Method for predicting physical properties of surface matrix in mountainous area

Abstract

The invention discloses a mountainous area surface matrix physical property prediction method, and relates to the technical field of geological exploration, and the method comprises the steps: obtaining mountainous area surface point cloud data, separating vegetation from ground points through a point cloud classification algorithm, and generating a digital elevation model and a digital surface model; based on the digital elevation model and the digital surface model, a quantum gravity gradiometer is adopted to move along a contour line for measurement, a gravity gradient tensor is obtained, terrain interference signals are eliminated, and a vertical density profile is obtained through inversion; identifying a density abnormal region according to the vertical density profile, acquiring an undisturbed soil column sample by adopting a drilling unit, determining a grain composition and moisture characteristic curve, performing DNA extraction and microorganism sequencing on the undisturbed soil column sample, and calculating the relative abundance of the nitro spirillum phylum. According to the method, the dynamic coupling model is constructed through fusion of quantum gravity gradient measurement and microbiological omics data, and the problem of insufficient vertical density-permeability coefficient coupling modeling precision of a traditional method is solved.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and in particular to a method for predicting the physical properties of surface matrix in mountainous areas. Background Technology

[0002] Currently, the prediction of the physical properties of surface matrix in mountainous areas mainly relies on the combination of geophysical exploration and remote sensing technologies. This involves acquiring three-dimensional point cloud data of the surface using unmanned aerial vehicles (UAVs), generating digital elevation models using Kriging interpolation algorithms, and then using ground resistivity measurement data to invert the density distribution of shallow matrix. This technology enables spatial prediction of parameters such as surface matrix porosity and permeability coefficient, showing high accuracy, especially in the characterization of shallow (<5m) matrix in vegetated areas. However, this method has significant limitations in predicting deep matrix (>10m). Its inversion model, relying on single physical field data, struggles to accurately characterize vertical density heterogeneity and fails to consider the influence of microbial activity on permeability, leading to widespread deviations in deep permeability coefficient prediction errors.

[0003] The shortcomings of existing technologies are mainly reflected in the insufficient accuracy of vertical density-permeability coupling modeling. Density and permeability are usually regarded as a static linear relationship, ignoring the dynamic modification of pore structure by microbial metabolism. Nitrifying spirilla can significantly change matrix pore connectivity by secreting extracellular polymers, but its impact has not been quantified and incorporated into the prediction model. The correction of topographic disturbances mostly adopts the empirical coefficient method, lacking a collaborative calculation mechanism with high-precision DEM, which further restricts the reliability of deep matrix prediction. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for predicting the physical properties of surface matrix in mountainous areas, which solves the problem of insufficient accuracy in the vertical density-permeability coefficient coupled modeling in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for predicting the physical properties of surface matrix in mountainous areas, comprising,

[0008] Acquire point cloud data of mountainous areas, separate vegetation and ground points using point cloud classification algorithms, and generate digital elevation models and digital surface models.

[0009] Based on the digital elevation model and digital surface model, a quantum gravity gradiometer is used to move along the contour line to measure the gravity gradient tensor and eliminate the terrain interference signal, and the vertical density profile is obtained by inversion.

[0010] Density anomaly zones were identified based on the vertical density profile. Uncirculated soil column samples were obtained using drilling units. Particle size distribution and moisture characteristic curves were measured. DNA was extracted and microbial sequencing was performed on the uncirculated soil column samples. The relative abundance of Nitrifying Spirochetes was calculated. A permeability correction model was established based on the vertical density profile, and the predicted permeability value was calculated.

[0011] The digital elevation model, vertical density profile, and permeability coefficient correction model are input into the geographic information unit, and a spatial interpolation algorithm is used to generate a spatial distribution map of the permeability coefficient.

[0012] Pumping tests were conducted in high-permeability zones based on the spatial distribution map of permeability coefficients to obtain measured permeability coefficients. The relative error between the measured permeability coefficients and the predicted permeability coefficients was calculated, and a verification report was generated.

[0013] As a preferred embodiment of the method for predicting the physical properties of surface matrix in mountainous areas according to the present invention, the method includes the following steps: acquiring point cloud data of the mountainous surface, separating vegetation and ground points using a point cloud classification algorithm, and generating a digital elevation model and a digital surface model.

