Seismic Facies Classification with Deep Learning and Wavelets

Overview

With the dramatic growth and complexity of seismic data, manual annotation of seismic facies has become a significant challenge. One of the challenges lies in classification where interfaces between different rock types inside the Earth are delineated in a seismic image – i.e. dividing the subsurface into regions that can be classified as distinct geologic facies. Delineating these facies requires months of efforts by geoscientists.

Can AI algorithms solve this? Can they be trained to recognize distinct geologic facies in seismic images, producing an interpretation that could pass for that of an expert geologist, or be used as a starting point to speed up human interpretation? 

In this presentation, we walk through how MathWorks helped solve this challenge and won the SEAM AI Applied Geoscience GPU Hackathon with a unique and innovative approach. We demonstrate the importance of applying signal processing techniques before applying AI algorithms. In addition, you will learn the following:

1. How MATLAB simplifies the application of advanced techniques like wavelets through interactive apps.
2. Creating Deep Learning models with just a few lines of MATLAB code
3. Exploring a seismic volume with Volume Viewer App
4. Accelerate algorithms on NVIDIA^® GPUs or the cloud without specialized programming or extensive knowledge of IT infrastructure.

About the Presenter

Akhilesh Mishra is a Sr. Application Engineer at MathWorks. He specializes in the signal/data processing, artificial intelligence and GPU computing workflows. He has been with MathWorks since 2016. Akhilesh holds a M.S. degree from University of Kansas where he was the signal processing lead in a group working on radar and sonar systems for sounding the ice sheets of Greenland and Antarctica to study global sea-level rise.