Geologic mapping groundwater filtration sampling method

Abstract

The present application relates to the technical field of hydrogeology, and specifically discloses a geological surveying and mapping groundwater filtering sampling method, which collects optical characteristic parameters, electrochemical response signals, suspended matter distribution characteristics and pressure pulsation data of groundwater bodies in real time, establishes a consistent characteristic data set in time and space, identifies hydrogeological characteristic indexes and their change rules through multi-scale fluctuation characteristic extraction and synchronism analysis, constructs a hydrogeological characteristic profile of groundwater with sampling depth as the benchmark, matches and analyzes in combination with a typical hydrogeological mode spectrum, determines the representativeness and reliability level of the sampling point, and finally generates a sampling optimization scheme and a quality control report; the present application realizes closed-loop control from data collection to quality evaluation, solves the problem of insufficient data representativeness and reliability caused by chemical disturbance and lack of systematic quality control in traditional sampling methods, and improves the accuracy of groundwater sampling data and the reliability of geological interpretation.

Description

Geological surveying groundwater filtration sampling methods Technical Field

[0001] This invention relates to the field of hydrogeology, specifically to a groundwater filtration and sampling method for geological mapping. Background Technology

[0002] Traditional groundwater sampling methods primarily rely on manual experience to select sampling points and combine simple on-site measurements with laboratory analysis. These methods have significant limitations: firstly, groundwater bodies are susceptible to environmental disturbances such as oxidation and pollution during sampling, leading to distortion of water sample chemical parameters; secondly, the lack of systematic collection and analysis of multi-dimensional characteristic data makes it difficult to establish a quantitative correlation between sampling data and hydrogeological conditions. Furthermore, existing technologies are insufficient in assessing the representativeness and reliability of sampling points, and sampling quality control largely depends on post-sampling laboratory testing, failing to enable real-time optimization and adjustment during the sampling process, thus affecting the accuracy and usability of the final data.

[0003] This invention addresses the core problem of insufficient data representativeness and reliability in traditional groundwater sampling due to chemical disturbances and a lack of systematic quality control. By establishing a complete technical chain from multi-dimensional data acquisition, spatiotemporal alignment, feature extraction to profile construction, it achieves refined control over the sampling process. Furthermore, through matching and analysis with typical hydrogeological model systems, it constructs a quantitative evaluation system for the representativeness and reliability of sampling points, fundamentally avoiding parameter distortion caused by improper sampling methods and significantly improving the accuracy of sampling data and the reliability of geological interpretation. Summary of the Invention

[0004] The purpose of this invention is to provide a groundwater filtration sampling method for geological mapping to solve the problems mentioned above.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] The groundwater filtration and sampling method for geological mapping includes the following steps:

[0007] S1: Real-time acquisition of multi-dimensional feature data of groundwater bodies, including: optical property parameters of water bodies, electrochemical response signals, suspended solids distribution characteristics and pressure pulsation data;

[0008] S2: Spatiotemporally align the collected multi-dimensional feature data to form a feature data set with spatiotemporal consistency;

[0009] S3: Perform multi-source feature extraction and correlation analysis on the spatiotemporally aligned feature dataset to separate and identify the feature indicators that characterize different hydrogeological conditions and their variation patterns during the sampling process;

[0010] S4: Spatiotemporally correlate the variation patterns of characteristic indicators with stratigraphic parameters to construct a groundwater hydrogeological feature profile with sampling depth as the benchmark and multiple characteristic indicators as the dimensions;

[0011] S5: Based on the underground hydrogeological feature profile, the representativeness and reliability level of the sampling points are determined by matching and analyzing with the typical hydrogeological model spectrum, and a sampling optimization plan and quality control report are generated.

[0012] As a further aspect of the present invention: the formation of a feature data set with spatiotemporal consistency specifically includes:

[0013] A unified timestamp sequence is assigned to the multi-dimensional feature data of each sampling point to establish a synchronized time reference.

[0014] The spatial location information of each sampling point is bound to the corresponding timestamp sequence to form time series data with spatial topological relationships;

[0015] Data consistency verification is performed on time series data with spatial topological relationships to generate a spatiotemporally consistent feature dataset.

[0016] As a further aspect of the present invention: the separation and identification of characteristic indicators representing different hydrogeological conditions and their variation patterns during the sampling process specifically includes:

[0017] Multi-scale fluctuation features are extracted from spatiotemporally consistent feature datasets. By calculating the gradient changes of feature parameters between adjacent sampling points, a multi-scale feature sequence reflecting the spatial heterogeneity of hydrogeological conditions is obtained.

[0018] Synchronicity index analysis is performed based on multi-scale feature sequences. By calculating the degree of coordinated change of different feature parameters during the sampling process, feature combinations with synchronous change characteristics are identified.

