MATLAB For Earth Sciences
Join our hands-on workshop on MATLAB for Earth Sciences, designed to empower researchers, students, and professionals with powerful computational tools for analyzing and visualizing Earth data. Learn to leverage MATLAB’s robust capabilities for geospatial analysis, data modeling, and simulation to tackle real-world challenges in geology, meteorology, oceanography, and environmental science. This workshop combines expert-led instruction with practical exercises to enhance your skills in data processing and interpretation.
Workshop Objectives
Master MATLAB fundamentals for Earth science applications.
Learn techniques for processing and visualizing geospatial and environmental data.
Develop skills in modeling and analyzing Earth science phenomena.
Apply MATLAB tools to solve real-world problems in Earth sciences.
Gain hands-on experience through practical, industry-relevant exercises.
About the Presenter
A highly accomplished geologist with expertise in structural geology, tectonics, and field mapping. He has a PhD in Geology from the University of Illinois at Urbana-Champaign and has held several teaching and research positions at prestigious universities in the United States. He is proficient in various geological software and programming languages, including Python, ArcGIS, and MATLAB. He has extensive experience in geological mapping, 3D modeling, and quantitative analysis of geological structures. He has also developed and taught several courses on Python programming for geoscientists.
Applied Machine Learning for Upstream and Subsurface Domains
This two-day hands-on workshop is designed for petroleum and subsurface professionals seeking practical skills in machine learning for upstream oil & gas operations. The program teaches participants how to apply machine learning to uncover patterns in production and reservoir data, discover equations from historical relationships, and build predictive models for vital engineering parameters. Real-world datasets from drilling and subsurface domains are used, focusing on automating reports, visualizing data, and interpreting model outputs for improved decision-making.