Fourier transform of large scale aeromagnetic field using a modified version of fast Fourier transform

1970 ◽  
Vol 81 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Prabhakar S. Naidu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Large Scale

scholarly journals A fast methodology for large-scale focusing inversion of gravity and magnetic data using the structured model matrix and the 2-D fast Fourier transform

2020 ◽  
Vol 223 (2) ◽  
pp. 1378-1397
Author(s):  
Rosemary A Renaut ◽  
Jarom D Hogue ◽  
Saeed Vatankhah ◽  
Shuang Liu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Linear Systems ◽  
Large Scale ◽  
Surface Measurement ◽  
Magnetic Data ◽  
Uniform Grid ◽  
Data Sets ◽  
Inversion Algorithm ◽  
Data Set

SUMMARY We discuss the focusing inversion of potential field data for the recovery of sparse subsurface structures from surface measurement data on a uniform grid. For the uniform grid, the model sensitivity matrices have a block Toeplitz Toeplitz block structure for each block of columns related to a fixed depth layer of the subsurface. Then, all forward operations with the sensitivity matrix, or its transpose, are performed using the 2-D fast Fourier transform. Simulations are provided to show that the implementation of the focusing inversion algorithm using the fast Fourier transform is efficient, and that the algorithm can be realized on standard desktop computers with sufficient memory for storage of volumes up to size n ≈ 106. The linear systems of equations arising in the focusing inversion algorithm are solved using either Golub–Kahan bidiagonalization or randomized singular value decomposition algorithms. These two algorithms are contrasted for their efficiency when used to solve large-scale problems with respect to the sizes of the projected subspaces adopted for the solutions of the linear systems. The results confirm earlier studies that the randomized algorithms are to be preferred for the inversion of gravity data, and for data sets of size m it is sufficient to use projected spaces of size approximately m/8. For the inversion of magnetic data sets, we show that it is more efficient to use the Golub–Kahan bidiagonalization, and that it is again sufficient to use projected spaces of size approximately m/8. Simulations support the presented conclusions and are verified for the inversion of a magnetic data set obtained over the Wuskwatim Lake region in Manitoba, Canada.

Large-Scale Fast Fourier Transform

2011 ◽  
pp. 629-642
Author(s):  
Yifeng Chen ◽  
Xiang Cui ◽  
Hong Mei
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Large Scale

Large-scale fast Fourier transform on a heterogeneous multi-core system

2012 ◽  
Vol 26 (2) ◽  
pp. 148-158 ◽  
Author(s):  
Yan Li ◽  
Jeffrey R Diamond ◽  
Xu Wang ◽  
Haibo Lin ◽  
Yudong Yang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Large Scale ◽  
Core System

scholarly journals Physical IC Design Layout of Memory-Based Real Fast Fourier Transform Architecture using 90nm Technology

2020 ◽  
Vol 8 (5) ◽  
pp. 3681-3685
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Communication Systems ◽  
High Speed ◽  
Large Scale ◽  
Low Complexity ◽  
Design Flow ◽  
Digital Subscriber Line ◽  
Ic Design ◽  
Subscriber Line

In this paper we present a low complexity physical IC layout for memory based Real Fast Fourier Transform (RFFT) architecture using 90nm technology. FFT architectures are the most important algorithms in the modern communication systems like and very high bit rate digital subscriber line (VDSL) asymmetric digital subscriber line (ADSL). In this FFT algorithm is based on radix-2 decimation-in-frequency. In order to meet the real time requirements of very large scale integration (VLSI), we designed a low complexity and high speed FFT architecture. The RFFT architecture was realised using Verilog hardware description language (HDL). This architecture is simulated using Native code launch of cadence and synthesized using RTL code complier of cadence tool. Each step of application specific integrated circuit (ASIC) physical IC design flow was synthesized using cadence Innovus 90nm technology and we optimize the design to reduce the area, power and timing requirements

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