CN114858238A - Underground water storage and transformation monitoring and early warning system - Google Patents
Underground water storage and transformation monitoring and early warning system Download PDFInfo
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Abstract
The invention discloses a groundwater accumulation monitoring and early warning system, which comprises: the underground water storage and transformation monitoring module and the underground water level early warning module can monitor the underground water storage change of a target area at a high resolution ratio. The underground water well water level of the target area is early warned through the underground water level early warning module, and early warning information is sent, so that the effect of early warning of excessive exploitation and exhaustion of underground water is achieved, and measures can be taken in time to process the underground water well water level.
Description
Technical Field
The invention relates to the technical field of underground water monitoring, in particular to an underground water accumulation monitoring and early warning system.
Background
Groundwater is an important component of fresh water, accounts for 33% of the total amount of fresh water in the world, and excessive exploitation and exhaustion of groundwater can cause sea level rise and influence natural runoff, so that ecological environment problems such as land salinization, groundwater quality deterioration and the like are caused. Meanwhile, excessive exploitation of underground water resources can directly cause problems of surface subsidence and the like, so that the ground is deformed, and the building is inclined and the wall is cracked possibly, thereby causing huge economic loss. Therefore, effective monitoring of groundwater reserve changes (i.e., storage changes) is an important basis for achieving water resource management.
Currently, monitoring underground water reserve changes mainly comprises using traditional observation means, remote sensing monitoring and establishing a hydrological model. The traditional observation means comprises underground water well observation and leveling observation, the underground water well observation is mainly measured by monitoring well distribution points, the method has high requirements on the number and representativeness of the monitoring points, meanwhile, a large amount of manpower is consumed to complete earlier-stage work such as point selection and distribution, the method is limited by objective conditions, and the change condition of underground water reserves is difficult to accurately reflect. The level observation is costly, occupies a lot of labour, the precision is lower, and spatial distribution is too sparse moreover, is difficult to catch aquifer and groundwater change's spatial detail, also can't accomplish incessant real-time supervision. The remote sensing monitoring is mainly used for researching underground water distribution by adopting optical and thermal infrared data, and has the problems of large uncertainty and the like. The hydrological model is widely applied to underground water reserve change monitoring, but complexity of a hydrological process is easily ignored, and accordingly a monitoring result is inaccurate. In addition, the gravity satellite is an emerging tool for monitoring underground water reserve change, and a common monitoring method mainly comprises the steps of estimating underground water reserve change based on a water quantity balance principle and calibrating a hydrological model by utilizing gravity satellite data. However, the underground water reserve monitoring based on the gravity satellite data still has the challenges of low spatial resolution, large uncertainty and the like.
Therefore, an underground water accumulation monitoring and early warning system is urgently needed.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention aims to provide an underground water storage change monitoring and early warning system which can carry out high-resolution monitoring on underground water storage change of a target area and has the function of early warning of underground water over-exploitation and exhaustion so as to take measures to treat in time.
In order to achieve the above object, the present invention provides a groundwater accumulation monitoring and early warning system, comprising: the underground water storage and transformation monitoring module and the underground water level early warning module; wherein the content of the first and second substances,
the underground water accumulation and transformation monitoring module is used for acquiring satellite radar data and gravity satellite data of a target area, restraining the gravity satellite data based on the satellite radar data and monitoring underground water storage capacity change of the target area;
the underground water level early warning module is used for acquiring underground water well water level time sequence observation data of the target area, carrying out abnormity judgment on underground water well water level change in the target area based on the underground water well water level time sequence observation data and an underground water well water level observation model, and sending underground water well water level early warning information of the target area if the judgment result shows that the underground water well water level change is abnormal;
the underground water well water level observation model is obtained by training based on historical underground water well water level time sequence observation data carrying an underground water well water level change abnormal time label.
In one embodiment of the present invention, the groundwater accumulation monitoring module includes:
the ground settlement data determining unit is used for determining the ground settlement data by adopting a synthetic aperture radar interferometry technology based on the satellite radar data; the ground settlement data comprise a ground settlement area and a ground settlement amount;
the initial variation determining unit is used for obtaining the initial variation of the groundwater storage capacity of the ground settlement area through inversion based on the gravity satellite data;
and the monitoring unit is used for monitoring the change of the groundwater water storage capacity based on the correlation between the initial variation and the ground settlement.
In an embodiment of the present invention, the ground settlement data determining unit is configured to:
carrying out data focusing on the satellite radar data to obtain single-view multi-type data;
generating a flat ground interferogram by adopting an auxiliary digital elevation model or a geometric removal mode based on the single-vision complex data;
determining the ground settlement data based on the land levelness interferogram.
In an embodiment of the present invention, the initial change amount determining unit is configured to:
based on the gravity satellite data, carrying out inversion to obtain the total land water reserve variation;
and determining the initial variable quantity based on the land water total reserve variable quantity and GLDAS assimilation system data.
