A method and technical framework for inversion of water pressure at the base of mountain glaciers based on remote sensing observations

Abstract

The present application provides a mountain glacier bottom water pressure inversion method and technical framework based on remote sensing observation, comprising: step 1, obtaining the pre-processed monthly scale glacier surface elevation data set and monthly scale glacier surface flow rate data set of the target time period of the target mountain glacier, and the bedrock surface elevation of the target mountain glacier; step 2, updating the geometric parameters of each month in the target time period of the target mountain glacier monthly; step 3, obtaining the mechanical parameters of each month in the target time period of the target mountain glacier based on the IGM model; step 4, obtaining the bed roughness; step 5, constructing the bottom water pressure solving formula of the target mountain glacier based on the regularized Coulomb friction law; step 6, obtaining the bottom water pressure data set of the target time period of the target mountain glacier; step 7, obtaining the bottom water pressure spatio-temporal distribution diagram of the target time period of the target mountain glacier and displaying. The present application can efficiently, stably and standardize the inversion of the bottom water pressure of the mountain glacier.

Description

Technical Field

[0001] This invention relates to a method and technical framework for inverting water pressure at the base of mountain glaciers based on remote sensing observation, belonging to the field of remote sensing image processing technology. Background Technology

[0002] Obtaining information on the base water pressure of mountain glaciers is a crucial prerequisite for understanding their dynamic evolution. Traditional in-situ observation methods (such as seismic wave methods and ground-penetrating radar) are limited by complex terrain and high costs, resulting in extremely small observation ranges. The method of quantitatively inverting base water pressure by coupling observational data obtained through remote sensing technology with glacier dynamic models has become the main development direction. However, it still has shortcomings in terms of data-model coupling capabilities and multi-parameter joint solutions. First, existing methods are mostly designed for polar ice sheets, employing "fixed geometric assumptions" and using traditional finite element ice flow models (such as Elmer / Ice) as solvers. When these methods are applied to mountain glaciers with significant surface elevation variations, the fixed geometric boundaries cannot accurately reflect the changes in overlying pressure at the glacier base. This boundary distortion forces traditional finite element ice flow models to drastically adjust the slip coefficient at the glacier base to compensate for systematic stress deviations when solving the mechanical equations, leading to numerical oscillations in the inversion process and difficulty in converging the inversion results. Secondly, the complex local topography of mountain glaciers requires high-resolution flow velocity and digital elevation model (DEM) data for inversion, which places an excessive computational burden on traditional finite element ice flow models when performing long-term, continuous high-temporal-resolution (e.g., monthly) inversions. Thirdly, even with the introduction of the more computationally efficient IGM model, limitations in the physical boundary constraints of its internal governing equations limit its output to dynamic variables such as bottom slip velocity and shear stress, preventing direct analysis of bottom water pressure. Furthermore, its fixed geometric boundaries fail to accurately reflect changes in overlying pressure at the glacier floor. Finally, existing methods suffer from low integration; data preprocessing, mechanical parameter inversion, and bottom water pressure calculation are fragmented, hindering widespread application. Summary of the Invention

[0003] The purpose of this invention is to provide a method and technical framework for inverting water pressure at the base of mountain glaciers based on remote sensing observation, so as to solve the problems existing in the prior art.

[0004] To address the aforementioned technical problems, this invention provides a method for retrieving water pressure at the base of mountain glaciers based on remote sensing observations, comprising:

[0005] Step 1: Obtain the preprocessed monthly-scale glacier surface elevation dataset and monthly-scale glacier surface velocity dataset for the target time period of the target mountain glacier, as well as the bedrock elevation of the target mountain glacier;

[0006] Step 2: Update the geometric parameters of the target mountain glacier for each month within the target time period. The geometric parameters include ice thickness H and ice surface slope α.

[0007] Step 3: Based on the IGM model, obtain the monthly mechanical parameters of the target mountain glacier within the target time period. The mechanical parameters include the base friction coefficient β and the base shear stress. , Substrate sliding velocity u b Model simulation of glacier surface flow velocity u s0 ;

[0008] Step 4: Obtain the bed surface roughness based on the average value of the base sliding velocity and the average value of the base shear stress within the target time period of the target mountain glacier;

[0009] Step 5: Construct a formula for calculating the bottom water pressure of the target mountain glacier based on the regularized Coulomb friction law;

[0010] Step 6: Obtain the bottom water pressure dataset for the target mountain glacier during the target time period: Based on the monthly geometric parameters obtained in Step 2, the monthly mechanical parameters obtained in Step 3, and the bed roughness, the bottom water pressure of the target mountain glacier during the target time period is obtained month by month through the bottom water pressure calculation formula and then corrected to obtain the bottom water pressure dataset for the target mountain glacier during the target time period.

