Coverage area rock augmentation height quantitative recovery method based on big data

Abstract

The invention relates to the technical field of applied geology, and discloses a coverage area rock augmentation height quantitative recovery method based on big data, which comprises the following steps: S1, obtaining a clastic zircon sample in a sedimentary basin of a research area, measuring U-Pb age and trace element data of the clastic zircon sample, and constructing a multi-dimensional clastic zircon database; s2, screening the data in the step S1, and removing Th / Ult; 0.1, Legt; the zircon data is 1 ppm, and the zircon from the S-type granite source is excluded. According to the coverage area rock augmentation height quantitative recovery method based on the big data, a technical chain of earth crust thickness inversion-paleo-elevation reconstruction is constructed based on multi-dimensional chipping zircon geochemical data, the denudation preservation state and development potential of mineral resources can be quantitatively analyzed, the method has universal guiding significance for deep prospecting, and the method is suitable for large-scale popularization and application. In the aspects of resource economy evaluation and exploration decision making, direct correlation analysis of denudation amount and buried depth data is realized.

Description

Technical Field

[0001] This invention relates to the field of applied geology technology, specifically to a method for quantitatively restoring the uplift height of rocks in covered areas based on big data. Background Technology

[0002] As the core material foundation for strategic emerging industries such as new energy and high-end equipment manufacturing, the exploration and development of key minerals are limited by the preservation state of the ore bodies—the exposed ore bodies on the surface are only the remnants of the original uplift height, and the assessment of deep mineral exploration potential requires the reconstruction of a complete uplift-erosion history. Traditional methods rely on magmatic rock samples (such as whole-rock Sr / Y ratios and paleontological isotopes) or thermochronological drilling data, which have significant drawbacks: 1. Topographical limitations: it is difficult to obtain representative samples in dangerous areas such as plateaus and glaciers; 2. High costs: the cost of a single drilling operation is relatively expensive; 3. Spatiotemporal discontinuities: discrete data can only construct step-like evolution curves and cannot continuously invert the uplift process.

[0003] Furthermore, existing technologies have failed to resolve the core contradiction in ore body evaluation—the disconnect between erosion amount and burial depth parameters. For example, whole-rock chemistry only reflects local crustal thickness and cannot correlate with the preservation potential of deep ore bodies; while the g1 ide algorithm can calculate erosion rate, it relies on dense boreholes and is difficult to cover large-area coverage areas, resulting in a prediction accuracy of less than 50% for deep target areas. Summary of the Invention

[0004] To address the problems mentioned in the background section, this invention provides the following technical solution: a method for quantitatively restoring the uplift height of rocks in covered areas based on big data, comprising the following steps:

[0005] S1. Obtain detrital zircon samples from sedimentary basins in the study area, determine their U-Pb ages and trace element data, and construct a multi-dimensional detrital zircon database.

[0006] S2. Filter the data from step S1, remove zircon data with Th / U < 0.1 and La > 1ppm, and exclude zircon from S-type granite sources;

[0007] S3. Based on the filtered data, calculate the zircon Eu outliers:

[0008]

[0009] S4. Inverting crustal thickness based on zircon Eu anomalies:

[0010] H cal = (84.2 ± 9.2) × Eu* + (24.5 ± 3.3)

[0011] Where H cal Thickness of the Earth's crust (unit: km);

[0012] S5. Calculation of ancient elevations based on the Airy equilibrium principle:

[0013]

[0014] Where h is the ancient elevation (unit: km), H ref =35km, ρ m =3.27g / cm 3 , ρ c =2.67g / cm 3 ;

[0015] S6. Based on thermal chronology data, calculate the regional erosion rate vector using the Glide inversion algorithm.

[0016] S7, combined with paleoelevation h and erosion rate Calculate the rock uplift, cumulative erosion, and erosion thickness;

[0017] S8. Output quantitative recovery results of rock uplift height in the covered area.

[0018] Preferably, the detrital zircon sample in step S1 includes river sand, weathered layer, or detrital zircon U-Pb age and trace element data from a publicly available database.

[0019] Preferably, the data filtering in step S2 further includes: removing the highest and lowest 10% outliers from the Eu / Eu* data within each 5 million-year time window.

