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Supervised modeling of depth-labeled core image and well log datasets

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# `coremdlr`

[![DOI](https://zenodo.org/badge/170920628.svg)](https://zenodo.org/badge/latestdoi/170920628)

*Note, this repo is part of research that has been accepted in **[Frontiers Earth Science](https://www.frontiersin.org/articles/10.3389/feart.2021.659611/abstract)**, please cite that paper with DOI (doi: 10.3389/feart.2021.659611). Full paper is here!*

This repo contains code for modeling lithology and facies in core photo + well log datasets, using both deep learning / computer vision and traditional machine learning approaches.

For pre-processing of UK core image data, check out **[CoreBreakout](https://joss.theoj.org/papers/10.21105/joss.01969.pdf)**.

## Requirements

Base requirements:

-   `Python 3.x`
-   `Numpy` + `Scipy` + `Matplotlib`
-   `scikit-image`
-   `scikit-learn`
-   `tensorflow.keras`

Individual scripts and notebooks may require some other libraries.

## Installation

Use `pip` to do a local develop mode install:

```bash
$ pip install -e path/to/coremdlr
```

## Organization

The `coremdlr` module contains a number of submodules:

**`coremdlr.config`** : settings and default paths / labels / dataset args / viz properties

**`coremdlr.datasets`** : loading / preprocessing / generating data

**`coremdlr.models`** : hierarchical set of generic model classes

**`coremdlr.networks`** : `tf.keras` network construction functions

**`coremdlr.layers/.ops`** : `tf.keras` custom layers and tensor operations

**`coremdlr.viz`** : plotting data and analysis (e.g., confusion matrices)

---

The final experiments for the paper mostly took place in `experiments`, and more specifically the `notebooks_*` subdirectories.

---

The `notebooks` folder contains assorted notebooks and a `figures` subdirectory in which paper figures were generated.

## Data

Current data consists of 12 UK Contiential Shelf wells from Q204 and Q205. Please check out the data folder for more information and licensing. The full dataset is here: https://figshare.com/articles/dataset/UKCS_Q204_Q205_Subsurface_Data_products_for_Machine_Learning_Study/14265689 including the images.

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