Hydrogeological parameter spatio-temporal quantitative expression method based on non-contact observation method

Abstract

The present application belongs to the technical field of hydrogeological parameter measurement, and discloses a kind of spatio-temporal quantitative expression method of hydrogeological parameter based on non-contact observation method, spatio-temporal quantitative expression method of hydrogeological parameter based on non-contact observation method includes: using the NMR geophysical prospecting method of different field source form, GPR geophysical prospecting method, TEM geophysical prospecting method collects different types, different structure geophysical data;And based on the different types, different structure geophysical data inversion obtains hydrogeological parameter;Based on D-S evidence theory, the hydrogeological parameter obtained by inversion is merged with the collected hydrogeological data to obtain the best estimate of the hydrogeological parameter.The present application merges the measured different types, different structure geophysical data and the collected hydrogeological data to obtain the best estimate of the hydrogeological parameter, and quantifies the uncertainty factor.

Description

Technical Field

[0001] This invention belongs to the field of hydrogeological parameter measurement technology, and in particular relates to a spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation. Background Technology

[0002] Currently, hydrogeological parameters are crucial for simulating groundwater flow and solute transport. Traditional hydrogeological exploration methods are costly, generate limited data, and struggle to accurately measure and describe the spatial variations of hydrogeological parameters. In contrast, geophysical exploration techniques offer advantages such as speed, convenience, non-destructive testing, multi-scale capability, and large data volume, making them an important means of acquiring hydrogeological parameters in recent years. However, geophysical measurements typically do not provide direct hydrogeological information; estimation of hydrogeological parameters requires establishing relationships between physical properties and hydrogeological parameters. Furthermore, different geophysical methods vary in measurement scale, resolution, and quality control. Therefore, integrating limited borehole and experimental data with a large volume of geophysical data is essential for achieving reliable hydrogeological parameter estimation and scale expansion.

[0003] Based on the above analysis, the problems and shortcomings of the existing technology are as follows: the existing hydrogeological exploration methods are costly, have limited data volume, and it is difficult to accurately measure and describe the spatial variation of hydrogeological parameters; at the same time, the existing technology has a large degree of uncertainty in predicting hydrogeological parameters at the regional scale. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation.

[0005] This invention is implemented as follows: a spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation, wherein the spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation includes:

[0006] Project Overview: Based on the differentiation patterns of geology, geomorphology, water system, and vegetation in the study area, geophysical transects are deployed vertically along the river channel from upstream to downstream. Ground-penetrating radar (GPR), transient electromagnetic transducer (TEM), and nuclear magnetic resonance (NMR) techniques are used to acquire electrical data for monitoring hydrogeological parameters from multiple sources and at multiple scales. A mathematical model is established to establish the relationship between electrical parameters (dielectric constant, apparent resistivity, relaxation time, initial amplitude, initial phase, etc.) and hydrogeological parameters (volume water content, porosity, specific yield, and permeability). The established mathematical model is validated and corrected using geological borehole data, pumping test data, and laboratory test data. Then, the DS conflict of evidence theory is applied to fuse a small amount of borehole and test data with a large amount of geophysical exploration data, achieving mutual support and complementary advantages among geophysical data, borehole data, and test data, thereby obtaining optimized results for the integration of multi-source hydrogeological parameters. Finally, spatial interpolation techniques are used to establish a 3D geological model of hydrogeological parameters, thus achieving scale expansion of hydrogeological parameters at the regional scale.

[0007] Based on the DS evidence theory, multi-source hydrological parameter information is fused, and combined with NMR, GPR, TEM and borehole data, the optimized results of multi-source hydrogeological parameter integration are obtained.

[0008] Furthermore, the fusion of multi-source hydrological parameter information based on DS evidence theory, combined with NMR, GPR, TEM, and borehole data, yields optimized results for the integration of multi-source hydrogeological parameters, including:

[0009] First, hydrogeological parameters were obtained by inversion using NMR geophysical methods, GPR geophysical methods, and TEM geophysical methods with different field source forms.

[0010] Secondly, based on the DS evidence theory, the hydrogeological parameters obtained by inversion are combined with the collected hydrogeological data to obtain the best estimate of the hydrogeological parameters.

