An Easy and Quick Option to Understand Your Geological Hierarchical Data with Python

Data visualisation plays a significant role within the geoscience and data science domains. It could possibly allow us to realize deeper insights into the subsurface, understanding geological structures and hierarchical relationships. The subsurface is usually subdivided into different categories starting from probably the most extensive scope of geological time, akin to Eras, Periods and Epochs, all the best way all the way down to lithological differences, akin to sandstone, limestone and shale.

When working with geological hierarchical data, the info may be visualised in several ways. This includes conventional geological timescale charts and tables to interactive sunburst charts.

Sunburst charts may be used to present data in a singular way and are a terrific strategy to visualise hierarchical data akin to geological hierarchical data. They accomplish that by utilizing multi-level concentric doughnut charts, which, depending on the tool used, may be fully interactive and help with drilling down from the very best to the bottom level.

To reveal these charts, we’ll use Plotly Express, a high-level data visualisation Python library, to take some data from a well on the Norwegian Continental Shelf and visualise the geological hierarchy, together with the lithological make-up of every formation. We can even see the right way to prepare the info from a well before creating the chart.

To start, we’ll need two libraries: pandas for loading and manipulating our data and plotly_express for creating our visualisation.

import pandas as pd
import plotly_express as px

Next, we’ll load our data from a CSV file. Details of the info used may be found at the underside of the article.

If you might have a LAS file as an alternative, you’ll be able to quickly load the LAS file using the LASIO library after which convert the info to a pandas dataframe.

df = pd.read_csv('Data/Xeek_Well_15-9-15.csv')