[0020] Preferably, the glide inversion algorithm in step S6 specifically includes:

[0021] Construct the time-space matrix A and the erosion rate vector

[0022]

[0023] Solving using a Bayesian inversion framework:

[0024]

[0025] in Let C be the prior erosion rate, and C be the covariance matrix. ∈ This is the error matrix.

[0026] Preferably, the calculation formula in step S7 is:

[0027] Rock uplift:

[0028] Cumulative erosion:

[0029] Erosion thickness:

[0030] Preferably, the output results in step S8 include: a spatiotemporal distribution map of the ancient Gao program sequence;

[0031] Crustal thickness evolution curve; erosion rate thermogram; ore body preservation potential evaluation map.

[0032] Preferably, the thermochronological data in step S6 is used to obtain the closure depth z by solving the heat conduction equation. c :

[0033]

[0034] Where κ is the thermal diffusivity and T is the temperature field.

[0035] Preferably, the ore body preservation potential evaluation map in step S8 is generated according to the following rules: when the cumulative erosion amount is less than the ore body burial depth, it is marked as a high-potential prospecting target area; when the cumulative erosion amount is greater than or equal to the ore body burial depth, it is marked as a low-potential area.

[0036] Compared with existing technologies, this invention provides a method for quantitatively restoring the uplift height of rocks in covered areas based on big data, which has the following beneficial effects:

[0037] 1. This method for quantitatively restoring the uplift height of rocks in covered areas based on big data, constructing a "crustal thickness inversion-paleoelevation reconstruction" technical chain based on multi-dimensional detrital zircon geochemical data, can quantitatively analyze the erosion and preservation status and development potential of mineral resources. It has universal guiding significance for deep mineral exploration. In terms of resource economic evaluation and exploration decision-making, this invention achieves direct correlation analysis between erosion volume and burial depth data. If the erosion volume of a ore-bearing rock mass is close to or exceeds the burial depth, it indicates that most of its ore has been eroded and lost, with limited remaining resources and low economic value. Conversely, if the erosion volume is significantly less than the burial depth, there may still be large-scale concealed ore bodies at depth, possessing high exploration potential. Production departments can use this information to scientifically determine whether to continue exploration or mining, effectively reducing the risk of blind exploration and significantly improving resource development efficiency.

[0038] 2. This method for quantitatively restoring the uplift height of rocks in covered areas based on big data reveals the spatiotemporal differences in the regional tectonic-erosion process by comparing the differences in erosion and uplift between different mining areas from the perspective of regional tectonic evolution. For example, areas with low erosion may retain more complete deep ore-bearing rock bodies. If the erosion rate matches the crustal thickness variation with a specific tectonic background (such as orogenic belt uplift), the spatial distribution pattern of concealed ore bodies can be predicted. This method provides a quantitative basis for accurately delineating potential ore-bearing target areas, significantly improving the targeting and success rate of mineral exploration. This technology breaks through the limitations of deposit type and is applicable to various types of deposits such as magmatic, sedimentary, and metamorphic deposits, providing a new and universal technical solution for mineral resource exploration. Attached Figure Description

[0039] Figure 1 This is a flowchart of the algorithm of the present invention;

[0040] Figure 2 This is a schematic diagram of the analysis results for the Gangdise region. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1-2 This invention provides a technical solution: a method for quantitatively restoring the uplift height of rocks in covered areas based on big data, comprising the following steps:

[0043] S1. Obtain detrital zircon samples from sedimentary basins in the study area, determine their U-Pb ages and trace element data, and construct a multi-dimensional detrital zircon database.

[0044] S2. Filter the data from step S1, remove zircon data with Th / U < 0.1 and La > 1ppm, and exclude zircon from S-type granite sources;

[0045] S3. Based on the filtered data, calculate the zircon Eu outliers:

[0046]

[0047] S4. Inverting crustal thickness based on zircon Eu anomalies:

[0048] H cal = (84.2 ± 9.2) × Eu * +(24.5±3.3)

[0049] Where H cal Thickness of the Earth's crust (unit: km);

[0050] S5. Calculation of ancient elevations based on the Airy equilibrium principle:

[0051]

[0052] Where h is the ancient elevation (unit: km), H ref =35km, ρ m =3.27g / cm 3 , ρ c =2.67g / cm 3 ;

[0053] S6. Based on thermal chronology data, calculate the regional erosion rate vector using the Glide inversion algorithm.

