Jun 3, 2021 · In this tutorial, we will be carrying out unsupervised learning classification using two clustering methods (K Means Clustering and Gaussian Mixture Modelling ) and comparing the results with an established Lithofacies curve. What is Clustering / Cluster Analysis?

Jun 3, 2021 · In this tutorial, we will be carrying out unsupervised learning classification using two clustering methods (K Means Clustering and Gaussian Mixture Modelling ) and comparing the results with an established Lithofacies curve. What is Clustering / Cluster Analysis?

This example notebook illustrates how to use K-Means clustering to divide up the subsurface using well log measurements. Please note there are no notes yet within the notebook about the steps. This will be added at a later stage. The data used for this notebook is a subset of the FORCE 2020 Lithology Competition dataset from Norway.

Jul 24, 2018 · HDBSCAN is the best clustering algorithm and you should always use it. Basically all you need to do is provide a reasonable min_cluster_size, a valid distance metric and you're good to go. For min_cluster_size I suggest using 3 since a cluster of 2 is lame and for metric the default euclidean works great so you don't even need to mention it.

How to use Unsupervised Learning to Cluster Well Log Data using Python - Medium; Exploring Well Log Data Using the Welly Python Library - Medium Link; Creating a Core Data Dashboard Using Matplotlib's subplot2grid functionality - Medium Link; Identifying Outliers in Well Log Data Using Boxplots in Matplotlib - Medium Link

Sep 21, 2021 · How to use Unsupervised Learning to Cluster Well Log Data Using Python As petrophysicists /geoscientists we commonly use well log plots (line plots with data vs depth), histograms, and crossplots (scatter plots) to analyse and explore well log data.

The code describes how unsupervised ML can be applied to well log data for efficient clustering. A part of the well log data set is provided.

Aug 7, 2022 · One of the most popular clustering methods to achieve the grouping of data based on similarities is K-means clustering. It is a very commonly used unsupervised machine learning algorithm that is relatively easy to understand and easy to implement within Python.

Dec 21, 2018 · Most simple way to get good results will be to break down the problem into two parts : Getting the features from the images: Using the raw pixels as features will give you poor results. Pass the images through a pre trained CNN (you can get several of those online).

Apr 17, 2021 · I am relatively new to the neural network, so I was trying to use it for unsupervised clustering. My data is in dataframe with 5 different columns (features), I wanted to get like 4 classes from this, see the full model below. when I give the option of generating 4 classes I get the error message: