Method for generating initial models for least squares migration using deep neural networks
US12060781B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 17, 2020 |
| Grant date | Aug 13, 2024 |
| Priority date | — |
| Expiry date | Sep 4, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01V2210/624
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and apparatus for generating a high-resolution seismic image, including extracting a reflectivity distribution from a geological model; utilizing the reflectivity distribution to label features of the model; generating forward-modeled data from the model; migrating the forward-modeled data to create a migrated image; and training a deep neural network with the labeled synthetic geological model and the migrated image to create a reflectivity prediction network. A method and apparatus includes: selecting a first subset of the field data; applying a low-pass filter to the first subset to generate a first filtered dataset; migrating the first filtered dataset to create a first migrated image; applying a high-pass filter to the first subset to generate a second filtered dataset; migrating the second filtered dataset to create a second migrated image; and training a deep neural network to predict a target distribution of high-frequency signal.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.