Method, device and equipment for automatic modeling of variogram based on deep learning

Abstract

The present invention discloses a method, device, and apparatus for automatic variogram modeling based on deep learning, which relates to the field of geostatistics and can solve the current technical problems of inaccurate and low efficiency in variogram modeling. The method comprises: constructing a variogram surface dataset to be trained and constructing an initial theoretical variogram parameter prediction model, wherein the variogram surface dataset to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained; training the initial theoretical variogram parameter prediction model based on the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model; obtaining experimental variogram values, calculating the variogram surface corresponding to the experimental variogram values, and using the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface, thereby obtaining a theoretical variogram based on the theoretical variogram parameters.

Description

Technical Field

[0001] The present invention relates to the field of geostatistics technology, and in particular to a method, device and equipment for automatic modeling of variogram based on deep learning. Background Art

[0002] In geostatistics, the theoretical variogram is a tool used to measure the differences or variations between data values ​​at different locations in geospatial space. It describes the spatial relationships and variability between data points, helping to understand patterns of data variation over time. It can reveal spatial correlations, trends, and structures in data, facilitating analyses such as spatial interpolation, spatial prediction, and geological resource assessment, and is of great significance in engineering practice.

[0003] The current conventional method for theoretical variogram modeling involves calculating the experimental variogram in a specified direction and then fitting it to obtain the theoretical variogram. However, the reliability of the calculated experimental variogram in a specified direction is closely related to the spatial distribution of the experimental variogram values. When the spatial distribution of the experimental variogram values ​​is strictly regular, parameters such as tolerance do not need to be considered; only the basic lag, search direction, and maximum search distance are considered. However, experimental variogram values ​​are often not strictly distributed. In such cases, considering only the basic lag will result in many sample points in the experimental variogram values ​​being excluded from the calculation, and the resulting experimental variogram will not fully represent the spatial structure of the experimental variogram values. In such cases, parameters that need to be set include azimuth, bandwidth, search radius, tolerance angle, lag distance, lag tolerance, and number of lags. This is computationally difficult, and parameter setting requires a certain level of industry knowledge and is subjective. Improper parameter setting can result in inaccurate experimental variograms, ultimately impacting the modeling effectiveness of the theoretical variogram model. On the other hand, when fitting the theoretical variogram from the experimental variogram, a scatter plot of the experimental variogram is drawn based on the experimental variogram. The range, arch height, and nugget value are then estimated from this scatter plot. The theoretical variogram model is then constructed using the estimated range, arch height, and nugget values. This is then compared with the experimental variogram scatter plot, and the range, arch height, and nugget values ​​are modified. This process is repeated until the theoretical and experimental variograms have the best possible match. Conventional fitting methods are inefficient and time-consuming. Summary of the Invention

[0004] In view of this, the present invention provides a method, device and equipment for automatic modeling of variogram based on deep learning, which can solve the current technical problems of inaccurate and low efficiency of variogram modeling.

[0005] According to a first aspect of the present invention, a method for automatic modeling of a variogram based on deep learning is provided, the method comprising:

[0006] Constructing a variogram surface data set to be trained and an initial theoretical variogram parameter prediction model, wherein the variogram surface data set to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained;

[0007] Training the initial theoretical variogram parameter prediction model according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model;

[0008] Acquire experimental variogram values, calculate the variogram surface corresponding to the experimental variogram values, and use the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain the theoretical variogram based on the theoretical variogram parameters.

[0009] Preferably, the step of constructing a variogram surface dataset to be trained includes:

[0010] Randomly generate multiple sets of variogram parameters to be trained;

[0011] Obtaining a preset coordinate range, calculating the trained variogram values ​​of all data points within the preset coordinate range using the trained variogram parameters of each group, and calculating the trained variogram surface corresponding to the trained variogram values;

[0012] Each group of the to-be-trained variogram parameters and the corresponding to-be-trained variogram surface is taken as a group of to-be-trained variogram surface data, and multiple groups of to-be-trained variogram data are determined as a to-be-trained variogram surface data set.

[0013] Preferably, the variogram parameters to be trained include the variogram type to be trained, and the step of constructing an initial theoretical variogram parameter prediction model includes:

[0014] Constructing a regression sub-model, wherein one regression sub-model corresponds to one type of the variogram to be trained;

[0015] A classification sub-model is constructed, and the classification sub-model and all the regression sub-models are determined as an initial theoretical variogram parameter prediction model.

[0016] Preferably, the constructing of the regression sub-model includes:

[0017] Construct the convolution layer, pooling layer, fully connected layer and regression sub-model output layer to obtain the regression sub-model;

[0018] The nodes of the output layer of the regression sub-model are determined according to the variogram parameters to be trained.

[0019] Preferably, the step of determining the nodes of the output layer of the regression sub-model according to the variogram parameters to be trained includes:

[0020] If the variogram surface dataset to be trained is two-dimensional, the variogram parameters to be trained are the variogram type to be trained, the nugget value, the main direction, the main direction range value, and the range ratio, and the nodes of the output layer of the regression sub-model are determined to be: the nugget value, the main direction, the main direction range value, and the range ratio;

[0021] If the variogram surface data set to be trained is three-dimensional, then the variogram parameters to be trained are the variogram type to be trained, the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value, and the nodes of the output layer of the regression sub-model are determined to be: the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value.

