Using Python Libraries and K-Nearest Neighbors Algorithms to Delineate Syn-Sedimentary Faults in Sedimentary Porous Media

Using Python Libraries and K-Nearest Neighbors Algorithms to Delineate Syn-Sedimentary Faults in Sedimentary Porous Media

Manuel Martín-Martín, Manuel Bullejos, David Cabezas, Francisco Javier Alcalá

This paper introduces a methodology based on Python libraries and machine learning k-Nearest Neighbors (KNN) algorithms to create an interactive 3D HTML model that combines 2D grain-size KNN-prediction vertical sections from which syn-sedimentary faults and other features in sedimentary porous media can be delineated. The grain-size physical parameter is associated to lithological classes. Grain-size data comes from a database of 433 boreholes in the Llobregat River Delta in NE Spain. This methodology is suitable to reduce the immeasurable uncertainty associated to the qualitative geological data used in complex numerical tools aimed at modelling different geological resources or Earth phenomena.

Marine and Petroleum Geology 153, 106283 (2023)
Corresponding Author: Francisco Javier Alcalá

InterPore Members can promote their publications to the community via the InterPore InJournals Section of the Newsletter. If you wish to do so, please submit your publication highlight to newsletter@InterPore.org. Clearly indicate which of the authors is an InterPore member (or the institute with an Institutional Membership). Note that we will not review the entries nor does InterPore endorse the published work. Furthermore, we publish on a “submitted first, published first” basis. The highlighted publication should be no older than 6 months (available online).

The highlight should be short (max 100 words) and contain an illustration. Please note that we offer this opportunity exclusively to InterPore members. If you would like to become a member, please have a look here.