A source analysis visualization method and device based on local singularity

Abstract

This application provides a method and apparatus for prototyping visualization based on local singularities. The method includes: acquiring a detrital zircon U-Pb age dataset of a target region; preprocessing the dataset and dividing it into age intervals; performing local singularity analysis on each age interval to calculate the singularity index of each age interval and constructing a local singularity spectrum; performing multidimensional scaling (MDS) analysis based on the singularity index to obtain visualization results in a low-dimensional space; and interpreting the visualization results in conjunction with geological background information to determine prototyping relationships and material migration paths. This application can accurately identify fine prototyping paths missed by traditional methods, significantly improving the precision and reliability of prototyping.

Description

Technical Field

[0001] This application relates to the field of geological exploration and provenance analysis technology, and in particular to a method and apparatus for visualizing provenance analysis based on local singularities. Background Technology

[0002] In geological research, detrital zircon U-Pb geochronology is a core technique for tracing sediment source regions, reconstructing paleogeographic patterns, and analyzing tectonic evolution history. By analyzing the U-Pb age distribution of detrital zircons, researchers can identify key information such as the magmatic activity periods and crustal formation ages of the source region, and further infer the material transport pathways and source-sink system relationships. However, existing source analysis methods have significant limitations in practical applications and cannot meet the needs of refined source analysis.

[0003] On the one hand, while local singularity spectral analysis can use fractal and multifractal theories to calculate singularity indices and enhance weak anomalous peaks in age spectra, this method is highly dependent on the subjective judgment of researchers. It requires manual comparison to confirm the geological significance of anomalous signals, which is not only inefficient but also prone to misjudgment due to differences in experience, making it impossible to achieve data-driven automated analysis. On the other hand, while multidimensional scaling analysis (MDS) can reduce high-dimensional age data to two- or three-dimensional space to intuitively show the similarity between samples, it is affected by geological factors (such as zircon preservation bias) and data processing factors (such as the selection of kernel density estimation bandwidth), which can easily lose weak anomalous information in age spectra, resulting in incomplete identification of provenance direction. For example, in the provenance analysis of Early Carboniferous mineralization in the central Guizhou region, traditional MDS can only identify the northern provenance direction and cannot capture western provenance signals or more refined material migration paths in the north. Meanwhile, while Hf isotope analysis can supplement age spectrum analysis to identify crustal accretion or recycling processes, it cannot directly quantify the provenance affinity between different samples and is often used in isolation, lacking deep integration with other analytical methods. In summary, existing technologies suffer from technical pain points such as "loss of weak anomaly information," "strong subjective dependence on provenance identification," and "insufficient synergy among multiple methods." Therefore, there is an urgent need for a provenance visualization method that combines weak anomaly enhancement capabilities, data-driven quantitative analysis, and multi-dimensional verification. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method and apparatus for visualization of provenance analysis based on local singularities. By enhancing weak provenance signals in detrital zircon age data through local singularity analysis, and then using MDS to achieve quantitative visualization, this method solves the defects of existing technologies, such as loss of weak anomaly information and reliance on subjective experience for provenance identification. It can accurately identify fine provenance paths missed by traditional methods, significantly improving the precision and reliability of provenance analysis, and providing efficient technical support for the reconstruction of provenance systems of complex geological bodies, the evolution of paleogeographic patterns, and resource exploration.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a method for visualizing source analysis based on local singularities, comprising the following steps:

[0007] Obtain the detrital zircon U-Pb age dataset of the target area, preprocess the dataset and divide it into age intervals;

[0008] Perform local singularity analysis on each age range, calculate the singularity index corresponding to each age range, and construct a local singularity spectrum;

[0009] Multidimensional scaling (MDS) analysis is performed based on the singularity index to obtain visualization results in a low-dimensional space.

[0010] By combining geological background information, the visualization results are interpreted to determine the source relationship and material migration path.

[0011] Secondly, embodiments of this application also provide a source analysis visualization device based on local singularities, the device comprising:

[0012] The preprocessing module is used to obtain the detrital zircon U-Pb age dataset of the target area, preprocess the dataset, and divide it into age intervals;

[0013] The module is used to perform local singularity analysis on each age range, calculate the singularity index corresponding to each age range, and construct the local singularity spectrum.

[0014] The analysis module is used to perform multidimensional scaling (MDS) analysis based on the singularity index to obtain visualization results in a low-dimensional space.

[0015] The interpretation module is used to interpret the visualization results in conjunction with geological background information to determine the source relationship and material migration path.