[0022] Preferably, the step of training the initial theoretical variogram parameter prediction model based on the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model includes:

[0023] Training the classification sub-model according to the variogram surface to be trained and the corresponding variogram type to be trained;

[0024] The regression sub-model corresponding to the variogram type to be trained is trained according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model.

[0025] Preferably, the method of predicting the theoretical variogram parameters corresponding to the variogram surface using the theoretical variogram parameter prediction model includes:

[0026] Mapping the variogram surface into the preset coordinate range to obtain a mapped variogram surface;

[0027] The mapped variogram surface is input into the theoretical variogram parameter prediction model to obtain the theoretical variogram parameters corresponding to the variogram surface.

[0028] According to a second aspect of the present invention, there is provided a device for automatic modeling of a variogram based on deep learning, the device comprising:

[0029] A construction module is used to construct a variogram surface data set to be trained and to construct an initial theoretical variogram parameter prediction model, wherein the variogram surface data set to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained;

[0030] A training module, configured to train the initial theoretical variogram parameter prediction model based on the variogram surface to be trained and the corresponding variogram parameters to be trained, to obtain a trained theoretical variogram parameter prediction model;

[0031] A prediction module is used to obtain experimental variogram values, calculate the variogram surface corresponding to the experimental variogram values, and use the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain a theoretical variogram based on the theoretical variogram parameters.

[0032] According to the third aspect of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned deep learning-based automatic modeling method of the variogram is implemented.

[0033] According to the fourth aspect of the present application, a computer device is provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the program, the above-mentioned deep learning-based automatic modeling method of variogram is implemented.

[0034] By means of the above technical scheme, the present invention provides a method, apparatus and equipment for automatic modeling of variogram based on deep learning. First, a variogram surface data set to be trained and an initial theoretical variogram parameter prediction model are constructed, wherein the variogram surface data set to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained; then, the initial theoretical variogram parameter prediction model is trained according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model; finally, the experimental variogram value is obtained, the variogram surface corresponding to the experimental variogram value is calculated, and the theoretical variogram parameter prediction model is used to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain the theoretical variogram according to the theoretical variogram parameters. Through the technical solution of the present invention, on the one hand, the theoretical variogram parameter prediction model obtained by training learns the relationship between the variogram surface to be trained and the variogram parameters to be trained, so that when the theoretical variogram parameter prediction model is applied, the theoretical variogram parameters can be directly obtained from the variogram surface corresponding to the experimental variogram value, without the need for repeated estimation through the experimental variogram scatter plot, thereby improving the efficiency of automatic variogram modeling. On the other hand, the existing technology and the present invention are both based on the experimental variogram value. However, the existing technology calculates the experimental variogram in the specified direction based on the experimental variogram value, which requires manual setting of some parameters, while the calculation of the variogram surface of the present invention uses the experimental variogram value in the entire space, and does not limit the direction. Therefore, the calculation of the variogram surface does not require manual setting of some parameters, and is thus not affected by subjective factors. The variogram surface corresponding to the experimental variogram value is used instead of the experimental variogram in the specified direction of the existing technology, thereby improving the accuracy of automatic variogram modeling.

[0035] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation on the local application. In the drawings:

[0037] Figure 1 A schematic diagram of a process for automatically modeling a variogram based on deep learning provided by an embodiment of the present invention is shown;

[0038] Figure 2A schematic diagram of a process for automatically modeling a variogram based on deep learning provided by an embodiment of the present invention is shown;

[0039] Figure 3 A schematic diagram of the structure of a variogram automatic modeling device based on deep learning provided by an embodiment of the present invention is shown;

[0040] Figure 4 A schematic structural diagram of another variogram automatic modeling device based on deep learning provided by an embodiment of the present invention is shown;

[0041] Figure 5 An example diagram of a two-dimensional variogram surface to be trained provided by an embodiment of the present invention is shown;

[0042] Figure 6 An example diagram of a three-dimensional variogram surface to be trained provided by an embodiment of the present invention is shown;

[0043] Figure 7 shows a two-dimensional experimental variogram value provided by an embodiment of the present invention;

[0044] Figure 8 shows a three-dimensional experimental variogram value provided by an embodiment of the present invention;

[0045] Figure 9 A two-dimensional variogram surface provided by an embodiment of the present invention is shown;

[0046] Figure 10 A three-dimensional variogram surface provided by an embodiment of the present invention is shown;

[0047] Figure 11 An existing two-dimensional theoretical variogram modeling result provided by an embodiment of the present invention is shown;

[0048] Figure 12 The figure shows a two-dimensional theoretical variogram modeling result of an embodiment of the present application provided by an embodiment of the present invention;

[0049] Figure 13 An existing three-dimensional theoretical variogram modeling result provided by an embodiment of the present invention is shown;

[0050] Figure 14 The figure shows a three-dimensional theoretical variogram modeling result of an embodiment of the present application provided by the embodiment of the present invention;

[0051] Figure 15 A two-dimensional Kriging estimation error histogram provided by an embodiment of the present invention is shown;

[0052] Figure 16A three-dimensional Kriging estimation error histogram provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0053] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0054] This embodiment provides a method for automatic modeling of variogram based on deep learning, such as Figure 1 As shown, the method includes:

[0055] 101. Construct a variogram surface dataset to be trained and an initial theoretical variogram parameter prediction model, wherein the variogram surface dataset to be trained includes the variogram surface to be trained and corresponding variogram parameters to be trained.