[0014] A drone equipped with a lidar was used to acquire high-density point cloud data in mountainous areas. Outlier filtering was performed on the high-density point cloud data in mountainous areas to obtain clean point cloud data.

[0015] Based on clean point cloud data, a cloth simulation filtering algorithm is used to separate ground points from non-ground points;

[0016] Irregular triangular mesh interpolation is performed on ground points to generate a digital elevation model, and inverse distance weighted interpolation is performed on high-density point cloud data in mountainous areas to generate a digital surface model.

[0017] As a preferred embodiment of the method for predicting the physical properties of surface matrix in mountainous areas according to the present invention, the method includes the following steps: based on a digital elevation model and a digital surface model, a quantum gravity gradiometer is used to move along contour lines to measure the gravity gradient tensor and eliminate terrain interference signals, and the vertical density profile is obtained by inversion.

[0018] Based on digital elevation model and digital surface model, slope angle is calculated and terrain correction weight map is generated;

[0019] Contour lines are extracted using a digital elevation model, the movement path of a quantum gravity gradiometer is planned, the quantum gravity gradiometer is moved along the planned path, gravity gradient tensor and BeiDou positioning data are collected, and the terrain correction weight map is combined to eliminate terrain gravity interference in the digital elevation model, thus obtaining corrected gravity gradient data.

[0020] The corrected gravity gradient data were processed using a Bayesian inversion algorithm to obtain the vertical density profile.

[0021] As a preferred embodiment of the method for predicting the physical properties of surface matrix in mountainous areas according to the present invention, the method includes the following steps: identifying density anomaly zones based on vertical density profiles, obtaining undisturbed soil column samples using drilling units, and determining particle size distribution and moisture characteristic curves.

[0022] Based on the vertical density profile, the absolute deviation and gradient marker density anomaly areas are calculated;

[0023] Drilling points are set up at the center and edge points of the density anomaly zone, and the priority of the drilling points is calculated.

[0024] Using a drilling rig and KML navigation positioning, undisturbed soil column samples were obtained in layers. The undisturbed soil column samples were screened, and the clay, silt, and sand content were determined using a Malvern Mastersizer 3000 laser particle size analyzer. A pressure plate apparatus was used to determine the moisture characteristic curve.

[0025] As a preferred embodiment of the method for predicting the physical properties of surface matrix in mountainous areas according to the present invention, the method includes the following steps: extracting DNA and sequencing microorganisms from undisturbed soil columns, calculating the relative abundance of Nitrifying Spirulina, establishing a permeability coefficient correction model based on vertical density profiles, and calculating the predicted permeability coefficient.

[0026] The undisturbed soil column sample was cut into layers, and three parallel samples were taken from each layer and stored in an ultra-low temperature environment to obtain the layer pattern.

[0027] DNA was extracted from each soil sample using the PowerSoil Pro Kit. After purification using a silica membrane, a DNA solution was obtained. The DNA solution was then amplified by PCR using 515F and 806R primers. The amplified products were then subjected to paired-end sequencing to obtain sequencing data.

[0028] Based on sequencing data, the relative abundance of the phylum Nitrifying Spirogyra was calculated using the QIIME2 process;

[0029] By combining the relative abundance of Nitrifying Spirulina with the vertical density profile, a permeability correction model was established, and the predicted permeability value was calculated.

[0030] As a preferred embodiment of the method for predicting the physical properties of surface matrix in mountainous areas according to the present invention, the method includes the following steps: inputting a digital elevation model, a vertical density profile, and a permeability coefficient correction model into a geographic information unit, and generating a spatial distribution map of the permeability coefficient using a spatial interpolation algorithm.

[0031] The digital elevation model, vertical density profile, and permeability coefficient correction model were uniformly converted to the CGCS2000 coordinate system to obtain a three-dimensional permeability coefficient matrix.

[0032] The empirical Bayesian Skripal algorithm is used to spatially interpolate the horizontal profile in the three-dimensional permeability coefficient matrix to obtain the horizontal interpolated permeability coefficient field. Piecewise cubic Hermite interpolation is used to smooth the vertical profile of the three-dimensional permeability coefficient matrix to obtain the vertical interpolated permeability coefficient field.

[0033] Based on the slope angle, the three-dimensional permeability coefficient field is corrected, and a spatial distribution map of the permeability coefficient is generated by slicing according to depth.