In an embodiment of the present invention, the gravity satellite data includes Level2 Level spherical harmonic coefficient gravity field model data;
correspondingly, the underground water accumulation monitoring module further comprises a data processing module for:
carrying out data preprocessing and data post-processing on the Level2 Level spherical harmonic coefficient gravity field model data;
the data preprocessing comprises static field deduction processing and low-order item replacement;
the data post-processing includes gaussian smoothing and decorrelation filtering.
In an embodiment of the present invention, the ground water level early warning module includes:
the characteristic extraction unit is used for inputting the underground well water level time series observation data into a characteristic extraction layer of the underground well water level observation model, and the characteristic extraction layer determines the space-time semantic characteristics of the underground well water level change based on an attention mechanism;
the characteristic recovery unit is used for inputting the space-time semantic characteristics to a characteristic recovery layer of the underground well water level observation model to obtain the underground well water level change information output by the characteristic recovery layer;
and the decision unit is used for inputting the underground water level change information into an underground water threshold decision layer of the underground water well water level observation model to obtain the judgment result output by the underground water threshold decision layer.
In one embodiment of the invention, the attention mechanism comprises a spatial attention mechanism and/or a channel attention mechanism.
In an embodiment of the present invention, the groundwater level warning module further includes a sending unit, the sending unit is in communication connection with a terminal device in the target area, and the sending unit is configured to:
and sending the water level early warning information of the underground water well to the terminal equipment.
In an embodiment of the present invention, the sending unit is communicatively connected to the terminal device in the target area through a 5G network.
In an embodiment of the present invention, the decision unit is specifically configured to:
comparing the groundwater level change information with a groundwater level change threshold based on the groundwater threshold decision layer;
if the comparison result is that the underground water level change information is larger than or equal to the underground water level change threshold, determining that the judgment result is that the underground water well water level change is abnormal;
otherwise, determining that the water level change of the underground water well is normal according to the judgment result.
Compared with the prior art, the underground water accumulation monitoring and early warning system comprises: the underground water storage and transformation monitoring module and the underground water level early warning module can monitor the underground water storage change of a target area at a high resolution ratio. The underground water well water level of the target area is early warned through the underground water level early warning module, and early warning information is sent, so that the effect of early warning of excessive exploitation and exhaustion of underground water is achieved, and measures can be taken in time to process the underground water well water level.
Drawings
Fig. 1 is a schematic structural diagram of a groundwater accumulation monitoring and warning system according to an embodiment of the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
Fig. 1 is a schematic structural diagram of an underground water storage monitoring and early warning system provided in an embodiment of the present invention, and as shown in fig. 1, the system includes: the underground water storage and transformation monitoring system comprises an underground water storage and transformation monitoring module 1 and an underground water level early warning module 2. Wherein the content of the first and second substances,
the underground water storage and transformation monitoring module 1 is used for acquiring satellite radar data and gravity satellite data of a target area, restraining the gravity satellite data based on the satellite radar data and monitoring underground water storage capacity change of the target area;
the underground water level early warning module 2 is used for acquiring underground water well water level time sequence observation data of the target area, carrying out abnormity judgment on underground water well water level change in the target area based on the underground water well water level time sequence observation data and an underground water well water level observation model, and sending underground water well water level early warning information of the target area if the judgment result shows that the underground water well water level change is abnormal;
the underground well water level observation model is obtained by training based on historical underground well water level time sequence observation data carrying an underground well water level change abnormal time label.
Specifically, in the embodiment of the present invention, the groundwater accumulation monitoring and warning system has two functions, one is a groundwater storage amount change (groundwater accumulation) monitoring function implemented by the groundwater accumulation monitoring module 1, and the other is a groundwater well water level warning function implemented by the groundwater level warning module 2.
For the underground water storage and transformation monitoring module 1, when the corresponding functions are realized, satellite radar data and gravity satellite data of a target area need to be acquired at first. Satellite radar data refers to data about a target area collected by a satellite, radar, etc., and is typically InSAR data. Gravity satellite data refers to data about a target area acquired by a GRACE gravity satellite, and is typically satellite gravity data.
By combining the satellite radar data with the gravity satellite data, the groundwater water storage capacity change of the target area can be monitored. The spatial resolution of the total land water storage capacity variation obtained by the inversion of the gravity satellite data is usually 350km multiplied by 350km, and the spatial resolution of the groundwater storage capacity variation obtained by the inversion is lower. The spatial resolution of the satellite radar data is usually 50m-200m, so that the spatial resolution of the variation of the groundwater storage capacity obtained by the inversion of the gravity satellite data can be restrained through the satellite radar data, the high-resolution monitoring on the groundwater storage capacity variation of a target area is further realized, and the spatial resolution of the variation of the groundwater storage capacity can be improved to 50km x 50 km.
