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Abstract The stress field perturbation caused by magmatic intrusions within volcanic systems induces strain in the surrounding region. This effect results in the opening and closing of microcracks in the vicinity of the intrusion, which can affect regional seismic velocities. In late November 2023, we deployed a distributed acoustic sensing interrogator to convert an existing 100‐km telecommunication fiber‐optic cable along the coast of Iceland's Reykjanes peninsula into a dense seismic array, which has run continuously. Measuring changes in surface wave moveout with ambient noise cross‐correlation, we observe up to 2% changes in Rayleigh wave phase velocity following eruptions in the peninsula's 2023–2024 sequence that are likely associated with magmatic intrusions into the eruption‐feeding dike. We apply a Bayesian inversion to compute the posterior distribution of potential dike opening models for each eruption by considering measurements for varying channel pairs and frequency bands, and assuming this velocity change is tied to volumetric strain associated with dike‐opening. Our results are in agreement with those based on geodetic measurement and provide independent constraints on the depth of the dike, demonstrating the viability of this novel inversion and new volcano monitoring directions through fiber sensing. 

Data reliability and availability, and serviceability (RAS) of erasure-coded data centers are highly affected by data repair induced by node failures. Compared to the recovery phase of the data repair, which is widely studied and well optimized, the failure identification phase of the data repair is less investigated. Moreover, in a traditional failure identification scheme, all chunks share the same identification time threshold, thus losing opportunities to further improve the RAS. To solve this problem, we propose RAFI, a novel risk-aware failure identification scheme. In RAFI, chunk failures in stripes experiencing different numbers of failed chunks are identified using different time thresholds. For those chunks in a high risk stripe (a stripe with many failed chunks), a shorter identification time is adopted, thus improving the overall data reliability and availability. For those chunks in a low risk stripe (one with only a few failed chunks), a longer identification time is adopted, thus reducing the repair network traffic. Therefore, the RAS can be improved simultaneously. We use both simulations and prototyping implementation to evaluate RAFI. Results collected from extensive simulations demonstrate the effectiveness and efficiency of RAFI on improving the RAS. We implement a prototype on HDFS to verify the correctness and evaluate the computational cost of RAFI.