The reconstruction of porous media is widely used in the study of fluid flows and engineering sciences. Some traditional reconstruction methods for porous media use the features extracted from real natural porous media and copy them to realize reconstructions. Currently, as one of the important branches of machine learning methods, the deep transfer learning (DTL) method has shown good performance in extracting features and transferring them to the predicted objects, which can be used for the reconstruction of porous media. Hence, a method for reconstructing porous media is presented by applying DTL to extract features from a training image (TI) of porous media to replace the process of scanning a TI for different patterns as in multiple-point statistical methods. The deep neural network is practically used to extract the complex features from the TI of porous media, and then, a reconstructed result can be obtained by transfer learning through copying these features. The proposed method was evaluated on shale and sandstone samples by comparing multiple-point connectivity functions, variogram curves, permeability, porosity, etc. The experimental results show that the proposed method is of high efficiency while preserving similar features with the target image, shortening reconstruction time, and reducing the burdens on CPU.

As a novel swarm intelligence optimization algorithm, cuckoo search (CS) has been successfully applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS has some disadvantages, such as premature convergence, easy to fall into local optimum and poor balance between exploitation and exploration. In order to improve the optimization performance of the CS algorithm, a new CS extension with multi-swarms and Q-Learning namely MP-QL-CS is proposed. The step size strategy of the CS algorithm is that an individual fitness value is examined based on a one-step evolution effect of an individual instead of evaluating the step size from the multi-step evolution effect. In the MP-QL-CS algorithm, a step size control strategy is considered as action, which is used to examine the individual multi-stepping evolution effect and learn the individual optimal step size by calculating the Q function value. In this way, the MP-QL-CS algorithm can increase the adaptability of individual evolution, and a good balance between diversity and intensification can be achieved. Comparing the MP-QL-CS algorithm with various CS algorithms, variants of differential evolution (DE) and improved particle swarm optimization (PSO) algorithms, the results demonstrate that the MP-QL-CS algorithm is a competitive swarm algorithm.

When using coarse-resolution remote sensing images, super-resolution reconstruction is widely desired, and can be realized by reproducing the intrinsic features from a set of coarse-resolution fraction data to fine-resolution remote sensing images that are consistent with the coarse fraction information. Prior models of spatial structures that encode the expected features at the fine (target) resolution are helpful to constrain the spatial patterns of remote sensing images to be generated at that resolution. These prior models can be used properly by multiple-point statistics (MPS), capable of extracting the intrinsic features of patterns from prior models such as training images, and copying them to the simulated regions using hard and soft conditional data, or even without any conditional data. However, because traditional MPS methods based on linear dimensionality reduction are not suitable to deal with nonlinear data, and isometric mapping (ISOMAP) can reduce the dimensionality of nonlinear data effectively, this paper presents a sequential simulation framework for generating super-resolution remote sensing images using ISOMAP and MPS. Using four different examples, it is demonstrated that the structural characteristics of super-resolution reconstruction of remote sensing images using this method, are similar to those of training images.

The Enhancement of WiFi Fingerprint Positioning Using Convolutional Neural Network

The 3D reconstruction of digital rocks assumes a paramount role within various engineering applications, necessitating careful consideration of the methods employed. The available approaches are divided into two groups, i.e., the physical experimental method and the numerical simulation method. Although the former is reliable, it incurs high costs and is limited by sample size constraints. The latter is cost-effective but suffers from prolonged processing times and sometimes suboptimal performance. However, the advent of deep learning has paved the way for integrating these techniques into 3D digital rock reconstruction. A standout amongst the method of deep learning techniques is the generative adversarial network (GAN). Nonetheless, existing models of GAN lack complete integration of multi-scale information. To address this issue, this study proposed style-transformer GAN (STGAN), a novel GAN model founded upon style-based GAN (StyleGAN) and Transformer. By utilizing the attention mechanisms of Transformer, STGAN ameliorates its ability to extract features from multi-scale training images. Additionally, the incorporation of style transfer further enhances the quality of the generated images. By comparing it with other deep learning algorithms, the efficiency and applicability of the proposed method are demonstrated.

Pore space reconstruction is of great significance to some fields such as the study of seepage mechanisms in porous media and reservoir engineering. Shale oil and shale gas, as unconventional petroleum resources with abundant reserves in the whole world, attract extensive attention and have a rapid increase in production. Shale is a type of complex porous medium with evident fluctuations in various mineral compositions, dense structure, and low hardness, leading to a big challenge for the characterization and acquisition of the internal shale structure. Numerical reconstruction technology can achieve the purpose of studying the engineering problems and physical problems through numerical calculation and image display methods, which also can be used to reconstruct a pore structure similar to the real pore spaces through numerical simulation and have the advantages of low cost and good reusability, casting light on the characterization of the internal structure of shale. The recent branch of deep learning, variational auto-encoders (VAEs), has good capabilities of extracting characteristics for reconstructing similar images with the training image (TI). The theory of Fisher information can help to balance the encoder and decoder of VAE in information control. Therefore, this paper proposes an improved VAE to reconstruct shale based on VAE and Fisher information, using a real 3D shale image as a TI, and saves the parameters of neural networks to describe the probability distribution. Compared to some traditional methods, although this proposed method is slower in the first reconstruction, it is much faster in the subsequent reconstructions due to the reuse of the parameters. The proposed method also has advantages in terms of reconstruction quality over the original VAE. The findings of this study can help for better understanding of the seepage mechanisms in shale and the exploration of the shale gas industry.