Subduction zone fault slip from seismic noise and GPS data

In Geosciences a class of phenomena that is widely studied given its real impact on human life are the tectonic faults slip. These landslides have different ways to manifest, ranging from aseismic events of slow displacement (slow slips) to ordinary earthquakes. An example of continuous slow slip event was identified in Cascadia, near the island of Vancouver (CA). This slow slip event is associated with a tectonic movements, when the overriding North America plate lurches southwesterly over the subducting Juan de Fuca plate. This region is located down-dip the seismogenic rupture zone, which has not been activated since 1700s but has been cyclically loaded by the slow slip movement. This fact requires some attention, since slow slip events have already been reported in literature as possible triggering factors for earthquakes. Nonetheless, the physical models to describe the slow slip events are still incomplete, which restricts the detailed knowledge of the movements and the associated tremor. In the original paper, the strategy adopted by the authors to address the limitation of the current models for the slow slip events was to use Random Forest machine learning algorithm to construct a model capable to predict GPS displacement measurement from the continuous seismic data. This investigation is sustained in the fact that the statistical features of the seismic data are a fingerprint of the fault displacement rate. Therefore, predicting GPS data from seismic data can make GPS measurements a proxy for investigating the fault slip physics and, additionally, correlate this slow slip events with associated tremors that can be studied in laboratory. The purpose of this report is to expose the methodology adopted by the authors and try to reproduce their results as coherent as possible with the original work.