Predicting Failures in Simulated Turbulence Data with Machine Learning Techniques

Abstract

Earthquake prediction has long been thought to be nearly impossible. However, machine learning gives us new possibilities to investigate. Machine learning has already been successfully applied to "lab-quakes", but these are much more periodic than real earthquakes. Real earthquake physics are often poorly understood, so to get a better understanding of the ml techniques it is important to also use data that is well understood. To test whether machine learning methods can also be used for non-periodic data, they are applied to simulated turbulence data. This data is used because the physics are fully understood and it gives a nice proxy to earthquakes. Machine learning techniques show promise on this dataset to predict when the next failure will occur. This prediction is most accurate when only medium-low frequencies of the acceleration data are used during prediction. This shows that it might be possible to apply these techniques to real earthquakes as well, as long as the data is properly preprocessed so that the machine learning models can extract the useful information.