Unveiling magma plumbing systems for volcanic eruptions and crustal accretion via active-seismic matrix imaging

Submarine eruptions, accounting for over 80% of Earth's volcanic activity, primarily occur along mid-ocean ridges, where shallow magmatic systems are accessible to high-resolution imaging. Yet, their remoteness often leaves them undetected. Recent seismic studies at the East Pacific Rise (EPR) 9°50'N-one of the most dynamic ridge segments, imaged the detailed architecture of the shallowest magma lens, but no data-constrained model yet explains how magma accumulates, migrates, or triggers eruptions. Similarly, the formation of oceanic crust remains poorly understood. While 2-D seismic data reveal only a few vertically stacked, transient magma lenses, our study applies matrix imaging, a novel technique in controlled-source seismology, to map the inner structure of on- and off-axis magma reservoirs. We uncover a conical on-axis reservoir and interconnected magma-rich zones throughout the crust. Combined with ophiolite evidence, these findings reveal that magma channels dominate the first 3 km for lower crust formation, while in situ crystallization prevails in the final 1 km, resolving a long-standing debate.

Physics-Based Learning of the Wave Speed Landscape in Complex Media

Wave velocity is a key parameter for imaging complex media, but in vivo measurements are typically limited to reflection geometries, where only backscattered waves from short-scale heterogeneities are accessible. As a result, conventional reflection imaging fails to recover large-scale variations of the wave velocity landscape. Here we show that matrix imaging overcomes this limitation by exploiting the quality of wave focusing as an intrinsic guide star. We model wave propagation as a trainable multi-layer network that leverages optimization and deep learning tools to infer the wave velocity distribution. We validate this approach through ultrasound experiments on tissue-mimicking phantoms and human breast tissues, demonstrating its potential for tumour detection and characterization. Our method is broadly applicable to any kind of waves and media for which a reflection matrix can be measured.