[0011] Furthermore, the hydrogeological parameters obtained by inversion using NMR geophysical methods, GPR geophysical methods, and TEM geophysical methods with different source forms include:

[0012] Different types and structures of geophysical data were collected using NMR, GPR, and TEM geophysical methods with different source forms; and hydrogeological parameters were obtained by inversion based on the different types and structures of geophysical data.

[0013] Furthermore, the spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation includes the following steps:

[0014] Step 1: Obtain hydrogeological background data, field and laboratory test data, and geophysical data; and summarize, organize, analyze, and calculate the acquired data.

[0015] Step two involves fusing stratigraphic division, lithological correlation, and hydrogeological parameters into the acquired data; and integrating a comprehensive characterization system for hydrogeological parameters.

[0016] Furthermore, in step two, the stratigraphic division and fusion, and lithological correlation and fusion of the acquired data include:

[0017] (1) Obtain hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data and TEM detection data;

[0018] (2) Construct an identification framework θ = {clay layer, fine sand, fine sand, uncertain} and obtain the basic probability allocation (BPA) of the identification framework θ based on the hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data and TEM detection data;

[0019] (3) Using Dempster's evidence synthesis rule, evidence is synthesized based on the basic probability allocation (BPA) of the identification framework θ of the hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data and TEM detection data, and the lithological composition of different layers is determined.

[0020] Furthermore, the acquisition of the basic probability allocation (BPA) of the identification framework θ based on the hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data, and TEM detection data includes:

[0021] The BPA of hydrogeological background data is determined by directly judging it as 0 or 1; the BPA of borehole sampling data is determined as 0 or 1 based on the composition of soil texture; where 0 represents no and 1 represents yes.

[0022] The BPA of GPR, NMR, and TEM data is determined by a membership function based on the classification range of electrical parameters dielectric constant, relaxation time T2, and apparent resistivity in different lithologies.

[0023] Furthermore, in step two, the fusion of hydrogeological parameters includes:

[0024] 1) Standardize the vertical resolution of hydrogeological parameters; calculate the permeability coefficient using the equivalent permeability coefficient method with the following formula:

[0025]

[0026] Where Kp represents the equivalent permeability coefficient, K iM represents the permeability coefficient of the i-th layer. i Indicates the thickness of the i-th stratum;

[0027] 2) Establish correlation analysis models between the electrical parameters of GPR and TEM and the hydrogeological parameters estimated by NMR, and test the correlation to select the optimal analysis model. The analysis models include: linear model, logarithmic model, exponential model, power model and polynomial fitting model.

[0028] 3) Using the analytical model, perform correlation analysis between the electrical parameters of GPR and TEM and the hydrogeological parameters estimated by NMR, and expand the vertical scale of hydrogeological parameters based on the correlation analysis results.

[0029] 4) Use pumping test and borehole test data to calibrate the range of hydrogeological parameters.

[0030] Furthermore, the spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation also includes:

[0031] A multi-point stochastic modeling method was adopted to fuse multi-source data, scale up the model, and construct a three-dimensional aquifer hydrogeological parameter model for simulation and evaluation of hydrogeological parameters.

[0032] Another object of the present invention is to provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the spatiotemporal quantitative expression method of hydrogeological parameters based on non-contact observation.

[0033] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the spatiotemporal quantitative expression method of hydrogeological parameters based on non-contact observation.

[0034] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0035] To address the scientific challenge of accurately acquiring detailed and extensive heterogeneous parameter information for the study area using traditional monitoring methods and pumping tests, this study applies non-contact observation techniques (NMR, GPR, TEM) to simultaneously monitor hydrogeological parameters using multiple methods, building upon traditional hydrogeological parameter exploration techniques. This elucidates the physical mechanisms by which NMR, GPR, and TEM techniques acquire hydrogeological parameters, collects borehole and experimental data, and integrates multi-source, multi-scale hydrogeological parameter data. This achieves complementary advantages in measurement scale, vertical resolution, and quality control, providing a holistic understanding of the regional hydrogeological conditions and integrating a characterization system for multi-source, multi-scale hydrogeological parameters. This offers a low-cost, high-precision means of acquiring hydrogeological parameters for data-scarce areas, providing technical support for reducing the uncertainty of numerical simulations of groundwater flow and solute transport.