[0034] As a preferred embodiment of the method for predicting the physical properties of surface matrix in mountainous areas according to the present invention, the method includes the following steps: conducting pumping tests in high-permeability zones based on a spatial distribution map of permeability coefficients to obtain measured permeability coefficients.

[0035] The boundary of the high-permeability target area was selected from the spatial distribution map of the permeability coefficient, and a complete well was drilled. Three radial survey lines were laid out, and Diver water level gauges were installed to obtain dynamic water level monitoring data. The Theis formula was used to invert the dynamic water level monitoring data to obtain the measured permeability coefficient.

[0036] As a preferred embodiment of the method for predicting the physical properties of surface matrix in mountainous areas according to the present invention, the method includes the following steps: calculating the relative error between the measured permeability coefficient and the predicted permeability coefficient, and generating a verification report.

[0037] The relative error value is calculated based on the measured permeability coefficient and the predicted permeability coefficient.

[0038] A relative error threshold is set based on the relative error value. When the relative error value is less than the relative error threshold, it is considered excellent; when the relative error value is equal to the relative error threshold, it is considered qualified; and when the relative error value is greater than the relative error threshold, it is considered unqualified. The boundaries of the high-permeability target area are graded and labeled to generate an error level distribution map.

[0039] The error level distribution map and relative error values ​​are integrated to generate a verification report.

[0040] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the method for predicting the physical properties of surface matrix in mountainous areas as described in the first aspect of the present invention.

[0041] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein, when executed by a processor, the computer program implements any step of the method for predicting the physical properties of surface matrix in mountainous areas as described in the first aspect of the present invention.

[0042] The beneficial effects of this invention are as follows: By fusing quantum gravity gradient measurement with microbiome data, a dynamic coupling model is constructed, which solves the problem of insufficient accuracy in vertical density-permeability coefficient coupling modeling of traditional methods. By using a quantum gravity gradient meter combined with high-precision digital elevation model terrain correction, the vertical density inversion resolution reaches 1m. The abundance of Nitrifying Spirochetes is incorporated as a microbial activity indicator into the permeability coefficient model. Through closed-loop verification by pumping tests, the entire process from point cloud data acquisition to the generation of permeability coefficient spatial distribution map is standardized. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a method for predicting the physical properties of surface matrix in mountainous areas.

[0045] Figure 2 This is a schematic diagram of a digital surface model and a digital elevation model.

[0046] Figure 3 This is a flowchart showing the relative abundance of the phylum Nitrifying Spirulina.

[0047] Figure 4 This is a schematic diagram for verifying the report. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0051] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for predicting the physical properties of surface matrix in mountainous areas, including the following steps:

[0052] S1. Obtain point cloud data of mountainous areas, separate vegetation and ground points through point cloud classification algorithm, and generate digital elevation model and digital surface model.

[0053] S1.1 Use a drone equipped with a lidar to acquire high-density point cloud data in mountainous areas, and perform outlier filtering on the high-density point cloud data in mountainous areas to obtain clean point cloud data.

[0054] Furthermore, a drone equipped with a lidar was used to fly along a pre-planned route to acquire high-density point cloud data of the mountainous surface. The flight altitude was set to 100 meters, and the point density reached 240 points per square meter. The acquired high-density point cloud data of the mountainous surface was processed using a statistical outlier filtering algorithm to remove abnormal points that deviate from the main distribution of the point cloud, resulting in clean point cloud data.

[0055] S1.2 Based on clean point cloud data, a cloth simulation filtering algorithm is used to separate ground points from non-ground points.

[0056] Furthermore, based on clean point cloud data, a fabric simulation filtering algorithm is used to separate ground points from non-ground points. The fabric resolution is set to 0.5 meters and the rigidity level to 3. By simulating the physical process of the fabric covering the point cloud surface, the point cloud is divided into ground points and non-ground points.

[0057] S1.3. Perform irregular triangular mesh interpolation on ground points to generate a digital elevation model, and perform inverse distance weighted interpolation on high-density point cloud data in mountainous areas to generate a digital surface model.

[0058] Furthermore, irregular triangular mesh interpolation is performed on the ground points. The Delaunay triangulation algorithm is used to connect adjacent ground points to construct a triangular mesh. A digital elevation model is generated through linear interpolation. For high-density point cloud data in mountainous areas, an inverse distance weighted interpolation algorithm is used, with a search radius of 5 meters and a power parameter of 2. The elevation value of each grid point is calculated to generate a digital surface model.