For the groundwater level early warning module 2, when the function is realized, groundwater well water level time sequence observation data of a target area need to be obtained first, and the groundwater well water level time sequence observation data can be obtained by arranging data obtained by observing water levels at different moments in a groundwater well of the target area through a water level observation device according to a time sequence. The water level observation device can be selected according to the requirement, and is not particularly limited herein.
And then, the observation data of the underground water well water level time sequence are input into an underground water well water level observation model, so that the abnormal judgment of the underground water well water level change in the target area can be realized, and the underground water well water level observation model can output the judgment result. The judgment result can comprise two conditions of normal water level change and abnormal water level change of the underground water well. When the water level change of the underground water well is abnormal as a result of the judgment, the water level of the underground water well in the target area needs to be pre-warned, and the pre-warning information of the water level of the underground water well in the target area can be sent out through the pre-warning module 2. The early warning information of the water level of the underground well can be the current value of the water level of the underground well and the difference value between the current value and the normal value, so that a person receiving the information can master the water level condition of the underground well in a target area, and measures can be taken conveniently and timely to process the water level condition.
The underground water well water level observation model can be constructed based on a neural network model and can be obtained through training of historical underground water well water level time sequence observation data carrying an underground water well water level change abnormal time label. In the embodiment of the invention, the underground water well water level observation model can be also called as a GNET deep learning model.
For example, historical groundwater well water level time series observation data can be input into an initial model, groundwater well water level change abnormity prediction time output by the initial model is obtained, then a loss function of the initial model is calculated through the groundwater well water level change abnormity prediction time and a groundwater well water level change abnormity time label, and model parameters of the initial model are updated based on the loss function. And replacing the historical observation data of the water level time sequence of the underground water well, and repeatedly executing the process until the loss function is converged, and finishing the training. And finally obtaining the underground water well water level observation model.
The groundwater accumulation monitoring and early warning system provided by the embodiment of the invention comprises: the underground water storage and transformation monitoring module and the underground water level early warning module can monitor the underground water storage change of a target area at a high resolution ratio. The underground water well water level of the target area is early warned through the underground water level early warning module, and early warning information is sent, so that the effect of early warning of excessive exploitation and exhaustion of underground water is achieved, and measures can be taken in time to process the underground water well water level.
On the basis of the above embodiment, in the groundwater accumulation monitoring and warning system provided in the embodiment of the present invention, the groundwater accumulation monitoring module includes:
the ground settlement data determining unit is used for determining the ground settlement data by adopting a synthetic aperture radar interferometry technology based on the satellite radar data; the ground settlement data comprise a ground settlement area and a ground settlement amount;
the initial variation determining unit is used for obtaining the initial variation of the groundwater storage capacity of the ground settlement area through inversion based on the gravity satellite data;
and the monitoring unit is used for monitoring the groundwater water storage capacity change based on the correlation between the initial variation and the ground settlement.
Specifically, in the embodiment of the present invention, the underground water accumulation monitoring module may include a ground settlement data determining unit, an initial variation determining unit, and a monitoring unit. Wherein:
and the ground settlement data determining unit can be used for determining the ground settlement data by adopting a synthetic aperture radar interferometry technique according to the satellite radar data. The synthetic aperture radar interferometry (InSAR) technique is a technique for mapping millimeter-scale displacement of the earth surface by using radar satellite measurement values, and can perform measurement at night and under any weather conditions by considering the constant change of the earth surface. With the InSAR technique, ground settlement data of the target area, which may be characterized by a Digital Elevation Model (DEM), may be determined, which may include the ground settlement area and the amount of ground settlement.
And the initial variation determining unit can be used for obtaining the initial variation of the groundwater storage capacity of the ground settlement area through inversion of gravity satellite data. The process of obtaining the initial variation may be to obtain the total reserve volume variation of the land water by inversion through gravity satellite data, and then determine the initial variation of the groundwater storage volume in the ground settlement area through the total reserve volume variation of the land water.
The monitoring unit can be used for monitoring the change of the groundwater water storage capacity through the correlation between the initial variation of the groundwater water storage capacity of the ground settlement area and the ground settlement capacity. Here, a correlation between two types of data with different spatial resolutions may be constructed, that is, a correlation between the initial variation of the groundwater storage amount in the ground subsidence area and the ground subsidence amount may be determined. Wherein the spatial resolution of the initial variation is lower than the spatial resolution of the ground settlement.
The correlation can be characterized by the corresponding relation of the two in the same ground settlement area, and the initial variable quantity can be subjected to space size reduction through the correlation, so that the high-resolution variable quantity of the groundwater water storage quantity can be determined, and the change of the groundwater water storage quantity can be monitored.