[0036] This invention integrates a small amount of borehole and experimental data with a large amount of geophysical data, enabling reliable estimation of hydrogeological parameters and scale expansion.

[0037] This invention combines complementary information from geophysical data of different types and structures with collected hydrogeological data to obtain the best estimate of hydrogeological parameters and quantifies the uncertainty factors.

[0038] This invention fills a technological gap in the domestic and international fields: hydrogeological parameters are crucial for simulating groundwater flow and solute transport. Traditional hydrogeological exploration methods are costly and generate limited data, leading to significant uncertainties in eco-hydrological process simulations. The 4D spatiotemporal quantitative expression of hydrogeological parameters is currently a challenging and hot topic in soil hydrology. Non-contact geophysical exploration technologies such as NMR, GPR, and TEM are fast, convenient, non-destructive, multi-scale, and generate large datasets, enabling precise characterization of the spatial variability of hydraulic parameters in aquifers. By establishing a mathematical link between geophysical exploration data and hydrogeological parameters, and through data fusion technology, high-resolution spatiotemporal quantitative expression of hydrogeological parameters is achieved. This leads to the establishment of a comprehensive characterization system for hydrogeological parameters in porous media, reducing the uncertainties caused by parameter variability in eco-hydrological process simulations. This invention represents a forward-looking approach in this field both domestically and internationally.

[0039] The technical solution of this invention solves a long-standing but unresolved technical problem: the Tarim River, under the combined influence of climate change and human activities, has undergone a long process of channel evolution, resulting in complex riverbed sedimentary patterns. Due to the uniformity of sedimentary particles and small electrical property differences in the Tarim River, high resolution is required for electrical resistivity tomography. Furthermore, the surface of the deep-buried groundwater zone is dry with high grounding resistance, making grounding-based geophysical exploration methods difficult. Well logging and experimental observation are expensive and can only obtain data from a limited number of points. Considering the characteristics of the hydrogeological conditions of the Tarim River, and comprehensively comparing the advantages and disadvantages of geophysical techniques, the invention selects the complementary non-contact observation techniques NMR, GPR, and TEM. These techniques can provide high-resolution structural maps of strata tens of meters below the surface, estimate aquifer characteristics, monitor groundwater flow and pollutant distribution, offer high coverage density, rapidly obtain large amounts of data, and are relatively inexpensive. The scientific and effective use of advanced monitoring technologies to establish a quantitative relationship between geophysical data and hydrogeological characteristics is conducive to the quantitative description of the structure and physicochemical processes of the vadose zone and aquifer, improves the understanding of hydrogeological conditions at the watershed scale, provides a scientific basis for obtaining effective spatiotemporal distribution of hydrogeological parameters, and is of great significance to the research on ecological protection and water resource management of the green corridor of the Tarim River. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation provided in this embodiment of the invention.

[0041] Figure 2 This is a flowchart of the spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation provided in an embodiment of the present invention;

[0042] Figure 3 This is a comparison chart of different geophysical exploration methods provided in the embodiments of the present invention at different research scales;

[0043] Figure 4 This is a schematic diagram of the 3D parameter model of porosity of the main stream of the Tarim River provided in an embodiment of the present invention;

[0044] Figure 5 This is a schematic diagram of the 3D parameter simulation field of the water yield of the Tarim River main stream provided in an embodiment of the present invention;

[0045] Figure 6 This is a schematic diagram of the geophysical exploration profile and measuring points provided in an embodiment of the present invention;

[0046] Figure 7 This is a lithological profile of the aquifer at the Yingbaza section provided in an embodiment of the present invention;

[0047] Figure 8This is a schematic diagram illustrating the changes in the number and thickness of soil layers along the upper, middle, and lower reaches of the Tarim River, provided in an embodiment of the present invention.

[0048] Figure 9 This is a schematic diagram illustrating the analysis of soil layer number and thickness variations at different distances from the river channel in the middle reaches of the Tarim River, provided by an embodiment of the present invention.

[0049] Figure 10 This is an embodiment of the present invention showing the apparent resistivity distribution of the Tarim River from the upper to the middle to the lower reaches (0-500m). (From left to right: Tarim River source, 14th Regiment, Shaya, Yingbaza, Aqike, Ahedong, Yingsu, Kurgan). Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] To enable those skilled in the art to fully understand how the present invention is specifically implemented, this section provides an explanatory description of the embodiments that expand upon the technical solutions of the claims.