[0019] Trend quantification is performed on feature combinations with synchronous change characteristics. By calculating the change gradient and fluctuation intensity of the feature sequence along the sampling path, feature indicators and change patterns representing different hydrogeological conditions are established.

[0020] As a further aspect of the present invention: the identification of feature combinations with synchronous change characteristics specifically includes:

[0021] The multi-scale feature sequence is divided into sliding windows, and the fluctuation trajectory of different feature parameters is calculated in each window to obtain the local change pattern of each feature parameter on the sampling path.

[0022] Based on the local change patterns of each feature parameter, the morphological similarity between the fluctuation trajectories of different feature parameters is calculated. By quantifying the degree of agreement between the fluctuation trajectories in key sampling sections, the intensity of the coordinated change between feature parameters is obtained.

[0023] Based on the intensity of coordinated change among feature parameters, a feature parameter coordinated change matrix is ​​constructed. By setting a threshold for the intensity of coordinated change, feature combinations with synchronous change characteristics are extracted from the feature parameter coordinated change matrix.

[0024] As a further aspect of the present invention: the establishment of characteristic indicators and variation patterns for different hydrogeological conditions specifically includes:

[0025] Sliding window segmentation is performed on feature combinations with synchronous change characteristics. Within each window, the change trend direction and change rate of each feature parameter are calculated to obtain the local trend characteristics of each feature parameter on the sampling path.

[0026] Based on the local trend characteristics of each feature parameter, the contribution of each feature to the overall trend is quantified by quantitatively evaluating the degree of consistency between the change magnitude of each feature within the window and the overall trend.

[0027] Based on the quantitative indicators of the contribution of each feature to the overall trend of change, a multi-feature trend contribution map with sampling location as the benchmark is constructed. By analyzing the spatial distribution pattern of feature contribution in the map, characteristic indicators and their changing patterns representing different hydrogeological conditions are established.

[0028] As a further aspect of the present invention: S4 specifically includes:

[0029] Based on the variation patterns of characteristic indicators, the location of changing stratigraphic interfaces is identified by calculating the variation gradient and fluctuation intensity of each characteristic indicator in the vertical direction.

[0030] Based on the identified stratigraphic interface locations, the sampling depth range is divided into stratigraphic units with different hydrogeological characteristics, and a stratigraphic unit sequence ordered by depth is established.

[0031] The multi-feature indicators within each stratigraphic unit are processed into a three-dimensional grid. A continuously distributed feature indicator surface is generated through a spatial interpolation algorithm to construct a groundwater hydrogeological feature profile that reflects the spatial variation of hydrogeological features.

[0032] As a further aspect of the present invention: the step of identifying the location of changing stratigraphic interfaces by calculating the vertical gradient and fluctuation intensity of each characteristic index specifically includes:

[0033] The variation pattern of the characteristic index is divided into sliding windows. Within each window, the gradient vector magnitude and orientation angle of the characteristic index in the vertical direction are calculated to obtain the gradient characteristic sequence reflecting the changes in the strata.

[0034] A gradient vector field is constructed based on the gradient feature sequence. By calculating the divergence characteristics of the gradient vector in spatial distribution, a divergence feature sequence that characterizes the changes in the stratigraphic interface is obtained.

[0035] Based on the extreme value distribution characteristics of the divergence feature sequence, the precise location and intensity of change of the stratigraphic interface can be determined by identifying the spatial location and distribution density of divergence extreme points.

[0036] As a further aspect of the present invention: obtaining the gradient feature sequence reflecting stratigraphic changes specifically includes:

[0037] The gradient feature sequence is divided into spatial grids, and a spatially continuous gradient vector field is constructed at each grid node based on the gradient vector magnitude and orientation angle.

[0038] In the gradient vector field, the divergence value of the gradient vector at each grid node is calculated by the spatial differential operator to obtain a divergence distribution map that reflects the changes in formation properties.

[0039] Feature curves in the divergence distribution map are extracted along the sampling depth direction. By performing extreme value detection and inflection point analysis on the feature curves, a divergence feature sequence characterizing the changes in the stratigraphic interface is generated.

[0040] As a further aspect of the present invention: S5 specifically includes:

[0041] Key feature index combinations are extracted from groundwater hydrogeological feature profiles. The spatial distribution of these key feature index combinations is compared with that of pre-stored typical hydrogeological model series. By calculating the matching degree of each key feature index combination in the vertical sequence, hydrogeological condition similarity index is obtained.

[0042] Based on the similarity index of hydrogeological conditions and the spatial distribution density of sampling points, the representativeness level of sampling points is determined by dividing them into multiple similarity thresholds, and the reliability level is evaluated based on the spatial continuity and stability of the combination of key feature indicators.

[0043] Based on the representativeness and reliability levels of the sampling points, the system identifies areas that require additional sampling, generates an optimization scheme that includes the optimal sampling point layout and sampling order, and outputs a quality control report containing the quality rating of each sampling point.