[0011] Step 7: Based on the bottom water pressure dataset obtained in Step 6, obtain and display the spatiotemporal distribution map of bottom water pressure in the target time period of the target mountain glacier.

[0012] In one specific implementation, step 2 includes the following steps: Step 2.1: Calculate the ice thickness for the current month by subtracting the bedrock elevation of the glacier from the current month's glacier surface elevation using a pixel-by-pixel calculation method; Step 2.2: Calculate the ice slope for the current month based on the current month's glacier surface elevation; Step 2.3: Repeat steps 2.1 to 2.2 to obtain the ice thickness and ice slope for all months within the target time period of the target mountain glacier.

[0013] In one specific implementation, step 3 includes the following steps: Step 3.1: Input the glacier surface flow velocity and geometric parameters of the current month into the IGM model, and the IGM model performs mechanical parameter inversion to obtain the mechanical parameters of the target mountain glacier for the current month; Step 3.2: Repeatedly execute step 3.1 to obtain the mechanical parameters of the target mountain glacier for all months within the target time period.

[0014] In one specific embodiment, the bed surface roughness is obtained through the following steps: Step 4.1, initial bed surface roughness A S0 Through public The calculation yielded the following result; Step 4.2: The initial bed surface roughness A was determined.S0 Correction to obtain bed surface roughness A S ,in This represents the average value of the base sliding velocity. This represents the average value of the base shear stress.

[0015] In one specific implementation, the bottom water pressure calculation formula is constructed through the following steps: Step 5.1, constructing the effective stress balance equation based on Terzaghi's effective stress principle: N=p i -p w In the formula, N is the effective pressure at the bottom of the glacier, p i For the static pressure of the ice body, p i =ρgH,p w ρ is the water pressure at the bottom of the glacier, g is the density of the ice, and g is the acceleration due to gravity.

[0016] Step 5.2: Based on the regularized Coulomb friction law, construct the constitutive relationship between the base shear stress, base sliding velocity, effective pressure at the glacier bottom, and bed surface characteristic parameters. The expression is: Where C is the interfacial friction constant of the ice bed, with a value of 0.16, and n is the Glen flow index, with a value of 3;

[0017] Step 5.3: By substituting the expression for the effective pressure N at the base of the glacier into the constitutive equation constructed in Step 5.2, the formula for calculating the water pressure at the base of the glacier is obtained: .

[0018] In one specific implementation, the preprocessed monthly glacier surface elevation dataset for the target time period of the target mountain glacier is based on ASTER imagery. The initial DEM of the ASTER imagery is extracted using photogrammetry techniques, and then regularized reconstruction is performed on the initial DEM to generate the dataset.

[0019] In one specific implementation, the preprocessed monthly glacier surface velocity dataset for the target time period of the target mountain glacier is generated by extracting the initial deformation field from Sentinel-2 images and then post-processing the extracted initial deformation field.

[0020] In one specific implementation, the elevation of the bedrock surface of the target mountain glacier is calculated by subtracting the ice thickness data from the ice surface elevation of the target mountain glacier represented by NASA DEM.

[0021] This invention provides a framework for remote sensing-based inversion of water pressure at the base of mountain glaciers, comprising:

[0022] Data acquisition and preprocessing module: Acquires and preprocesses remote sensing images of the target mountain glacier for the target time period, and obtains the preprocessed monthly-scale glacier surface elevation dataset and monthly-scale glacier surface velocity dataset for the target mountain glacier for the target time period, as well as the glacier bedrock elevation of the target mountain glacier. The preprocessed monthly-scale glacier surface elevation dataset and glacier bedrock elevation for the target mountain glacier for the target time period are output to the geometric parameter update module, and the preprocessed monthly-scale glacier surface velocity for the target mountain glacier for the target time period is output to the IGM dynamics workflow inversion module.