[0052] like Figures 1-2 As shown, the spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation provided in this embodiment of the invention includes the following steps:

[0053] S101: Acquire hydrogeological background data, field and laboratory test data, and geophysical data; and summarize, organize, analyze, and calculate the acquired data.

[0054] S102 involves fusing stratigraphic division, lithological correlation, and hydrogeological parameters into the acquired data; and integrating a comprehensive characterization system for hydrogeological parameters.

[0055] In step S102, the stratigraphic division and fusion, and lithological correlation and fusion of the acquired data provided in this embodiment of the invention include:

[0056] (1) Obtain hydrogeological background data, borehole sampling data, ground penetrating radar GPR detection data, nuclear magnetic resonance (NMR) detection data, and transient electromagnetic TEM detection data;

[0057] (2) Construct an identification framework θ = {clay layer, fine sand, fine sand, uncertain}, and obtain the basic probability allocation function BPA of the identification framework θ based on the hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data and TEM detection data;

[0058] (3) Using Dempster's evidence synthesis rule, evidence is synthesized based on the basic probability allocation (BPA) of the identification framework θ of the hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data and TEM detection data, and the lithological composition of different layers is determined.

[0059] The basic probability allocation (BPA) for obtaining the identification framework θ based on the hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data, and TEM detection data provided in this embodiment of the invention includes:

[0060] The BPA of hydrogeological background data is determined by directly judging it as 0 or 1; the BPA of borehole sampling data is determined as 0 or 1 based on the composition of soil texture; where 0 represents no and 1 represents yes.

[0061] The BPA of GPR, NMR, and TEM data is determined by a membership function based on the classification range of electrical parameters dielectric constant, relaxation time T2, and apparent resistivity in different lithologies.

[0062] In step S102, the hydrogeological parameter fusion provided in this embodiment of the invention includes:

[0063] 1) Standardize the vertical resolution of hydrogeological parameters; calculate the permeability coefficient using the equivalent permeability coefficient method with the following formula:

[0064]

[0065] Where Kp represents the equivalent permeability coefficient, K i M represents the permeability coefficient of the i-th layer. i Indicates the thickness of the i-th stratum;

[0066] 2) Establish correlation analysis models between the electrical parameters of GPR and TEM and the hydrogeological parameters estimated by NMR, and test the correlation to select the optimal analysis model. The analysis models include: linear model, logarithmic model, exponential model, power model and polynomial fitting model.

[0067] 3) Using the analytical model, perform correlation analysis between the electrical parameters of GPR and TEM and the hydrogeological parameters estimated by NMR, and expand the vertical scale of hydrogeological parameters based on the correlation analysis results.

[0068] 4) Use pumping test and borehole test data to calibrate the range of hydrogeological parameters.

[0069] The spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation provided in this embodiment of the invention further includes:

[0070] A multi-point stochastic modeling method was adopted to fuse multi-source data, scale up the model, and construct a three-dimensional aquifer hydrogeological parameter model for simulation and evaluation of hydrogeological parameters.

[0071] The technical solution of the present invention will be further described below with reference to specific embodiments.

[0072] This invention utilizes three geophysical exploration methods with different source forms to invert and obtain the hydrogeological parameters of the aquifer in the main stream of the Tarim River. Under the same geoelectric conditions, the anomaly morphology and magnitude of the geophysical parameters obtained by the three methods are comparable. The difference lies in the measurement scale and resolution of the three methods in the horizontal and vertical (depth) directions (e.g., Figure 3 As shown, the principles for obtaining hydrogeological parameters also differ. NMR technology can directly distinguish between aquifers and non-aquifers in multiple media; it can quickly provide quantitative interpretation and hydrogeological parameters; its average detection depth is 70m, but its anti-interference ability is relatively weak. TEM technology divides aquifer layers based on the electrical differences between the aquifer and its surrounding medium, with strong depth resolution and large exploration depth, reaching a maximum depth of over 400m. GPR technology has a fast data acquisition speed, high horizontal and vertical accuracy, and relatively intuitive images, but its disadvantage is a shallow detection depth, with an average detection depth of 30m. Field sampling borehole data ranges from 1-4m, with an average depth of 20m. The basic objective of this invention is to combine complementary information from different types and structures of geophysical data with collected hydrogeological data to obtain the best estimate of hydrogeological parameters and quantify uncertainties.