[0054] S7, combined with paleoelevation h and erosion rate Calculate the rock uplift, cumulative erosion, and erosion thickness;

[0055] S8. Output quantitative recovery results of rock uplift height in the covered area.

[0056] Furthermore, the detrital zircon samples in step S1 include river sand, weathered layers, or detrital zircon U-Pb age and trace element data from publicly available databases.

[0057] Furthermore, the data filtering in step S2 also includes: removing the highest and lowest 10% outliers from the Eu / Eu* data within each 5 million-year time window.

[0058] Furthermore, the glide inversion algorithm in step S6 specifically includes:

[0059] Construct the time-space matrix A and the erosion rate vector

[0060]

[0061] Solving using a Bayesian inversion framework:

[0062]

[0063] in Let C be the prior erosion rate, and C be the covariance matrix. ∈ This is the error matrix.

[0064] Furthermore, the calculation formula in step S7 is as follows:

[0065] Rock uplift:

[0066] Cumulative erosion:

[0067] Erosion thickness:

[0068] Furthermore, the output results in step S8 include: a spatiotemporal distribution map of the ancient Gao program sequence;

[0069] Crustal thickness evolution curve; erosion rate thermogram; ore body preservation potential evaluation map.

[0070] Furthermore, the thermochronological data in step S6 are used to obtain the closure depth z by solving the heat conduction equation. c :

[0071]

[0072] Where κ is the thermal diffusivity and T is the temperature field.

[0073] Furthermore, the orebody preservation potential evaluation map in step S8 is generated according to the following rules: when the cumulative erosion amount is less than the orebody burial depth, it is marked as a high-potential prospecting target area; when the cumulative erosion amount is greater than or equal to the orebody burial depth, it is marked as a low-potential area.

[0074] The technical solution of the present invention will be described in detail below with reference to embodiments. Those skilled in the art can implement the present invention based on this description. The embodiments take the recovery of the uplift height of the eastern Gangdise ore body as an example, but the application of the present invention is not limited to this area.

[0075] 1. Data Acquisition and Processing of Detrital Zircon

[0076] Step 1: Non-invasive sample acquisition

[0077] Sampling strategy: Divide the river basin into 5 sub-regions (W1-W5) and collect zircon samples from river sand debris (single-point sampling amount ≥500g);

[0078] Data integration: Supplementing publicly available databases (such as EarthChem) with eligible zircon U-Pb ages and trace element data;

[0079] Instrument configuration: LA-ICP-MS (laser ablation inductively coupled plasma mass spectrometry) was used to test the U-Pb age and rare earth element content (Eu, Sm, Gd, etc.) of zircon.

[0080] Step 2: Data Filtering

[0081] Perform four levels of filtering to ensure data reliability:

[0082] Remove metamorphic zircons with Th / U ratio < 0.1 (to avoid interference from Th-enriched minerals);

[0083] Exclude contamination data with La > 1 ppm (eliminate the influence of inclusions);

[0084] Identify and remove zircon from S-type granite (criterion: phosphorus content > 50 ppm);

[0085] For every 5 million years, the highest / lowest 10% of Eu / Eu* outliers are removed (to enhance fault tolerance).

[0086] 2. Calculation of key parameters

[0087] Step 3: Zircon Eu Anomaly Calculation

[0088] Calculated using standardized formulas:

[0089]

[0090] Input: Concentration data of trace elements after screening (unit: ppm);

[0091] Output: Eu* value range 0.42-0.68 (high pressure environment value > 0.6).

[0092] Step 4: Crustal Thickness Inversion

[0093] Based on the empirical model of Tang et al. (2020):

[0094] H cal =84.2×Eu * +24.5 (±9.2, ±3.3)

[0095] Results: The crustal thickness in the eastern Gangdise region is 55-82 km. Figure 2 (Color scale diagram);

[0096] Verification: The error with the seismic wave inversion results is <8% (80 Ma period).

