Method for predicting subsurface features from seismic using deep learning dimensionality reduction for regression
US11698471B2 · kind B2 · utility
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19Claims
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Key dates
| Filing date | Sep 10, 2019 |
| Grant date | Jul 11, 2023 |
| Priority date | — |
| Expiry date | Nov 10, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01V2210/642
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A method for training a backpropagation-enabled regression process is used for predicting values of an attribute of subsurface data. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A predicted value of the attribute has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.