@article{(Open Science Index):https://publications.waset.org/pdf/10008470,
	  title     = {Anisotropic Total Fractional Order Variation Model in Seismic Data Denoising},
	  author    = {Jianwei Ma and  Diriba Gemechu},
	  country	= {},
	  institution	= {},
	  abstract     = {In seismic data processing, attenuation of random noise
is the basic step to improve quality of data for further application
of seismic data in exploration and development in different gas
and oil industries. The signal-to-noise ratio of the data also highly
determines quality of seismic data. This factor affects the reliability
as well as the accuracy of seismic signal during interpretation
for different purposes in different companies. To use seismic data
for further application and interpretation, we need to improve the
signal-to-noise ration while attenuating random noise effectively.
To improve the signal-to-noise ration and attenuating seismic
random noise by preserving important features and information
about seismic signals, we introduce the concept of anisotropic
total fractional order denoising algorithm. The anisotropic total
fractional order variation model defined in fractional order bounded
variation is proposed as a regularization in seismic denoising. The
split Bregman algorithm is employed to solve the minimization
problem of the anisotropic total fractional order variation model
and the corresponding denoising algorithm for the proposed method
is derived. We test the effectiveness of theproposed method for
synthetic and real seismic data sets and the denoised result is
compared with F-X deconvolution and non-local means denoising
algorithm.},
	    journal   = {International Journal of Geological and Environmental Engineering},
	  volume    = {12},
	  number    = {1},
	  year      = {2018},
	  pages     = {40 - 44},
	  ee        = {https://publications.waset.org/pdf/10008470},
	  url   	= {https://publications.waset.org/vol/133},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 133, 2018},
	}