scholarly journals A wavenumber-domain iterative approach for 3D imaging of magnetic anomalies and gradients with depth constraints

2019 ◽  
Vol 16 (6) ◽  
pp. 1032-1047
Author(s):  
Yatong Cui ◽  
Lianghui Guo
Keyword(s):  
Large Scale ◽  
3D Imaging ◽  
Three Dimensional ◽  
Magnetic Anomalies ◽  
Magnetic Data ◽  
Iterative Approach ◽  
Quantitative Interpretation ◽  
Wavenumber Domain ◽  
Large Scale Data ◽  
Scale Data

Abstract Three-dimensional magnetic inversion, based on the least-square and regularization algorithm in the space domain, is an important tool for quantitative interpretation of magnetic data. However, the common 3D inversion approaches usually require great numbers of forward and inversion calculations and cause low efficiency for inverting large-scale data. Three-dimensional imaging is an alternate rapid tool for qualitative and quantitative interpretation of magnetic data. In this paper, we present a wavenumber-domain iterative approach for 3D imaging of magnetic anomalies and gradients, which could increase imaging efficiency and is suitable for rapidly imaging large-scale data. The wavenumber-domain formulas for forward modeling and imaging of total magnetic anomaly, three magnetic components, magnetic gradients and magnetic full-tensor gradients are deduced and provided. A depth-scale factor and the constraints of magnetic interface are included into the imaging formulas to enhance depth resolution. An iterative algorithm is adopted for the imaging to reduce the fitting error and improve the imaging accuracy. Tests on synthetic and real data from the Sichuan basin, China, verified the feasibility of the presented approaches.

3D-RadViz: Three Dimensional Radial Visualization for Large-Scale Data Visualization

2021 ◽  
Author(s):  
Abdelrahman Elewah ◽  
Abeer A. Badawi ◽  
Haytham Khalil ◽  
Shahryar Rahnamayan ◽  
Khalid Elgazzar
Keyword(s):  
Data Visualization ◽  
Large Scale ◽  
Three Dimensional ◽  
Large Scale Data ◽  
Radial Visualization ◽  
Scale Data

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

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