Utilizing Plotly Specific Sunburst Charts to Discover Geological Information | by Andy McDonald | Jul, 2023

An Simple and Fast Approach to Perceive Your Geological Hierarchical Information with Python

Geological hierarchy represented on an interactive Plotly Specific Sunburst chart. Picture by the creator.

Information visualisation performs an important function within the geoscience and information science domains. It will probably permit us to achieve deeper insights into the subsurface, understanding geological constructions and hierarchical relationships. The subsurface is usually subdivided into totally different classes starting from probably the most intensive scope of geological time, corresponding to Eras, Durations and Epochs, all the way in which right down to lithological variations, corresponding to sandstone, limestone and shale.

When working with geological hierarchical information, the information may be visualised in a number of methods. This consists of standard geological timescale charts and tables to interactive sunburst charts.

Sunburst charts can be utilized to current information in a singular method and are a good way to visualise hierarchical information corresponding to geological hierarchical information. They achieve this by utilizing multi-level concentric doughnut charts, which, relying on the software used, may be totally interactive and assist with drilling down from the very best to the bottom degree.

To exhibit these charts, we’ll use Plotly Express, a high-level information visualisation Python library, to take some information from a properly on the Norwegian Continental Shelf and visualise the geological hierarchy, together with the lithological make-up of every formation. We may also see easy methods to put together the information from a properly earlier than creating the chart.

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

import pandas as pd
import plotly_express as px

Subsequent, we’ll load our information from a CSV file. Particulars of the information used may be discovered on the backside of the article.

When you have a LAS file as a substitute, you’ll be able to rapidly load the LAS file utilizing the LASIO library after which convert the information to a pandas dataframe.

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

Leave a Reply

Your email address will not be published. Required fields are marked *