[0073] This invention addresses the issue of small sample sizes of borehole hydrogeological parameters used for verification, and the absence of contradictions or strong conflicts among information sources. It selects the DS evidence theory, which does not require a large sample size and possesses strong reasoning capabilities for uncertain information, to achieve the fusion of multi-source hydrogeological parameter information. By synthesizing the mutual support and complementary advantages of NMR, GPR, TEM, and borehole data, the optimized result of multi-source hydrogeological parameter integration is ultimately obtained. The research approach to achieving this result is as follows: Figure 3 As shown.

[0074] Permeability coefficient is a crucial parameter for identifying and validating groundwater numerical simulation models, and its spatial heterogeneity is one of the main sources of uncertainty in numerical simulations. Currently, at the small scale, field observation, sampling, and testing are the basic means of obtaining permeability coefficients. However, due to limited sampling depth, significant soil sample disturbance, and limited representativeness, the measured K value is generally underestimated. Pumping tests can objectively reflect the actual situation with high accuracy, but they are labor-intensive and costly, making them unsuitable for obtaining large-scale parameters. Different methods can obtain comparable stratigraphic and lithological data, but the estimated permeability coefficient values ​​vary significantly. NMR technology corrects for permeability coefficient inversion based on borehole data, resulting in data that are relatively close, slightly smaller than the K value obtained from pumping tests. In contrast, TEM and GPR technologies, using empirical formulas for electrical parameters, yield relatively larger K values. GPR technology provides more accurate results for lithological stratigraphy, while TEM technology has advantages in exploration depth, fusing data from stratigraphic division, lithology, and hydrogeological parameters, respectively.

[0075] (1) Stratigraphic stratification and lithological determination of water-bearing rock groups

[0076] The Dempster evidence fusion algorithm constructs an identification framework, performs a Basic Probability Assignment (BPA) on the hypotheses within that framework, and calculates the confidence interval, composed of the confidence function and likelihood function, representing the degree of confirmation. Using Dempster's rule of combination, it combines evidence from different sources to reflect the combined effect of multiple pieces of evidence. Identify a finite number of mass functions m1, m2, ... m on the framework θ n Dempster's rule of evidence synthesis is:

[0077]

[0078]

[0079] In the formula: K is called the normalization factor, 1-K is... This reflects the degree of conflict in the evidence.

[0080] Objective: To determine the lithological composition of different strata by constructing an identification framework θ = {clay layer, silty sand, fine sand, uncertain}. Let set A represent hydrogeological background data, set B represent borehole sampling data, set C represent GPR data, set D represent NMR data, and set E represent TEM data. Using m... A m B m C m D m ELet B and C represent the basic probability assignments (BPAs) of the identification framework θ obtained based on the corresponding information sets. The BPA is determined by the following parameters: A set can be directly judged as 0 (no) or 1 (yes); B set is determined as 0 or 1 based on the soil texture composition; and C, D, and E sets are determined by membership functions based on the classification range of electrical parameters (dielectric constant, relaxation time T2, and apparent resistivity) for different lithologies. Taking layer 13-15m as an example, the calculated BPAs are shown in Table 1 (the A set in clay layer S1 is not 0 or 1 because it includes different lithologies). Considering that the focal element B data in this case is empty, it is not included in the calculation. Evidence A, C, D, and E are fused. Table 2 shows the fusion results: as multiple pieces of evidence are fused, the uncertainty in lithology judgment gradually decreases, and the basic probability assignment of this layer as a silty fine sand layer gradually becomes more prominent, indicating that the target is indeed a silty fine sand layer.

[0081] Table 1. BPA from various data sources

[0082]

[0083]

[0084] Table 24 Basic Probability Assignments After Synthesizing Evidence

[0085] clay layer 0.702 0.269 0.162 fine sand 0.266 0.701 0.825 fine sand 0.000 0.018 0.009 uncertain 0.032 0.012 0.004

[0086] (2) Data fusion of hydrogeological parameters

[0087] Stratigraphic strata and lithology obtained by different methods are comparable and can be further confirmed using the DS evidence fusion method. However, the estimated permeability coefficient values ​​differ significantly. NMR technology corrects for permeability coefficient inversion based on borehole data, resulting in a closer correlation between the two sets of data, with the value slightly smaller than the K value obtained from pumping tests. In contrast, TEM and GPR technologies yield relatively larger K values ​​calculated using empirical formulas for electrical parameters. Furthermore, they possess different vertical resolutions. To conduct correlation comparisons of multi-source data, it is first necessary to standardize the vertical resolution of hydrogeological parameters. The permeability coefficient can be determined using the equivalent permeability coefficient method (Equation 10).

