Accurately inferring realistic subsurface structures poses a considerable challenge due to the impact of morphology on flow and transport behaviors. Traditional subsurface characterization relies on two primary types of data: hard data, derived from direct subsurface measurements, and soft data, encompassing remotely sensed geophysical information and its interpretation. Existing deep‐learning‐based methodologies predominantly focus on the transition from multiple observations to subsurface structures. However, implicit non‐linear correlations among diverse data sources often remain underutilized, leading to potential bias and errors. In this study, we introduce a multiple‐condition fusion network (MCF‐Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple‐source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. The integration of these features is achieved through multiple‐condition fusion blocks, designed to capture representative characteristics. These blocks are also adept at reconstructing heterogeneous structures and facilitating hydrological parameterization. MCF‐Net exhibits accuracy in estimating subsurface structures across various types of subsurface observations. Experimental results underscore the utility and superiority of MCF‐Net in applications of hydrogeological modeling.

Multiple-point statistics (MPS) has shown promise in representing complicated subsurface structures. For a practical three-dimensional (3-D) application, however, one of the critical issues is the difficulty in obtaining a credible 3-D training image. However, bidimensional (2-D) training images are often available because established workflows exist to derive 2-D sections from scattered boreholes and/or other samples. In this work, we propose a locality-based MPS approach to reconstruct 3-D geological models on the basis of such 2-D cross sections (3DRCS), making 3-D training images unnecessary. Only several local training subsections closer to the central uninformed node are used in the MPS simulation. The main advantages of this partitioned search strategy are the high computational efficiency and a relaxation of the stationarity assumption. We embed this strategy into a standard MPS framework. Two probability aggregation formulas and their combinations are used to assemble the probability density functions (PDFs) from different subsections. Moreover, a novel strategy is adopted to capture more stable PDFs, where the distances between patterns and flexible neighborhoods are integrated on multiple grids. A series of sensitivity analyses demonstrate the stability of the proposed approach. Several hydrogeological 3-D application examples illustrate the applicability of the 3DRCS approach in reproducing complex geological features. The results, in comparison with previous MPS methods, show better performance in portraying anisotropy characteristics and in CPU cost.

The classification of hyperspectral remote sensing images is difficult due to the curse of dimensionality. Therefore, it is necessary to find an effective way to reduce the dimensions of such images. The Relief-F method has been introduced for supervising dimensionality reduction, but the band subset obtained by this method has a large number of continuous bands, resulting in a reduction in the classification accuracy. In this paper, an improved method—called Partitioned Relief-F—is presented to mitigate the influence of continuous bands on classification accuracy while retaining important information. Firstly, the importance scores of each band are obtained using the original Relief-F method. Secondly, the whole band interval is divided in an orderly manner, using a partitioning strategy according to the correlation between the bands. Finally, the band with the highest importance score is selected in each sub-interval. To verify the effectiveness of the proposed Partitioned Relief-F method, a classification experiment is performed on three publicly available data sets. The dimensionality reduction methods Principal Component Analysis (PCA) and original Relief-F are selected for comparison. Furthermore, K-Means and Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH) are selected for comparison in terms of partitioning strategy. This paper mainly measures the effectiveness of each method indirectly, using the overall accuracy of the final classification. The experimental results indicate that the addition of the proposed partitioning strategy increases the overall accuracy of the three data sets by 1.55%, 3.14%, and 0.83%, respectively. In general, the proposed Partitioned Relief-F method can achieve significantly superior dimensionality reduction effects.

This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation coefficients of the target pixel and the relevant pixels, and the target pixel can be reconstructed by using the pixels and the corresponding correlation coefficients in the optimal sub-window. By combining NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM), a classification model, namely NSW-PCA-SVM, is obtained. This paper conducts experiments on three public datasets, and verifies the effectiveness of the proposed model by comparing with two basic models, i.e., SVM and PCA-SVM, and six state-of-the-art models, i.e., CDCT-WF-SVM, CDCT-2DCT-SVM, SDWT-2DWT-SVM, SDWT-WF-SVM, SDWT-2DCT-SVM and Two-Stage. The proposed approach has the following advantages in overall accuracy (OA)—take the experimental results on the Indian Pines dataset as an example: (1) Compared with SVM (OA = 53.29%) and PCA-SVM (OA = 58.44%), NSW-PCA-SVM (OA = 91.40%) effectively utilizes the spatial information of HSI and improves the classification accuracy. (2) The performance of the proposed model is mainly determined by two parameters, i.e., the window size in NSW and the number of principal components in PCA. The two parameters can be adjusted independently, making parameter adjustment more convenient. (3) When the sample size of the training set is small (20 samples per class), the proposed NSW-PCA-SVM approach achieves 2.38–18.40% advantages in OA over the six state-of-the-art models.