[0044] The beneficial effects of this invention are:

[0045] (1) Through the synchronous acquisition of multi-dimensional feature data and strict spatiotemporal alignment processing, combined with multi-source feature extraction and correlation analysis techniques, the variation patterns of characteristic indicators under different hydrogeological conditions were effectively identified and quantified. The stratigraphic interface identification method using gradient vector field and divergence analysis can accurately depict the spatial variation of hydrogeological feature profiles, avoiding data distortion problems caused by chemical disturbance and incomplete phase separation in traditional sampling. Based on the matching analysis and dual-level evaluation system of typical hydrogeological model series, the representativeness and reliability of sampling points were scientifically quantified, ensuring that the sampling data truly reflects the actual hydrogeological conditions.

[0046] (2) By establishing a complete technical chain from data collection to quality assessment, a closed-loop sampling quality control system has been formed. Based on the spatial interpolation optimization method of representativeness level and reliability level, it is possible to accurately identify areas that need supplementary sampling and generate the optimal sampling point layout and sampling sequence scheme. The percentage scoring system in the quality control report provides a clear quality rating for each sampling point, making the entire sampling process traceable and assessable, effectively avoiding duplicate sampling and resource waste, and improving the efficiency and economy of field sampling work. Attached Figure Description

[0047] The invention will now be further described with reference to the accompanying drawings.

[0048] Figure 1 is a flowchart of the method of the present invention. Detailed Implementation

[0049] 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.

[0050] Please refer to Figure 1. This invention is a groundwater filtration and sampling method for geological mapping, comprising the following steps:

[0051] S1: Real-time acquisition of multi-dimensional feature data of groundwater bodies, including: optical property parameters of water bodies, electrochemical response signals, suspended solids distribution characteristics and pressure pulsation data;

[0052] S2: Spatiotemporally align the collected multi-dimensional feature data to form a feature data set with spatiotemporal consistency;

[0053] S3: Perform multi-source feature extraction and correlation analysis on the spatiotemporally aligned feature dataset to separate and identify the feature indicators that characterize different hydrogeological conditions and their variation patterns during the sampling process;

[0054] S4: Spatiotemporally correlate the variation patterns of characteristic indicators with stratigraphic parameters to construct a groundwater hydrogeological feature profile with sampling depth as the benchmark and multiple characteristic indicators as the dimensions;

[0055] S5: Based on the underground hydrogeological feature profile, the representativeness and reliability level of the sampling points are determined by matching and analyzing with the typical hydrogeological model spectrum, and a sampling optimization plan and quality control report are generated.

[0056] In S1, multi-dimensional characteristic data of groundwater bodies are collected in real time. This multi-dimensional characteristic data includes: optical property parameters of the water body, electrochemical response signals, suspended solids distribution characteristics, and pressure pulsation data, specifically including:

[0057] Optical characteristic parameters are acquired using a multispectral water quality sensor installed in the sampling flow path. This sensor incorporates a light-emitting unit and a receiving unit with specific wavelengths. The light-emitting unit emits visible and ultraviolet light signals that penetrate the flowing water, while the receiving unit detects changes in transmitted light intensity and fluorescence characteristics, thereby obtaining optical parameters such as turbidity, color, and the content of fluorescent substances in the water. These parameters reflect the composition and concentration of dissolved organic matter and colloidal substances in the water.

[0058] Electrochemical response signal acquisition was achieved using a contact-type multi-electrode sensing probe. This probe comprises a measurement system consisting of a working electrode, a reference electrode, and a counter electrode. When the probe is in direct contact with flowing groundwater, a specific sequence of scanning potentials is applied, and the resulting current response is detected to obtain electrochemical characteristic parameters of the water body, such as redox potential, conductivity, and ion activity. These parameters characterize the chemical composition and reactivity of the water body.

[0059] The distribution characteristics of suspended solids were collected using a laser diffraction particle analyzer. This analyzer generates a laser beam in the sampling flow path that penetrates the water sample. By detecting the scattering angle and intensity of the laser light by the particles, the particle size distribution and concentration changes of the suspended particles are determined. This non-contact measurement method can accurately obtain the quantity distribution and size composition of suspended particles in water.

[0060] Pressure pulsation data acquisition was achieved using a high-frequency response piezoelectric pressure sensor. This sensor directly contacts the water flow through an insulating diaphragm, converting pressure fluctuations into electrical signals. After signal conditioning, the time-domain waveform of the pressure pulsation is output. The pressure pulsation data reflects the dynamic characteristics and flow field changes of groundwater flowing within the aquifer.

