Fiber-optic volcano-tectonic fault imaging with coherent wavefields and deep learning

在人口稠密的火山地区进行高分辨率的地下地球物理成像，对于评估和减轻潜在的灾害至关重要。最近出现的分布式声学传感（DAS）允许具有成本效益的连续多通道地震应变数据的光纤电缆的测量，特别适合于城市地区。在这个项目中，我建议将联合收割机为多通道地震数据设计的既定处理方案与深度学习技术相结合，用于相干波场增强和分离，以便在埃特纳山（意大利）东侧采集的光纤测量中识别和成像由火山-构造相互作用驱动的小规模断层。

High resolution geophysical imaging of the subsurface in densely populated volcanic regions is crucial for assessing and mitigating potential hazards. The recently emerging distributed acoustic sensing (DAS) allows the cost-efficient measurement of continuous multi-channel seismic strain data with fiber-optic cables and is particularly suited for urban areas. In this project, I propose to combine established processing schemes designed for multi-channel seismic data and deep learning techniques for coherent wavefield enhancement and separation in order to identify and image small-scale faults driven by volcano-tectonic interaction in fiber-optic measurements acquired on the eastern flank of Mount Etna (Italy).