[0016] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the source analysis visualization method based on local singularities as described in any of the first aspects.

[0017] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the source analysis visualization method based on local singularities as described in any one of the first aspects.

[0018] The embodiments of this application have the following beneficial effects:

[0019] This technical process, which involves preprocessing and age range division of detrital zircon U-Pb data, constructing a singularity spectrum through local singularity analysis, visualizing weak anomalies through multidimensional scaling analysis, and interpreting provenance relationships in conjunction with geological background, overcomes the shortcomings of existing provenance analysis methods, such as insufficient extraction of weak anomaly information and reliance on subjective experience for provenance identification. It achieves automated enhancement of weak provenance signals and quantitative visualization of provenance relationships in detrital zircon age data. This process can transform weak anomalies (such as low-abundance age range signals) that are difficult to identify using traditional methods into significant singularity index anomalies, and can intuitively present the provenance affinity between samples through multidimensional scaling analysis. This significantly improves the precision and reliability of provenance path analysis, providing an efficient and accurate technical means for provenance analysis and geological exploration of complex geological bodies such as orogenic belts and sedimentary basins. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating steps S101-S104 provided in the embodiments of this application;

[0022] Figure 2 This is a flowchart illustrating steps S201-S203 provided in the embodiments of this application;

[0023] Figure 3 This is a flowchart illustrating steps S301-S302 provided in the embodiments of this application;

[0024] Figure 4 This is a schematic diagram of a source analysis visualization method based on local singularities provided in an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of the structure of the source analysis visualization device based on local singularities provided in the embodiments of this application;

[0026] Figure 6 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0028] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0029] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0030] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0031] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application and is not intended to limit this application.

[0033] See Figure 1 , Figure 1This is a flowchart illustrating steps S101-S104 of the source analysis visualization method based on local singularities provided in this application embodiment, which will be combined with... Figure 1 Steps S101-S104 are explained below.

[0034] In step S101, the detrital zircon U-Pb age dataset of the target area is obtained, and the dataset is preprocessed and divided into age intervals.

[0035] In step S102, local singularity analysis is performed on each age range to calculate the singularity index corresponding to each age range and construct a local singularity spectrum.

[0036] In step S103, multidimensional scaling analysis (MDS) is performed based on the singularity index to obtain visualization results in low-dimensional space.

[0037] In step S104, the visualization results are interpreted in conjunction with geological background information to determine the source relationship and material migration path.

[0038] In existing technologies, local singularity analysis can only generate singularity spectra, requiring manual subjective identification of weak anomalies; multidimensional scaling analysis directly reduces dimensionality based on age data, which easily loses weak information; however, the embodiments of this application, through a series of processes of "local singularity analysis → singularity index MDS → geological background verification", combine nonlinear singularity index with MDS for the first time, achieving the synergy of "weak anomaly enhancement" and "data-driven quantification", solving the dual pain points of "subjective dependence" and "loss of weak information" in existing technologies.

[0039] Specifically, the first step is data acquisition and preprocessing. Detrital zircon U-Pb age datasets from the target area are typically obtained using mainstream testing techniques such as LA-ICP-MS. Preprocessing requires removing outlier data (such as ages exceeding geologically reasonable ranges or isotope ratios deviating from standard values) to avoid interference from outliers in subsequent analysis. Age intervals are divided using a fixed step size (e.g., 1-100 Ma) to discretize continuous age data into standardized intervals, providing a unified computational unit for subsequent multi-scale analysis and ensuring the comparability of data from different samples.

[0040] Next, local singularity analysis and singularity spectrum construction are performed. By constructing multi-scale windows, the age distribution characteristics under different observation accuracies are simulated, the average age density is calculated, and a power-law relationship is established. Essentially, fractal theory is used to capture the nonlinear changes of data at the "local-global" scale. Anomalies in the singularity index k value (such as being significantly lower than the surrounding interval) correspond to weak peaks that are masked in the age spectrum. Weak source signals that are difficult to identify in traditional methods are transformed into quantifiable k-value anomalies, which are finally presented intuitively through the singularity spectrum.

[0041] Next, MDS analysis and visualization are performed. The core advantage of MDS is that it reduces the dimensionality of high-dimensional anomaly index vectors (such as each sample containing dozens of k values) to two-dimensional / three-dimensional space, quantifying the provenance affinity between samples through "distance". The closer the distance between the scatter points after dimensionality reduction, the more similar the anomaly spectra (i.e., weak anomaly distribution characteristics) of the samples are, and the higher the provenance correlation. Compared with traditional MDS that directly uses age data, this step, based on the dimensionality reduction results of the anomaly index, can retain weak anomaly information and avoid the loss of key provenance signals.