[0056] 102. Train the initial theoretical variogram parameter prediction model according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model.

[0057] For step 101 of the embodiment, the variogram surface data set to be trained includes multiple groups of variogram surface data to be trained, each group of variogram surface data to be trained includes a variogram surface to be trained and a corresponding group of variogram parameters to be trained, a group of variogram parameters to be trained includes multiple variogram parameters to be trained, and each variogram surface to be trained is labeled with the corresponding multiple variogram parameters to be trained.

[0058] Taking the example that a variogram surface data set to be trained includes 3 groups of variogram surface data to be trained, and a group of variogram parameters to be trained includes 3 variogram parameters to be trained, the variogram surface data set to be trained includes: {[variogram surface to be trained 1, variogram parameter to be trained 11, variogram parameter to be trained 12, variogram parameter to be trained 13]; [variogram surface to be trained 2, variogram parameter to be trained 21, variogram parameter to be trained 22, variogram parameter to be trained 23]; [variogram surface to be trained 3, variogram parameter to be trained 31, variogram parameter to be trained 32, variogram parameter to be trained 33]}.

[0059] For steps 101 and 102 of the embodiment, the initial theoretical variogram parameter prediction model is untrained, and the variogram surface data set to be trained is divided into a training set, a test set, and a validation set according to a preset ratio. The initial theoretical variogram parameter prediction model is trained according to the training set, the test set, and the validation set to obtain a trained theoretical variogram parameter prediction model, wherein the initial theoretical variogram parameter prediction model can be a convolutional neural network (CNN), which is not limited here. During the training process, as an implementation method, the epoch is set to 300, and Adam is selected as the optimizer. Adam uses momentum as the parameter update direction, can adaptively adjust the learning rate to improve gradient descent, has efficient and stable optimization performance, can usually converge quickly and has high training efficiency, and the weight decay parameter in the Adam optimizer is used to perform L2 regularization on the weight parameters of the model. By applying a penalty term to the weight parameters, the model can be better generalized to new data and reduce the risk of overfitting. The weight decay parameter of the Adam optimizer was set to 0.0001. During training, StepLR was used as the learning rate adjuster (implementing a strategy of adjusting the learning rate by step size). The step size and decay factor of StepLR were set to 10 and 0.5, respectively. This means that after every 10 training cycles, the learning rate is updated by half its current value. The initial learning rate was set to 0.001, and the batch size was set to 32. Model training was performed with these parameter settings.

[0060] 103. Obtain an experimental variogram value, calculate a variogram surface corresponding to the experimental variogram value, and use the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain a theoretical variogram based on the theoretical variogram parameters.

[0061] In this embodiment, the specific process for calculating the variogram surface corresponding to the experimental variogram values ​​is similar to the prior art and will not be repeated here. When applying the theoretical variogram parameter prediction model, the input is the variogram surface corresponding to the experimental variogram values, and the output is the theoretical variogram parameters. These theoretical variogram parameters are the theoretical variogram parameters required to automatically model the experimental variogram values ​​into the theoretical variogram.

[0062] The trained theoretical variogram parameter prediction model can effectively capture the trends and overall characteristics of the variogram surface. By applying deep learning methods to the field of variogram modeling, the theoretical variogram parameters are automatically optimized, thereby improving the accuracy and efficiency of automatic variogram modeling.

[0063] The present invention provides a method, device and equipment for automatic modeling of a variogram based on deep learning. First, a variogram surface data set to be trained and an initial theoretical variogram parameter prediction model are constructed, wherein the variogram surface data set to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained; then, the initial theoretical variogram parameter prediction model is trained according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model; finally, an experimental variogram value is obtained, the variogram surface corresponding to the experimental variogram value is calculated, and the theoretical variogram parameter prediction model is used to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain a theoretical variogram according to the theoretical variogram parameters. Through the technical solution of the present invention, on the one hand, the theoretical variogram parameter prediction model obtained by training learns the relationship between the variogram surface to be trained and the variogram parameters to be trained, so that when the theoretical variogram parameter prediction model is applied, the theoretical variogram parameters can be directly obtained from the variogram surface corresponding to the experimental variogram value, without the need for repeated estimation through the experimental variogram scatter plot, thereby improving the efficiency of automatic variogram modeling. On the other hand, the existing technology and the present invention are both based on the experimental variogram value. However, the existing technology calculates the experimental variogram in the specified direction based on the experimental variogram value, which requires manual setting of some parameters, while the calculation of the variogram surface of the present invention uses the experimental variogram value in the entire space, and does not limit the direction. Therefore, the calculation of the variogram surface does not require manual setting of some parameters, and is thus not affected by subjective factors. The variogram surface corresponding to the experimental variogram value is used instead of the experimental variogram in the specified direction of the existing technology, thereby improving the accuracy of automatic variogram modeling.

[0064] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another automatic modeling method of the variogram based on deep learning is provided, such as Figure 2 As shown, the method includes:

[0065] 201. Construct a variogram surface dataset to be trained, wherein the variogram surface dataset to be trained includes a variogram surface to be trained and corresponding variogram parameters to be trained, and the variogram parameters to be trained include a variogram type to be trained.