[0023] Geometric parameter update module: Based on the preprocessed monthly glacier surface elevation dataset and monthly glacier bedrock elevation of the target mountain glacier during the target time period output by the data acquisition and preprocessing module, the geometric parameters of the target mountain glacier during the target time period are updated monthly and output to the IGM dynamic process inversion module and bottom water pressure calculation module.

[0024] The IGM dynamics workflow inversion module: Based on the monthly surface velocity data of the target mountain glacier during the target time period and the geometric parameters of the target mountain glacier during the target time period, the IGM model is used to obtain the mechanical parameters of the target mountain glacier during the target time period each month and output them to the bed roughness calculation module and the bottom water pressure calculation module.

[0025] Bed surface roughness calculation module: Calculates the bed surface roughness based on the mechanical parameters of the target mountain glacier for each month within the target time period and outputs it to the bottom water pressure calculation module;

[0026] Bottom water pressure calculation and correction module: The bottom water pressure calculation formula of the target mountain glacier is constructed based on the regularized Coulomb friction law. According to the bed surface roughness, the mechanical parameters of the target mountain glacier in the target time period and the geometric parameters of the target mountain glacier in the target time period, the bottom water pressure dataset of the target mountain glacier in the target time period is obtained through the bottom water pressure calculation formula and output to the bottom water pressure visualization module.

[0027] Bottom water pressure visualization module: Based on the bottom water pressure dataset of the target mountain glacier within the target time period, obtain and display the spatial distribution field and spatiotemporal distribution map of bottom water pressure within the target time period of the target mountain glacier. Generate and display the corresponding monthly scale spatiotemporal distribution map of mechanical parameters based on the monthly mechanical parameters of the target mountain glacier within the target time period, and display the bed roughness.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows.

[0029] 1. This invention dynamically updates the geometric parameters of mountain glaciers to match the drastic changes in elevation, thereby eliminating stress calculation bias caused by "fixed geometric assumptions." Simultaneously, it employs an IGM model based on a physical information neural network to solve for mechanical parameters. Addressing the issue that the IGM model cannot directly analyze bottom water pressure, this invention constructs a bottom water pressure calculation formula based on a regularized Coulomb friction law. By solving for the bottom water pressure of mountain glaciers based on mechanical parameters, this invention significantly reduces computational dimensionality and solution complexity, effectively improving the stability and efficiency of long-term mechanical parameter inversion, thus efficiently, stably, and in a standardized manner inverting the bottom water pressure of mountain glaciers.

[0030] 2. This invention integrates the data acquisition and preprocessing module, the geometric parameter update module, the IGM dynamic process inversion module, the bed roughness calculation module, the bottom water pressure calculation and correction module, and the bottom water pressure visualization module into a standardized, automated, and low-operation-threshold bottom water pressure inversion technology framework for mountain glaciers, which enables efficient, stable, and standardized inversion of bottom water pressure in mountain glaciers. Attached Figure Description

[0031] Figure 1 This is a flowchart of a method for retrieving bottom water pressure in mountain glaciers based on remote sensing observations.

[0032] Figure 2 This is a map of the target mountain glacier in an embodiment of the present invention (the dashed lines represent the flow lines in the glacier).

[0033] Figure 3 This is a preprocessed monthly glacier surface elevation dataset (along the glacier streamline) from an embodiment of the present invention.

[0034] Figure 4 This is a preprocessed dataset of monthly glacier surface velocity (along the glacier streamline) from an embodiment of the present invention.

[0035] Figure 5 The diagram shows the spatiotemporal distribution of mechanical parameters (along the streamlines in the glacier) in an embodiment of the present invention, where a is the spatiotemporal distribution of the flow velocity on the glacier surface simulated by the model, b is the spatiotemporal distribution of the sliding flow velocity on the base, c is the spatiotemporal distribution of the shear stress on the base, and d is the spatiotemporal distribution of the friction coefficient on the base.

[0036] Figure 6 The surface roughness (along the streamline in the glacier) is the bed roughness in an embodiment of the present invention.

[0037] Figure 7 This is a monthly average distribution map of bottom water pressure (along the glacier streamline) according to an embodiment of the present invention.