[0042] Finally, geological background and provenance interpretation are performed. Provenance analysis needs to be combined with regional geological background (such as magmatic activity periods, stratigraphic contact relationships, and paleocurrent direction) to avoid misjudgments based solely on data. For example, the age range corresponding to an anomaly in the singularity index of a sample needs to be matched with the magmatic activity ages of known provenance areas in the region to confirm the provenance association. The embodiments of this application combine data results with geological realities to improve the reliability and practicality of the method.

[0043] In some embodiments, preprocessing of the dataset includes removing outlier data, and the age range division is performed by continuous division with a fixed step size, ranging from 1 to 100 Ma.

[0044] Removing outliers is a prerequisite for ensuring the accuracy of subsequent analyses. In detrital zircon U-Pb testing, outliers may occur due to instrument errors, sample contamination, etc. (e.g., young zircons <100 Ma appearing in ancient strata samples, or¹ 76 Pb / ¹ 74 If the Pb ratio exceeds the standard range, it will lead to distortion of the statistical results for the age range if not removed, which will affect the accuracy of the singularity index calculation. This limitation clearly defines the core operation of preprocessing to avoid the failure of the method implementation due to the neglect of abnormal data by those skilled in the art.

[0045] The step size ranges from 1 to 100 Ma, which is a design based on a balance between geological realities and computational efficiency.

[0046] Too small a step size (e.g., <1Ma) will result in too many intervals (e.g., 3000 intervals are needed for 0-3000Ma), which will greatly increase the amount of computation and easily introduce random errors.

[0047] Excessively large step sizes (e.g., >100 Ma) can result in too few intervals, making it impossible to capture fine age distribution features, and weak anomalies may be masked by merging.

[0048] The 1-100 Ma range covers mainstream provenance analysis scenarios: for example, for young sedimentary basins (such as the Cenozoic), a step size of 1-20 Ma can be selected; for ancient metamorphic rock areas (such as the Precambrian), a step size of 50-100 Ma can be selected, balancing accuracy and efficiency.

[0049] In some embodiments, see Figure 2 , Figure 2 This is a flowchart illustrating steps S201-S203 provided in the embodiments of this application. The local singularity analysis includes steps S201-S203, which will be explained in conjunction with each step.

[0050] In step S201, a multi-scale analysis window with increasing scale is constructed with each age range as the center.

[0051] In step S202, the average age density under each scale window is calculated, and a power-law relationship between the average age density and the window scale is established.

[0052] In step S203, the singularity index is obtained by linear regression fitting, wherein the linear regression uses the logarithm of the window scale as the independent variable and the logarithm of the mean age density as the dependent variable.

[0053] Here, constructing incrementally scaling windows centered on each age range essentially simulates an observational perspective "from local to global":

[0054] Small-scale windows (such as n=1, containing only the central interval) reflect subtle changes in local age density;

[0055] Large-scale windows (e.g., n=6, containing 11 intervals) reflect the overall trend of global age distribution;

[0056] Only through comparison of multi-scale windows can weak source signals that are "locally abnormal but globally insignificant" be captured (e.g., a certain interval has a slightly higher density at a small scale, but it is averaged at a large scale, which traditional methods cannot identify, while multi-scale analysis can capture this anomaly through density change patterns).

[0057] Mean age density ρ(L) n The power-law relationship between ρ(ε) and window size n (ρ(ε)∝cε) -k The essence of ρ(ε) is to describe the fractal characteristics of age data. If the data has no anomalies (uniform distribution), ρ(ε) has a linear relationship with n, and the power law relationship does not hold. If there are weak anomalies (small difference between local density and surrounding density), then ρ(ε) changes non-linearly with n, which conforms to the power law relationship. The value of k directly reflects the strength of the anomaly (the smaller the value of k, the more significant the anomaly).

[0058] Taking the logarithm of the power-law relationship, the slope of the linear regression is the singularity index k, whose physical meaning is:

[0059] The larger the absolute value of the slope of the regression line, the faster the age density changes with scale, and the more significant the weak anomaly.

[0060] The goodness-of-fit R² (e.g., R² > 0.9) can verify the reliability of the power-law relationship. If R² is too low, it indicates that there is no significant singularity in the interval, thus excluding false positive anomalies.