In the embodiment of the invention, the underground water storage capacity change monitoring module monitors the underground water storage capacity change through the correlation between the initial variation of the underground water storage capacity in the ground settlement area and the ground settlement capacity, so that the monitoring precision can be improved.
On the basis of the above embodiment, in the groundwater accumulation monitoring and warning system provided in the embodiment of the present invention, the ground settlement data determining unit is configured to:
carrying out data focusing on the satellite radar data to obtain single-view multiplex data;
generating a flat ground interferogram by adopting an auxiliary digital elevation model or a geometric removal mode based on single-vision complex data;
determining the ground subsidence data based on the land planum interferogram.
Specifically, in the embodiment of the present invention, when determining the ground settlement data, the ground settlement data determining unit may first perform data focusing on the satellite radar data to obtain the single-view multi-view data. Data focusing, i.e., data conversion, since satellite radar data is generally RAW format data, RAW format data can be converted into Single Look Complex (SLC) data by using the ω -k algorithm of f. It will be appreciated that the single-view multiplex type data may be a master-slave pair.
Then, a land-based interferogram may be generated from the single-view replica data using a secondary Digital Elevation Model (DEM) or geometric elimination. Before that, baseline estimation can be performed on the single-vision multi-type data, so that the single-vision multi-type data can be registered.
Here, the land interference pattern may be generated by combining an intensity pattern of the single-view complex data, a composite phase, and a slant distance generated by the DEM, and may be a multi-view land interference pattern. The process can be realized in two modes, wherein one mode adopts an auxiliary DEM, and the other mode adopts a mode without the auxiliary DEM.
For high resolution SAR or large topography, an auxiliary DEM approach is typically employed. The distance difference between a point on the ground and a sensor can be calculated through the phase difference of a master image and a slave image in the single-view complex type data, the distance difference can be obtained through complex conjugate multiplication of the master image and the slave image, the fringes of an interference image reflect terrain or displacement information and are similar to contour lines, and finally a flat-removing interference image is output.
For other cases, the initial interferogram is obtained by means of unassisted DEM, and the relevant terrain (or ellipsoid) and normal phase components need to be subtracted from the initial interferogram in a geometric removal manner, so as to obtain a flat-ground interferogram.
Finally, through the interference pattern on the flat ground, the ground settlement data can be determined. In the process, self-adaptive filtering and coherent generation, phase unwrapping, orbit redefinition, phase conversion height and geocoding can be sequentially carried out on the flat interference pattern, and the DEM is obtained. The representation of the ground settlement data can be realized through the DEM.
The adaptive filtering and the phase generation refer to filtering the interference pattern of the flat ground to remove phase noise caused by the interference of the flat ground. A coherence map of the interference (describing the phase quality) and a filtered primary image intensity map are generated simultaneously.
Since the interference phase can only be modulo 2 pi, the phase will start over and cycle as long as the phase change exceeds 2 pi. The phase unwrapping is to perform phase unwrapping on the flattened and filtered phase, so as to solve the problem of 2 pi ambiguity, and further perform phase editing according to specific data characteristics to correct errors of the phase unwrapping.
The orbital redefinition means that the orbit and the phase deviation of the satellite are corrected, orbit refining and phase deviation calculation are carried out, and possible slope phase is eliminated. This step is critical to the correct translation of the unwound phase into elevation or deformation values. This step must be done whether the DEM or the deformation result is generated.
And (3) elevation conversion and geocoding, wherein the step is to combine the absolute calibrated and unwrapped actual phase with the synthesized phase, convert the absolute calibrated and unwrapped actual phase into DEM and carry out geocoding, namely map projection.
In the embodiment of the invention, in the process of determining the ground settlement data, the land leveling interferogram can be generated by adopting an auxiliary digital elevation model or a geometric removal mode, so that the ground settlement data can be more accurate.
On the basis of the foregoing embodiment, in the groundwater accumulation monitoring and warning system provided in the embodiment of the present invention, the initial variation determining unit is configured to:
based on the gravity satellite data, carrying out inversion to obtain the total land water reserve variation;
and determining the initial variable quantity based on the land water total reserve variable quantity and GLDAS assimilation system data.
Specifically, in the embodiment of the present invention, when the initial variation determining unit determines the initial variation, the total land water reserve variation may be obtained through inversion according to the gravity satellite data. The process can calculate the density change of the earth surface, and then convert the density change into the change of equivalent water height so as to invert the distribution situation of the land water reserves.
And then, determining the initial variable quantity according to the total land water reserve variable quantity and the GLDAS assimilation system data. The VIC model in the GLDAS assimilation system is a semi-distributed macro hydrological model. By utilizing the VIC model of the GLDAS, the water content of soil with the depth of 0-200 cm, attributes of canopy water, surface water and the like can be simulated. Therefore, the GLDAS assimilation system data may be GLDAS hydrological model data. Furthermore, initial variation of groundwater storage capacity in the ground subsidence area can be obtained by deducting GLDAS hydrological model data from the land water total storage capacity variation.