[0059] S2. Based on the digital elevation model and digital surface model, a quantum gravity gradiometer is used to move along the contour line to measure the gravity gradient tensor and eliminate terrain interference signals, and the vertical density profile is obtained by inversion.

[0060] S2.1. Based on the digital elevation model and digital surface model, calculate the slope angle and generate a terrain correction weight map.

[0061] Specifically, the expression is,

[0062]

[0063] in, The slope angle, Rate of change of elevation in the y-direction Let x be the rate of change of elevation in the x-direction. Let z be the rate of change of elevation in the z-direction. This represents the rate of change of elevation.

[0064] It should be noted that the calculated slope angle is normalized to obtain the terrain correction weight map.

[0065] S2.2. Use the digital elevation model to extract contour lines, plan the movement path of the quantum gravity gradiometer, move the quantum gravity gradiometer along the planned movement path, collect gravity gradient tensor and BeiDou positioning data, combine with the terrain correction weight map to eliminate terrain gravity interference of the digital elevation model, and obtain the corrected gravity gradient data.

[0066] Specifically, the expression is,

[0067]

[0068] in, For the ll component of the corrected gravity gradient tensor, To observe the ll component of the gravity gradient tensor, G is the gravitational constant, ρ0 is the average density, h(x′,y′) is the deviation of the elevation of (x′,y′) in the digital elevation model from the reference surface, r is the horizontal distance, dx′ is the length element along the x′ direction, and dy′ is the width element along the y′ direction.

[0069] S2.3. The corrected gravity gradient data is processed using the Bayesian inversion algorithm to obtain the vertical density profile.

[0070] Furthermore, the corrected gravity gradient data is processed using a Bayesian inversion algorithm to establish a likelihood function. The posterior distribution is then iteratively solved using a Markov chain Monte Carlo sampling method, with each chain iterating 10,000 times. The mean of the last 2,000 samples is taken as the vertical density profile.

[0071] S3. Identify density anomaly zones based on vertical density profiles, obtain undisturbed soil column samples using drilling units, and determine particle size distribution and moisture characteristic curves.

[0072] S3.1 Calculate the absolute deviation and gradient-marked density anomaly areas based on the vertical density profile.

[0073] Specifically, the expression is,

[0074]

[0075] Where X represents the density anomaly region, ρ(m) represents the current density, dρ represents the density change, and dz represents the depth change in the z-direction.

[0076] S3.2 Drilling points are set up at the center and edge points of the density anomaly zone, and the priority of the drilling points is calculated.

[0077] Specifically, the expression is,

[0078]

[0079] Where P is the probe priority, and Δρ is the absolute difference between the average density ρ0, the absolute density deviation ρ(m), and the current density.

[0080] S3.3 Using a drilling rig, navigate and position according to KML, obtain undisturbed soil column samples in layers, screen the undisturbed soil column samples, use a Malvern Mastersizer 3000 laser particle size analyzer to determine the clay, silt, and sand content, and use a pressure plate apparatus to determine the moisture characteristic curve.

[0081] Furthermore, using a Geoprobe 6620DT drilling rig and locating according to the KML navigation file, boreholes were drilled at the center and edge points of the marked density anomaly area to obtain undisturbed soil column samples with a diameter of 5 cm and a length of 1 m in layers, with sampling depths spaced 10 cm apart. After removing coarse particles by passing the undisturbed soil column samples through a 2 mm sieve, the contents of clay (<0.002 mm), silt (0.002-0.05 mm), and sand (>0.05 mm) were determined using a Malvern Mastersizer 3000 laser particle size analyzer. The test was repeated 3 times and the average value was taken. The moisture characteristic curve of the soil column samples was determined using a 1500 pressure plate apparatus at pressures of 0, 10, 33, 100, 500, and 1500 kPa.

[0082] S4. DNA extraction and microbial sequencing were performed on the undisturbed soil column samples. The relative abundance of Nitrifying Spirochetes was calculated. A permeability correction model was established based on the vertical density profile, and the predicted permeability value was calculated.

[0083] S4.1. The undisturbed soil column sample is cut into layers, and three parallel samples are taken from each layer and stored in an ultra-low temperature environment to obtain a layer pattern.

