# Facies classification from well logs using an inception convolutional network

arXiv: https://arxiv.org/abs/1706.00613 · HTML: https://mlsanity.com/paper/1706.00613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation](https://mlsanity.com/paper/2604.21501/) (2026)

## Abstract (arXiv)

The idea to use automated algorithms to determine geological facies from well logs is not new (see e.g Busch et al. (1987); Rabaute (1998)) but the recent and dramatic increase in research in the field of machine learning makes it a good time to revisit the topic. Following an exercise proposed by Dubois et al. (2007) and Hall (2016) we employ a modern type of deep convolutional network, called textitinception network (Szegedy et al., 2015), to tackle the supervised classification task and we discuss the methodological limits of such problem as well as further research opportunities.