In the embodiment of the invention, a method for determining the initial variation is provided, and GLDAS assimilation system data is introduced, so that the result of the initial variation can be more accurate.
On the basis of the above embodiment, in the groundwater accumulation monitoring and early warning system provided in the embodiment of the present invention, the gravity satellite data includes Level2 Level spherical harmonic coefficient gravity field model data;
correspondingly, the underground water accumulation monitoring module further comprises a data processing module for:
carrying out data preprocessing and data post-processing on the Level2 Level spherical harmonic coefficient gravity field model data;
the data preprocessing comprises static field deduction processing and low-order item replacement;
the data post-processing includes gaussian smoothing and decorrelation filtering.
Specifically, in the embodiment of the present invention, the gravity satellite data may include Level2 Level spherical harmonic coefficient gravity field model data and Mascon data.
For the spherical harmonic coefficient gravity field model data, a data processing module in the underground water accumulation monitoring module can be used for carrying out data preprocessing and data post-processing on the spherical harmonic coefficient gravity field model data.
The data pre-processing may include static field subtraction processing and low-order term replacement. Because the original gravity field comprises a static field and a time-varying field, after the static field is deducted, the time-varying part of the gravity field can be obtained. Meanwhile, since the satellite cannot measure the change of the geocentric, the coefficient of the first order term in the spherical harmonic coefficients is 0, and therefore, the replacement needs to be performed according to the corresponding technical document. At the same time, the accuracy of the measured earth's ellipticity may be less problematic, resulting in C 20 The item accuracy is low, and C measured by Satellite Laser Ranging (SLR) is required 20 The item is replaced. Here, the technical documents may include TN-13a, TN-14, and the like.
The data post-processing may include gaussian smoothing and decorrelation filtering. When the Gaussian filtering operation is performed, a Gaussian function kernel can be constructed first, the spherical harmonic coefficient is filtered, the weight of the high-order coefficient is reduced, and the purpose of suppressing the high-order error is achieved. When constructing the gaussian function kernel, a suitable filter radius needs to be selected, and the filter radius is selected according to the target region. The gaussian function kernel is specifically as follows:
wherein r represents the distance between two points on the earth surface and is called as a Gaussian filter smoothing radius; a is the radius of the earth, W is a weighting factor, W 0 Is a 0 th order weight factor, W 1 Is a 1 st order weight factor, W l+1 Is the weighting factor of order l +1, and b is the bias term.
The decorrelation filtering may be a de-banding operation, and may be implemented by using a Swenson de-banding filtering method. Swenson de-banding is a sliding window polynomial based de-correlation filter. Wherein, because the spherical harmonic coefficients of the low order do not have obvious correlation, the decorrelation filtering operation is not carried out on the spherical harmonic coefficients. And strong correlation exists among the spherical harmonic coefficients of high orders, namely when the order is more than n, the correlation of the spherical harmonic coefficients is eliminated by filtering. Because Swenson de-banding filtering considers the difference of parities of different orders under the same number (order), the filtering spherical harmonic coefficient can be written as:
wherein, l is the order of the spherical harmonic coefficient of the time-varying gravity field, m is the number of times, w is the width of the sliding window, Λ is the Swenson convolution kernel parameter, and the calculation formula is as follows:
wherein the content of the first and second substances,
it will be appreciated that the calculation of Λ is a polynomial least squares fit and p is the degree of the fitted polynomial.
In the embodiment of the invention, the data preprocessing and the data post-processing are carried out on the spherical harmonic coefficient gravity field model data, so that the monitoring difficulty of the groundwater storage capacity change of the target area can be reduced, and the monitoring accuracy is improved.
On the basis of the above embodiment, the groundwater accumulation monitoring and warning system provided in the embodiment of the present invention includes:
the characteristic extraction unit is used for inputting the observation data of the underground water well water level time sequence into a characteristic extraction layer of the underground water well water level observation model, and the characteristic extraction layer determines the time-space semantic characteristics of the underground water well water level change based on an attention mechanism;
the characteristic recovery unit is used for inputting the space-time semantic characteristics to a characteristic recovery layer of the underground well water level observation model to obtain the underground well water level change information output by the characteristic recovery layer;
and the decision unit is used for inputting the underground water level change information into an underground water threshold decision layer to obtain the judgment result output by the underground water threshold decision layer.
Specifically, in the embodiment of the present invention, the groundwater level early warning module may include a feature extraction unit, a feature recovery unit, and a decision unit, and the groundwater well water level observation model may include a feature extraction layer, a feature recovery layer, and a groundwater threshold decision layer.
By the feature extraction unit, the observation data of the underground water well water level time sequence can be input into a feature extraction layer of the underground water well water level observation model, and the feature extraction layer determines the time-space semantic features of the underground water well water level change based on an attention mechanism.