[0061] In S2, the collected multi-dimensional feature data is spatiotemporally aligned to form a feature data set with spatiotemporal consistency, specifically including:

[0062] In achieving spatiotemporal alignment of multi-dimensional feature data, the first step is to establish a unified time reference. This step is achieved through the time signal provided by the Global Positioning System (GPS) receiver, specifically using its output second pulse signal as the synchronization trigger reference for data acquisition at each sampling point. Each sampling point's data acquisition device records the current sampling time upon receiving the second pulse signal, and simultaneously records a millisecond-level time count calculated from the device's startup. This assigns a timestamp sequence with a unified reference to the multi-dimensional feature data of each sampling point. The timestamp recording format is year, month, day, hour, minute, second, plus a millisecond value, ensuring that the time records of all sampling points achieve millisecond-level synchronization accuracy.

[0063] After time base synchronization is completed, spatial location and time series binding processing is required. This step uses real-time dynamic measurement of the Global Positioning System (GPS) to obtain the three-dimensional spatial coordinates of each sampling point, with the coordinate system being the National Geodetic Coordinate System. The longitude, latitude, and elevation data of each sampling point are associated with its corresponding timestamp sequence to form time series data with spatial topological relationships. The recording accuracy of spatial coordinates reaches the centimeter level, and each spatial coordinate point is associated with a specific timestamp, forming a complete spatiotemporal coordinate set.

[0064] Finally, data consistency verification is performed using a sliding window method. A sliding window with 30 consecutive sampling points is set, and the local variance of each feature data point is calculated within the window. If the difference between the feature value of a data point and the mean of the local sequence formed by its five preceding and following data points exceeds twice the local variance, the data point is identified as an outlier. For identified outliers, linear interpolation of the three preceding and following valid data points is used to replace them. After verification, a spatiotemporally consistent feature data set is formed, providing a reliable data foundation for subsequent analysis.

[0065] Throughout the spatiotemporal alignment process, time synchronization accuracy was controlled within 1 millisecond, spatial coordinate positioning accuracy reached the centimeter level, the sliding window size for data consistency verification was fixed at 30 sampling points, and the outlier threshold was set to twice the local variance. The specific values ​​of these parameters were determined based on actual sampling requirements and equipment accuracy to ensure that the final spatiotemporal consistent feature data set met the accuracy requirements of geological mapping.

[0066] In S3, multi-source feature extraction and correlation analysis are performed on the spatiotemporally aligned feature dataset to separate and identify characteristic indicators representing different hydrogeological conditions and their variation patterns during the sampling process. Specifically, this includes:

[0067] After completing spatiotemporal alignment, multi-source feature extraction and correlation analysis were performed on the feature dataset. First, multi-scale fluctuation feature extraction was conducted using a three-level scale analysis: the first-level scale window contained 10 consecutive sampling points, the second-level scale window contained 30 consecutive sampling points, and the third-level scale window contained 50 consecutive sampling points. Within each scale window, the gradient change value of the feature parameters between adjacent sampling points was calculated. The gradient calculation formula was: the feature value of the subsequent sampling point minus the feature value of the previous sampling point, divided by the distance between the two points. The gradient change values ​​of the three scales were weighted and fused with weighting coefficients of 0.3, 0.5, and 0.2 to obtain a multi-scale feature sequence reflecting the spatial heterogeneity of hydrogeological conditions. This processing can capture the hydrogeological variation characteristics at different scales.

[0068] When performing synchronicity index analysis based on multi-scale feature sequences, a sliding window method is employed, with a fixed window size of 50 sampling points. Within each window, the morphological similarity of the fluctuation trajectories of different feature parameters is calculated. Morphological similarity is determined by calculating the consistency of the direction of change and the matching degree of the change amplitude of two fluctuation trajectories at the same sampling position, specifically using a cosine similarity algorithm combined with Euclidean distance. When the morphological similarity calculation result of two feature parameters is greater than 0.8, they are considered to have strong cooperative change characteristics. Based on the cooperative change intensity values ​​among all feature parameters, a feature parameter cooperative change matrix is ​​constructed. This matrix is ​​symmetric, with diagonal elements all equal to 1. A cooperative change intensity threshold of 0.7 is set, and feature parameter combinations that meet this threshold condition are extracted from the matrix as feature combinations with synchronous change characteristics.

[0069] When quantifying the trend of identified feature combinations exhibiting synchronous changes, a sliding window segmentation method is employed, with a window size of 20 sampling points and a sliding step size of 5 sampling points. Within each window, the direction and rate of change of each feature parameter are calculated. The direction of change is determined by the slope of linear regression, and the rate of change is calculated as the ratio of the range of feature values ​​within the window to the window width. Based on the local trend characteristics of each feature parameter, the contribution of each feature to the overall trend is quantified by calculating the degree of agreement between the change amplitude of each feature within the window and the overall trend. The contribution is calculated by multiplying the ratio of the feature change amplitude to the overall change amplitude by a consistency coefficient between the feature change direction and the overall change direction.

