tectonic cycle, further classify the key fingerprint decoding parameter set through K-means clustering analysis, distinguish the source supply mode of different sedimentary stages, construct a three-dimensional correlation framework of parameter-source-cycle, and quantitatively reveal the dynamic evolution of the source area and the coupling mechanism of the source-sink system in the filling process of the sedimentary basin; Step six specifically includes: The deposition phase distribution characteristics, the deposition process homogenization degree value and the deposition process fingerprint decoding parameter are integrated to construct an initial source-sink system conceptual model, a spatial constraint condition of a deposition path and an initial distribution range of process parameters are defined, a parameter space is randomly sampled by an inverse Monte Carlo model with the fingerprint decoding parameter as a constraint condition, a deposition process including transport, sorting and mixing is simulated, a residual error between a simulation result and a measured regional deposition phase distribution characteristic and a deposition process homogenization degree value is calculated, a parameter distribution of a source supply rate, a transport distance and a deposition rate is iteratively adjusted until the residual error converges to a preset convergence threshold, a spatial migration track of a sediment in the optimized inverse Monte Carlo model is extracted, a path geometry and a branch / merging point are covered, a mixing efficiency of a deposition process is quantified by combining a homogenization degree value, a path rationality is verified by a temporal and spatial variation of the fingerprint decoding parameter, and a source-sink system deposition path network and a quantitative solution of key process parameters are output; Specific working contents are as follows: the deposition phase distribution characteristics (covering a deposition system type, a spatial distribution pattern), the deposition process homogenization degree value and the deposition process fingerprint decoding parameter are integrated to construct an initial source-sink system conceptual model, the spatial constraint condition of the deposition path is defined based on geological survey and data fusion, including a paleotopographic slope, a hydrodynamic zoning and an obstacle body distribution, and the initial distribution range of the process parameters is set, covering the source supply rate, the transport distance and the deposition rate; the parameter space is randomly sampled by the inverse Monte Carlo model with the fingerprint decoding parameter as the constraint condition, the whole process of the sediment transport, sorting and mixing is simulated, wherein the output of the inverse Monte Carlo model includes a virtual deposition phase distribution map, a homogenization degree simulation value and a material migration path, the residual error between the simulation result and the measured regional deposition phase distribution characteristic and the deposition process homogenization degree value is calculated, the model deviation is quantified, and the parameter distribution of the source supply rate, the transport distance and the deposition rate is iteratively adjusted to gradually narrow the residual error range until the preset convergence threshold is met, that is, the residual error square sum is less than 0.05, so as to ensure that the simulation result is highly consistent with the geological observation; the spatial migration track of the sediment is extracted from the optimized inverse Monte Carlo model, the path geometry (linearity / curvature), the branch and merging point position and connectivity are analyzed, the source-sink system deposition path network is constructed, the mixing efficiency of the deposition process (high value corresponds to strong mixing, and low value reflects conservative transport) is quantified by combining the homogenization degree value, and the path rationality is verified by the temporal and spatial variation (such as the longitudinal variation of the heavy mineral combination) of the fingerprint decoding parameter, and finally the quantitative solution of the key process parameters, including the source contribution proportion, the transport distance, the deposition rate and the mixing intensity, is output, so as to reveal the dynamic coupling mechanism of the source-sink system and provide a quantifiable process model for the analysis of the sedimentary basin evolution.