Wherein, the feature extraction layer can be a convolution layer, and the feature extraction layer can include a convolution module, a pooling module, and an activation module. The convolution module is used for performing convolution operation on the alternative region through a convolution kernel by adopting an attention mechanism, the pooling module is used for performing pooling operation on a result of the convolution operation, and the activation module is used for performing activation operation on the result of the pooling operation.
The groundwater well water level observation model may include one or more feature extraction layers, and when a plurality of feature extraction layers are included, the sizes of convolution kernels of convolution modules in the feature extraction layers may be the same or different, and are not specifically limited herein. For example, the number of the feature extraction layers is 3, and the 3 feature extraction layers are connected in sequence, and the sizes of convolution kernels of convolution modules in the previous feature extraction layers are reduced in sequence. The convolution kernel size of the convolution module in the first feature extraction layer may be 512 × 512, the convolution kernel size of the convolution module in the second feature extraction layer may be 256 × 256, and the convolution kernel size of the convolution module in the third feature extraction layer may be 128 × 128.
When the convolution module carries out convolution operation on the alternative region through convolution kernel, convolution operation can be carried out on the time sequence of the underground water well water level time sequence observation data by utilizing a series of convolution sliding calculation modes according to the size of the convolution kernel and the sliding step length, and therefore the time-space semantic characteristics of underground water well water level changes are obtained.
The activation module needs to change linear connection in the underground water well water level observation model into a nonlinear relation by using an activation function such as a Sigmoid function or a ReLu function, so that the mapping condition of input and output of the underground water well water level observation model is more consistent with the real condition, and the automatic decision-making can be performed on the space-time semantic features extracted by the feature extraction layer.
In the underground water well water level observation model, a plurality of characteristic extraction layers are arranged to extract the space-time semantic characteristics of underground water well water level changes, and meanwhile, the space-time semantic characteristics extracted by the previous characteristic extraction layers can be operated with the current characteristic extraction layer, so that the shallow space-time semantic characteristics and the deep space-time semantic characteristics are more fully utilized, and the operation efficiency is improved.
Through the characteristic recovery unit, the space-time semantic characteristics can be input into the characteristic recovery layer of the underground well water level observation model, and underground well water level change information output by the characteristic recovery layer is obtained. Here, the feature recovery layer may also be a convolution layer, and the feature extraction layer may include a deconvolution module, a pooling module, and an activation module. The deconvolution module is used for carrying out deconvolution operation on the time-space semantic features through convolution kernel, the pooling module is used for carrying out pooling operation on the result of the deconvolution operation, and the activation module is used for carrying out activation operation on the result of the pooling operation.
The underground water well water level observation model can comprise one or more feature recovery layers, the number of the feature recovery layers is equal to that of the feature extraction layers, and when a plurality of feature recovery layers exist, the sizes of convolution kernels of deconvolution modules in the feature recovery layers can be the same or different, and the sizes are not specifically limited. For example, the number of the feature recovery layers is 3, and 3 feature recovery layers are connected in sequence, and the sizes of convolution kernels of the deconvolution modules in the former feature recovery layer are reduced in sequence. The size of the convolution kernel of the deconvolution module in the first feature recovery layer may be 128 x 128, the size of the convolution kernel of the deconvolution module in the second feature recovery layer may be 256 x 256, and the size of the convolution kernel of the deconvolution module in the third feature recovery layer may be 512 x 512.
The pooling module and the activating module in the feature recovery layer are consistent with the pooling module and the activating module in the feature extraction layer in structure and function, and are not described herein again.
Through the decision unit, the underground water level change information can be input into an underground water threshold decision layer of the underground water well water level observation model, and a judgment result output by the underground water threshold decision layer is obtained. And after the underground water well water level observation model is completed, the underground water threshold decision layer stores the underground water level change threshold. Therefore, the groundwater level threshold decision layer can compare the groundwater level change information with the groundwater level change threshold, and further perform abnormity judgment on groundwater well water level changes in the target area.
In the embodiment of the invention, the attention mechanism of the characteristic extraction layer is introduced into the underground water well water level observation model, so that the obtained space-time semantic characteristics are more accurate, the accuracy of underground water well water level change information can be improved, and the accuracy of the judgment result is further improved.
On the basis of the above embodiments, the groundwater accumulation monitoring and early warning system provided in the embodiments of the present invention includes a spatial attention mechanism and/or a channel attention mechanism.
Specifically, in the embodiment of the present invention, the attention mechanism introduced at the feature extraction layer may include at least one of a spatial attention mechanism by which the spatiotemporal semantic features may be obtained from spatial assist and a temporal attention mechanism by which the spatiotemporal semantic features may be obtained from temporal assist.
