Seismic Phases Picking with Artificial Intelligence: A Novel Approach for Distributed Acoustic Sensing Data Analysis

The analysis of signals acquired through Distributed Acoustic Sensing (DAS) technology offers an innovative method for seismic monitoring. However, owing to the high noise levels, the analysis of DAS data presents significant challenges in taking full advantage of dense temporal and spatial sampling. This is particularly true in accurately picking phase arrival times on DAS data. 

Currently, some techniques have been proposed to address the picking problem on DAS data both from classical methods and through use of machine learning approaches, including the notable model named PhaseNet-DAS. Despite this, challenges persist, especially in real-time seismic monitoring applications and in the presence of high-frequency and high-density data.

In this context, we propose a novel model that leverages visual features and is based on the fundamental principles of Transformers, a class of Artificial Intelligence models, widely recognized for their ability to model complex relationships in sequential data. Our proposed model shows its effectiveness in learning seismic wave characteristics from DAS data, enabling an efficient phase picking.

To demonstrate the effectiveness of our approach, we present preliminary results on the use of our model to DAS data acquired in the seismically active area of Campi Flegrei caldera. The experimental results show the benefits of our method in exploiting DAS technology for enhancing seismic monitoring.