Derivation of lithofacies from geophysical logs : a review of methods from manual picking to machine learning

Newell, Andrew J.; Woods, Mark A.; Graham, Romaine L.; Christodoulou, Vyron. 2021 Derivation of lithofacies from geophysical logs : a review of methods from manual picking to machine learning. Nottingham, UK, British Geological Survey, 43pp. (OR/21/006) (Unpublished)

Before downloading, please read NORA policies.

Preview

Text
OR21006.pdf
Download (3MB) | Preview

Abstract/Summary

The aims of this report are to document: 1. A range of methods that are currently used by the BGS stratigraphers to extract lithological information from geophysical logs (includes manual classification, cut-off analysis, mineral composition by linear inversion). 2. Alternative methods which, at present, are not routinely applied but are sufficiently practical and accessible that they could become important, including unsupervised (k-mean clustering) and supervised machine learning approaches. The report does not aim or claim to be a complete inventory of all possible methods to derive lithological information from geophysical logs. The authors welcome correspondence and information on any additional methods that are available or emerging.

Item Type:	Publication - Report
Funders/Sponsors:	British Geological Survey
Additional Information. Not used in RCUK Gateway to Research.:	This item has been internally reviewed, but not externally peer-reviewed.
Additional Keywords:	Groundwater, GroundwaterBGS
Date made live:	14 Apr 2021 12:20 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/530055

Item Type:

Publication - Report

Funders/Sponsors:

British Geological Survey

Additional Information. Not used in RCUK Gateway to Research.:

This item has been internally reviewed, but not externally peer-reviewed.

Additional Keywords:

Groundwater, GroundwaterBGS

Date made live:

14 Apr 2021 12:20 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/530055