For the attention mechanism, the attention mechanism aims to tell the key area of the underground water well water level observation model learning, mathematical operation can be adopted to improve the weight of the underground water well water level change area through the attention mechanism, and the new weight obtained through the attention mechanism is combined with the coiled layer in the channel dimension, so that the accuracy of the underground water well water level change information is improved.
The underground water well water level observation model can use two attention mechanisms, namely a space attention mechanism and a channel attention mechanism. The channel attention mechanism can be expressed by the following formula:
M c (F)＝δ(MLP(AvgPool(F))+MLP(MaxPool(F)))
wherein δ is an activation function, F is an intermediate feature map (intermediate feature map), and MLP is a multilayer perceptron with shared weights.
Unlike the channel attention mechanism, which focuses mainly on positional information, the spatial attention mechanism first uses maximal pooling and average pooling in the dimensions of the channel to obtain two different feature layers, which are then merged and a convolution operation is used to generate a spatial attention profile. The spatial attention mechanism is calculated as follows:
M c (F)＝δ(f n*n ([AvgPool(f)]；MaxPool(F)])
wherein f is n*n Convolution operation for n x n.
In the embodiment of the invention, different attention mechanisms are introduced, so that the extraction of the space-time semantic features can be assisted from different angles, and the extraction accuracy can be improved.
On the basis of the above embodiment, in the groundwater accumulation monitoring and warning system provided in the embodiment of the present invention, the groundwater level warning module further includes a sending unit, the sending unit is in communication connection with the terminal device in the target area, and the sending unit is configured to:
and sending the water level early warning information of the underground water well to the terminal equipment.
Specifically, in the embodiment of the present invention, the ground water level early warning module further includes a sending unit, the sending unit may be in communication connection with the terminal device in the target area, and a user holding the terminal device may be a user of all mobile devices in the target area.
Through the sending unit, the early warning information of the water level of the underground well can be sent to the terminal equipment, so that a user holding the terminal equipment can timely master the early warning information of the water level of the underground well.
On the basis of the above embodiment, in the groundwater accumulation monitoring and warning system provided in the embodiment of the present invention, the sending unit is in communication connection with the terminal device in the target area through a 5G network, so that information can be quickly transmitted between the sending unit and the terminal device.
On the basis of the above embodiment, in the groundwater accumulation monitoring and early warning system provided in the embodiment of the present invention, the decision unit is specifically configured to:
comparing the groundwater level change information with a groundwater level change threshold based on the groundwater threshold decision layer;
if the comparison result is that the underground water level change information is larger than or equal to the underground water level change threshold, determining that the judgment result is that the underground water well water level change is abnormal;
otherwise, determining that the water level change of the underground water well is normal according to the judgment result.
Specifically, in the embodiment of the present invention, the decision unit may compare the groundwater level change information with a groundwater level change threshold through a groundwater threshold decision layer, and determine a result of the comparison, and if the result of the comparison is that the groundwater level change information is greater than or equal to the groundwater level change threshold, may determine that the result of the determination is that the groundwater well water level change is abnormal. Otherwise, if the comparison result shows that the underground water level change information is smaller than the underground water level change threshold, the judgment result can be determined that the underground water well water level change is normal.
In summary, the underground water accumulation monitoring and early warning system provided in the embodiment of the present invention performs spatial downscaling at a function level through a correlation between the underground water change monitored by the GRACE and the surface subsidence monitored by the InSAR technology, so as to improve the spatial resolution of the underground water inverted by the GRACE data. In the data acquisition and broadcasting process, the 5G wireless network transmission module is adopted for data transmission, so that the data quality and speed can be considered, and the early warning response can be rapidly realized. In the process of establishing the underground water monitoring model, GNSS data, underground water reserve measured data and InSAR data can be combined for comprehensive analysis, so that the resolution, the precision and the reliability of the underground water monitoring by the GRACE gravity satellite can be improved. In the process of data training and decision making, a plurality of avoidance design schemes may appear, namely, a data source simultaneously adopts a high-resolution satellite remote sensing image to carry out comprehensive training and decision making. In the process of designing the deep learning network model, similar processing degree or faster processing speed can be achieved through an updated network structure.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (10)
1. The utility model provides an underground water holds and becomes monitoring and early warning system which characterized in that includes: the underground water storage and transformation monitoring module and the underground water level early warning module; wherein the content of the first and second substances,
the underground water accumulation and transformation monitoring module is used for acquiring satellite radar data and gravity satellite data of a target area, restraining the gravity satellite data based on the satellite radar data and monitoring underground water storage capacity change of the target area;
the underground water level early warning module is used for acquiring underground water well water level time sequence observation data of the target area, carrying out abnormity judgment on underground water well water level change in the target area based on the underground water well water level time sequence observation data and an underground water well water level observation model, and sending underground water well water level early warning information of the target area if the judgment result shows that the underground water well water level change is abnormal;
the underground water well water level observation model is obtained by training based on historical underground water well water level time sequence observation data carrying an underground water well water level change abnormal time label.