[0038] Figure 8 This is a spatiotemporal distribution map of bottom water pressure (along the streamline of the glacier) according to an embodiment of the present invention. Detailed Implementation

[0039] The present invention will now be described in detail with reference to the embodiments and accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0040] refer to Figure 1 A method for inverting water pressure at the base of a mountain glacier based on remote sensing observation includes step 1: obtaining a preprocessed monthly glacier surface elevation dataset and a monthly glacier surface velocity dataset for the target time period of the target mountain glacier, and the bedrock elevation of the target mountain glacier;

[0041] Preferably, the preprocessed monthly glacier surface elevation dataset for the target time period of the target mountain glacier is based on ASTER imagery. The initial DEM of the ASTER imagery is extracted using photogrammetry techniques, and then regularized reconstruction is performed on the initial DEM to generate the dataset.

[0042] Preferably, the preprocessed monthly glacier surface velocity dataset for the target time period of the target mountain glacier is generated by extracting the initial deformation field from Sentinel-2 images and then postprocessing the extracted initial deformation field.

[0043] Preferably, the elevation of the bedrock surface of the target mountain glacier is calculated by subtracting the ice thickness data of the target mountain glacier from the ice surface elevation published by NASA DEM;

[0044] This embodiment performs bottom water pressure inversion on a mountain glacier from January 2016 to January 2020. The target mountain glacier in this embodiment is a reference. Figure 2 The preprocessed monthly glacier surface elevation dataset in this embodiment is a reference. Figure 3 The preprocessed monthly glacier surface velocity dataset in this embodiment is referenced. Figure 4 ;

[0045] Step 2: Update the geometric parameters of the target mountain glacier monthly within the target time period. The geometric parameters include ice thickness H and ice surface slope α. This breaks the "fixed geometric assumption" of the existing technology and effectively eliminates the systematic bias in stress calculation caused by fixed geometry.

[0046] Step 3: Based on the IGM model, obtain the monthly mechanical parameters of the target mountain glacier within the target time period. The mechanical parameters include the base friction coefficient β and the base shear stress. , Substrate sliding velocity u b Model simulation of glacier surface flow velocity u s0 Using the IGM model can significantly reduce the computational dimensionality and solution complexity, thereby improving the efficiency of mechanical parameter inversion.

[0047] Step 4: Obtain the bed surface roughness based on the average value of the base sliding velocity and the average value of the base shear stress within the target time period of the target mountain glacier. The bed surface roughness reference in this embodiment is... Figure 6 It exhibits the numerical distribution characteristics and spatial heterogeneity of bed surface roughness;

[0048] Step 5: Construct a formula for calculating the bottom water pressure of the target mountain glacier based on the regularized Coulomb friction law;

[0049] Step 6: Obtain the bottom water pressure dataset for the target mountain glacier during the target time period: Based on the monthly geometric parameters obtained in Step 2, the monthly mechanical parameters obtained in Step 3, and the bed roughness, the bottom water pressure of the target mountain glacier during the target time period is obtained month by month through the bottom water pressure calculation formula and then corrected to obtain the bottom water pressure dataset for the target mountain glacier during the target time period, thus realizing the bottom water pressure solution based on dynamic parameters.

[0050] Step 7: Based on the bottom water pressure dataset obtained in Step 6, obtain and display the spatiotemporal distribution map of bottom water pressure in the target time period of the target mountain glacier. Specifically, obtain the spatial distribution field of bottom water pressure in the target time period of the target mountain glacier based on the bottom water pressure dataset in the target time period of the target mountain glacier, and obtain the spatiotemporal distribution map of bottom water pressure based on the spatial distribution field of bottom water pressure in the target time period of the target mountain glacier. The bottom water pressure spatial distribution map in this embodiment is referenced. Figure 7 The spatiotemporal distribution diagram of bottom water pressure in this embodiment is referenced. Figure 8 It presents the spatial distribution characteristics and seasonal evolution patterns of bottom water pressure in this embodiment, which can provide bottom water pressure data support for the analysis of the dynamic evolution mechanism of mountain glaciers, risk assessment of glacier instability disasters, and early warning.

[0051] Based on actual needs, monthly spatiotemporal distribution maps of mechanical parameters can be generated and displayed according to the monthly mechanical parameters of the target mountain glacier within the target time period. These maps can display surface roughness and include spatiotemporal distribution maps of the simulated glacier surface flow velocity, substrate sliding flow velocity, substrate shear stress, and substrate friction coefficient. The spatiotemporal distribution maps of mechanical parameters in this embodiment are for reference only. Figure 5 .

