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A Geologist’s Introduction to Object-Oriented Programming in Python: Using geological data to explain the basics of OOP

Object-Oriented Programming (OOP) is a programming paradigm based on the concept of “objects” (Wikipedia). A bunch of objects that are arranged into one group can be called a class. OOP can make the developer focus more on manipulating the object rather than manipulating the logic.

For example, in a showroom, there are various types of vehicles, as follows:

  • One jeep has 4 doors, 4 wheels, and green color
  • One motorcycle has no door, 2 wheels, and white color
  • One minibus has 6 doors, 4 wheels, and red color

If we apply OOP concepts in this example, we can say that we have a vehicle class with 3 objects (jeep, motorcycle, & minibus), and each object has 3 different attributes (door, wheel, & color). But there are enough car analogies in this world already, so in this article, we will apply the OOP concept on geological data. Read more…

Python for Geoscientists: Beyond the machine learning

This day, almost all of our analysis is able to be processed by some particular software. Any kind of dataset, seismic, well log, and the other, any phase of analysis, pre-processing, processing, interpretation are covered. Unfortunately, many of us are not lucky enough to have access to this software or sometimes, the software owned by our organization does not cover the alternative method that we would like to try on the data. Referring to my past experience, I would like to encourage my fellow geoscientists to unleash the ability of this programming language beyond the SKLEARN module. There are plenty of Python’s modules that could be useful for us to do some simple geological and geophysical analysis to the advanced one. We can do from a simple rock physics cross plot to the petrophysical analysis / seismic inversion, or from reduce to pole filter on aeromagnetic data to subsurface magnetic body modeling. Read more…

Simple EDV & Two Way ANOVA Applications in Core Sample Data: Case Study from Volve Open Datasets

Creating Presentable Data Visualization with Altair: Case Study using Geothermal Heatflow Data

Linear vs Non-Linear Dimensionality Reduction in Well Log Data: An Effective Way to Visualize and Interpret a Large Well Log Dataset