[0066] For this embodiment, as an implementation method, constructing the variogram surface data set to be trained includes: randomly generating multiple groups of variogram parameters to be trained; obtaining a preset coordinate range, and within the preset coordinate range, using the variogram parameters to be trained of each group to calculate the variogram values ​​to be trained of all data points, and calculating the variogram surface to be trained corresponding to the variogram values ​​to be trained; taking the variogram parameters to be trained of each group and the corresponding variogram surface to be trained as a group of variogram surface data to be trained, and determining the multiple groups of variogram data to be trained as the variogram surface data set to be trained.

[0067] Among them, the variogram surface data set to be trained is divided into two-dimensional and three-dimensional. Regardless of whether the variogram surface data set to be trained is two-dimensional or three-dimensional, the construction method is the same. Each set of variogram parameters to be trained includes multiple variogram parameters to be trained, one of these variogram parameters to be trained is a variogram type to be trained, and the variogram types to be trained include: spherical model, exponential model and Gaussian model.

[0068] If the variogram surface data set to be trained is two-dimensional, the variogram parameters to be trained are the variogram type to be trained, the nugget value, the main direction, the main direction range value and the range ratio; if the variogram surface data set to be trained is three-dimensional, the variogram parameters to be trained are the variogram type to be trained, the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value.

[0069] Among them, such as Figure 5 As shown in the figure, there is a set of two-dimensional variogram surface examples to be trained, and the corresponding set of two-dimensional variogram parameters to be trained are: model type, exponential model; nugget value, 0.1540, main direction, 57.99°, main direction range value, 651.77, range ratio, 0.61. Figure 6 As shown in the figure, a set of three-dimensional variogram surface examples to be trained are displayed through the X-axis section, Y-axis section, Z-axis section and XY diagonal section, and the corresponding set of three-dimensional variogram parameters to be trained are: model type, exponential model; main direction angle, 161.47°; secondary direction angle, 16.32°; short direction angle, -82.21°; nugget value, 0.3952; main direction range value, 98.14; secondary direction range value, 65.75; short direction range value, 46.12.

[0070] The specific construction method is as follows: if the variogram surface dataset to be trained is two-dimensional, a preset coordinate range is obtained (this preset coordinate range is two-dimensional), and within the preset coordinate range, the variogram values ​​to be trained for all data points (the data points are two-dimensional data points) are calculated using the variogram parameters to be trained (two-dimensional) for each group, and the variogram surface to be trained corresponding to the variogram values ​​is calculated. If the variogram surface dataset to be trained is three-dimensional, a preset coordinate range is obtained (this preset coordinate range is three-dimensional), and within the preset coordinate range, the variogram values ​​to be trained for all data points (the data points are three-dimensional data points) are calculated using the variogram parameters to be trained (three-dimensional) for each group, and the variogram surface to be trained corresponding to the variogram values ​​is calculated.

[0071] The specific calculation process for calculating the training variogram surface corresponding to the training variogram value is referred to the prior art and will not be described in detail here. Preferably, the constructed training variogram surface dataset can be preprocessed, including data standardization, size adjustment and other operations.

[0072] 202. Construct an initial theoretical variogram parameter prediction model.

[0073] For this embodiment, as an implementation method, the construction of the initial theoretical variogram parameter prediction model includes: constructing a regression sub-model, wherein one regression sub-model corresponds to one type of the variogram to be trained; constructing a classification sub-model, and determining one classification sub-model and all the regression sub-models as the initial theoretical variogram parameter prediction model.

[0074] Among them, the initial theoretical variogram parameter prediction model includes a two-dimensional initial theoretical variogram parameter prediction model and a three-dimensional initial theoretical variogram parameter prediction model. Regardless of the two-dimensional initial theoretical variogram parameter prediction model or the three-dimensional one, when the variogram types to be trained are spherical model, exponential model and Gaussian model, the initial theoretical variogram parameter prediction model is a classification sub-model and three regression sub-models. The classification sub-model is used to learn the variogram type to be trained. When there are three types of variograms to be trained, the classification sub-model output layer has three results. The three regression sub-models are the regression sub-model corresponding to the spherical model, the regression sub-model corresponding to the exponential model, and the regression sub-model corresponding to the Gaussian model.

[0075] Specifically, for the regression sub-model corresponding to the spherical model, if it is a two-dimensional initial theoretical variogram parameter prediction model, it is used to learn the nugget value, main direction, main direction range value and range ratio corresponding to the spherical model; if it is a three-dimensional initial theoretical variogram parameter prediction model, it is used to learn the main direction angle, secondary direction angle, short direction angle, nugget value, main direction range value, secondary direction range value and short direction range value corresponding to the spherical model. For the regression sub-model corresponding to the exponential model, if it is a two-dimensional initial theoretical variogram parameter prediction model, it is used to learn the nugget value, main direction, main direction range value and range ratio corresponding to the exponential model; if it is a three-dimensional initial theoretical variogram parameter prediction model, it is used to learn the main direction angle, secondary direction angle, short direction angle, nugget value, main direction range value, secondary direction range value and short direction range value corresponding to the exponential model. For the regression sub-model corresponding to the Gaussian model, if it is a two-dimensional initial theoretical variogram parameter prediction model, it is used to learn the nugget value, main direction, main direction range value and range ratio corresponding to the Gaussian model; if it is a three-dimensional initial theoretical variogram parameter prediction model, it is used to learn the main direction angle, secondary direction angle, short direction angle, nugget value, main direction range value, secondary direction range value and short direction range value corresponding to the Gaussian model.