[0052] Specifically, step 2 includes the following steps: Step 2.1: Calculate the ice thickness for the current month by subtracting the bedrock elevation from the glacier surface elevation for the current month using a pixel-by-pixel calculation method; Step 2.2: Calculate the ice slope for the current month based on the glacier surface elevation for the current month; Step 2.3: Repeat steps 2.1 to 2.2 to obtain the ice thickness and ice slope for all months within the target time period for the target mountain glacier.

[0053] Specifically, step 3 includes the following steps: Step 3.1: Input the glacier surface flow velocity and geometric parameters of the current month into the IGM model. The IGM model performs mechanical parameter inversion to obtain the mechanical parameters of the target mountain glacier for the current month; Step 3.2: Repeat step 3.1 to obtain the mechanical parameters of the target mountain glacier for all months within the target time period.

[0054] Specifically, the process of inverting mechanical parameters using the IGM model in step 3.1 is as follows: The IGM model decomposes the simulated glacier surface velocity into intra-ice shear deformation velocity and base sliding velocity, as shown in the formula: u s0 =u deform +u b , where u deform This refers to the flow velocity during shear deformation within the ice.

[0055] The intra-ice shear deformation velocity is calculated using the following formula: Where A is the ice rheological parameter, the driving stress of the glacier base is calculated pixel by pixel based on the ice thickness H and the ice surface slope α. , ;

[0056] The ice-rock interface sliding process is quantitatively described by Weertman's sliding law, expressed as follows: In the formula, under the shallow ice approximation, the base shear stress is equal to the glacier base driving stress, β is the base friction coefficient, and m is the Weertman slip index, which is 3.

[0057] Using the glacier surface flow velocity as a constraint, a total cost function is constructed as the minimization objective for parameter optimization. By iteratively solving the minimization model, the deviation between the glacier surface flow velocity and the actual glacier surface flow velocity is simulated, and the optimal base friction coefficient is obtained. The total cost function consists of a velocity mismatch term and a spatial regularization term, and its specific expression is shown in the following equation: In the formula, J(β) is the total loss function; u s σ represents the surface velocity of the glacier obtained through remote sensing. u The uncertainty of the glacier surface velocity is used to balance the observation error weights; λ is the regularization weight coefficient; R(β) is the spatial regularization term used to suppress numerical oscillations in the inversion process and ensure the spatial smoothness of the friction coefficient field; M is the total number of effective observation pixels; and i is an integer. During the inversion process, the substrate friction coefficient β is iteratively optimized using the gradient descent algorithm to minimize the loss function J(β). The final output is the converged β field, and the simulated surface velocity, substrate shear stress, and substrate sliding velocity are obtained simultaneously.

[0058] Specifically, the surface roughness is obtained through the following steps: Step 4.1, initial surface roughness A S0Through formula The calculation yielded the following result; Step 4.2: The initial bed surface roughness A was determined. S0 Correction to obtain bed surface roughness A S ,in This represents the average value of the base sliding velocity. This represents the average value of the base shear stress.

[0059] To mitigate the interference between the original observations and model inversion errors, the obtained initial bed surface roughness A needs to be adjusted. S0 The correction is implemented, and the specific steps are as follows: First, a threshold method is used for masking to remove invalid solutions; second, the relative error of the parameters is evaluated based on the standard deviation of the time series data, and twice the standard deviation is deducted as a safety penalty term, thereby effectively suppressing the propagation and amplification of errors; finally, a Gaussian space smoothing algorithm is introduced to filter out residual computational noise.

[0060] Specifically, the bottom water pressure calculation formula is constructed through the following steps:

[0061] Step 5.1: Construct the effective stress equilibrium equation based on Terzaghi's effective stress principle: N=p i -p w In the formula, N is the effective pressure at the bottom of the glacier, p i For the static pressure of the ice body, p i =ρgH,p w ρ is the water pressure at the bottom of the glacier, g is the density of the ice, and g is the acceleration due to gravity.