[0070] Based on the quantification of the contribution of each feature to the overall trend, a multi-feature trend contribution map is constructed with the sampling location as the baseline. The horizontal axis of this map represents the sampling location, and the vertical axis represents the contribution value of each feature. By analyzing the spatial distribution of feature contributions in the map, critical locations of significant changes in contribution are identified. When the contribution of a certain feature remains above 0.6 for three consecutive sampling windows, that feature is determined as the dominant feature indicator for that section. Simultaneously, by analyzing the spatial distribution of contributions of different features, characteristic indicators and their variation patterns representing different hydrogeological conditions are established, including determining the effective range and trend characteristics of each characteristic indicator.

[0071] During feature extraction, the window size for multi-scale analysis is adjusted according to the sampling density. A three-level window (10 / 30 / 50) is used when the sampling point spacing is less than 1 meter, and a three-level window (5 / 15 / 25) is used when the sampling point spacing is greater than 1 meter. The threshold for co-variation intensity can be adjusted within the range of 0.6-0.9 based on specific hydrogeological conditions, with a lower threshold used in homogeneous aquifers and a higher threshold used in strongly heterogeneous areas. The trend contribution map is analyzed using spatial clustering, dividing adjacent sampling intervals with similar contribution variation characteristics into the same hydrogeological unit, thereby establishing a complete hydrogeological condition characteristic index system.

[0072] In S4, the variation patterns of characteristic indicators are spatiotemporally correlated with stratigraphic parameters to construct a groundwater hydrogeological feature profile based on sampling depth and multiple characteristic indicators, specifically including:

[0073] After extracting the variation patterns of characteristic indicators, spatiotemporal correlation analysis between these indicators and formation parameters is required. First, based on the variation patterns of the characteristic indicators, a sliding window segmentation method is used to identify formation interfaces. The sliding window size is set to 5 consecutive sampling points, and the sliding step size is 1 sampling point. Within each window, the magnitude and orientation angle of the gradient vector of the characteristic indicator in the vertical direction are calculated. The magnitude of the gradient vector is obtained by taking the square root of the sum of the squares of the changes in the characteristic indicator values ​​within the window, and the orientation angle is determined by calculating the angle between the direction of the characteristic indicator change and the vertical direction. The magnitude and orientation angle of the gradient vector calculated for each window are arranged in depth order to form a gradient feature sequence reflecting formation changes. This step effectively captures the physical property variation characteristics at formation interfaces.

[0074] When constructing the gradient vector field based on the gradient feature sequence, a three-dimensional spatial grid partitioning method is adopted, with the grid size set to 0.5 m × 0.5 m × 0.2 m (length × width × depth). At each grid node, the gradient vector value of that node is calculated using the inverse distance weighted interpolation method based on the gradient vector magnitude and orientation angle of the surrounding 8 sampling points, with the distance weight exponent in the inverse distance weighting set to 2. By traversing all grid nodes, a gradient vector field with spatial continuity is constructed. This vector field can comprehensively characterize the spatial variation law of groundwater hydrogeological features in the study area.

[0075] When calculating divergence characteristics in a gradient vector field, the spatial differential operator of the central difference method is employed. For each grid node, the difference in its gradient vector with its six adjacent grid nodes (front, back, left, right, up, and down) is calculated. The divergence value of that node is obtained by weighted summation of the differences in each direction, with the weighting coefficients determined based on the grid spacing. By calculating the divergence values ​​of all grid nodes, a divergence distribution map reflecting changes in formation properties is obtained. Positive divergence values ​​indicate regions of enhanced formation properties, negative values ​​indicate regions of weakened formation properties, and values ​​near zero indicate formation interfaces.

[0076] When extracting the characteristic curve from the divergence distribution map along the sampling depth direction, the divergence values ​​corresponding to all depths at each sampling well are extracted, forming a divergence-depth characteristic curve. Extreme value detection is performed on this characteristic curve using a sliding window extreme value identification method with a window size of 7 data points. When the divergence value of a data point is greater than the divergence values ​​of the three data points before and after it, it is identified as an extreme point. Simultaneously, inflection point analysis is performed, calculating the rate of change of the first derivative of the characteristic curve. When the sign of the first derivative changes for three consecutive data points, it is identified as an inflection point. Based on the spatial distribution of extreme points and inflection points, a divergence characteristic sequence characterizing changes in the stratigraphic interface is generated.

[0077] When identifying the location of stratigraphic interfaces based on the extreme value distribution characteristics of divergence feature sequences, a divergence extreme value threshold is set at 30% of the principal extreme points. When the absolute divergence value of an extreme point exceeds this threshold, it is determined to be a valid stratigraphic interface marker. The precise location of the stratigraphic interface is determined by calculating the distribution density of valid extreme points in the vertical direction. The distribution density is calculated by statistically analyzing the number of extreme points per meter of depth. When the distribution density is greater than two extreme points per meter, a significant stratigraphic interface is considered to exist at that depth. Simultaneously, the intensity of stratigraphic interface change is determined based on the magnitude of the divergence value of the extreme points; a larger absolute divergence value indicates a more intense stratigraphic interface change.