[0076] As an implementation method, for two-dimensional, the structure of the classification sub-model (in order) is: classification sub-model input layer (Input), first convolutional layer (Conv1), batch normalization layer (i.e. Batch Normalization), activation function layer (Relu), maximum pooling layer (MaxPool), second convolutional layer (Conv2), third convolutional layer (Conv3), fourth convolutional layer (Conv4), fifth convolutional layer (Conv5), global average pooling layer (Global Average Pooling), fully connected layer (Fully connected layer, i.e. FC), classification sub-model output layer (Output).

[0077] For three-dimensional, the structure of the classification sub-model is shown in Table 1:

[0078]

[0079] The basic idea of ​​Dropout in Table 1 is to randomly dropout (discard) the activation of some neurons during training to prevent the model from overfitting and make the model more robust.

[0080] Wherein, the construction of the regression sub-model includes: constructing a convolutional layer, a pooling layer, a fully connected layer and a regression sub-model output layer to obtain a regression sub-model; determining the nodes of the regression sub-model output layer according to the parameters of the variogram to be trained. As an embodiment, for two dimensions, the structure of the regression sub-model (in order) is: regression sub-model input layer (Input), first convolutional layer (Conv1), first pooling layer (Pool1), second convolutional layer (Conv2), second pooling layer (Pool2), third convolutional layer (Conv3), third pooling layer (Pool3), fourth convolutional layer (Conv4), fourth pooling layer (Pool4), fifth convolutional layer (Conv5), fifth pooling layer (Pool5), global average pooling layer (Global AveragePooling, i.e. FC), fully connected layer (Fully connected layer), regression sub-model output layer (Output).

[0081] For three-dimensional, the structure of the regression sub-model is shown in Table 2:

[0082] Table 2:

[0083]

[0084] The flatten in Table 2 refers to the tensor operation.

[0085] For two-dimensional and three-dimensional data, in addition to the different structures of the regression sub-models, the nodes of the regression sub-model output layer are also different. Specifically, determining the nodes of the regression sub-model output layer based on the variogram parameters to be trained includes: if the variogram surface dataset to be trained is two-dimensional, the variogram parameters to be trained are the variogram type to be trained, the nugget value, the main direction, the main direction range value, and the range ratio, and determining the nodes of the regression sub-model output layer as: the nugget value, the main direction, the main direction range value, and the range ratio; if the variogram surface dataset to be trained is three-dimensional, the variogram parameters to be trained are the variogram type to be trained, the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value, and the short direction range value, and determining the nodes of the regression sub-model output layer as: the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value, and the short direction range value.

[0086] 203. Train the classification sub-model according to the variogram surface to be trained and the corresponding variogram type to be trained.

[0087] 204. Train the regression sub-model corresponding to the variogram type to be trained according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model.

[0088] For the embodiment steps 203 and 204, it is divided into using the two-dimensional variogram surface data set to be trained to train the two-dimensional initial theoretical variogram parameter prediction model (two-dimensional classification sub-model and two-dimensional regression sub-model), and using the three-dimensional variogram surface data set to be trained to train the three-dimensional initial theoretical variogram parameter prediction model (three-dimensional classification sub-model and three-dimensional regression sub-model).

[0089] In which, both the two-dimensional classification sub-model and the three-dimensional classification sub-model are used to learn the type of the variogram to be trained. For training the two-dimensional initial theoretical variogram parameter prediction model (two-dimensional classification sub-model and two-dimensional regression sub-model) using the two-dimensional variogram surface dataset to be trained, specifically, if the variogram type to be trained is a spherical model, then the regression sub-model corresponding to the two-dimensional spherical model is trained; if the variogram type to be trained is an exponential model, then the regression sub-model corresponding to the two-dimensional exponential model is trained; if the variogram type to be trained is a Gaussian model, then the regression sub-model corresponding to the two-dimensional Gaussian model is trained. For training the three-dimensional initial theoretical variogram parameter prediction model (three-dimensional classification sub-model and three-dimensional regression sub-model) using the three-dimensional variogram surface dataset to be trained, specifically, if the variogram type to be trained is a spherical model, then the regression sub-model corresponding to the three-dimensional spherical model is trained; if the variogram type to be trained is an exponential model, then the regression sub-model corresponding to the three-dimensional exponential model is trained; if the variogram type to be trained is a Gaussian model, then the regression sub-model corresponding to the three-dimensional Gaussian model is trained.

[0090] Finally, a two-dimensional theoretical variogram parameter prediction model and a three-dimensional theoretical variogram parameter prediction model are obtained.

[0091] 205. Obtain an experimental variogram value, calculate a variogram surface corresponding to the experimental variogram value, and use the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain a theoretical variogram based on the theoretical variogram parameters.

[0092] For this embodiment, the use of the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface includes: mapping the variogram surface into the preset coordinate range to obtain the mapped variogram surface; inputting the mapped variogram surface into the theoretical variogram parameter prediction model to obtain the theoretical variogram parameters corresponding to the variogram surface.

