A Pragmatic Approach for Integrating Submarine DAS Data with Onshore Seismometer Networks: A Case Study in the Canary Islands

Distributed Acoustic Sensing (DAS) technology offers a valuable opportunity to enhance seismological monitoring of ocean floors. Typically, in a standard monitoring network, most seismic stations are installed on land, with only a few ocean-bottom seismometers available. Consequently, monitoring oceanic seismicity is intrinsically challenging due to the lack of observations near seismic sources. In this context, DAS systems, for instance, those installed on fibers connecting islands and land-islands, can help bridge this observational gap. However, integrating the spatially dense DAS data (meter scale) with the often sparser seismometer data (kilometer scale) is not straightforward. Specifically, inverting DAS alongside seismometer P- and S-wave arrival times can lead to biased location results due to the numerical disparity between datasets and/or outliers not identified. Moreover, employing the full set of DAS arrival times can be computationally intensive, limiting its feasibility for real-time monitoring and integration into routine seismological software.

Automated weighting methods can help mitigate bias introduced by arrival time outliers in data inversion. This is particularly useful for DAS data, where user control over individual channels is limited. However, suppose the goal is to use DAS as a complementary tool to a seismometer network, and meter-scale spatial density is not essential. In that case, DAS data selection/sub-sampling can improve computational efficiency. To this end, we propose an approach that, for a given seismological network and DAS system, a) automatically identifies “reliable” DAS channels using a machine learning classifier trained on specific data attributes and b) further selects a subset of DAS channels to achieve similar interchannel spacing to the network. The proposed strategy generates a final set of DAS P- and S-wave arrival times with a number of observations comparable to the network. To test the benefits of this procedure on oceanic seismic monitoring, we use data from a fiber optic cable northeast of Gran Canaria and seismometers operated by the Instituto Geográfico Nacional (IGN) in the Canary Islands. We then compare event locations obtained using: a) IGN-only P- and S-wave arrival times, b) DAS-only P- and S-wave arrival times (unselected), and c) IGN and selected DAS P- and S-wave arrival times using the proposed method. Event locations are estimated using a hierarchical Markov Chain Monte Carlo approach.

Preliminary results show promising improvements in the location uncertainty of oceanic seismicity when the proposed data integration approach is applied. Additionally, a refined location catalog, incorporating a more detailed velocity model, is compiled using standard monitoring software alongside the proposed data selection approach.