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.

翻译：在地球科学领域，鉴于其对人类生活的实际影响，构造断层滑动是一类被广泛研究的现象。这些滑移表现为不同形式，从慢速位移的无震事件（慢滑事件）到普通地震。在卡斯卡迪亚地区（加拿大温哥华岛附近）发现了一个持续慢滑事件的实例。该慢滑事件与构造运动相关，表现为上覆的北美板块向西南方向俯冲于胡安·德富卡板块之上。这一区域位于地震孕震破裂带的下倾方向，自18世纪以来尚未被激活，但已因慢滑运动而周期性加载。这一事实需引起关注，因为已有文献报道慢滑事件可能成为地震的触发因素。然而，描述慢滑事件的物理模型仍不完整，这限制了对运动及其相关震颤的详细认知。在原论文中，作者为应对当前慢滑事件模型的局限性，采用随机森林机器学习算法构建了一个能够从连续地震数据预测GPS位移测量的模型。该研究的依据在于，地震数据的统计特征是断层位移速率的指纹特征。因此，从地震数据预测GPS数据可使GPS测量成为研究断层滑动物理过程的代理指标，并进一步将这些慢滑事件与可在实验室中研究的相关震颤关联起来。本报告旨在阐述作者采用的方法，并尽可能与原作一致地复现其结果。