[0078] Based on the identified stratigraphic interfaces, the sampling depth range was divided into stratigraphic units with different hydrogeological characteristics. The division principle was to use significant stratigraphic interfaces as boundaries, with each stratigraphic unit having a thickness of no less than 0.5 meters. For each stratigraphic unit, its top and bottom plate depths, thicknesses, and the average values ​​and ranges of characteristic indicators within the unit were recorded. All these stratigraphic units were arranged in depth order to establish a depth-ordered stratigraphic unit sequence, where each unit contains a complete description of its hydrogeological characteristics.

[0079] When performing three-dimensional meshing of multiple feature indicators within each stratigraphic unit, the Kriging interpolation method is employed. First, the geometry and spatial distribution of each stratigraphic unit are determined. Then, each unit is discretized using a grid size of 1 meter × 1 meter × 0.5 meters. At each grid node, the feature indicator value is calculated using Kriging interpolation based on the feature indicator values ​​of surrounding known sampling points. The variation function in the Kriging interpolation adopts a spherical model, and the range is determined based on the spatial correlation of the actual data, generally adjusted within the range of 5-20 meters.

[0080] When generating continuously distributed feature index surfaces using spatial interpolation algorithms, a triangular meshing method is employed to connect discrete grid nodes into a continuous surface. For each feature index, its distribution surface within different stratigraphic units is generated. These surfaces are arranged sequentially according to the stratigraphic unit sequence to ultimately construct a groundwater geological feature profile reflecting the spatial variation of hydrogeological characteristics. This profile, based on sampling depth, includes multiple dimensions of feature indices and can comprehensively demonstrate the vertical distribution patterns and lateral variation characteristics of groundwater geological features within the study area.

[0081] During stratigraphic interface identification, the sliding window size can be adjusted according to the complexity of the stratigraphy; a window of 7 sampling points is used for simple strata, while a window of 3 sampling points is used for complex strata. The divergence extreme value threshold can be adjusted within the range of 20%-40% based on the actual data characteristics; a higher threshold is used when the data noise is high. The minimum thickness of the stratigraphic unit division can be adjusted according to research needs; 0.5 meters is used for routine analysis, and 0.2 meters can be used for fine analysis. The grid size for kriging interpolation is determined based on the sampling density; a smaller grid size can be used when the sampling points are dense to improve the resolution of the profile. All these parameter settings are optimized and adjusted based on the actual data characteristics and research objectives to ensure that the final constructed groundwater hydrogeological profile accurately reflects the actual hydrogeological conditions.

[0082] In S5, based on the groundwater hydrogeological profile, the representativeness and reliability level of sampling points are determined by matching and analyzing with typical hydrogeological model series, and a sampling optimization plan and quality control report are generated, specifically including:

[0083] After constructing the groundwater hydrogeological feature profile, it is necessary to conduct quality assessment and optimization of sampling points based on this profile. First, key feature index combinations are extracted from the groundwater hydrogeological feature profile. These indicators include, but are not limited to, vertical conductivity gradient, redox potential change rate, and temperature anomaly coefficient. The spatial distribution of these key feature index combinations is then compared with a pre-stored typical hydrogeological model spectrum. The comparison process employs a layer-by-layer comparison method, dividing the actual profile into 1-meter thickness layers and calculating the matching degree between each key feature index and the typical model within each layer. The matching degree calculation uses a weighted average method, with weights determined according to the importance of the feature indexes: important indicators have a weight of 0.6, and secondary indicators have a weight of 0.4. By calculating the weighted sum of the matching degrees of each key feature index in the vertical sequence, a hydrogeological condition similarity index is obtained, with a value ranging from 0 to 1.

[0084] Based on the similarity index of hydrogeological conditions and the spatial distribution density of sampling points, the representativeness level of sampling points is determined through a multi-level similarity threshold system. Three similarity thresholds are set: 0.8, 0.6, and 0.4. When the similarity index is greater than 0.8, the representativeness level is rated as Level 1; between 0.6 and 0.8, it is Level 2; between 0.4 and 0.6, it is Level 3; and below 0.4, it is Level 4. Simultaneously, the reliability level is assessed based on the spatial continuity and stability of key characteristic indicators. Continuity is determined by calculating the correlation between characteristic indicators of adjacent sampling points: a correlation coefficient greater than 0.9 indicates high continuity, 0.7 to 0.9 indicates medium continuity, and below 0.7 indicates low continuity. Stability is assessed by calculating the coefficient of variation of characteristic indicators in the vertical sequence: a coefficient of variation less than 0.1 indicates high stability, 0.1 to 0.3 indicates medium stability, and greater than 0.3 indicates low stability.

