Modelling P waves in seismic noise correlations: advancing fault monitoring using train traffic sources. Issue 3 (25th September 2021)

• Record Type: Journal Article
• Authors: Sager, Korbinian; Tsai, Victor C; Sheng, Yixiao; Brenguier, Florent; Boué, Pierre; Mordret, Aurélien; Igel, Heiner
• Is Part Of: Geophysical journal international. Volume 228:Issue 3(2022)
• Page Start: 1556
• Page End: 1567
• Publication Date: 2021-09-25
• Subjects: interferometry -- theoretical seismology -- numerical modeling -- seismic noise -- body waves; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggab389
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: The theory of Green's function retrieval essentially requires homogeneously distributed noise sources. Even though these conditions are not fulfilled in nature, low-frequency (<1 Hz) surface waves generated by ocean–crust interactions have been used successfully to image the crust with unprecedented spatial resolution. In contrast to low-frequency surface waves, high-frequency (>1 Hz) body waves have a sharper, more localized sensitivity to velocity contrasts and temporal changes at depth. In general, their retrieval using seismic interferometry is challenging, and recent studies focus on powerful, localized noise sources. They have proven to be a promising alternative but break the assumptions of Green's function retrieval. In this study, we present an approach to model correlations between P waves for these scenarios and analyse their sensitivity to 3-D Earth structure. We perform a series of numerical experiments to advance our understanding of these signals and prepare for an application to fault monitoring. In the considered cases, the character of the signals strongly diverges from Green's function retrieval, and the sensitivity to structure has significant contributions in the source direction. An accurate description of the underlying physics allows us to reproduce observations made in the context of monitoring the San Jacinto Fault in California using train-generated seismic waves. This approach provides new perspectives for detecting and localizing temporal velocity changes previously unnoticed by commonly exploited surface-wave reconstructions.