Zircon Hf-isotopic mapping can be regarded as a useful tool for evaluating the coupling relationship between lithospheric structure and metallic mineralization. Hence, this method shows important significance for mineral prediction. To explore this potential, the published granite zircon Hf isotope data from the Sanjiang Tethyan Orogen were systematically compiled. This study uses the Kriging weighted interpolation in the Mapgis software system to contour Hf isotopes, revealing a relation between the crustal structure and metallogenesis. The mapping results suggest that the Changning–Menglian suture zone is the boundary between ancient and juvenile crust, viz., the western terranes have ancient crust attributes, whereas the eastern terranes exhibit the properties of new juvenile crust. In addition, this study also found that the mineralization and element types in the Sanjiang Tethyan Orogen have a coupling relationship with the crustal structure. The distribution of porphyry Cu-Mo-Au deposits is mainly controlled by the new juvenile crust, whereas the magmatic-hydrothermal Sn-W and porphyry Mo-W(-Cu) deposits are closely related to the reworked ancient crust. The results of zircon Hf isotope mapping prove that the formation and spatial distribution of deposits are related to the composition and properties of the crust. Hf isotope mapping can reveal the regional metallogenic rules and explore metallogenic prediction and metallogenic potential evaluation.

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Hyperspectral images possess a continuous and analogous spectral nature, enabling the classification of distinctive information by analyzing the subtle variations between adjacent spectra. Meanwhile, a hyperspectral dataset includes redundant and noisy information in addition to larger dimensions, which is the primary barrier preventing its use for land cover categorization. Despite the excellent feature extraction capability exhibited by convolutional neural networks, its efficacy is restricted by the constrained receptive field and the inability to acquire long-range features due to the limited size of the convolutional kernels. We construct a dual-stream self-attention fusion network (DSSFN) that combines spectral and spatial information in order to achieve the deep mining of global information via a self-attention mechanism. In addition, dimensionality reduction is required to reduce redundant data and eliminate noisy bands, hence enhancing the performance of hyperspectral classification. A unique band selection algorithm is proposed in this study. This algorithm, which is based on a sliding window grouped normalized matching filter for nearby bands (SWGMF), can minimize the dimensionality of the data while preserving the corresponding spectral information. Comprehensive experiments are carried out on four well-known hyperspectral datasets, where the proposed DSSFN achieves higher classification results in terms of overall accuracy (OA), average accuracy (AA), and kappa than previous approaches. A variety of trials verify the superiority and huge potential of DSSFN.

Based on the statistical characteristics of remote sensing data, the spatial geometric structure characteristics of spectral data and distribution of background, interference and alteration information in characteristic space were researched through the analysis of two-dimensional and three-dimensional scatter diagrams. The results indicate that the hyper-space of remote sensing multi-data aggregation belongs to low-dimensional geometric structure, i.e. hyperplane form, and anomalous point groups including alteration information usually dissociate out of hyperplane. Scatter diagrams of remote sensing data band are mainly presented as two distribution forms of single-ellipse and dual-ellipse. Clarifying the relations of three objects of background, disturbance and alteration information in remote sensing images provides an important technical thought and guidance for accurately detecting and extracting remote sensing alteration information.