Inversion of seismic source parameters from satellite InSAR data based on deep learning
TLDR
Zhang et al. as mentioned in this paper proposed a deep learning approach of earthquake source parameters inversion using ResNet (abbreviated as ESPI-ResNet) from satellite InSAR data.About:
This article is published in Tectonophysics.The article was published on 2021-11-15 and is currently open access. It has received 2 citations till now. The article focuses on the topics: Interferometric synthetic aperture radar & Inversion (meteorology).read more
Citations
More filters
Journal ArticleDOI
Surface deformation simulation for InSAR detection using a machine learning approach on the hantangang river volcanic field: A case study on the orisan mountain
Muhammad Fulki Fadhillah,Wahyu Luqmanul Hakim,Sungjae Park,Dae-Hwan Kim,Yu-Chul Park,Chang Hwan Kim,Chang-Wook Lee +6 more
TL;DR: In this paper , a convolutional neural network (CNN) was trained with the unwrapped simulated interferogram data and its performance was evaluated using the CNN algorithms on the validation dataset.
Journal ArticleDOI
Bibliometric Analysis of the Permafrost Research: Developments, Impacts, and Trends
Qingsong Du,Guoyu Li,Dun Chen,Yu Zhou,Shunshun Qi,Fei Wang,Yuncheng Mao,Jun Zhang,Ya-peng Cao,Kai-Bo Gao,Gang Wu,Chunyan Li,Yapeng Wang +12 more
TL;DR: Wang et al. as discussed by the authors conducted a bibliometric analysis of 13,697 articles in the field of permafrost research from 1942 to 2021, collected from the Web of Science core collection database.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.