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 achieve deeper insights into the subsurface, understanding geological structures and hierarchical relationships. The subsurface is usually subdivided into different categories starting from essentially the most extensive scope of geological time, resembling Eras, Periods and Epochs, all the way in which right down to lithological differences, resembling sandstone, limestone and shale.
When working with geological hierarchical data, the information will be visualised in several ways. This includes conventional geological timescale charts and tables to interactive sunburst charts.
Sunburst charts will be used to present data in a singular way and are an ideal approach to visualise hierarchical data resembling geological hierarchical data. They accomplish that through the use of multi-level concentric doughnut charts, which, depending on the tool used, will be fully interactive and help with drilling down from the very best to the bottom level.
To reveal these charts, we are going to 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 find out how to prepare the information from a well before creating the chart.
To start, we are going to 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 are going to load our data from a CSV file. Details of the information used will be found at the underside of the article.
If you’ve a LAS file as an alternative, you may quickly load the LAS file using the LASIO library after which convert the information to a pandas dataframe.
df = pd.read_csv('Data/Xeek_Well_15-9-15.csv')