[0061] By using linear regression, complex multi-scale data can be transformed into a single k-value, enabling quantitative characterization of weak anomalies and providing standardized input for subsequent MDS analysis.

[0062] In some embodiments, the scale n of the multi-scale analysis window is greater than or equal to 1. When the scale is n, the window covers (2n-1) consecutive age intervals, and the maximum value of n is 3-10.

[0063] n=1 is the minimum scale window, which only includes the central interval. Its purpose is to obtain "purely local" age density data as a benchmark for multiscale analysis. If n<1, the window cannot cover the complete age interval, and the data is meaningless.

[0064] The maximum value of n, ranging from 3 to 10, is the result of an optimization between "anomaly detection accuracy" and "computational cost".

[0065] If the maximum value of n is less than 3 (e.g., n=2), the window size is insufficient and cannot fully reflect the scale change from "local to global". The fitting accuracy of the power law relationship is low (R²<0.8), and it is difficult to accurately calculate the value of k.

[0066] If the maximum value of n is greater than 10 (e.g., n=15), the window covers too many intervals (e.g., covering 29 intervals when n=15), which will include signals from irrelevant age intervals in the calculation, causing local anomalies to be diluted and the sensitivity of weak anomaly identification to decrease.

[0067] Experimental verification shows that when n=3-10, the goodness of fit of the power law relationship R² is generally >0.9, the weak anomaly identification rate is >90%, and the computational load is moderate (e.g., for each sample with 68 intervals, only 6×68=408 window densities need to be calculated when n=6), balancing accuracy and efficiency.

[0068] In some embodiments, see Figure 3 , Figure 3 This is a flowchart illustrating steps S301-S302 provided in the embodiments of this application. The multidimensional scaling analysis includes steps S301-S302, which will be explained in conjunction with each step.

[0069] In step S301, the singularity index of each age range is represented as a high-dimensional vector.

[0070] In step S302, the difference distance between high-dimensional vectors is calculated to form a distance matrix.

[0071] In step S303, based on the characteristics of the distance matrix, a classic MDS, a metric MDS, or a non-metric MDS is selected for dimensionality reduction to obtain two-dimensional or three-dimensional visualized coordinates.

[0072] Here, representing the singularity index as a high-dimensional vector is a prerequisite for MDS analysis. The singularity index of each sample corresponds to multiple age intervals (e.g., 68 intervals correspond to 68-dimensional vectors). Each dimension of the high-dimensional vector represents the weak anomalous features of the sample in a specific age interval, and the overall distribution of the vector reflects the weak anomalous "fingerprint" of the sample. Through this representation, the weak anomalous features of the sample are transformed into mathematical objects that can be processed by MDS.

[0073] The distance matrix is ​​the core carrier for quantifying the source differences between samples. The "difference distance" is essentially a measure of the similarity between the high-dimensional vectors (i.e., weak anomaly fingerprints) of two samples. The smaller the distance, the closer the weak anomaly distributions of the two samples are, and the higher the source correlation. The symmetry of the distance matrix (e.g., the distance between sample A and B = the distance between sample B and A) ensures the mathematical rationality of MDS analysis and provides reliable input for subsequent dimensionality reduction.

[0074] MDS type selection and dimensionality reduction adaptively choose the optimal dimensionality reduction method based on data characteristics to ensure the accuracy of the dimensionality reduction results:

[0075] Classical MDS is suitable for scenarios where the distance matrix satisfies Euclidean distance properties (such as the triangle inequality). It directly reduces dimensionality through eigenvalue decomposition, resulting in high computational efficiency and good stability. Metric MDS is suitable for scenarios where the distance matrix exhibits semi-metric properties (partially violating the triangle inequality). It optimizes the distance through linear functions, corrects data bias, and ensures that the distance still reflects sample differences after dimensionality reduction. Non-metric MDS is suitable for scenarios where only the rank relationship of the distance needs to be preserved (such as when the data is noisy). It optimizes the stress function through monotonic function transformation, prioritizing the accuracy of the "relative differences" between samples.

[0076] The three types of selection logic ensure that this method can adapt to scenarios with different data quality (such as sample size and data noise level), thus improving the versatility of the method.

[0077] In some embodiments, the dissimilarity distance is calculated by the KS test and is defined as the maximum vertical deviation between the kernel density estimation functions corresponding to two high-dimensional vectors.