[0085] Based on the representativeness and reliability levels of the sampling points, a spatial interpolation method is used to identify areas requiring supplementary sampling. Areas with a representativeness level of three or below and a reliability level of low continuity are marked as requiring supplementary sampling. An optimized scheme containing the optimal sampling point layout and sampling order is generated. The new sampling points are deployed according to the following principles: in areas with low representativeness levels, the sampling points are densified at 500-meter intervals; in areas with low reliability levels, the sampling points are verified at 250-meter intervals. The sampling order follows the sequence from low representativeness level to high representativeness level, and from low reliability level to high reliability level.

[0086] The final output is a quality control report containing the quality ratings for each sampling point. The quality rating combines representativeness and reliability levels, using a 100-point scale. Level 1 representativeness with high continuity and reliability receives 90-100 points; Level 2 representativeness with medium continuity and reliability receives 70-89 points; Level 3 representativeness with low continuity and reliability receives 50-69 points; and Level 4 representativeness receives below 49 points. The report details the spatial location of each sampling point, the values ​​of various characteristic indicators, the representativeness level, the reliability level, and the final quality score, providing a complete data quality assessment basis for subsequent hydrogeological analysis.

[0087] The working principle of this invention is as follows: By real-time acquisition of multi-dimensional characteristic data such as optical properties, electrochemical response signals, suspended matter distribution characteristics, and pressure pulsation data of groundwater bodies, a characteristic data set with spatiotemporal consistency is established. Through multi-scale fluctuation feature extraction, synchronization index analysis, and trend quantification of the spatiotemporally aligned data, characteristic indicators representing different hydrogeological conditions and their variation patterns are separated and identified. The variation patterns of these characteristic indicators are spatiotemporally correlated with stratigraphic parameters to construct a groundwater hydrogeological characteristic profile based on sampling depth. Finally, based on the matching analysis of this profile with typical hydrogeological model systems, the representativeness and reliability level of the sampling points are determined, and a sampling optimization scheme and quality control report are generated, achieving refined control of the groundwater sampling process and scientific evaluation of sampling quality.

[0088] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A groundwater filtration and sampling method for geological surveying, characterized in that, Includes the following steps: S1: Real-time acquisition of multi-dimensional feature data of groundwater bodies, including: optical property parameters, electrochemical response signals, suspended matter distribution characteristics, and pressure pulsation data; S2: Spatiotemporal alignment of the acquired multi-dimensional feature data to form a feature data set with spatiotemporal consistency; S3: Multi-source feature extraction and correlation analysis of the spatiotemporally aligned feature data set to separate and identify characteristic indicators representing different hydrogeological conditions and their variation patterns during the sampling process; S4: Spatiotemporal correlation of the variation patterns of the characteristic indicators with stratigraphic parameters to construct a system based on sampling depth. S5: Based on the groundwater hydrogeological feature profile, by matching and analyzing with typical hydrogeological model systems, the representativeness and reliability level of the sampling points are determined, and a sampling optimization plan and quality control report are generated. Specifically, this includes: extracting key feature index combinations from the groundwater hydrogeological feature profile, comparing the spatial distribution of the key feature index combinations with the pre-stored typical hydrogeological model systems, and obtaining hydrogeological condition similarity indexes by calculating the matching degree of each key feature index combination in the vertical sequence; according to hydrogeological... The conditional similarity index, combined with the spatial distribution density of sampling points, determines the representativeness level of sampling points through multi-level similarity thresholds, and assesses the reliability level based on the spatial continuity and stability of key feature index combinations. The similarity thresholds are 0.8, 0.6, and 0.

4. Continuity is determined by calculating the correlation between feature indices of adjacent sampling points: a correlation coefficient greater than 0.9 indicates high continuity, 0.7 to 0.9 indicates medium continuity, and less than 0.7 indicates low continuity. Stability is assessed by calculating the coefficient of variation of feature indices in the vertical sequence: a coefficient of variation less than 0.1 indicates high stability. Stability is assessed with a value of 0.1 to 0.3 indicating moderate stability and a value greater than 0.3 indicating low stability. Based on the representativeness and reliability levels of the sampling points, areas requiring supplementary sampling are identified, an optimized scheme including the optimal sampling point layout and sampling order is generated, and a quality control report containing the quality rating of each sampling point is output. The deployment of new sampling points follows these principles: in areas with low representativeness levels, sampling points are densified at 500-meter intervals; in areas with low reliability levels, sampling points are verified at 250-meter intervals. The sampling order proceeds from low representativeness level to high representativeness level and from low reliability level to high reliability level.