[0093] Among them, the experimental variogram value includes the two-dimensional experimental variogram value (such as Figure 7 ) and the three-dimensional experimental variogram value (such as Figure 8 ), the method for calculating the variogram surface corresponding to the experimental variogram value refers to the existing technology and will not be repeated here. Since the theoretical variogram parameter prediction model is trained within the preset coordinate range, in order to ensure the prediction accuracy, it is necessary to map the variogram surface to the preset coordinate range. Specifically, if it is a two-dimensional experimental variogram value, the two-dimensional variogram surface (such as Figure 9 ), map this two-dimensional variogram surface to a two-dimensional preset coordinate range to obtain a two-dimensional mapped variogram surface, input the two-dimensional mapped variogram surface into a two-dimensional theoretical variogram parameter prediction model to obtain the two-dimensional theoretical variogram parameters corresponding to the two-dimensional variogram surface, and the automatic modeling of the two-dimensional theoretical variogram is completed when the two-dimensional theoretical variogram parameters are known (such as Figure 12 The automatic modeling result of the two-dimensional theoretical variation function of the embodiment of this application is Figure 11 The existing two-dimensional modeling results can be compared). The automatic modeling of the three-dimensional theoretical variogram is similar and will not be described here (eg Figure 10 The three-dimensional variogram surface is calculated from the three-dimensional experimental variogram value, such as Figure 14 The automatic modeling result of the three-dimensional theoretical variation function of the embodiment of this application is Figure 13 The existing three-dimensional modeling results can be compared).

[0094] After obtaining the theoretical variogram, the theoretical variogram can be used to analyze the characteristics of spatial data. It is mainly used in Kriging interpolation to estimate the value of unknown locations based on the spatial correlation between sample points at known locations. It can be widely used in geological resource assessment, pollutant diffusion prediction, surface water or atmospheric data modeling, etc. Figure 15 and Figure 16 As shown, whether it is two-dimensional or three-dimensional, the experimental variogram fitting model in the figure is the existing technology, and the CNN fitting model is the automatic modeling result of the embodiment of the present application. The two are used to perform Kriging valuation respectively, and the obtained Kriging valuation results are as follows: Figure 15 As shown, the three-dimensional Figure 16 As shown, from the comparison of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) of the error comparison, the kriging valuation error in the actual application of the CNN fitting model is smaller than that of the experimental variogram fitting model. Therefore, it can be confirmed that the automatic modeling results of the embodiment of the present application are more accurate than those of the prior art.

[0095] The present invention provides a method, device and equipment for automatic modeling of a variogram based on deep learning. First, a variogram surface data set to be trained and an initial theoretical variogram parameter prediction model are constructed, wherein the variogram surface data set to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained; then, the initial theoretical variogram parameter prediction model is trained according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model; finally, an experimental variogram value is obtained, the variogram surface corresponding to the experimental variogram value is calculated, and the theoretical variogram parameter prediction model is used to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain a theoretical variogram according to the theoretical variogram parameters. Through the technical solution of the present invention, on the one hand, the theoretical variogram parameter prediction model obtained by training learns the relationship between the variogram surface to be trained and the variogram parameters to be trained, so that when the theoretical variogram parameter prediction model is applied, the theoretical variogram parameters can be directly obtained from the variogram surface corresponding to the experimental variogram value, without the need for repeated estimation through the experimental variogram scatter plot, thereby improving the efficiency of automatic variogram modeling. On the other hand, the existing technology and the present invention are both based on the experimental variogram value. However, the existing technology calculates the experimental variogram in the specified direction based on the experimental variogram value, which requires manual setting of some parameters, while the calculation of the variogram surface of the present invention uses the experimental variogram value in the entire space, and does not limit the direction. Therefore, the calculation of the variogram surface does not require manual setting of some parameters, and is thus not affected by subjective factors. The variogram surface corresponding to the experimental variogram value is used instead of the experimental variogram in the specified direction of the existing technology, thereby improving the accuracy of automatic variogram modeling.

[0096] Further, as Figure 1 and Figure 2 The embodiment of the present invention provides a device for automatically modeling a variogram based on deep learning, such as Figure 3 As shown, the device includes: a construction module 31, a training module 32, and a prediction module 33;

[0097] A construction module 31 is used to construct a variogram surface dataset to be trained and an initial theoretical variogram parameter prediction model, wherein the variogram surface dataset to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained;

[0098] A training module 32 is configured to train the initial theoretical variogram parameter prediction model based on the variogram surface to be trained and the corresponding variogram parameters to be trained, to obtain a trained theoretical variogram parameter prediction model;

[0099] The prediction module 33 is used to obtain the experimental variogram value, calculate the variogram surface corresponding to the experimental variogram value, and use the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain the theoretical variogram based on the theoretical variogram parameters.

[0100] Accordingly, in order to construct a variogram surface data set to be trained, the construction module 31 can be specifically used to randomly generate multiple groups of variogram parameters to be trained; obtain a preset coordinate range, and within the preset coordinate range, use the variogram parameters to be trained of each group to calculate the variogram values ​​to be trained of all data points, and calculate the variogram surface to be trained corresponding to the variogram values ​​to be trained; use the variogram parameters to be trained of each group and the corresponding variogram surface to be trained as a group of variogram surface data to be trained, and determine the multiple groups of variogram data to be trained as a variogram surface data set to be trained.