[0084] Furthermore, the undisturbed soil column sample was cut into layers at 10 cm intervals. Each layer was separated using a sterile ceramic scalpel, and three parallel samples were taken from each layer, each weighing 50 grams. The parallel samples were immediately placed into cryovials pre-cooled to -80°C, each pre-filled with 1 ml of RNAlater solution. After sealing, the cryovials were transferred to an ultra-low temperature freezer at -80°C for storage. The stratified samples were numbered sequentially by depth, and the depth of the sampling location from the top was recorded (accurate ±1 mm). A stratification record table containing the depth number, storage location, and sampling time was generated. The sample numbers were labeled on the outside of the cryovials, and the record table strictly corresponded to the cryovial numbers. All operations were performed in a 4°C clean bench to avoid sample contamination and DNA degradation.

[0085] S4.2. DNA was extracted from each soil layer using the PowerSoil Pro Kit. After purification with a silica membrane, a DNA solution was obtained. PCR amplification of the DNA solution was performed using 515F and 806R primers. The amplified products were then subjected to paired-end sequencing to obtain sequencing data.

[0086] Furthermore, DNA was extracted from each soil layer using the PowerSoil Pro Kit. 0.25 g of frozen soil sample was weighed and added to C1 buffer, vortexed for 10 minutes (3000 rpm), and then incubated at 65°C for 10 minutes to lyse cells. After centrifugation, the supernatant was passed through a silica membrane to adsorb DNA, and 50 μL of DNA solution was obtained by elution. PCR amplification was performed on the DNA solution using 515F and 806R primers. The reaction volume was 25 μL (containing 12.5 μL of 2×Taq Master Mix, 1 μL of each primer, and 2 μL of DNA template). The amplification program was: 95°C for 3 minutes; 35 cycles (95°C for 30 seconds, 55°C for 30 seconds, 72°C for 45 seconds); 72°C for 10 minutes. After purification with magnetic beads, the amplified products were sequenced at 2×150 bp using the Illumina NovaSeq 6000 platform. ≥50,000 valid sequences were obtained from each sample, generating FASTQ format sequencing data.

[0087] S4.3. Based on sequencing data, the relative abundance of the phylum Nitrifying Spirulina was calculated using the QIIME2 process.

[0088] Specifically, the expression is,

[0089]

[0090] Where A is the relative abundance of the Nitrifying Spirulina phylum, Q is the number of Nitrifying Spirulina sequences, and T is the total number of bacterial sequences.

[0091] S4.4 Combine the relative abundance of Nitrifying Spirochetes with the vertical density profile to establish a permeability correction model and calculate the predicted permeability value.

[0092] Specifically, the expression is,

[0093]

[0094] Among them, K pre ρ(z) represents the predicted permeability coefficient, K0 represents the baseline permeability coefficient, α represents the microbial enhancement coefficient, β represents the density inhibition coefficient, and ρ(z) represents the vertical density profile.

[0095] S5. Vertical density profile and permeability coefficient correction model are input into geographic information units, and spatial interpolation algorithm is used to generate a spatial distribution map of permeability coefficient.

[0096] S5.1. Convert the digital elevation model, vertical density profile, and permeability coefficient correction model to the CGCS2000 coordinate system to obtain the three-dimensional permeability coefficient matrix.

[0097] Furthermore, the digital elevation model, vertical density profile, and permeability correction model were uniformly converted to the CGCS2000 coordinate system using the spatial reference tool of ArcGIS Pro software. The conversion parameters adopted the seven-parameter Bursa model. The converted digital elevation model maintained a 0.05-meter resolution grid structure. The vertical density profile was resampled at 1-meter intervals to the same planar grid as the digital elevation model. The permeability correction model was discretized with the same grid size. The three were then aligned and superimposed according to spatial coordinates to generate a three-dimensional permeability matrix. The matrix cell size was 0.05 meters, and each cell stored the permeability value at the corresponding location. The data was stored in a NetCDF4 layered format.

[0098] S5.2. Using the empirical Bayesian Skripal algorithm, spatial interpolation is performed on the horizontal profile in the three-dimensional permeability coefficient matrix to obtain the horizontal interpolated permeability coefficient field. Piecewise cubic Hermite interpolation is used to smooth the vertical profile of the three-dimensional permeability coefficient matrix to obtain the vertical interpolated permeability coefficient field.

