Earthquakes are a major cause of life and property destruction. It is known that earthquakes radiate energy in the form of surface and body seismic waves. P-wave and S-waves are types of body waves. Both waves can be detected and recorded at an earthquake station. These waves can be analyzed to detect earthquakes. Most of the earthquake prediction techniques today are a combination of geophysics and signal processing, which are relatively complex. Machine learning can be used to learn the behavior of seismic waves and help in early detection. Machine learning can also be employed to process massive amounts of raw seismic data. The goal of this project is to distinguish between earthquakes and noise. Recordings of seismic waves from earthquake stations contain significant noise, for example from mining explosions or surface vibrations caused by vehicle traffic. It is necessary to distinguish between noise and actual earthquake signals. In this project machine learning classification techniques will be used for this purpose.
Narvekar, Nishita, "DISTINGUISHING EARTHQUAKES AND NOISE USING RANDOM FOREST ALGORITHM" (2018). Master's Projects. 606.