[0078] The Kolmogorov-Smirnov (KS) test is a non-parametric test method for measuring the similarity between two probability distributions. The calculated D_KS distance is defined as the "maximum vertical deviation between the two kernel density estimation functions," and it has the following advantages:

[0079] Sensitive to weak differences: Compared to Euclidean distance (which is susceptible to extreme values), KS distance focuses on differences in the overall distribution and can capture subtle differences in the trends of two singularity spectra, making it suitable for identifying differences in weakly anomalous distributions;

[0080] No need to assume distribution type: The distribution of the singularity index of detrital zircon has no fixed pattern (non-normal distribution). The KS test does not require a pre-set distribution model and is calculated directly based on the actual distribution of the data, making it more widely applicable.

[0081] Results standardization: The D_KS distance ranges from 0 to 1, where 0 represents that the two samples are completely identical and 1 represents that they are completely different. This makes it easy to intuitively judge the magnitude of the difference in the source material between the samples (e.g., D_KS < 0.15 represents that the source material is highly similar and D_KS > 0.30 represents that the source material is significantly different).

[0082] The distance matrix calculated by KS distance is well-suited for the dimensionality reduction requirements of MDS:

[0083] If the singularity spectra (weakly anomalous distributions) of two samples are similar, the KS distance is small, and the distance between the scattered points after MDS dimensionality reduction is close, which intuitively reflects the source affinity. If the samples have weakly anomalous differences (such as anomalous k values ​​in a certain age range), the KS distance will increase significantly, and the scattered points will be separated after MDS dimensionality reduction, avoiding the problem of weak differences being masked in traditional methods.

[0084] For example, the KS distance between GBV samples and northern samples is >0.30, and they aggregate independently in MDS, accurately identifying the western source. In contrast, traditional MDS does not use KS distance, resulting in samples overlapping and being indistinguishable.

[0085] In some embodiments, the method further includes a step of verification using detrital zircon Hf isotope data, which includes depleted crustal model age (TDM) and initial εHf value.

[0086] Hf isotopes in detrital zircons (TDM age, initial εHf value) are key indicators for determining the crustal properties of the source region, complementing the "weak anomaly location" of the singularity index.

[0087] The singularity index indicates that "a weak source signal exists in a certain age range", but it cannot determine whether the source region corresponding to the signal is the newly formed crust or the recycling of ancient crust;

[0088] Hf isotopes directly determine the source region attributes through εHf values. Positive εHf values ​​(e.g., +2 to +6) indicate newly formed crust (the source region was newly formed crustal material when zircon crystallized), while negative εHf values ​​(e.g., -5 to -9) indicate ancient crustal recycling (the source region was ancient crustal remelting when zircon crystallized). TDM age reflects the time when the source rock migrated from the depleted mantle, which can further limit the formation age of the source region.

[0089] For example, the singularity anomaly in the 1450-1500 Ma range of the XRY sample, combined with εHf(t) = +3.2 to +5.6, confirms that the anomaly corresponds to the source of newly formed crustal material, matches the magmatic activity in the northern Zunyi area, and excludes interference from other source areas.

[0090] The specific procedure for Hf isotope verification is as follows:

[0091] Identify anomalous intervals in the singularity spectrum (such as age intervals with significantly abnormal k values);

[0092] Targeted testing of Hf isotopes in zircon within this range (to avoid mismatches caused by random testing);

[0093] The Hf isotope results are compared with the Hf characteristics of known source regions in the region to verify whether the source region corresponding to the singularity anomaly is reasonable.

[0094] For example, a sample exhibits a singular anomaly in the 2500-2800 Ma range. Zircon samples in this range show εHf(t) = -8 to -6 and TDM = 2900-3100 Ma, which is consistent with the characteristics of the ancient basement in the western part of the region (εHf(t) = -9 to -4 and TDM = 3000-3200 Ma). This confirms that the anomaly corresponds to the provenance of the ancient basement in the west, thus avoiding misidentification of other provenance areas with similar ages as the target provenance.

[0095] Please see Figure 4 , Figure 4 This is a schematic diagram of the source analysis visualization method based on local singularities provided in the embodiments of this application, such as... Figure 4 As shown, the technical process comparison framework between traditional source analysis methods and this solution can be explained from three dimensions: data input, process differences, and technical improvements.

[0096] I. Data Input Layer.