[0097] Step 5: Ancient Elevation Reconstruction

[0098] Transformation based on the Airy equalization principle:

[0099]

[0100] Parameter: Reference thickness H _ ref = 35km, mantle density ρ_m = 3.27g / cm³ 3 The density of the Earth's crust, ρ_c, is 2.67 g / cm³. 3 ;

[0101] Output: Ancient elevation 3.7-8.6km

[0102] 3. Analysis of erosion rate and uplift

[0103] Step 6: Use the Glide algorithm to invert the erosion rate

[0104] Input parameters:

[0105] Thermodynamic parameters: Surface temperature 0℃, basement temperature 1120℃, thermal diffusivity 20km 2 / Myr;

[0106] Prior value: Initial erosion rate Covariance σ_pr = 7;

[0107] Matrix construction:

[0108]

[0109] Inversion solution:

[0110]

[0111] Result: Erosion rate vector ( Figure 2 Heat map, red area > 1km / Myr).

[0112] Step 7: Restoration of Height

[0113] Calculate three major quantitative indicators:

[0114]

[0115] 4. Application in mineral exploration decision-making

[0116] Step 8: Visualization and Target Delineation

[0117] Tools: GMT plotting library for generating spatiotemporal distribution maps ( Figure 2 );

[0118] Decision-making rules:

[0119] High potential areas: erosion amount < burial depth (e.g., W2 basin: 4.3km < 5.2km);

[0120] Risk zone: Erosion amount > burial depth (e.g., W4 watershed: 6.1km > 4.8km).

[0121] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for quantitatively restoring the height of rock uplift in covered areas based on big data, characterized in that, Includes the following steps: S1. Obtain detrital zircon samples from sedimentary basins in the study area, determine their U-Pb ages and trace element data, and construct a multi-dimensional detrital zircon database. S2. Filter the data from step S1, remove zircon data with Th / U < 0.1 and La > 1ppm, and exclude zircon from S-type granite sources; S3. Based on the filtered data, calculate the zircon Eu outliers: S4. Inverting crustal thickness based on zircon Eu anomalies: H cal =(84.2±9.2)×Eu * +(24.5±3.3) Where H cal Thickness of the Earth's crust (unit: km); S5. Calculation of ancient elevations based on the Airy equilibrium principle: Where h is the ancient elevation (unit: km), H ref =35km, ρ m =3.27g / cm 3 , ρ c =2.67g / cm 3 ; S6. Based on thermal chronology data, calculate the regional erosion rate vector using the Glide inversion algorithm. S7, combined with paleoelevation h and erosion rate Calculate the rock uplift, cumulative erosion, and erosion thickness; S8. Output quantitative recovery results of rock uplift height in the covered area.

2. The method for quantitatively restoring the height of rock uplift in covered areas based on big data according to claim 1, characterized in that, The detrital zircon samples in step S1 include river sand, weathered layers, or detrital zircon U-Pb age and trace element data from publicly available databases.

3. The method for quantitatively restoring the height of rock uplift in covered areas based on big data according to claim 1, characterized in that, The data filtering in step S2 also includes: removing the highest and lowest 10% outliers from the Eu / Eu* data within each 5 million-year time window.

4. The method for quantitatively restoring the height of rock uplift in covered areas based on big data according to claim 1, characterized in that, The glide inversion algorithm in step S6 specifically includes: Construct the time-space matrix A and the erosion rate vector Solving using a Bayesian inversion framework: in Let C be the prior erosion rate, and C be the covariance matrix. ∈ This is the error matrix.

5. The method for quantitatively restoring the height of rock uplift in covered areas based on big data according to claim 1, characterized in that, The calculation formula in step S7 is: Rock uplift: Cumulative erosion: Erosion thickness:

6. The method for quantitatively restoring the height of rock uplift in covered areas based on big data according to claim 1, characterized in that, The output results in step S8 include: a spatiotemporal distribution map of the ancient high-altitude sequence; a crustal thickness evolution curve; a thermal map of erosion rate; and an evaluation map of the ore body preservation potential.

7. The method for quantitatively restoring the height of rock uplift in covered areas based on big data according to claim 1, characterized in that, The thermochronological data in step S6 is used to obtain the closure depth z by solving the heat conduction equation. c : Where κ is the thermal diffusivity and T is the temperature field.

8. The method for quantitatively restoring the height of rock uplift in covered areas based on big data according to claim 1, characterized in that, In step S8, the ore body preservation potential evaluation map is generated according to the following rules: when the cumulative erosion amount is less than the ore body burial depth, it is marked as a high-potential prospecting target area; when the cumulative erosion amount is greater than or equal to the ore body burial depth, it is marked as a low-potential area.

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