[0021] In embodiment 3, as Figure 1As shown, on the basis of embodiments 1-2, the application also provides a complex source-sink system-based sedimentary path process fingerprint decoding system for implementing the complex source-sink system-based sedimentary path process fingerprint decoding method described above, comprising: A data collection and integration module is used to collect geochemical characteristic data of target layers in a study area, perform standardization processing and quality inspection, construct a sedimentary geochemical big data set of the study area, ensure data integrity and standardization, and provide a high-precision basis for subsequent analysis; A stratigraphic feature modeling module is used to establish a standard stratigraphic feature library in combination with typical stratigraphic sections of a basin, analyze regional sedimentary facies distribution characteristics, construct a regional sedimentary facies distribution characteristic set, and clarify stratigraphic age, lithological combination and contact relationship, thereby supporting geochemical facies analysis; A sedimentary process analysis module is used to identify the homogenization degree of sedimentary processes in a source-sink system through multidimensional scale analysis, analyze sedimentary process modification intensity, reveal sediment transport and mixing rules, and distinguish between conservative and modified transport paths; A fingerprint decoding parameter module is used to construct a sedimentary process fingerprint decoding parameter system covering sedimentology, heavy mineral combination, geochemistry and chronology, and realize quantitative tracking of source signals; An inverse Monte Carlo model simulation module is used to simulate regional sedimentary facies distribution characteristics, sedimentary process homogenization degree values and sedimentary process fingerprint decoding parameters, use iterative simulation of an inverse Monte Carlo model, optimize source supply and sedimentary path parameters, quantitatively depict source-sink system dynamic processes, and output source-sink system sedimentary path networks and key process parameter quantitative solutions.

[0022] The above is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for decoding the fingerprint of a sedimentary pathway process based on a complex source-sink system, characterized in that, The method comprises the following steps: Step 1: Collect and organize the geochemical characteristics data of the target layer system in the study area, and establish a sedimentary geochemical big data set of the study area; Step 2: Combined with the typical stratigraphic section of the basin, the standard stratigraphic characteristics and its sedimentary geochemical characteristics are established, and the regional sedimentary facies distribution characteristics are analyzed; Step 3: Based on the multi-dimensional scale analysis of element geochemistry, the homogenization degree of the sedimentary process in the source-sink system is identified; Step 4: Based on the comparison of the standard stratigraphic sedimentary geochemical characteristics and the source area characteristics along the sedimentary path, the sedimentary process fingerprint decoding parameters are constructed; Step 5: On the basis of constructing the sedimentary process fingerprint decoding parameters, based on the PCA data algorithm, the key fingerprint decoding parameter set is constructed, and the quantitative source-sink supply ratio and cyclicity of the potential source area are revealed; Step 6: Combined with the regional sedimentary facies distribution characteristics, the sedimentary process homogenization degree value and the sedimentary process fingerprint decoding parameters, the sedimentary path and process in the source-sink system are quantitatively characterized through the iterative simulation of the inverse Monte Carlo model.

2. The method of claim 1, wherein the method is based on a complex source- sink system of deposition path process fingerprinting. The step one specifically comprises: The range of the study area and the target layer system are determined, the public database, the geological exploration report and the journal literature are searched, the major elements, trace elements, rare earth elements and heavy mineral data of the target layer system in the study area are batch grabbed, the fields are aligned according to the unified well name, layer system and depth coordinates, the missing and out-of-tolerance data are removed, the instrument drift is corrected by using the ISO reference value, the metadata table is established, and the original data is standardized and stored; The in-storage data is subjected to three-level quality inspection: in the first step, the major element discrete value is screened out with a relative deviation of the repeated sample of less than 5%, in the second step, the contaminated sample is removed with an abnormal coefficient of the rare earth element normalized after the ballast meteorite of more than 3σ, and in the third step, the weathering recirculation influence is tested by using the heavy mineral stability coefficient, the qualified sample is marked, and a quality report is generated; The qualified sample data is coupled according to the depth sequence and the spatial grid, the major trace element matrix, the rare earth element vector and the heavy mineral combination fingerprint table are constructed, the NoSQL database is stored, the well-layer-depth three-level index and the spatial R-Tree index are established, and the sedimentary geochemical big data set of the study area is formed.