[0101] Accordingly, the variogram parameters to be trained include the variogram type to be trained. In order to construct an initial theoretical variogram parameter prediction model, the construction module 31 may specifically include: a first construction unit 311 and a second construction unit 312;

[0102] A first construction unit 311 is specifically configured to construct a regression sub-model, wherein one regression sub-model corresponds to one type of variogram to be trained;

[0103] The second construction unit 312 can be specifically used to construct a classification sub-model, and determine the classification sub-model and all the regression sub-models as an initial theoretical variogram parameter prediction model.

[0104] Correspondingly, in order to construct a regression sub-model, the first construction unit 311 can be specifically used to construct a convolution layer, a pooling layer, a fully connected layer and an output layer to obtain a regression sub-model; and determine the nodes of the output layer according to the variogram parameters to be trained.

[0105] Correspondingly, in order to determine the nodes of the output layer according to the variogram parameters to be trained, the first construction unit 311 can be specifically used to determine, if the variogram surface data set to be trained is two-dimensional, the variogram parameters to be trained are the variogram type to be trained, the nugget value, the main direction, the main direction range value and the range ratio, and the nodes of the output layer are determined to be: the nugget value, the main direction, the main direction range value and the range ratio; if the variogram surface data set to be trained is three-dimensional, the variogram parameters to be trained are the variogram type to be trained, the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value, and the nodes of the output layer are determined to be: the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value.

[0106] Correspondingly, in order to train the initial theoretical variogram parameter prediction model according to the variogram surface to be trained and the corresponding variogram parameters to be trained, and obtain the trained theoretical variogram parameter prediction model, the training module 32 can be specifically used to train the classification sub-model according to the variogram surface to be trained and the corresponding variogram type to be trained; train the regression sub-model corresponding to the variogram type to be trained according to the variogram surface to be trained and the corresponding variogram parameters to be trained, and obtain the trained theoretical variogram parameter prediction model.

[0107] Correspondingly, in order to use the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface, the prediction module 33 can be specifically used to map the variogram surface to the preset coordinate range to obtain the mapped variogram surface; input the mapped variogram surface into the theoretical variogram parameter prediction model to obtain the theoretical variogram parameters corresponding to the variogram surface.

[0108] It should be noted that for other corresponding descriptions of the functional units involved in the automatic modeling device for variogram based on deep learning provided in this embodiment, please refer to Figures 1 to 2 The corresponding description will not be repeated here.

[0109] Based on the above Figures 1 to 2 The method shown in FIG. 1 is a method for performing the above-mentioned operations. Accordingly, this embodiment further provides a storage medium, which may be volatile or non-volatile, and stores a computer program thereon. When the program is executed by a processor, the above-mentioned operations are performed. Figures 1 to 2 The automatic modeling method of variogram based on deep learning is shown.

[0110] Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present invention.

[0111] Based on the above Figures 1 to 2 The method shown and Figure 3 、 Figure 4 In order to achieve the above-mentioned purpose, the embodiment of the virtual device shown in the figure further provides a computer device, which includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figures 1 to 2 The automatic modeling method of variogram based on deep learning is shown.

[0112] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and may optionally include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface), etc.

[0113] Those skilled in the art will understand that the computer device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0114] The storage medium may also include an operating system and network communication module. Operating systems are programs that manage the hardware and software resources of the computer device, supporting the execution of information processing programs and other software and / or programs. The network communication module facilitates communication between components within the storage medium and with other hardware and software within the information processing device.

[0115] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform, or by hardware.

[0116] The present invention provides a method, device and equipment for automatic modeling of a variogram based on deep learning. First, a variogram surface data set to be trained and an initial theoretical variogram parameter prediction model are constructed, wherein the variogram surface data set to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained; then, the initial theoretical variogram parameter prediction model is trained according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model; finally, an experimental variogram value is obtained, the variogram surface corresponding to the experimental variogram value is calculated, and the theoretical variogram parameter prediction model is used to predict the theoretical variogram parameters corresponding to the variogram surface, so as to obtain a theoretical variogram according to the theoretical variogram parameters. Through the technical solution of the present invention, on the one hand, the theoretical variogram parameter prediction model obtained by training learns the relationship between the variogram surface to be trained and the variogram parameters to be trained, so that when the theoretical variogram parameter prediction model is applied, the theoretical variogram parameters can be directly obtained from the variogram surface corresponding to the experimental variogram value, without the need for repeated estimation through the experimental variogram scatter plot, thereby improving the efficiency of automatic variogram modeling. On the other hand, the existing technology and the present invention are both based on the experimental variogram value. However, the existing technology calculates the experimental variogram in the specified direction based on the experimental variogram value, which requires manual setting of some parameters, while the calculation of the variogram surface of the present invention uses the experimental variogram value in the entire space, and does not limit the direction. Therefore, the calculation of the variogram surface does not require manual setting of some parameters, and is thus not affected by subjective factors. The variogram surface corresponding to the experimental variogram value is used instead of the experimental variogram in the specified direction of the existing technology, thereby improving the accuracy of automatic variogram modeling.

[0117] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and that the modules or processes in the accompanying drawings are not necessarily required for the implementation of the present invention. Those skilled in the art will appreciate that the modules in the devices in the implementation scenarios can be distributed in the devices of the implementation scenarios according to the implementation scenario descriptions, or can be modified accordingly and located in one or more devices different from the implementation scenarios. The modules in the above-mentioned implementation scenarios can be combined into one module, or can be further split into multiple submodules.