2. A groundwater fouling monitoring and pre-warning system according to claim 1, wherein the groundwater fouling monitoring module comprises:
the ground settlement data determining unit is used for determining the ground settlement data by adopting a synthetic aperture radar interferometry technology based on the satellite radar data; the ground settlement data comprise a ground settlement area and a ground settlement amount;
the initial variation determining unit is used for obtaining the initial variation of the groundwater storage capacity of the ground settlement area through inversion based on the gravity satellite data;
and the monitoring unit is used for monitoring the change of the groundwater water storage capacity based on the correlation between the initial variation and the ground settlement.
3. A groundwater importation monitoring and pre-warning system according to claim 2, wherein the ground subsidence data determination unit is configured to:
carrying out data focusing on the satellite radar data to obtain single-view multi-type data;
generating a flat ground interferogram by adopting an auxiliary digital elevation model or a geometric removal mode based on the single-vision complex data;
determining the ground subsidence data based on the land planum interferogram.
4. A groundwater accumulation monitoring and warning system as claimed in claim 2, wherein the initial variation determining unit is configured to:
based on the gravity satellite data, carrying out inversion to obtain the total land water reserve variation;
and determining the initial variable quantity based on the land water total reserve variable quantity and GLDAS assimilation system data.
5. A groundwater importation monitoring and early warning system as claimed in claim 1, wherein the gravity satellite data comprises Level2 Level spherical harmonic coefficient gravity field model data;
correspondingly, the underground water accumulation monitoring module further comprises a data processing module for:
carrying out data preprocessing and data post-processing on the Level2 Level spherical harmonic coefficient gravity field model data;
the data preprocessing comprises static field deduction processing and low-order item replacement;
the data post-processing includes gaussian smoothing and decorrelation filtering.
6. A groundwater importation monitoring and pre-warning system according to any of claims 1 to 5, wherein the groundwater level pre-warning module comprises:
the characteristic extraction unit is used for inputting the observation data of the underground water well water level time sequence into a characteristic extraction layer of the underground water well water level observation model, and the characteristic extraction layer determines the time-space semantic characteristics of the underground water well water level change based on an attention mechanism;
the characteristic recovery unit is used for inputting the space-time semantic characteristics to a characteristic recovery layer of the underground well water level observation model to obtain the underground well water level change information output by the characteristic recovery layer;
and the decision unit is used for inputting the underground water level change information into an underground water threshold decision layer of the underground water well water level observation model to obtain the judgment result output by the underground water threshold decision layer.
7. A groundwater impoundment monitoring and forewarning system as claimed in claim 6, wherein the attentiveness mechanism comprises a spatial attentiveness mechanism and/or a channel attentiveness mechanism.
8. A groundwater accumulation monitoring and pre-warning system as claimed in claim 6, wherein the groundwater level pre-warning module further comprises a sending unit, the sending unit is in communication connection with a terminal device in the target area, and the sending unit is configured to:
and sending the water level early warning information of the underground water well to the terminal equipment.
9. A groundwater accumulation monitoring and pre-warning system according to claim 8, wherein the sending unit is in communication connection with a terminal device in the target area through a 5G network.
10. A groundwater accumulation monitoring and pre-warning system according to claim 6, wherein the decision unit is specifically configured to:
comparing the groundwater level change information with a groundwater level change threshold based on the groundwater threshold decision layer;
if the comparison result is that the underground water level change information is larger than or equal to the underground water level change threshold, determining that the judgment result is that the underground water well water level change is abnormal;
otherwise, determining that the water level change of the underground water well is normal according to the judgment result.
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| CN115630686A (en) * | 2022-10-11 | 2023-01-20 | 首都师范大学 | Method for recovering land water reserve abnormity from satellite gravity data by machine learning |
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| CN115240082A (en) * | 2022-09-26 | 2022-10-25 | 四川省冶金地质勘查局水文工程大队 | Geological disaster monitoring and early warning method based on deformation monitoring and deep learning |
| CN115240082B (en) * | 2022-09-26 | 2022-12-13 | 四川省冶金地质勘查局水文工程大队 | Geological disaster monitoring and early warning method based on deformation monitoring and deep learning |
| CN115630686A (en) * | 2022-10-11 | 2023-01-20 | 首都师范大学 | Method for recovering land water reserve abnormity from satellite gravity data by machine learning |
| CN115953453A (en) * | 2023-03-03 | 2023-04-11 | 国网吉林省电力有限公司信息通信公司 | Transformer substation geological deformation monitoring method based on image dislocation analysis and Beidou satellite |
| CN115953453B (en) * | 2023-03-03 | 2023-08-15 | 国网吉林省电力有限公司信息通信公司 | Substation geological deformation monitoring method based on image dislocation analysis and Beidou satellite |
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