[0099] Furthermore, the empirical Bayesian skrygian algorithm is used to spatially interpolate each horizontal profile in the three-dimensional permeability coefficient matrix. The nugget value of the semi-variogram model is set to 0.02, the sill value to 1.0, the range to 50 meters, and the search radius to 25 meters, generating a horizontally interpolated permeability coefficient field with a resolution of 0.05 meters. Piecewise cubic Hermite interpolation is used on the vertical profile of the three-dimensional permeability coefficient matrix, ensuring the continuity of the first derivative at 1-meter intervals in each depth layer. Non-physical oscillations are eliminated through constraints (the derivative of the current point is equal to the slope between two adjacent points), generating a vertically interpolated permeability coefficient field. The horizontally interpolated permeability coefficient field and the vertically interpolated permeability coefficient field are then fused by weighted calculation according to grid points to obtain the three-dimensional permeability coefficient field.

[0100] S5.3. Based on the slope angle, the three-dimensional permeability coefficient field is corrected, and a spatial distribution map of the permeability coefficient is generated by slicing according to depth.

[0101] Furthermore, based on the slope angle calculated by the digital elevation model, the three-dimensional permeability field is corrected for topography. The corrected three-dimensional permeability field is sliced ​​into three depth ranges: 0-5 meters, 5-10 meters, and 10-20 meters. Each slice generates a spatial distribution map of the permeability coefficient with a resolution of 0.05 meters.

[0102] S6. Based on the spatial distribution map of permeability coefficient, a pumping test was conducted in the high-permeability zone to obtain the measured permeability coefficient.

[0103] The boundary of the high-permeability target area was selected from the spatial distribution map of the permeability coefficient, and a complete well was drilled. Three radial survey lines were laid out, and Diver water level gauges were installed to obtain dynamic water level monitoring data. The Theis formula was used to invert the dynamic water level monitoring data to obtain the measured permeability coefficient.

[0104] Specifically, the expression is,

[0105]

[0106] Among them, K obs U is the measured permeability coefficient, Q is the dynamic water level monitoring data, o is the hydraulic conductivity, and W(u) is the exponential integral function.

[0107] It should be noted that the permeability coefficient values ​​selected in the spatial distribution map are greater than 1×10. -4 A continuous area with a permeability of m / s was used as the high-permeability target area. The boundary coordinates of the target area were extracted to generate an SHP format vector file. A complete well with a diameter of 150 mm was drilled at the geometric center of the high-permeability target area using a Geoprobe 6620DT drilling rig. The filter pipe length matched the aquifer thickness. Three radial survey lines were laid out at a 120-degree angle with the complete well as the center. An observation well was set at a distance of 5 m, 10 m, and 20 m on each survey line, for a total of 9 observation points. Diver CTD water level gauges (accuracy ±1 mm) were installed in all wells to synchronously record water level changes at 1-minute intervals for 72 hours or until the water level fluctuation was less than 1 cm / hour. A water level dynamic monitoring dataset (containing three columns of data: time, well number, and water level depth) was obtained. The data was stored in CSV format and corresponded to the well coordinates.

[0108] S7. Calculate the relative error between the measured permeability coefficient and the predicted permeability coefficient, and generate a verification report.

[0109] S7.1 Calculate the relative error value based on the measured permeability coefficient and the predicted permeability coefficient.

[0110] Specifically, the expression is,

[0111]

[0112] Where δ is the relative error value.

[0113] S7.2. Set a relative error threshold based on the relative error value. When the relative error value is less than the relative error threshold, it is considered excellent. When the relative error value is equal to the relative error threshold, it is considered qualified. When the relative error value is greater than the relative error threshold, it is considered unqualified. Classify and label the boundary of the high-permeability target area and generate an error level distribution map.

[0114] Furthermore, based on the relative error calculated from the measured permeability coefficient and the predicted permeability coefficient, a relative error threshold of 15% was set. The boundaries of high-permeability target areas were divided into three levels according to the error value: areas with a relative error value <15% were marked as excellent (green), areas with a relative error value =15% were marked as qualified (yellow), and areas with a relative error value >15% were marked as unqualified (red). An error level distribution map was generated using the spatial analysis tools of ArcGIS Pro. The boundary vector of the high-permeability target area and the well location coordinates were overlaid on the map, and the error value and level of each area were marked.

[0115] S7.3 Integrate the error level distribution map and relative error values ​​to generate a verification report.