3. The method of claim 1, wherein the method is based on a complex source- sink system of deposition path process fingerprinting. The step two specifically comprises: According to the geological background and the target layer system of the study area, the representative basin typical stratigraphic section is selected as the research object, the stratigraphic age framework is constrained through the chronology test, the sedimentary sequence order is divided combined with the sedimentary cycle analysis, the standard stratigraphic characteristic library is established, the lithology description, the paleontological combination, the sedimentary structure and the logging response characteristics are covered, the stratigraphic division template is formed, the geochemical element data of the key layer in the section are synchronously extracted, and the stratigraphic-geochemical correlation matrix is constructed; Based on the established standard stratigraphic characteristic library, the geochemical facies analysis is carried out on the target layer system, the heavy mineral combination is further used to verify the source direction, the Q-type clustering is used to convert the geochemical parameters into quantifiable sedimentary facies marks, and an element-mineral-sedimentary facies conversion model is established; Based on the standard stratigraphic characteristics and sedimentary facies markers, the data of multiple drilling wells and seismic profiles in the basin are integrated to construct a three-dimensional sedimentary facies model, and the discrete well point data are processed by gridding to generate a continuous sedimentary facies distribution map by using the Kriging interpolation method. Combined with the regional tectonic evolution history, the relationship between the sedimentary facies migration path and the basin filling process is analyzed to form the regional sedimentary facies distribution characteristics set.

4. The method of claim 1, wherein the method is based on a complex source- sink system of deposition path process fingerprinting. The step three specifically includes: Based on the target layer system of the study area, representative samples of the source area and the sink area are collected, and whole rock major and trace element analysis is performed. The original geochemical data are standardized by using the Z-score standardization method to unify the dimension, and a standardized data set containing the geochemical parameters of the source-sink samples is constructed; The standardized geochemical data are taken as the input, the non-metric multidimensional scaling analysis algorithm is used, the high-dimensional geochemical data are reduced to 2-3 dimensional space based on the Euclidean distance matrix, the distribution scatter plot of the source-sink samples is generated, and the goodness of fit is evaluated by the stress value evaluation model. Then, the reconstruction degree of the original provenance signal by the sedimentary process is quantified by calculating the coefficient of variation of the Euclidean distance of the source-sink samples in the multidimensional scaling space. Based on the multidimensional scaling analysis result, the expansion ratio of the distribution range of the sink area samples in the multidimensional scaling space relative to the source area is defined as the homogenization degree, that is, the ratio of the standard deviation of the Euclidean distance between the source-sink samples to the average Euclidean distance.

5. The method of claim 4, wherein the method is based on a complex source- sink system of deposition path process fingerprinting. The calculation expression of the coefficient of variation of the Euclidean distance is as follows: ； ； ； wherein: is the coefficient of variation of Euclidean distance, defined as the ratio of the standard deviation of Euclidean distance between source-sink sample pairs to the average Euclidean distance; is the standard deviation of Euclidean distance between source-sink sample pairs, is the average of Euclidean distance between source-sink sample pairs, is the Euclidean distance between the ith source sample and the jth sink sample, M is the number of source samples, and N is the number of sink samples. The calculation expression of the homogenization degree is as follows: ； In the formula, P% is the homogenization degree, representing the expansion ratio of the distribution range of the sink sample in the multidimensional scale analysis space relative to the source; is the distribution range of the sink sample in the multidimensional scale analysis space; is the distribution range of the source sample in the multidimensional scale analysis space.

6. The method of claim 1, wherein the method is based on a complex source- sink system of deposition path process fingerprinting. The step four specifically includes: Based on the sedimentary path of the study area, representative samples of the source area outcrop, transport channel and sedimentary center are collected, covering the whole process from source to sink. Simultaneously, sedimentology analysis, heavy mineral combination identification, whole rock geochemical test and detrital zircon U-Pb chronology determination are performed, and the original data are standardized: a multi-source data set covering sedimentology, mineralogy, geochemistry and chronology is constructed; By comparing the multi-dimensional data of the source area and the sedimentary center samples, the preserved or reconstructed provenance signal in the sedimentary process is identified. By comprehensively analyzing the multi-disciplinary evidence, the fingerprint parameters sensitive to the transport process are extracted, including the heavy mineral stability index, the rare earth element fractionation coefficient and the age spectrum similarity, to form a sedimentary process decoding index system.