[0097] Geochemical data were analyzed using LA-ICP-MS (laser ablation inductively coupled plasma mass spectrometry), and two core data types were acquired simultaneously:

[0098] Detrital zircon U-Pb age: used to analyze the magmatic activity stages and crustal formation age of the source region;

[0099] Zircon Hf isotope ratio: used to determine the crustal properties of the source area (new crust / old crust recycling), providing multi-dimensional verification for subsequent analysis.

[0100] II. Process Differences: Traditional Methods vs. This Solution

[0101] (1) Traditional source analysis methods.

[0102] The process is as follows:

[0103] U-Pb age spectrum construction → combining ε_Hf(t) spectrum → determining source direction → MDS analysis → traditional visualization dimensionality reduction results.

[0104] Limitations: Direct analysis based on U-Pb age spectrum is prone to losing weak anomaly information; after MDS dimensionality reduction, the source discrimination is low, making it difficult to identify fine transport paths.

[0105] (2) This plan.

[0106] The process is as follows:

[0107] Local singularity analysis → Generation of local singularity index and local singularity spectrum (LSA spectrum) → Enhancement of weak anomalies by combining ε_Hf(t) spectrum → Discrimination of source direction after weak anomaly enhancement → MDS analysis → Visualization of dimensionality reduction results after weak anomaly enhancement.

[0108] This application introduces a local singularity analysis step, which first "enhances weak anomalies in U-Pb age data into quantifiable singularity indices / spectrums", and then combines Hf isotope and MDS analysis to ultimately achieve the preservation of weak anomaly information and fine visualization of provenance relationships.

[0109] Therefore, this application's embodiments transform weak provenance signals (such as trace zircon age peaks) that are masked in traditional methods into significant quantitative indicators through the conversion of "local singularity analysis → singularity index / spectrum," thus solving the problem of "loss of weak information." Simultaneously, it integrates the "time dimension" of U-Pb age with the "crustal attribute dimension" of Hf isotopes, and then uses MDS analysis to achieve quantitative visualization of high-dimensional data, avoiding the subjective dependence of traditional methods. Comparing "traditional visualization dimensionality reduction results" with "dimensionality reduction results after weak anomaly enhancement," this scheme can more clearly distinguish provenance affinity (such as different migration paths within the same provenance region, or subtle differences between different provenance regions), providing a more accurate basis for provenance analysis.

[0110] In summary, the embodiments of this application have the following beneficial effects:

[0111] By enhancing weak anomalies through local singularity analysis, quantifying singularity index MDS, and validating multi-dimensional data, the overall efficiency of provenance analysis is significantly improved. Compared with existing technologies, it can transform weak provenance signals masked in detrital zircon U-Pb age spectra into quantifiable singularity index anomalies, thus improving the identification rate of weak anomalies. Through KS distance calculation and MDS dimensionality reduction, it solves the problem of ambiguous provenance distinction caused by the loss of weak information in traditional MDS, thereby improving the visualization accuracy of provenance affinity and clearly identifying fine migration paths missed by traditional methods, such as "XRY→GBC" and "HTW→MT". At the same time, it integrates Hf isotope data and geological background to form a closed-loop analysis system of data-driven and geological verification, improving the reliability of provenance analysis results and providing more accurate and efficient technical support for the reconstruction of provenance systems of complex geological bodies, the evolution of paleogeographic patterns, and resource exploration.

[0112] Based on the same inventive concept, this application also provides a source analysis visualization device based on local singularities, which corresponds to the source analysis visualization method based on local singularities in the first embodiment. Since the principle of the device in this application is similar to the above-mentioned source analysis visualization method based on local singularities, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0113] like Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of the source analysis visualization device 500 based on local singularities provided in this application embodiment. The source analysis visualization device 500 based on local singularities includes:

[0114] Preprocessing module 501 is used to obtain the detrital zircon U-Pb age dataset of the target area, preprocess the dataset and divide it into age intervals;

[0115] Module 502 is used to perform local singularity analysis on each age range, calculate the singularity index corresponding to each age range, and construct the local singularity spectrum.

[0116] Analysis module 503 is used to perform multidimensional scaling analysis (MDS) based on the singularity index to obtain visualization results in a low-dimensional space;

[0117] The interpretation module 504 is used to interpret the visualization results in conjunction with geological background information to determine the source relationship and material migration path.

[0118] Those skilled in the art should understand that Figure 5 The functions of each unit in the local singularity-based source analysis visualization device 500 shown can be understood by referring to the relevant description of the aforementioned local singularity-based source analysis visualization method. Figure 5The functions of each unit in the local singularity-based source analysis visualization device 500 shown can be implemented by a program running on a processor or by specific logic circuits.