[0116] Furthermore, the error level distribution map and the relative error value calculation results were imported into ArcGIS Pro software for spatial correlation analysis. The error level distribution map was stored in GeoTIFF format, containing spatial distribution information of three types of areas: excellent areas (green, relative error value less than 15%), qualified areas (yellow, relative error value equal to 15%), and unqualified areas (red, relative error value greater than 15%). The relative error value calculation results were stored in CSV format, recording the borehole number, measured permeability coefficient, predicted permeability coefficient, and relative error value of each high-permeability target area boundary. Spatial connections were established in ArcGIS Pro, linking the raster data of the error level distribution map with the attribute table of the relative error value calculation results through the borehole number field, generating a comprehensive dataset containing spatial location and error attributes. A verification report was generated based on the comprehensive dataset. The report included three parts: error spatial distribution map, error statistics table (area proportion of each level area), borehole data comparison table, and model correction suggestions.

[0117] This embodiment also provides a computer device applicable to the method for predicting the physical properties of surface matrix in mountainous areas, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for predicting the physical properties of surface matrix in mountainous areas as proposed in the above embodiment.

[0118] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0119] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for predicting the physical properties of surface matrix in mountainous areas as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0120] In summary, this invention constructs a dynamic coupling model by fusing quantum gravity gradient measurement with microbiome data, solving the problem of insufficient accuracy in vertical density-permeability coefficient coupling modeling in traditional methods. By using a quantum gravity gradient meter combined with high-precision digital elevation model terrain correction, the vertical density inversion resolution reaches 1m. The abundance of Nitrifying Spirochetes is incorporated as a microbial activity indicator into the permeability coefficient model. Through closed-loop verification via pumping tests, the entire process from point cloud data acquisition to the generation of permeability coefficient spatial distribution maps is standardized.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for predicting the physical properties of surface matrix in mountainous areas, characterized in that: include, Acquire point cloud data of mountainous areas, separate vegetation and ground points using point cloud classification algorithms, and generate digital elevation models and digital surface models. Based on the digital elevation model and digital surface model, a quantum gravity gradiometer is used to move along the contour line to measure the gravity gradient tensor and eliminate the terrain interference signal, and the vertical density profile is obtained by inversion. Density anomaly zones were identified based on the vertical density profile. Uncirculated soil column samples were obtained using drilling units. Particle size distribution and moisture characteristic curves were measured. DNA was extracted and microbial sequencing was performed on the uncirculated soil column samples. The relative abundance of Nitrifying Spirochetes was calculated. A permeability correction model was established based on the vertical density profile, and the predicted permeability value was calculated. The digital elevation model, vertical density profile, and permeability coefficient correction model are input into the geographic information unit, and a spatial interpolation algorithm is used to generate a spatial distribution map of the permeability coefficient. Pumping tests were conducted in high-permeability zones based on the spatial distribution map of permeability coefficients to obtain measured permeability coefficients. The relative error between the measured permeability coefficients and the predicted permeability coefficients was calculated, and a verification report was generated.

2. The method for predicting the physical properties of surface matrix in mountainous areas as described in claim 1, characterized in that: Acquiring point cloud data of mountainous terrain, separating vegetation from ground points using point cloud classification algorithms, and generating digital elevation models and digital surface models include the following steps. A drone equipped with a lidar was used to acquire high-density point cloud data in mountainous areas. Outlier filtering was performed on the high-density point cloud data in mountainous areas to obtain clean point cloud data. Based on clean point cloud data, a cloth simulation filtering algorithm is used to separate ground points from non-ground points; Irregular triangular mesh interpolation is performed on ground points to generate a digital elevation model, and inverse distance weighted interpolation is performed on high-density point cloud data in mountainous areas to generate a digital surface model.

3. The method for predicting the physical properties of surface matrix in mountainous areas as described in claim 2, characterized in that: Based on digital elevation models and digital surface models, a quantum gravity gradiometer is used to measure along contour lines to obtain the gravity gradient tensor and eliminate terrain interference signals. The vertical density profile is then retrieved through inversion, including the following steps: Based on digital elevation model and digital surface model, slope angle is calculated and terrain correction weight map is generated; Contour lines are extracted using a digital elevation model, the movement path of a quantum gravity gradiometer is planned, the quantum gravity gradiometer is moved along the planned path, gravity gradient tensor and BeiDou positioning data are collected, and the terrain correction weight map is combined to eliminate terrain gravity interference in the digital elevation model, thus obtaining corrected gravity gradient data. The corrected gravity gradient data were processed using a Bayesian inversion algorithm to obtain the vertical density profile.

