AIML in Geoscience

This topic will explore unsupervised learning techniques and generative adversarial networks for tasks like clustering seismic data or creating synthetic geologic models for reservoir simulation.

Unsupervised Learning and Generative Adversarial Networks in Geoscience

Participants will learn how to streamline and automate repetitive tasks in geoscience using Python programming, saving time and increasing efficiency in data processing and analysis.

Semi-automation the tedious works in Geoscience

This topic will focus on using machine learning techniques to analyze subsurface data for resource assessment purposes, such as identifying potential oil or mineral deposits based on geophysical data.

Subsurface characterization applicable to resource assessment

Participants will learn how to use machine learning algorithms to create forward seismic models, which can help in simulating and predicting seismic responses in different geologic settings.

Forward seismic modeling

This topic will cover the use of shallow learning techniques, such as clustering and regression, to analyze seismic attributes for seismic interpretation, aiding in understanding subsurface structures and geological features.

'Shallow' learning; seismic attributes

Participants will gain hands-on experience in using Python programming language and Jupyter notebooks for geoscience applications, such as data processing, visualization, and modeling.

Python Programming for Geoscience

This topic will delve into the use of regression and classification algorithms for analyzing geoscience data, such as predicting rock properties or classifying seismic facies based on attributes.

Regression and Classification in Geoscience

Participants will learn about neural networks and deep learning algorithms for geoscience applications, such as image segmentation in seismic interpretation or predicting reservoir properties from well logs.