[0118] The serial numbers of the present invention are for descriptive purposes only and do not represent the advantages or disadvantages of the implementation scenarios. The above disclosures are only a few specific implementation scenarios of the present invention, but the present invention is not limited to them. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present invention.

Claims

1. A variogram automatic modeling method based on deep learning, characterized in that: The method comprises: Constructing a variogram surface data set to be trained and an initial theoretical variogram parameter prediction model, wherein the variogram surface data set to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained; Training the initial theoretical variogram parameter prediction model according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model; Obtaining an experimental variogram value, calculating a variogram surface corresponding to the experimental variogram value, and using the theoretical variogram parameter prediction model to predict theoretical variogram parameters corresponding to the variogram surface, so as to obtain a theoretical variogram based on the theoretical variogram parameters; The variogram parameters to be trained include the variogram type to be trained, and the construction of the initial theoretical variogram parameter prediction model includes: Constructing a regression sub-model, wherein one regression sub-model corresponds to one type of the variogram to be trained; Constructing a classification sub-model, and determining the classification sub-model and all the regression sub-models as an initial theoretical variogram parameter prediction model; The constructing of the regression sub-model includes: Construct the convolution layer, pooling layer, fully connected layer and regression sub-model output layer to obtain the regression sub-model; Determine the nodes of the output layer of the regression sub-model according to the variogram parameters to be trained; The step of determining the nodes of the output layer of the regression sub-model according to the variogram parameters to be trained includes: If the variogram surface dataset to be trained is two-dimensional, the variogram parameters to be trained are the variogram type to be trained, the nugget value, the main direction, the main direction range value, and the range ratio, and the nodes of the output layer of the regression sub-model are determined to be: the nugget value, the main direction, the main direction range value, and the range ratio; If the variogram surface data set to be trained is three-dimensional, then the variogram parameters to be trained are the variogram type to be trained, the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value, and the nodes of the output layer of the regression sub-model are determined to be: the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value.

2. The method according to claim 1, characterized in that The step of constructing a variogram surface dataset to be trained includes: Randomly generate multiple sets of variogram parameters to be trained; Obtaining a preset coordinate range, calculating the trained variogram values ​​of all data points within the preset coordinate range using the trained variogram parameters of each group, and calculating the trained variogram surface corresponding to the trained variogram values; Each group of the to-be-trained variogram parameters and the corresponding to-be-trained variogram surface is taken as a group of to-be-trained variogram surface data, and multiple groups of to-be-trained variogram data are determined as a to-be-trained variogram surface data set.

3. The method according to claim 1, characterized in that The step of training the initial theoretical variogram parameter prediction model according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model includes: Training the classification sub-model according to the variogram surface to be trained and the corresponding variogram type to be trained; The regression sub-model corresponding to the variogram type to be trained is trained according to the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model.

4. The method according to claim 2, characterized in that Predicting the theoretical variogram parameters corresponding to the variogram surface using the theoretical variogram parameter prediction model includes: Mapping the variogram surface into the preset coordinate range to obtain a mapped variogram surface; The mapped variogram surface is input into the theoretical variogram parameter prediction model to obtain the theoretical variogram parameters corresponding to the variogram surface.

5. A variogram automatic modeling device based on deep learning, characterized in that: The device comprises: A construction module is used to construct a variogram surface data set to be trained and to construct an initial theoretical variogram parameter prediction model, wherein the variogram surface data set to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained; A training module, configured to train the initial theoretical variogram parameter prediction model based on the variogram surface to be trained and the corresponding variogram parameters to be trained, to obtain a trained theoretical variogram parameter prediction model; a prediction module, configured to obtain experimental variogram values, calculate a variogram surface corresponding to the experimental variogram values, and predict theoretical variogram parameters corresponding to the variogram surface using the theoretical variogram parameter prediction model, so as to obtain a theoretical variogram based on the theoretical variogram parameters; The building module includes: a first building unit and a second building unit; The first construction unit is used to construct a regression sub-model, wherein one regression sub-model corresponds to one type of variogram to be trained; The second construction unit is used to construct a classification sub-model, and determine the classification sub-model and all the regression sub-models as an initial theoretical variogram parameter prediction model; The first construction unit is used to construct a convolution layer, a pooling layer, a fully connected layer and an output layer to obtain a regression sub-model; and determine the nodes of the output layer according to the variogram parameters to be trained; The first construction unit is used to, if the variogram surface data set to be trained is two-dimensional, then the variogram parameters to be trained are the variogram type to be trained, nugget value, main direction, main direction range value and range ratio, and determine that the nodes of the output layer are: the nugget value, the main direction, the main direction range value and the range ratio; if the variogram surface data set to be trained is three-dimensional, then the variogram parameters to be trained are the variogram type to be trained, the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value, and determine that the nodes of the output layer are: the main direction angle, the secondary direction angle, the short direction angle, the nugget value, the main direction range value, the secondary direction range value and the short direction range value.

6. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for automatic modeling of variogram based on deep learning as described in any one of claims 1 to 4 is implemented.

7. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein: When the processor executes the computer program, the method for automatic modeling of variogram based on deep learning as described in any one of claims 1 to 4 is implemented.

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