[0119] In one possible implementation, preprocessing the dataset includes removing outlier data, and the age range division is performed using a fixed step size, ranging from 1 to 100 Ma.

[0120] In one possible implementation, the construction module 502 performs local singularity analysis including:

[0121] A multi-scale analysis window with increasing scale is constructed with each age range as the center.

[0122] Calculate the average age density under each scale window and establish a power-law relationship between the average age density and the window scale;

[0123] The singularity index was obtained by fitting a linear regression, with the logarithm of the window scale as the independent variable and the logarithm of the mean age density as the dependent variable.

[0124] In one possible implementation, the scale n of the multi-scale analysis window is greater than or equal to 1. When the scale is n, the window covers (2n-1) consecutive age intervals, and the maximum value of n is between 3 and 10.

[0125] In one possible implementation, the analysis module 503 performs multidimensional scaling analysis including:

[0126] The singularity index of each age range is represented as a high-dimensional vector;

[0127] Calculate the difference distance between high-dimensional vectors to form a distance matrix;

[0128] Based on the characteristics of the distance matrix, classic MDS, metric MDS, or non-metric MDS are selected for dimensionality reduction to obtain two-dimensional or three-dimensional visualized coordinates.

[0129] In one possible implementation, the dissimilarity distance is calculated by the KS test and is defined as the maximum vertical deviation between the kernel density estimation functions corresponding to two high-dimensional vectors.

[0130] In one possible implementation, the method further includes a step of verification using detrital zircon Hf isotope data, which includes depleted crustal model age (TDM) and initial εHf value.

[0131] The aforementioned local singularity-based provenance analysis visualization device significantly improves the overall efficiency of provenance analysis by enhancing weak anomalies through local singularity analysis, quantifying singularity index MDS, and verifying multi-dimensional data. Compared with existing technologies, it can transform the masked weak provenance signals in the detrital zircon U-Pb age spectrum into quantifiable singularity index anomalies, improving the weak anomaly identification rate. Through KS distance calculation and MDS dimensionality reduction, it solves the problem of ambiguous provenance distinction caused by the loss of weak information in traditional MDS, thereby improving the accuracy of provenance affinity visualization and clearly identifying fine migration paths missed by traditional methods, such as "XRY→GBC" and "HTW→MT". At the same time, it integrates Hf isotope data and geological background to form a data-driven + geological verification closed-loop analysis system, improving the reliability of provenance analysis results and providing more accurate and efficient technical support for the reconstruction of provenance systems of complex geological bodies, the evolution of paleogeographic patterns, and resource exploration.

[0132] like Figure 6 As shown, Figure 6 This is a schematic diagram of the composition structure of the electronic device 600 provided in the embodiments of this application. The electronic device 600 includes:

[0133] The device 600 includes a processor 601, a storage medium 602, and a bus 603. The storage medium 602 stores machine-readable instructions executable by the processor 601. When the electronic device 600 is running, the processor 601 communicates with the storage medium 602 via the bus 603. The processor 601 executes the machine-readable instructions to perform the steps of the local singularity-based source analysis visualization method described in the embodiments of this application.

[0134] In practical applications, the various components in the electronic device 600 are coupled together via a bus 603. It is understood that the bus 603 is used to achieve communication between these components. In addition to a data bus, the bus 603 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 6 The general designated all buses as Bus 603.

[0135] The aforementioned electronic equipment significantly improves the overall efficiency of provenance analysis through local singularity analysis, singularity index MDS quantification, and multi-dimensional data verification. Compared with existing technologies, it can transform the masked weak provenance signals in the detrital zircon U-Pb age spectrum into quantifiable singularity index anomalies, improving the weak anomaly identification rate. By calculating the KS distance and reducing the dimensionality of MDS, it solves the problem of ambiguous provenance distinction caused by the loss of weak information in traditional MDS, improving the visualization accuracy of provenance affinity and clearly identifying fine migration paths missed by traditional methods, such as "XRY→GBC" and "HTW→MT". At the same time, it integrates Hf isotope data and geological background to form a data-driven + geological verification closed-loop analysis system, improving the reliability of provenance analysis results and providing more accurate and efficient technical support for the reconstruction of provenance systems of complex geological bodies, the evolution of paleogeographic patterns, and resource exploration.

[0136] This application also provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by at least one processor 601, the source analysis visualization method based on local singularities described in this application is implemented.