[0062] Step 5.2: Based on the regularized Coulomb friction law, construct the constitutive relationship between the base shear stress, base sliding velocity, effective pressure at the glacier bottom, and bed surface characteristic parameters. The expression is: Where C is the interfacial friction constant of the ice bed, with a value of 0.16, and n is the Glen flow index, with a value of 3;

[0063] Step 5.3: By substituting the expression for the effective pressure N at the base of the glacier into the constitutive equation constructed in Step 5.2, the formula for calculating the water pressure at the base of the glacier is obtained: .

[0064] The steps for correcting the bottom water pressure of the target mountain glacier for all months within the target time period are as follows: First, cubic spline interpolation is used to fit the time-series variation curve, eliminating high-frequency abnormal fluctuations and retaining the seasonal evolution pattern of glacier water pressure; then, combined with the glacier's ice surface slope characteristics, a nonlocal mean smoothing algorithm is used to suppress computational noise in areas with high topographic relief. After the above correction and processing, a spatiotemporally continuous and accurate bottom water pressure dataset for the target mountain glacier within the target time period is finally obtained.

[0065] This invention provides a framework for remote sensing-based inversion of water pressure at the base of mountain glaciers, comprising:

[0066] Data Acquisition and Preprocessing Module: Acquires and preprocesses remote sensing images of the target mountain glacier for the target time period, obtaining preprocessed monthly-scale glacier surface elevation datasets and monthly-scale glacier surface velocity datasets for the target mountain glacier for the target time period, as well as the bedrock elevation of the target mountain glacier. The preprocessed monthly-scale glacier surface elevation datasets and bedrock elevations for the target mountain glacier for the target time period are output to the geometric parameter update module, and the preprocessed monthly-scale glacier surface velocity datasets for the target mountain glacier for the target time period are output to the IGM dynamics workflow inversion module. Specifically, it acquires and preprocesses monthly-scale glacier surface elevation ASTER images for the target mountain glacier for the target time period and outputs them to the geometric parameter update module; it acquires and preprocesses monthly-scale glacier surface velocity Sentinel-2 images for the target mountain glacier for the target time period and outputs them to the IGM dynamics workflow inversion module; and it acquires NASA... The DEM publishes the ice surface elevation data and ice thickness data of the target mountain glacier, and subtracts the two to obtain the bedrock surface elevation of the target mountain glacier. The bedrock surface elevation of the target mountain glacier is then output to the geometric parameter update module.

[0067] Geometric parameter update module: Based on the preprocessed monthly glacier surface elevation dataset and glacier bedrock elevation of the target mountain glacier during the target time period output by the data acquisition and preprocessing module, the geometric parameters of the target mountain glacier during the target time period are updated monthly and output to the IGM dynamic process inversion module and bottom water pressure calculation module.

[0068] The IGM dynamics workflow inversion module: Based on the monthly surface velocity data of the target mountain glacier during the target time period and the geometric parameters of the target mountain glacier during the target time period, the IGM model is used to obtain the mechanical parameters of the target mountain glacier during the target time period each month and output them to the bed roughness calculation module and the bottom water pressure calculation module.

[0069] Bed surface roughness calculation module: Calculates the bed surface roughness based on the mechanical parameters of the target mountain glacier for each month within the target time period and outputs it to the bottom water pressure calculation module;

[0070] Bottom water pressure calculation and correction module: Based on the regularized Coulomb friction law, the bottom water pressure calculation formula of the target mountain glacier is constructed. According to the bed surface roughness, the mechanical parameters of the target mountain glacier in each month of the target time period, and the geometric parameters of the target mountain glacier in each month of the target time period, the bottom water pressure dataset of the target mountain glacier in the target time period is obtained through the bottom water pressure calculation formula and output to the bottom water pressure visualization module. The bottom water pressure calculation and correction module, as the post-processing module of the IGM dynamics workflow inversion module, realizes the bottom water pressure solution based on mechanical parameters.

[0071] Bottom water pressure visualization module: Based on the bottom water pressure dataset of the target mountain glacier within the target time period, obtain and display the spatial distribution field and spatiotemporal distribution map of the bottom water pressure of the target mountain glacier within the target time period;

[0072] Based on actual needs, the mechanical parameters and bed roughness of the target mountain glacier can be input into the bottom water pressure visualization module for each month within the target time period. The bottom water pressure visualization module can generate and display the corresponding monthly spatiotemporal distribution map of mechanical parameters based on the monthly mechanical parameters of the target mountain glacier within the target time period. The bottom water pressure visualization module can display the bed roughness. The spatiotemporal distribution map of monthly mechanical parameters includes the spatiotemporal distribution map of the surface flow velocity of the model simulated glacier, the spatiotemporal distribution map of the base sliding flow velocity, the spatiotemporal distribution map of the base shear stress, and the spatiotemporal distribution map of the base friction coefficient.

