Dissertation/Thesis Abstract

Interpolation with prediction -error filters and training data
by Curry, William, Ph.D., Stanford University, 2008, 190; 3313828
Abstract (Summary)

With finite capital and logistical means, interpolation is a necessary part of seismic processing, especially for such data-dependent methods as surface-related multiple elimination. One such method is to first estimate a prediction-error filter (PEF) on a training data set, and then use that PEF to estimate missing data, where each step is a least-squares problem. This approach is useful because it can interpolate multiple simultaneous, aliased slopes, but requires regularly-sampled data. I adapt this approach to interpolate irregularly-sampled data, marine data with a large near-offset gap, and 3D prestack marine data with many dimensions.

I estimate a PEF from irregularly-sampled data in order to interpolate these data onto a regular grid. I do this by regridding the data onto multiple different grids and estimate a PEF simultaneously on all of the regridded data. I use this approach to interpolate both irregularly-sampled 3D synthetic data and 2D prestack land data using nonstationary PEFs.

Marine data typically contains a near-offset gap of several traces, which can be larger when surface obstacles are present, such as offshore platforms. Most methods that depend on lower-frequency information from the data fail for these large gaps. I estimate nonstationary PEFs from pseudoprimary data, which is generated by cross-correlating data within each shot, so that the correlation of multiples with primaries creates data at the near offsets that were not originally recorded. I use this approach in t-x-y and f-x-y, on both the Sigsbee2B 2D prestack synthetic dataset, and a 2D prestack field data set. I also explore the feasibility of this approach for 3D data.

Finally, I estimate nonstationary PEFs in many dimensions using the approximation that slope is constant as a function of frequency, and interpolate data in two, three, four, and five dimensions simultaneously by using nonstationary PEFs on frequency slices. I interpolate both prestack 3D synthetic as well as field data to the densities required for future processing.

Indexing (document details)
School: Stanford University
School Location: United States -- California
Source: DAI-B 69/05, Dissertation Abstracts International
Subjects: Geophysics
Keywords: Interpolation, Prediction-error filters, Prestack data, Training data
Publication Number: 3313828
ISBN: 978-0-549-62957-3
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