Did a Late Paleoproterozoic-Early Mesoproterozoic Landmass Exist in the Eastern Cathaysia Block? New Evidence from Detrital Zircon U-Pb Geochronology and Sedimentary Indicators

The South China Craton comprises the Yangtze and Cathaysia blocks and is one of the largest Precambrian continental blocks in East Asia. However, the early geological and geographical evolution of the Cathaysia block is relatively poorly understood, due to the sparse exposure of pre-Neoproterozoic rocks and reworking during Phanerozoic polyphase magmatism and metamorphism. In this contribution, we carried out detrital zircon U-Pb geochronology and sedimentary analyses on five Proterozoic meta-sedimentary rocks collected from the northeastern Cathaysia block, which belong to the previously defined Chencai, Mayuan, and Mamianshan Groups (strata). LA-ICP-MS U-Pb dating results of the detrital zircons show various ~1.85–1.35 Ga maximum depositional ages. They are significantly older than the previously constrained Neoproterozoic formation ages of these Proterozoic strata of northeastern Cathaysia, suggesting that their deposition and formation were probably initiated as early as the late Paleoproterozoic. Provenance analyses reveal that the late Paleoproterozoic to early Mesoproterozoic detrital zircons with igneous-origin were derived from in situ contemporary crystalline basements in eastern Cathaysia. In addition, by implication, the easternmost part of Cathaysia was probably an emerged area (i.e., the “proto-Cathaysia Land”) under active erosion. It had a ~NWW orientation and provided detrital sediments to the neighboring marine basin (i.e., the Cathaysia Sea) during the late Paleoproterozoic to early Mesoproterozoic. Finally, the Paleoproterozoic evolution of Cathaysia was involved in the assembly of the Nuna supercontinent. Our results, together with the published data, reveal a distinct late Paleoproterozoic (~1.8 Ga) detrital zircon age peak, which seems to support the view that eastern Cathaysia had close tectonic affinities with terranes such as the Precambrian terranes of current northern India, in the framework of the Nuna supercontinent reconstruction.</jats:p

Feature extraction of hyperspectral images with semi-supervised graph learning

We propose a semisupervised graph learning (SEGL) method for feature extraction of hyperspectral remote sensing imagery in this paper. The proposed SEGL method aims to build a semisupervised graph that can maximize the class discrimination and preserve the local neighborhood information by combining labeled and unlabeled samples. In our semisupervised graph, we connect labeled samples according to their label information and unlabeled samples by their nearest neighborhood information. By sorting the mean distance between a unlabeled sample and labeled samples of each class, we connect the unlabeled sample with all labeled samples belonging to its nearest neighborhood class. Moreover, the proposed SEGL better models the actual differences and similarities between samples, by setting different weights to the edges of connected samples. Experimental results on four real hyperspectral images (HSIs) demonstrate the advantages of our method compared to some related feature extraction methods

Time constraints on the closure of the Paleo–South China Ocean and the Neoproterozoic assembly of the Yangtze and Cathaysia blocks: Insight from new detrital zircon analyses

International audienceThe South China Block was built up by the assembly of the Yangtze and Cathaysia blocks along the Neoproterozoic Jiangnan Orogenic Belt. The timing of the Jiangnan Orogeny remains controversial. The widespread orogeny–related Neoproterozoic angular unconformity that separates the underlying folded Sibao (ca.1000–820 Ma) and overlying Danzhou (ca.800–720 Ma) Groups was investigated. Six sedimentary samples, below and above the unconformity in three distal localities (Fanjingshan, Madiyi, and Sibao) yield detrital zircon with UPb ages ranging from 779 ± 16 Ma to 3006 ± 36 Ma, with a prominent peak at ca. 852 Ma. The youngest ages of 832 ± 11 Ma and 779 ± 16 Ma are revealed for the underlying Sibao and overlying Danzhou Groups, respectively. The detrital zircon UPb age relative probability plot of the Jiangnan Orogen matches well with those of the Yangtze and Cathaysia blocks since ca. 865 Ma. Integrating geological, geochemical and geochronological results, we suggest that the Paleo–South China Ocean began to subduct under the Yangtze block at ca. 1000 Ma, and was partly closed at ca. 865 Ma. Afterwards, the Yangtze and Cathaysia blocks initially collide at 865 Ma, forming the Jiangnan Orogen. This collision resulted in not only the folding of the Sibao Group, but also sediment deposition in a syn-collisional setting, which makes the upper part of the Sibao Group. The youngest S-type granite dated at ca. 820 Ma that intruded in the Sibao Group marks the late stage of the Jiangnan Orogeny

LRAD-Net: An Improved Lightweight Network for Building Extraction From Remote Sensing Images

The building extraction method of remote sensing images that uses deep learning algorithms can solve the problems of low efficiency and poor effect of traditional methods during feature extraction. Although some semantic segmentation networks proposed recently can achieve good segmentation performance in extracting buildings, their huge parameters and large amount of calculation lead to great obstacles in practical application. Therefore, we propose a lightweight network (named LRAD-Net) for building extraction from remote sensing images. LRAD-Net can be divided into two stages: encoding and decoding. In the encoding stage, the lightweight RegNet network with 600 million flop (600 MF) is finally selected as our feature extraction backbone net though lots of experimental comparisons. Then, a multiscale depthwise separable atrous spatial pyramid pooling structure is proposed to extract more comprehensive and important details of buildings. In the decoding stage, the squeeze-and-excitation attention mechanism is applied innovatively to redistribute the channel weights before fusing feature maps with low-level details and high-level semantics, thus can enrich the local and global information of the buildings. What&#x0027;s more, a lightweight residual block with polarized self-attention is proposed, it can incorporate features extracted from the space of maps and different channels with a small number of parameters, and improve the accuracy of recovering building boundary. In order to verify the effectiveness and robustness of proposed LRAD-Net, we conduct experiments on a self-annotated UAV dataset with higher resolution and three public datasets (the WHU aerial image dataset, the WHU satellite image dataset and the Inria aerial image dataset). Compared with several representative networks, LRAD-Net can extract more details of building, and has smaller number of parameters, faster computing speed, stronger generalization ability, which can improve the training speed of the network without affecting the building extraction effect and accuracy