[0073] By simply inputting the original remote sensing data and basic physical parameters into the mountain glacier bottom water pressure inversion technology framework based on remote sensing observation provided by this invention, the entire chain of calculations from data processing to water pressure inversion can be completed. The modules of the mountain glacier bottom water pressure inversion technology framework based on remote sensing observation provided by this invention adopt a loosely coupled design, which not only ensures computational efficiency and stability, but also facilitates parameter adjustment, function expansion and algorithm iteration. The mountain glacier bottom water pressure inversion technology framework based on remote sensing observation provided by this invention is adapted to the characteristics of mountain glaciers, and has a low operating threshold and high versatility.

[0074] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions and substitutions can be made without departing from the inventive concept, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for inverting water pressure at the base of mountain glaciers based on remote sensing observations, characterized in that, include: Step 1: Obtain the preprocessed monthly-scale glacier surface elevation dataset and monthly-scale glacier surface velocity dataset for the target time period of the target mountain glacier, as well as the bedrock elevation of the target mountain glacier; Step 2: Update the geometric parameters of the target mountain glacier for each month within the target time period. The geometric parameters include ice thickness H and ice surface slope α. Step 3: Based on the IGM model, obtain the monthly mechanical parameters of the target mountain glacier within the target time period. The mechanical parameters include the base friction coefficient β and the base shear stress. , Substrate sliding velocity u b Model simulation of glacier surface flow velocity u s0 ; Step 4: Obtain the bed surface roughness based on the average value of the base sliding velocity and the average value of the base shear stress within the target time period of the target mountain glacier; Step 5: Construct a formula for calculating the bottom water pressure of the target mountain glacier based on the regularized Coulomb friction law; Step 6: Obtain the bottom water pressure dataset for the target mountain glacier during the target time period: Based on the monthly geometric parameters obtained in Step 2, the monthly mechanical parameters obtained in Step 3, and the bed roughness, the bottom water pressure of the target mountain glacier during the target time period is obtained month by month through the bottom water pressure calculation formula and then corrected to obtain the bottom water pressure dataset for the target mountain glacier during the target time period. Step 7: Based on the bottom water pressure dataset obtained in Step 6, obtain and display the spatiotemporal distribution map of bottom water pressure in the target time period of the target mountain glacier.

2. The method for inverting water pressure at the base of mountain glaciers based on remote sensing observation as described in claim 1, characterized in that, Step 2 includes the following steps: Step 2.1: Subtract the bedrock elevation of the glacier from the glacier surface elevation of the current month by calculating the glacier surface elevation of the current month using a pixel-by-pixel method to obtain the ice thickness of the current month; Step 2.2: Calculate the ice surface slope of the current month based on the glacier surface elevation of the current month; Step 2.3: Repeat steps 2.1 to 2.2 to obtain the ice thickness and ice surface slope of the target mountain glacier for all months within the target time period.

3. The method for inverting water pressure at the base of mountain glaciers based on remote sensing observation as described in claim 2, characterized in that, Step 3 includes the following steps: Step 3.1: Input the glacier surface flow velocity and geometric parameters of the current month into the IGM model. The IGM model performs mechanical parameter inversion to obtain the mechanical parameters of the target mountain glacier for the current month; Step 3.2: Repeat Step 3.1 to obtain the mechanical parameters of the target mountain glacier for all months within the target time period.

4. The method for inverting water pressure at the base of mountain glaciers based on remote sensing observation as described in claim 3, characterized in that, The surface roughness is obtained through the following steps: Step 4.1, Initial surface roughness A S0 Through formula The calculation yielded the following result; Step 4.2: The initial bed surface roughness A was determined. S0 Correction to obtain bed surface roughness A S ,in This represents the average value of the base sliding velocity. This represents the average value of the base shear stress.

