Towards a generic fibreoptic earthquake detection and location algorithm for arbitrary fibre geometries and hybrid fibre-seismometer networks

Detecting earthquakes in continuous seismic data is essential for various applications. Such applications include real-time natural or anthropogenic hazard monitoring and generating earthquake catalogues for body-wave tomography. Although numerous detection algorithms have been developed specifically for fibreoptic data, they are typically only applicable for certain fibre geometries, and generally struggle with P and S wave phase association. Furthermore, integrating fibreoptic and conventional seismometer data into current algorithms remains challenging. Here, we present an automated, faster-than-real-time earthquake back-migration method, adapted to incorporate arbitrary 3D fibre geometries and include all available seismic observations from any instrumentation. Crucially, the strength of this method lies in stacking energy based on physics-derived time-shifts, locating the event during the detection process. Unlike machine learning methods, it does not require a training dataset so is therefore readily applicable to new scenarios. We demonstrate the performance of the method to detect near-surface seismicity at an alpine glacier and crustal seismicity from the ongoing Sundhnúkur eruption near Grindavik, Iceland. We also show how subsequent automated phase-arrival refinement can provide sufficient quality picks for travel-time tomography. As we present these results, we highlight how fibreoptic sensitivity limitations can be quantified and accounted for during earthquake detection, how different data sources can be combined, and we identify areas where advances are yet to be made. Generic earthquake detection algorithms such as that presented here are essential for harnessing the dense spatial sampling that fibreoptic sensing provides, while at the same time accounting for fundamental fibreoptic sensitivity limitations.