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The southern Great Xing'an Range is located in the eastern Central Asian Orogenic Belt, where voluminous igneous rocks developed during the Late Mesozoic period. The east slope of the southern Great Xing'an Range has been the topic of numerous debates on the level of influence of the Mongol-Okhotsk and the Paleo-Pacific regimes in the Late Mesozoic period. Therefore, this area is a suitable region in which to study the temporal changes in magma sources and tectono-magmatic evolution. In this paper, whole-rock geochemical data, zircon U-Pb geochronology, and zircon Hf isotope studies were carried out on the granitoids in the east slope area of the southern Great Xing'an Range. LA-ICP-MS zircon U-Pb dating revealed the ages of four granitoid samples: 135.0 ± 0.6 Ma, 130.7 ± 1.4 Ma, 130.4 ± 1.0 Ma, and 127.6 ± 0.8 Ma, respectively. The Hf isotope values 176Hf/177Hf = 0.282751–0.283015, εHf (t) = +2.0~+11.5, and T2DM = 583~1442 Ma suggest that the magma was generated by partial melting of Meso- and Neoproterozoic accreted and thickened low crust. The whole-rock geochemical data implied that these granitoids are A-type granite and their formation is closely linked to the subduction of the Paleo-Pacific Ocean plate. These geochemical, isotopic, and geochronological data suggest that the Early Cretaceous magmatism in the east slope area of the southern Great Xing'an Range formed in an extensional back-arc tectonic setting associated with the slab roll-back of the Paleo-Pacific plate subduction. [ABSTRACT FROM AUTHOR]

Intelligent prospecting and prediction are important research foci in the field of mineral resource exploration. To solve the problem of the performance degradation of deep convolutional neural networks, enhancing the attention to target information and suppressing unnecessary feature information, this paper proposes a new prospecting prediction method based on a two-dimensional convolutional neural network (CNN2D). This method mainly uses known Cu deposits as the positive sample labels, adopts the sliding window method for data enhancement, and uses the window area as a unit to extract spatial variation features. It is important to supplement squeeze-and-excitation networks (SENets) to add an attention mechanism to the channel dimension, assign a weight value to each feature layer, and finally make prospecting predictions by matching the features of the known deposit window area and the features of the unknown window area. This method allows the neural network to focus on certain characteristic channels and realizes prospecting prediction in the case where there are few known deposits so that the deep learning method can be more effectively used for the prospecting prediction of mineralization. Based on geological data, geochemical exploration data of water system sediments, and aeromagnetic data, and via this method, this study carried out prospecting prediction of Cu deposits in the Zhunuo area of Tibet and predicted 12 favorable Cu prospecting prediction areas. Combined with previous research results and field exploration, the predicted result is consistent with the established mineralization and prospecting pattern and has good prospects for Cu deposit prospecting. [ABSTRACT FROM AUTHOR]