scholarly journals Online Kernel-Based Graph Topology Identification with Partial-Derivative-Imposed Sparsity

Author(s):  
Mircea Moscu ◽  
Ricardo Borsoi ◽  
Cedric Richard
2021 ◽  
Author(s):  
Rohan Money ◽  
Joshin Krishnan ◽  
Baltasar Beferull-Lozano

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Lizong Zhang ◽  
Fengming Zhang ◽  
Xiaolei Li ◽  
Chunlei Wang ◽  
Taotao Chen ◽  
...  

2021 ◽  
Vol 11 (7) ◽  
pp. 3010
Author(s):  
Hao Liu ◽  
Xuewei Liu

The lack of an initial condition is one of the major challenges in full-wave-equation depth extrapolation. This initial condition is the vertical partial derivative of the surface wavefield and cannot be provided by the conventional seismic acquisition system. The traditional solution is to use the wavefield value of the surface to calculate the vertical partial derivative by assuming that the surface velocity is constant. However, for seismic exploration on land, the surface velocity is often not uniform. To solve this problem, we propose a new method for calculating the vertical partial derivative from the surface wavefield without making any assumptions about the surface conditions. Based on the calculated derivative, we implemented a depth-extrapolation-based full-wave-equation migration from topography using the direct downward continuation. We tested the imaging performance of our proposed method with several experiments. The results of the Marmousi model experiment show that our proposed method is superior to the conventional reverse time migration (RTM) algorithm in terms of imaging accuracy and amplitude-preserving performance at medium and deep depths. In the Canadian Foothills model experiment, we proved that our method can still accurately image complex structures and maintain amplitude under topographic scenario.


2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Haipeng Peng ◽  
Lixiang Li ◽  
Jürgen Kurths ◽  
Shudong Li ◽  
Yixian Yang

Nowadays, the topology of complex networks is essential in various fields as engineering, biology, physics, and other scientific fields. We know in some general cases that there may be some unknown structure parameters in a complex network. In order to identify those unknown structure parameters, a topology identification method is proposed based on a chaotic ant swarm algorithm in this paper. The problem of topology identification is converted into that of parameter optimization which can be solved by a chaotic ant algorithm. The proposed method enables us to identify the topology of the synchronization network effectively. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed method.


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