Talk #1  "Some Applications of Wavelet Transform in Seismic Data Processing"  

 

Ground-roll, swell noise, guided waves, and random noise are just some of most persistent types of noise in land, marine and OBC data. These types of noise can be hard to remove with traditional Fourier-based methods without damage to the signal. Wavelet Transforms present a relatively new set of tools to aid seismic processing. These tools have been successfully applied for compressing and de-noising purposes. In this talk I will give a brief theoretical background for Wavelet Transforms and provide details about some of the wavelet-transform-based filters and algorithms used in the research community. I will show results of both 1D and 2D Stationary Wavelet Transform (SWT) - based filters for groundroll and coherent noise removal in prestack data. I will also show 2D SWT filters for acquisition footprints and random noise suppression in poststack data. More recent applications of SWT would include F-X deconvolution and resolution enhancement in wavelet transform domain.  

 

Talk #2  "High - Order Seislet Transform"  

 

The seislet transform is a wavelet-like transform that analyzes seismic data by following variable slopes of seismic events across different scales. In the case of multiple components, the transform turns into a frame (an invertible overcomplete representation) and becomes suitable for analyzing data with multiple frequencies (in 1-D) or multiple plane-wave slopes (in 2-D and 3-D). We extend this approach to a higher order, using t he Cohen-Daubechies-Feauveau 9/7 biorthogonal wavelet transform (the basis for the JPEG2000 compression scheme) as a template. Using synthetic and field-data examples, we demonstrate that the new transform can provide a better compression rate for seismic events than the Fourier transform, discrete wavelet transform, or the low-order seislet transform. Therefore, the high-order seislet transform can be more suitable for data processing tasks such as data regularization and noise attenuation.