[0088]

[0089] In the formula: Kp is the equivalent permeability coefficient, K i Let M be the permeability coefficient of the i-th layer. i Let be the thickness of the i-th stratum.

[0090] When fusing permeability coefficients from multiple sources at a single point, the amount of data truly usable for verification is scarce due to differences in measurement scales, making it difficult to guarantee the reliability of the verification results. This invention argues that NMR monitoring data from direct water-finding technology is less affected by other factors in the inversion of hydrogeological parameters, and the results can be considered relatively reliable. TEM parameter estimation is affected by variables such as mineralization, temperature, and lithological factors, increasing the uncertainty of hydrogeological parameter estimation. Furthermore, uncertainty already arises when acquiring dielectric constants using GPR technology. Therefore, correlation analysis models (linear, logarithmic, exponential, power, and polynomial models) are established between the electrical parameters of GPR and TEM and the hydrogeological parameters estimated by NMR, and their correlations are tested. The optimal model is selected for predicting hydrogeological parameters. Finally, the range of hydrogeological parameters is calibrated using a small amount of pumping and borehole test data.

[0091] The correlation analysis plot of TEM electrical parameter apparent resistivity and NMR inverted porosity data shows that the fourth-order polynomial fit between apparent resistivity and porosity is the best, R0 2 The correlation coefficient reached 0.858. The correlation between TEM electrical parameters and NMR results allows for the expansion of hydrogeological parameters to the vertical scale.

[0092] To demonstrate the inventiveness and technical value of the technical solution of this invention, this section provides specific product or related technology application examples of the technical solution claimed.

[0093] The present invention provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the spatiotemporal quantitative expression method of hydrogeological parameters based on non-contact observation.

[0094] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the spatiotemporal quantitative expression method of hydrogeological parameters based on non-contact observation.

[0095] The embodiments of the present invention have achieved some positive results during the research and development or use process, and have indeed great advantages compared with the prior art. The following content describes them in conjunction with the data, charts and other information of the experimental process.

[0096] This invention represents a preliminary attempt to improve geophysical methods for estimating hydrogeological parameters. A multi-point stochastic modeling approach was employed, and a three-dimensional aquifer hydrogeological parameter model was constructed through multi-source data fusion and scale expansion. This model successfully simulated and evaluated the hydrogeological parameters of the study area. Figure 4 , Figure 5 As shown.

[0097] The multi-point geostatistics employed in this invention can more realistically reflect the original fluctuation characteristics of hydrogeological parameters. However, the acquired data is still fragmented, locally sampled data. Quantifying the 3D spatial variability characteristics of hydrogeological parameters still requires spatial continuity modeling that can reflect the scale characteristics of spatial information and process models, as well as the error propagation characteristics of the problem domain reflected by the spatial pattern.

[0098] The Tarim River's alluvial plain stretches in an east-west strip between the Taklamakan Desert and the alluvial-diluvial plain at the northern foot of the Tianshan Mountains, situated in a piedmont depression between the Tianshan geosyncline and the Tarim Plateau. Historically, the Tarim River was a notoriously volatile river. In the north, the uplift caused by the Tianshan fold structure extended the alluvial fan-shaped plain southward, forcing the river to shift southward. Conversely, the accumulation of alluvial deposits and aeolian sands in the southern alluvial plain forced the river to turn northward, creating a vast and deep plain. Due to the uplift in the south of the Tarim River, there are numerous ancient river channels on the southern side of the main stream. The sediments along these ancient channels are relatively coarse, and the groundwater is relatively abundant and of relatively good quality, resulting in a complex stratigraphic structure with alternating layers of fluvial alluvial sand and silty soil.

