Seismic signal processing method with Gaussian slowness-period packets
US-9091789-B2 · Jul 28, 2015 · US
US10895655B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10895655-B2 |
| Application number | US-201815989280-A |
| Country | US |
| Kind code | B2 |
| Filing date | May 25, 2018 |
| Priority date | Jun 29, 2017 |
| Publication date | Jan 19, 2021 |
| Grant date | Jan 19, 2021 |
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A computer-implemented method for generating missing frequencies within geophysical data, said method including: decomposing, with a computer, geophysical data into frequencies and slowness or wavenumber along time and one or more spatial dimensions; estimating, with a computer, a filter across known frequencies within the geophysical data for each time, spatial dimension, and slowness or wavenumber sample; extrapolating or interpolating, with a computer, the missing frequencies from the known frequencies by applying the filter; recomposing, with a computer, the known frequencies and the missing frequencies back into time and the one or more spatial dimensions; and prospecting for hydrocarbons with geophysical data that includes the missing frequencies.
Opening claim text (preview).
What is claimed is: 1. A computer-implemented method for generating missing frequencies within geophysical data, said method comprising: decomposing, with a computer, the geophysical data into frequencies and slowness or wavenumber along time and one or more spatial dimensions, wherein the decomposing includes applying a GaSP transform; estimating, with a computer, a filter across known frequencies within the geophysical data for each time, spatial dimension, and slowness or wavenumber sample; extrapolating or interpolating, with a computer, the missing frequencies from the known frequencies by applying the filter; recomposing, with a computer, the known frequencies and the missing frequencies back into time and the one or more spatial dimensions, which generates updated geophysical data that includes the missing frequencies; and prospecting for hydrocarbons with the updated geophysical data. 2. The method of claim 1 , wherein the decomposing includes applying a transform that decomposes the geophysical data into slowness or wavenumber and a linearly-spaced range of frequencies. 3. The method of claim 1 , wherein the filter is a Wiener prediction filter. 4. The method of claim 3 , wherein the extrapolating or interpolating includes extrapolating or interpolating along frequency for each time, spatial, and slowness or wavenumber dimension. 5. The method of claim 3 , wherein a length of the Wiener prediction filter is 1. 6. The method of claim 1 , wherein estimating includes estimating the filter with autoregression across frequencies. 7. The method of claim 1 , further comprising executing a full wavefield inversion process with the updated geophysical data, which includes generating a physical property model, wherein the prospecting for hydrocarbons includes deriving a hydrocarbon location from the physical property model. 8. The method of claim 7 , further comprising causing a well to be drilled at the hydrocarbon location. 9. The method of claim 7 , further comprising generating a subsurface image that depicts representations of subsurface reflectors at the hydrocarbon location. 10. The method of claim 1 , wherein the missing frequencies are from a hole in a spectrum of the geophysical data caused by a ghost notch. 11. The method of claim 1 , wherein the geophysical data is seismic data. 12. The method of claim 1 , wherein the filter is a prediction filter.
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