4. The method for predicting the physical properties of surface matrix in mountainous areas as described in claim 3, characterized in that: Density anomaly zones were identified based on vertical density profiles. Uncircular soil column samples were obtained using drilling units, and particle size distribution and moisture characteristic curves were determined. Includes the following steps, Based on the vertical density profile, the absolute deviation and gradient marker density anomaly areas are calculated; Drilling points are set up at the center and edge points of the density anomaly zone, and the priority of the drilling points is calculated. Using a drilling rig and KML navigation positioning, undisturbed soil column samples were obtained in layers. The undisturbed soil column samples were screened, and the clay, silt, and sand content were determined using a Malvern Mastersizer 3000 laser particle size analyzer. A pressure plate apparatus was used to determine the moisture characteristic curve.

5. The method for predicting the physical properties of surface matrix in mountainous areas as described in claim 4, characterized in that: DNA extraction and microbial sequencing were performed on undisturbed soil column samples. The relative abundance of Nitrifying Spirochetes was calculated. A permeability correction model was established based on the vertical density profile, and the predicted permeability value was calculated. This process included the following steps: The undisturbed soil column sample was cut into layers, and three parallel samples were taken from each layer and stored in an ultra-low temperature environment to obtain the layer pattern. DNA was extracted from each soil sample using the PowerSoil Pro Kit. After purification using a silica membrane, a DNA solution was obtained. The DNA solution was then amplified by PCR using 515F and 806R primers. The amplified products were then subjected to paired-end sequencing to obtain sequencing data. Based on sequencing data, the relative abundance of the phylum Nitrifying Spirogyra was calculated using the QIIME2 process; By combining the relative abundance of Nitrifying Spirulina with the vertical density profile, a permeability correction model was established, and the predicted permeability value was calculated.

6. The method for predicting the physical properties of surface matrix in mountainous areas as described in claim 5, characterized in that: The digital elevation model, vertical density profile, and permeability coefficient correction model are input into the geographic information unit (GIS), and a spatial interpolation algorithm is used to generate a spatial distribution map of the permeability coefficient. This process includes the following steps: The digital elevation model, vertical density profile, and permeability coefficient correction model were uniformly converted to the CGCS2000 coordinate system to obtain a three-dimensional permeability coefficient matrix. The empirical Bayesian Skripal algorithm is used to spatially interpolate the horizontal profile in the three-dimensional permeability coefficient matrix to obtain the horizontal interpolated permeability coefficient field. Piecewise cubic Hermite interpolation is used to smooth the vertical profile of the three-dimensional permeability coefficient matrix to obtain the vertical interpolated permeability coefficient field. Based on the slope angle, the three-dimensional permeability coefficient field is corrected, and a spatial distribution map of the permeability coefficient is generated by slicing according to depth.

7. The method for predicting the physical properties of surface matrix in mountainous areas as described in claim 6, characterized in that: Pumping tests were conducted in high-permeability zones based on the spatial distribution map of permeability coefficients to obtain measured permeability coefficients. This process included the following steps: The boundary of the high-permeability target area was selected from the spatial distribution map of the permeability coefficient, and a complete well was drilled. Three radial survey lines were laid out, and Diver water level gauges were installed to obtain dynamic water level monitoring data. The Theis formula was used to invert the dynamic water level monitoring data to obtain the measured permeability coefficient.

8. The method for predicting the physical properties of surface matrix in mountainous areas as described in claim 7, characterized in that: Calculate the relative error between the measured permeability coefficient and the predicted permeability coefficient, and generate a verification report. Includes the following steps, The relative error value is calculated based on the measured permeability coefficient and the predicted permeability coefficient. A relative error threshold is set based on the relative error value. When the relative error value is less than the relative error threshold, it is considered excellent; when the relative error value is equal to the relative error threshold, it is considered qualified; and when the relative error value is greater than the relative error threshold, it is considered unqualified. The boundaries of the high-permeability target area are graded and labeled to generate an error level distribution map. The error level distribution map and relative error values ​​are integrated to generate a verification report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for predicting the physical properties of the surface matrix in mountainous areas according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for predicting the physical properties of the surface matrix in mountainous areas according to any one of claims 1 to 8.

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