Subsurface lithological model with machine learning
US12007519B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 13, 2023 |
| Grant date | Jun 11, 2024 |
| Priority date | — |
| Expiry date | Feb 13, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01V2210/673
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.