[0099] NMR technology applications

[0100] Based on factors such as geology, land use, and groundwater depth, ground exploration sections were arranged perpendicular to the main channel of the Tarim River from its upper reaches to its lower reaches. Figure 6 Hydrogeological surveys were conducted during both the dry and wet seasons. NMR technology was used, employing single-turn square coils (90m x 90m) and the FID (Pluselength = 50ms, Tr = 4s) and CPMG (Pluselength = 20ms, Tr = 4s, Echoes = 4) methods. Depending on the level of environmental noise, 16-32 replicates were established. After noise reduction of the observation data, hydrogeological parameters such as aquifer depth, thickness, water content per unit volume, aquifer porosity, and conductivity were retrieved.

[0101] The aquifers in the study area have a uniform lithology, mainly composed of sandy and clayey strata. The sandy strata are primarily silt and fine sand, with some sections containing medium-coarse sand and gravelly medium-coarse sand. The clayey strata are mainly sub-clay, often appearing as thin interlayers. Taking the Yingbaza section as an example (10 sites), the lithological variation pattern is as follows: the upper part is dominated by silty and fine sand, interbedded with thin layers of sub-clay and sub-sand; the lower part has an increase in clayey layers, interbedded with sandy layers, and the bottom is sandy. Figure 7 T2 obtained from NMR measurement points *Based on the distribution of aquifer types in Table 3, a lithological profile of the strata was drawn. Calculations show that silt soil accounts for approximately 55.02% of the total strata thickness, fine sand approximately 25.9%, sub-clay approximately 8.24%, medium sand 2.03%, coarse sand 1.97%, gravel 0.47%, and other types 6.36%. Along the paleochannels, the sediment particles are relatively coarse, forming a shallow, freshwater zone.

[0102] Table 3 Approximate Relationship between NMR Relaxation Time T2* and Aquifer Type [5]

[0103] <30 Sub-clay layer 30-60 silt layer 60-120 fine sand layer 120-180 medium to coarse sand layer 180-300 Coarse sand and gravelly sand layers 300-600 Gravel layer 600-1000 Surface water bodies

[0104] GPR technology application

[0105] The results of surface stratification of the Tarim River main stream soil using 250MHz ground-penetrating radar are as follows: Figure 8 , Figure 9 As shown. The study analyzed the variation patterns of soil layer number and thickness at different distances from the river channel in the upper, middle, and lower reaches of the Tarim River, obtaining the spatial variability of soil layer number and thickness. The interpretation depth of the 250MHz ground-penetrating radar was within 4m. The number of soil differentiation layers did not differ significantly across river sections. The thickness of the first layer was less than 0.5m in the upper and middle reaches, while it ranged from 0.88 to 1.64m in the middle and lower reaches. The thickness of the second layer was 0.25-0.78m, the third layer was 0.18-2.28m, and the fourth layer had an average thickness of 0.669m. Perpendicular to the river channel, the thickness of the surface soil layer on the south bank of the Tarim River initially decreased and then increased with distance from the river channel, while the thickness on the north bank of the Tarim River showed a decreasing trend.

[0106] TEM technology applications

[0107] The transient electromagnetic measurement inversion results show a depth of approximately 400m, revealing a region with high surface resistivity that gradually decreases with depth. Based on these properties, the area is essentially divided into three layers: a high-resistivity surface layer, presumed to be overlying Quaternary sediments; a middle layer, presumed to be a water-bearing sandstone layer with low resistivity; and a lower layer of medium- to low-resistivity material, possibly Tertiary sandstone. Figure 10 The region has a relatively arid climate, and the differences reflected in resistivity are a comprehensive reflection of changes in lithology and groundwater mineralization.