7. The method of claim 1, wherein the method is based on a complex source- sink system of deposition path process fingerprinting. The step five specifically includes: The parameters of the heavy mineral stability index, the rare earth element fractionation coefficient and the age spectrum similarity are integrated, the detrital zircon age spectrum and the major and trace element ratio are supplemented to form a high-dimensional fingerprint data set. The data are standardized by using the Z-score method, and the collinearity between the parameters is tested by the correlation matrix to remove the redundant variables to form a standardized fingerprint data set; Based on the standardized fingerprint data set, the PCA analysis is performed to calculate the eigenvalues and eigenvectors of the covariance matrix, determine the number of principal components, identify the main control parameters of each principal component through the loading matrix, and screen the parameters with significant load to construct a key fingerprint decoding parameter set; The key fingerprint decoding parameter set is used to establish a provenance supply ratio quantitative model. Through multiple linear regression, each parameter is fitted with the known end-member value of the provenance area to calculate the contribution proportion of different provenances to the sediments. Combined with time series analysis, the periodic fluctuation characteristics of the key fingerprint decoding parameter set are identified to divide the sedimentary cycle level including the Milankovitch cycle and tectonic cycle. Further, K-means clustering analysis is performed on the key fingerprint decoding parameter set to distinguish the provenance supply mode in different sedimentary stages, and a parameter-provenance-cycle three-dimensional correlation framework is constructed to quantitatively reveal the dynamic evolution of the provenance area and the coupling mechanism of the source-sink system in the filling process of the sedimentary basin.

8. The method of claim 7, wherein the method is based on a complex source- sink system of deposition path process fingerprinting. The step six specifically includes: Integrating regional sedimentary facies distribution characteristics, sedimentary process homogenization degree values and sedimentary process fingerprint decoding parameters, an initial source-sink system conceptual model is constructed, and the spatial constraint conditions of the sedimentary path and the initial distribution range of the process parameters are defined; Taking the fingerprint decoding parameters as the constraint conditions, the parameter space is randomly sampled by the inverse Monte Carlo model to simulate the sedimentary process including transportation, sorting and mixing, and the residual error between the simulation results and the measured regional sedimentary facies distribution characteristics and sedimentary process homogenization degree values is calculated. The parameter distribution of the provenance supply rate, the transportation distance and the sedimentation rate is iteratively adjusted until the residual error converges to a preset convergence threshold; The spatial migration trajectory of the sediments in the optimized inverse Monte Carlo model is extracted, covering the path geometry and branch / convergence points. The mixing efficiency of the sedimentary process is quantified by combining the sedimentary process homogenization degree values, the path rationality is verified by the temporal and spatial variation of the fingerprint decoding parameters, and the source-sink system sedimentary path network and the quantitative solution of the key process parameters are output.

9. A system for complex source-sink system based sedimentary pathway process fingerprint decoding, for implementing the complex source-sink system based sedimentary pathway process fingerprint decoding method according to any one of claims 1-8, characterized in that, It includes: A data acquisition and integration module is used to collect geochemical characteristic data of the target layer system in the study area, perform standardization processing and quality inspection, and construct a sedimentary geochemistry big data set of the study area; A stratigraphic feature modeling module is used to establish a standard stratigraphic feature library in combination with typical stratigraphic sections of the basin, and analyze regional sedimentary facies distribution characteristics to construct a regional sedimentary facies distribution characteristic set; A sedimentary process analysis module is used to identify the homogenization degree of the sedimentary process in the source-sink system through multidimensional scaling analysis and analyze the sedimentary process reconstruction intensity; A fingerprint decoding parameter module is used to construct a sedimentary process fingerprint decoding parameter system covering sedimentology, heavy mineral assemblage, geochemistry and geochronology; An inverse Monte Carlo model simulation module is used to integrate regional sedimentary facies distribution characteristics, sedimentary process homogenization degree values and sedimentary process fingerprint decoding parameters, optimize the provenance supply and sedimentary path parameters by iterative simulation of the inverse Monte Carlo model, and output the source-sink system sedimentary path network and the quantitative solution of the key process parameters.

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