Exploring Earth’s Vibrations Using Machine Learning: From Giraffe Footfalls to Volcanic Eruptions
Our planet undergoes continuous unrest across a broad spectrum of scales, ranging from the gentle footfalls of giraffes to the immense forces unleashed during volcanic eruptions and megathrust earthquakes. Seismic sensors bear witness to this diverse unrest, capturing valuable insights into active geological features, surface dynamics, and life on our planet. However, the complexity and volume of seismic data pose challenges to efficiently extracting meaningful information hidden within vast datasets. Automated algorithms analyzing continuous data streams offer a promising solution, capable of uncovering hidden signal patterns and providing new perspectives on the task at hand. In this seminar, I will introduce machine learning strategies leveraging wavelet transforms (a scattering network) to derive meaningful and continuous patterns from seismograms, identifying groups of seismic signals in a data-driven manner. I will discuss case studies revealing insights into 1) the signal-modifying effects of surface freezing, 2) a continuously evolving magmatic plumbing system (see attached image), and 3) the possibility to track wildlife with seismic sensors. The study cases show that continuous seismograms are indeed a goldmine of information, spawning new areas of inquiry within seismology.