5. The method for inverting water pressure at the base of mountain glaciers based on remote sensing observation as described in claim 4, characterized in that, The bottom water pressure calculation formula is constructed through the following steps: Step 5.1, construct the effective stress balance equation based on Terzaghi's effective stress principle: N=p i -p w In the formula, N is the effective pressure at the bottom of the glacier, p i For the static pressure of the ice body, p i =ρgH,p w ρ is the water pressure at the bottom of the glacier, g is the density of the ice, and g is the acceleration due to gravity. Step 5.2: Based on the regularized Coulomb friction law, construct the constitutive relationship between the base shear stress, base sliding velocity, effective pressure at the glacier bottom, and bed surface characteristic parameters. The expression is: Where C is the interfacial friction constant of the ice bed, with a value of 0.16, and n is the Glen flow index, with a value of 3; Step 5.3: By substituting the expression for the effective pressure N at the base of the glacier into the constitutive equation constructed in Step 5.2, the formula for calculating the water pressure at the base of the glacier is obtained: .

6. The method for inverting water pressure at the base of mountain glaciers based on remote sensing observation as described in claim 5, characterized in that, The preprocessed monthly glacier surface elevation dataset for the target time period of the target mountain glacier is based on ASTER imagery. The initial DEM of the ASTER imagery is extracted using photogrammetry techniques, and then regularized reconstruction is performed on the initial DEM to generate the dataset.

7. The method for inverting water pressure at the base of mountain glaciers based on remote sensing observation as described in claim 6, characterized in that, The preprocessed monthly glacier surface velocity dataset for the target time period of the target mountain glacier is generated by extracting the initial deformation field from Sentinel-2 images and then post-processing the extracted initial deformation field.

8. The method for inverting water pressure at the base of mountain glaciers based on remote sensing observation as described in claim 7, characterized in that, The elevation of the bedrock surface of the target mountain glacier is calculated by subtracting the ice thickness data from the ice surface elevation of the target mountain glacier represented by NASA DEM.

9. A system for retrieving water pressure at the base of mountain glaciers based on remote sensing observations, characterized in that, include: Data acquisition and preprocessing module: Acquires and preprocesses remote sensing images of the target mountain glacier for the target time period, and obtains the preprocessed monthly-scale glacier surface elevation dataset and monthly-scale glacier surface velocity dataset for the target mountain glacier for the target time period, as well as the glacier bedrock elevation of the target mountain glacier. The preprocessed monthly-scale glacier surface elevation dataset and glacier bedrock elevation for the target mountain glacier for the target time period are output to the geometric parameter update module, and the preprocessed monthly-scale glacier surface velocity for the target mountain glacier for the target time period is output to the IGM dynamics workflow inversion module. Geometric parameter update module: Based on the preprocessed monthly glacier surface elevation dataset and monthly glacier bedrock elevation of the target mountain glacier during the target time period output by the data acquisition and preprocessing module, the geometric parameters of the target mountain glacier during the target time period are updated monthly and output to the IGM dynamic process inversion module and bottom water pressure calculation module. The IGM dynamics workflow inversion module: Based on the monthly surface velocity data of the target mountain glacier during the target time period and the geometric parameters of the target mountain glacier during the target time period, the IGM model is used to obtain the mechanical parameters of the target mountain glacier during the target time period each month and output them to the bed roughness calculation module and the bottom water pressure calculation module. Bed surface roughness calculation module: Calculates the bed surface roughness based on the mechanical parameters of the target mountain glacier for each month within the target time period and outputs it to the bottom water pressure calculation module; Bottom water pressure calculation and correction module: The bottom water pressure calculation formula of the target mountain glacier is constructed based on the regularized Coulomb friction law. According to the bed surface roughness, the mechanical parameters of the target mountain glacier in the target time period and the geometric parameters of the target mountain glacier in the target time period, the bottom water pressure dataset of the target mountain glacier in the target time period is obtained through the bottom water pressure calculation formula and output to the bottom water pressure visualization module. Bottom water pressure visualization module: Based on the bottom water pressure dataset of the target mountain glacier within the target time period, obtain and display the spatial distribution field and spatiotemporal distribution map of bottom water pressure within the target time period of the target mountain glacier. Generate and display the corresponding monthly scale spatiotemporal distribution map of mechanical parameters based on the monthly mechanical parameters of the target mountain glacier within the target time period, and display the bed roughness.

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