Utilizing Plotly Categorical Sunburst Charts to Discover Geological Knowledge | by Andy McDonald | Jul, 2023

An Straightforward and Fast Approach to Perceive Your Geological Hierarchical Knowledge with Python

Geological hierarchy represented on an interactive Plotly Categorical Sunburst chart. Picture by the writer.

Knowledge visualisation performs an important position within the geoscience and information science domains. It may possibly permit us to achieve deeper insights into the subsurface, understanding geological constructions and hierarchical relationships. The subsurface is commonly subdivided into completely different classes starting from probably the most intensive scope of geological time, akin to Eras, Durations and Epochs, all the best way all the way down to lithological variations, akin to sandstone, limestone and shale.

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

Sunburst charts can be utilized to current information in a novel approach and are a good way to visualise hierarchical information akin to geological hierarchical information. They accomplish that 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 are going to use Plotly Express, a high-level information visualisation Python library, to take some information from a effectively on the Norwegian Continental Shelf and visualise the geological hierarchy, together with the lithological make-up of every formation. We may even see the best way to put together the info from a effectively earlier than creating the chart.

To start, we are going to 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 are going to load our information from a CSV file. Particulars of the info used may be discovered on the backside of the article.

If in case you have a LAS file as an alternative, you possibly can rapidly load the LAS file utilizing the LASIO library after which convert the info to a pandas dataframe.

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

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