[0108] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0109] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for spatio-temporal quantitative expression of hydrogeological parameters based on non-contact observation method, characterized in that, The spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation includes: based on the differentiation patterns of geology, geomorphology, water system, and vegetation in the study area, geophysical transects are arranged vertically along the river channel from upstream to downstream. Ground-penetrating radar (GPR), transient electromagnetic TEM, and nuclear magnetic resonance (NMR) methods are used to acquire electrical data of hydrogeological parameters from multiple sources and at multiple scales; a mathematical relationship model between electrical parameters and hydrogeological parameters is established, and the established mathematical relationship model is verified and corrected using geological borehole data, pumping test data, and indoor test data; then, the DS evidence conflict theory is used to fuse a small amount of borehole and test data with a large amount of geophysical exploration data to obtain the optimized result of multi-source hydrogeological parameter integration; finally, spatial interpolation technology is used to establish a 3D geological model of hydrogeological parameters, thereby realizing the scale expansion of hydrogeological parameters at the regional scale. The DS evidence conflict theory is used to fuse multi-source hydrological parameter information, combining NMR, GPR, TEM, and borehole data to obtain optimized results of multi-source hydrogeological parameter integration, including: First, hydrogeological parameters were obtained by inversion using NMR geophysical methods, GPR geophysical methods, and TEM geophysical methods with different field source forms. Secondly, based on the DS evidence conflict theory, the hydrogeological parameters obtained by inversion are combined with the collected hydrogeological data to obtain the best estimate of the hydrogeological parameters. The spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation includes the following steps: Step 1: Obtain hydrogeological background data, field and laboratory test data, and geophysical data; and summarize, organize, analyze, and calculate the acquired data. Step two involves fusing stratigraphic division, lithological correlation, and hydrogeological parameters into the acquired data; and integrating a comprehensive characterization system for hydrogeological parameters. In step two, the stratigraphic division and fusion, and lithological correlation and fusion of the acquired data include: (1) Obtain hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data and TEM detection data; (2) Constructing a recognition framework and obtaining a basic probability assignment (BPA) of the recognition framework based on the hydrogeological background information, the drilling sampling data, the GPR detection data, the NMR detection data, and the TEM detection data . (3) An identification framework based on the aforementioned hydrogeological background data, borehole sampling data, GPR detection data, NMR detection data, and TEM detection data, using the Dempster evidence synthesis method. The basic probability allocation (BPA) is used to synthesize evidence and determine the lithological composition of different strata.

2. The method of claim 1, wherein the method is characterized by, The hydrogeological parameters obtained by inversion using NMR geophysical methods, GPR geophysical methods, and TEM geophysical methods with different source forms include: Different types and structures of geophysical data were collected using NMR, GPR, and TEM geophysical methods with different source forms; and hydrogeological parameters were obtained by inversion based on the different types and structures of geophysical data.

3. The method of claim 1, wherein the method is characterized by, The acquisition is based on the hydrogeological background information, drilling sampling data, GPR detection data, NMR detection data and TEM detection data recognition framework The basic probability assignment BPA includes: The BPA of hydrogeological background data is determined by directly judging it as 0 or 1; the BPA of borehole sampling data is determined as 0 or 1 based on the composition of soil texture; where 0 represents no and 1 represents yes. The BPA of GPR, NMR, and TEM data is determined by a membership function based on the classification range of electrical parameters dielectric constant, relaxation time T2, and apparent resistivity in different lithologies.

4. The method of claim 1, wherein the method is characterized by, Step two, the fusion of hydrogeological parameters, includes: 1) Standardize the vertical resolution of hydrogeological parameters; calculate the permeability coefficient using the equivalent permeability coefficient method with the following formula: where Kp represents the equivalent permeability coefficient, K i represents the permeability coefficient of the i-th layer, M i represents the thickness of the i-th layer 2) Establish correlation analysis models between the electrical parameters of GPR and TEM and the hydrogeological parameters estimated by NMR, and test the correlation to select the optimal analysis model. The analysis models include: linear model, logarithmic model, exponential model, power model and polynomial fitting model. 3) Using the analytical model, perform correlation analysis between the electrical parameters of GPR and TEM and the hydrogeological parameters estimated by NMR, and expand the vertical scale of hydrogeological parameters based on the correlation analysis results. 4) Use pumping test and borehole test data to calibrate the range of hydrogeological parameters.

5. The method of claim 1, wherein the method is characterized by, The spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation also includes: A multi-point stochastic modeling method was adopted to fuse multi-source data, scale up the model, and construct a three-dimensional aquifer hydrogeological parameter model for simulation and evaluation of hydrogeological parameters.

6. A computer device, comprising: The computer device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor causes the processor to perform the steps of the spatiotemporal quantitative expression method of hydrogeological parameters based on non-contact observation as described in any one of claims 1-5.

7. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the spatiotemporal quantitative expression method for hydrogeological parameters based on non-contact observation as described in any one of claims 1-5.

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