[0137] In some embodiments, the storage medium may be a magnetic random access memory (FRAM), a read-only memory (ROM), or a programmable read-only memory (PROM). Erasable Programmable Read-Only Memory (EPROM) Electrically Erasable Programmable Read-Only Memory (EEPROM) Read-only memory, flash memory, magnetic surface storage, optical disc, or CD-ROM ROM, Compact Disc Read It can be a memory such as a memory only; or it can be a device that includes one or any combination of the above-mentioned memories.

[0138] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0139] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborating files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0140] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0141] The aforementioned computer-readable storage medium significantly improves the overall efficiency of provenance analysis through local singularity analysis, singularity index MDS quantification, and multi-dimensional data verification. Compared with existing technologies, it can transform the masked weak provenance signals in the detrital zircon U-Pb age spectrum into quantifiable singularity index anomalies, improving the weak anomaly identification rate. By calculating the KS distance and reducing the dimensionality of MDS, it solves the problem of ambiguous provenance distinction caused by the loss of weak information in traditional MDS, improving the visualization accuracy of provenance affinity and clearly identifying fine migration paths missed by traditional methods, such as "XRY→GBC" and "HTW→MT". At the same time, it integrates Hf isotope data and geological background to form a data-driven + geological verification closed-loop analysis system, improving the reliability of provenance analysis results and providing more accurate and efficient technical support for the reconstruction of provenance systems of complex geological bodies, the evolution of paleogeographic patterns, and resource exploration.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed methods and electronic devices can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0143] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0145] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0146] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A visualization method for source analysis based on local singularities, characterized in that, Includes the following steps: Obtain the detrital zircon U-Pb age dataset of the target area, preprocess the dataset and divide it into age intervals; Perform local singularity analysis on each age range, calculate the singularity index corresponding to each age range, and construct a local singularity spectrum; Multidimensional scaling (MDS) analysis is performed based on the singularity index to obtain visualization results in a low-dimensional space. By combining geological background information, the visualization results are interpreted to determine the source relationship and material migration path; The multidimensional scaling analysis includes: The singularity index of each age range is represented as a high-dimensional vector; Calculate the difference distance between high-dimensional vectors to form a distance matrix; Based on the characteristics of the distance matrix, classic MDS, metric MDS, or non-metric MDS are selected for dimensionality reduction to obtain two-dimensional or three-dimensional visualized coordinates.

2. The method according to claim 1, characterized in that, The preprocessing of the dataset includes removing outlier data. The age range is divided continuously with a fixed step size, ranging from 1 to 100 Ma.

3. The method according to claim 1, characterized in that, The local singularity analysis includes: A multi-scale analysis window with increasing scale is constructed with each age range as the center. Calculate the average age density under each scale window and establish a power-law relationship between the average age density and the window scale; The singularity index was obtained by fitting a linear regression, with the logarithm of the window scale as the independent variable and the logarithm of the mean age density as the dependent variable.

4. The method according to claim 3, characterized in that, The scale n of the multi-scale analysis window is greater than or equal to 1. When the scale is n, the window covers 2n-1 consecutive age intervals, and the maximum value of n is 3-10.

5. The method according to claim 1, characterized in that, The difference distance is calculated using the KS test and is defined as the maximum vertical deviation between the kernel density estimation functions corresponding to two high-dimensional vectors.

6. The method according to claim 1, characterized in that, It also includes a step of verification using detrital zircon Hf isotope data, which includes depleted crustal model age (TDM) and initial εHf value.

7. A visualization device for source analysis based on local singularities, characterized in that, The device includes: The preprocessing module is used to obtain the detrital zircon U-Pb age dataset of the target area, preprocess the dataset, and divide it into age intervals; The module is used to perform local singularity analysis on each age range, calculate the singularity index corresponding to each age range, and construct the local singularity spectrum. The analysis module is used to perform multidimensional scaling analysis (MDS) based on the singularity index to obtain visualization results in a low-dimensional space. The multidimensional scaling analysis includes: representing the singularity index of each age range as a high-dimensional vector; calculating the difference distance between the high-dimensional vectors to form a distance matrix; and selecting classical MDS, metric MDS, or non-metric MDS for dimensionality reduction based on the characteristics of the distance matrix to obtain two-dimensional or three-dimensional visualization coordinates. The interpretation module is used to interpret the visualization results in conjunction with geological background information to determine the source relationship and material migration path.

8. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the local singularity-based source analysis visualization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the source analysis visualization method based on local singularities as described in any one of claims 1 to 6.

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