scholarly journals Differential equation-based seismic data filtering

1992 ◽  
Author(s):  
Jianchao Li ◽  
K. Larner
Keyword(s):  
Differential Equation ◽  
Seismic Data ◽  
Data Filtering

scholarly journals Differential‐equation‐based seismic data filtering

Geophysics ◽  
1993 ◽  
Vol 58 (12) ◽  
pp. 1809-1819 ◽  
Author(s):  
Jianchao Li ◽  
Ken Larner
Keyword(s):  
Differential Equation ◽  
Seismic Data ◽  
Lower Cost ◽  
Seismic Signal ◽  
Seismic Data Processing ◽  
Data Filtering ◽  
Time And Space ◽  
Convolution Operation ◽  
Computational Advantage ◽  
Space Point

Suppressing noise and enhancing useful seismic signal by filtering is one of the important tasks of seismic data processing. Conventional filtering methods are implemented through either the convolution operation or various mathematical transforms. We describe a methodology for studying and implementing filters, which, unlike conventional filtering methods, is based on solving differential equations in the time and space domain. We call this differential‐equation‐based filtering (DEBF). DEBF does not require that seismic data be stationary, so filtering parameters can vary with every time and space point. Examples with two‐dimensional (2-D) synthetic and field seismic data demonstrate that the DEBF method accomplishes the desired time‐ and space‐varying temporal and move‐out filtering at lower cost than conventional frequency‐wavenumber‐domain filtering. The computational advantage in 3-D would be much greater.

scholarly journals Differential equation-based seismic data filtering

1992 ◽  
Author(s):  
Jianchao Li ◽  
K. Larner
Keyword(s):  
Differential Equation ◽  
Seismic Data ◽  
Data Filtering

Application of spatial contiguity analysis to seismic data filtering

1994 ◽  
Author(s):  
Abdelaziz Faraj
Keyword(s):  
Seismic Data ◽  
Data Filtering ◽  
Spatial Contiguity

Seismic data filtering using a Gabor representation

1994 ◽  
Vol 32 (2) ◽  
pp. 467-472 ◽  
Author(s):  
J.E. Womack ◽  
J.R. Cruz
Keyword(s):  
Seismic Data ◽  
Data Filtering

scholarly journals ADAPTIVE SINGULAR VALUE DECOMPOSITION FILTERING TO ENHANCE REFLECTORS AND GEOLOGICAL STRUCTURES IN 3D SEISMIC DATA

2016 ◽  
Vol 34 (2) ◽  
Author(s):  
Washington Oliveira Martins ◽  
Milton José Porsani ◽  
Michelângelo G. da Silva
Keyword(s):  
Singular Value Decomposition ◽  
Adaptive Filtering ◽  
Seismic Data ◽  
Singular Value ◽  
3D Seismic ◽  
Data Filtering ◽  
Geological Structures ◽  
Value Decomposition ◽  
Seismic Volumes ◽  
3D Seismic Data

ABSTRACT. We applied an adaptive seismic data filtering method, based on the singular value decomposition (SVD) to improve the identification of reflectors and geological structures in 3D stacked seismic volumes...Keywords: seismic data processing, SVD filtering, 3D pos-stacked filtering, adaptive filtering. RESUMO. Nós aplicamos um método de filtragem adaptativa de dados sísmicos, baseado na decomposição em valores singulares (SVD), para melhorar a identificação de refletores e estruturas geológicas em volumes sísmicos empilhados 3D...Palavras-chave: processamento sísmico, filtragem SVD, filtragem pós-stack 3D, filtragem adaptativa.

Skeletonization of aeromagnetic data

Geophysics ◽  
2004 ◽  
Vol 69 (2) ◽  
pp. 478-488 ◽  
Author(s):  
D. Eaton ◽  
K. Vasudevan
Keyword(s):  
Seismic Data ◽  
Line Drawing ◽  
Aeromagnetic Data ◽  
Gridded Data ◽  
Data Filtering ◽  
Directional Bias ◽  
Great Slave Lake ◽  
Syntactic Pattern ◽  
Strike Direction ◽  
Oriented Parallel

Skeletonization is a syntactic pattern‐recognition method that is applied to gridded data to produce an automatic line drawing, with an associated event catalog. Previous implementations of skeletonization have been tailored for seismic data. Here, we modify that technique to render it more suitable for other types of gridded data, with particular emphasis on aeromagnetic maps. A modification from previous schemes is the use of a two‐pass approach, to reduce the effects of an otherwise problematic directional bias that discriminates against events oriented parallel to columns of the grid. The method can be used effectively for filtering aeromagnetic data on the basis of strike direction, event linearity, event amplitude, and polarity. It is based on the delineation of peak‐trough pairs (cycles), which are traced throughout the grid to form contiguous events. Cycles and events are characterized by attributes that include amplitude, polarity, and pulse width. Events are further characterized by length, average strike direction, and linearity. The event attributes are stored in a catalog, thus enabling one to perform attribute‐based analysis and data filtering. We illustrate our algorithm using two regional aeromagnetic examples from different parts of the Canadian Shield. The first, from the Great Slave Lake shear zone, is dominated by linear anomaly trends produced by faults and mafic dikes. The second, from the Manicouagan region of northeastern Quebec, contains abundant subcircular and arcuate anomaly patterns caused by large intrusive complexes and a meteorite impact structure.

Image-based processing techniques applied to seismic data filtering

2013 ◽  
Vol 104 ◽  
pp. 17-26 ◽  
Author(s):  
Jalal Ferahtia ◽  
Tahar Aïfa ◽  
Kamel Baddari ◽  
Noureddine Djarfour ◽  
Saïd El Adj
Keyword(s):  
Seismic Data ◽  
Data Filtering ◽  
Processing Techniques

scholarly journals Application of feedback connection artificial neural network to seismic data filtering

2008 ◽  
Vol 340 (6) ◽  
pp. 335-344 ◽  
Author(s):  
Noureddine Djarfour ◽  
Tahar Aïfa ◽  
Kamel Baddari ◽  
Abdelhafid Mihoubi ◽  
Jalal Ferahtia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Data ◽  
Data Filtering ◽  
Feedback Connection ◽  
Artificial Neural
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