2. The groundwater filtration and sampling method for geological mapping according to claim 1, characterized in that, The process of forming a spatiotemporally consistent feature data set specifically includes: assigning a unified timestamp sequence to the multi-dimensional feature data of each sampling point to establish a synchronized time reference; binding the spatial location information of each sampling point with the corresponding timestamp sequence to form a time series data with spatial topological relationships; and performing data consistency verification on the time series data with spatial topological relationships to generate a spatiotemporally consistent feature data set.

3. The groundwater filtration and sampling method for geological mapping according to claim 1, characterized in that, The process of separating and identifying characteristic indicators representing different hydrogeological conditions and their variation patterns during the sampling process specifically includes: extracting multi-scale fluctuation features from a spatiotemporally consistent feature data set; obtaining a multi-scale feature sequence reflecting the spatial heterogeneity of hydrogeological conditions by calculating the gradient changes of feature parameters between adjacent sampling points; performing synchronicity index analysis based on the multi-scale feature sequence; identifying feature combinations with synchronous change characteristics by calculating the degree of coordinated change of different feature parameters during the sampling process; and performing trend quantification characterization on feature combinations with synchronous change characteristics by calculating the gradient and fluctuation intensity of the feature sequence along the sampling path to establish characteristic indicators and variation patterns representing different hydrogeological conditions.

4. The groundwater filtration and sampling method for geological mapping according to claim 3, characterized in that, The identification of feature combinations with synchronous change characteristics specifically includes: dividing the multi-scale feature sequence into sliding windows, calculating the fluctuation trajectory of different feature parameters within each window, and obtaining the local change pattern of each feature parameter on the sampling path; based on the local change pattern of each feature parameter, calculating the morphological similarity between the fluctuation trajectories of different feature parameters, and obtaining the intensity of coordinated change between feature parameters by quantifying the degree of agreement between fluctuation trajectories in key sampling sections; constructing a feature parameter coordinated change matrix according to the intensity of coordinated change between feature parameters, and extracting feature combinations with synchronous change characteristics from the feature parameter coordinated change matrix by setting a threshold for the intensity of coordinated change.

5. The groundwater filtration and sampling method for geological mapping according to claim 3, characterized in that, The establishment of characteristic indicators and variation patterns for different hydrogeological conditions specifically includes: dividing feature combinations with synchronous variation characteristics into sliding windows; calculating the direction and rate of change of each feature parameter within each window to obtain the local trend characteristics of each feature parameter along the sampling path; based on the local trend characteristics of each feature parameter, quantitatively evaluating the degree of agreement between the variation amplitude of each feature within the window and the overall trend to obtain a quantified index of the contribution of each feature to the overall trend; and constructing a multi-feature trend contribution map based on the sampling location, and establishing characteristic indicators and variation patterns for different hydrogeological conditions by analyzing the spatial distribution of feature contributions in the map.

6. The groundwater filtration and sampling method for geological mapping according to claim 1, characterized in that, S4 specifically includes: based on the variation law of characteristic indicators, by calculating the variation gradient and fluctuation intensity of each characteristic indicator in the vertical direction, identifying the location of the changing stratigraphic interface; according to the identified stratigraphic interface location, dividing the sampling depth interval into stratigraphic units with different hydrogeological characteristics, and establishing a stratigraphic unit sequence ordered by depth; performing three-dimensional meshing processing on the multiple characteristic indicators in each stratigraphic unit, generating a continuously distributed characteristic indicator surface through spatial interpolation algorithm, and constructing a groundwater hydrogeological feature profile reflecting the spatial variation of hydrogeological characteristics.

7. The groundwater filtration and sampling method for geological mapping according to claim 6, characterized in that, The method of identifying the location of changing stratigraphic interfaces by calculating the vertical gradient and fluctuation intensity of each characteristic index includes: dividing the characteristic index variation pattern into sliding windows, calculating the gradient vector magnitude and orientation angle of the characteristic index in the vertical direction within each window to obtain a gradient characteristic sequence reflecting stratigraphic changes; constructing a gradient vector field based on the gradient characteristic sequence, and obtaining a divergence characteristic sequence characterizing stratigraphic interface changes by calculating the divergence characteristics of the gradient vector in spatial distribution; and determining the precise location and intensity of change of the stratigraphic interface by identifying the spatial location and distribution density of divergence extreme points based on the extreme value distribution characteristics of the divergence characteristic sequence.

8. The groundwater filtration and sampling method for geological mapping according to claim 7, characterized in that, The specific steps for obtaining the gradient feature sequence reflecting formation changes include: dividing the gradient feature sequence into spatial grids; constructing a spatially continuous gradient vector field at each grid node based on the gradient vector magnitude and orientation angle; calculating the divergence value of the gradient vector at each grid node using a spatial differential operator in the gradient vector field to obtain a divergence distribution map reflecting formation property changes; extracting feature curves from the divergence distribution map along the sampling depth direction; and generating a divergence feature sequence characterizing formation interface changes by performing extreme value detection and inflection point analysis on the feature curves.

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