|Guest Speaker - David Kirschner, Shell||13:30 – 13:50|
|Guest Speaker Q&A||13:50 – 14:00|
|MathWorks (incl. Q&A): MATLAB for Deep Learning||14:00 – 15:00|
In the first part, David Kirschner will show how an LSTM network was used for signal processing to automatically pick the arrival times of seismic P- and S-waves. The network picks events accurately and quickly, obviating the need for human intervention. Although the deep learning model was trained to detect small, local earthquakes in an onshore fold-thrust belt, it is effective in picking small earthquakes in other geologic settings and large earthquakes recorded on global seismic networks based on some initial results.
The aim of the second part is to provide an overview of how MATLAB enables you to take advantage of disruptive technologies like deep learning.
Deep Learning is a key technology driving the current Artificial Intelligence (AI) megatrend. You may have heard of some mainstream applications of deep learning, but how many of them would you consider applying to your engineering and science applications? MathWorks developers have purpose-built MATLAB's deep learning functionality for engineering and science workflows. We understand that success goes beyond just developing a deep learning model. Ultimately, models need to be incorporated into an entire system design workflow to deliver a product or a service to the market.