Ground roll attenuation using unsupervised deep learning
US12313800B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 22, 2021 |
| Grant date | May 27, 2025 |
| Priority date | — |
| Expiry date | Apr 17, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20212
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine-implemented method, at least one non-transitory computer-readable medium storing instructions, and a computing system are provided for attenuating noise. A computing system receives a seismic image and generates a first image using a first neural network configured to identify low-frequency ground roll in a seismic image, and a second image using a second neural network configured to identify reflections in the seismic image. A combined image is generated by combining the first image and the second image. The first neural network and the second neural network are adjusted to reduce a difference between the combined image and the seismic image using frequency constraint to guide separation of the seismic image into the first image and the second image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.