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ABSTRACT Detecting offshore earthquakes in real time is challenging for traditional land-based seismic networks due to insufficient station coverage. Application of distributed acoustic sensing (DAS) to submarine cables has the potential to extend the reach of seismic networks and thereby improve real-time earthquake detection and earthquake early warning (EEW). We present a complete workflow of a modified point-source EEW algorithm, which includes a machine-learning-based model for P- and S-wave phase picking, a grid-search location method, and a locally calibrated empirical magnitude estimation equation. Examples are shown with offshore earthquakes from the SeaFOAM DAS project using a 52-km-long submarine cable in Monterey Bay, California, demonstrating the robustness of the proposed workflow. When comparing to the current onshore network, we can expect up to 6 s additional warning time for earthquakes in the offshore San Gregorio fault zone, representing a substantial improvement to the existing ShakeAlert EEW system. 

Abstract Distributed acoustic sensing (DAS) is being explored in a variety of environments as a promising technology for the recording of seismic signals in dense array configurations. There is a particular interest for deploying DAS arrays on the ocean floor, presenting formidable challenges for conventional seismology. Taking advantage of the availability of a dark fiber on the Monterey Bay Accelerated Research System (MARS) 52 km offshore cable at Monterey Bay, California, in July 2022, we installed a DAS interrogator at the shore end of the cable with the intention of acquiring continuous data for a period of one year. Here, we describe the experiment and present examples of observations over the first six months of the deployment. 

Observations of physical Earth processes used to be the exclusive domain of governmental agencies. In the United States, NASA satellites observe surface changes, National Oceanic and Atmospheric Administration buoys monitor the ocean and the atmosphere, and US Geological Survey (USGS) seismometers detect earthquakes, allowing scientists to tackle questions that were unimaginable before these observational networks were built. Today, much larger observational networks exist in the private sector that could also be harnessed to study Earth processes and reduce the impact of natural hazards. The development of public-private partnerships is therefore increasingly key for Earth scientists to use the complete observational dataset needed to answer fundamental scientific questions and solve societal challenges. 

Numerous pairs of evolutionarily divergent mammalian species have been shown to produce hybrid offspring. In some cases, F 1 hybrids are able to produce F 2 s through matings with F 1 s. In other instances, the hybrids are only able to produce offspring themselves through backcrosses with a parent species owing to unisexual sterility (Haldane's Rule). Here, we explicitly tested whether genetic distance, computed from mitochondrial and nuclear genes, can be used as a proxy to predict the relative fertility of the hybrid offspring resulting from matings between species of terrestrial mammals. We assessed the proxy's predictive power using a well-characterized felid hybrid system, and applied it to modern and ancient hominins. Our results revealed a small overlap in mitochondrial genetic distance values that distinguish species pairs whose calculated distances fall within two categories: those whose hybrid offspring follow Haldane's Rule, and those whose hybrid F 1 offspring can produce F 2 s. The strong correlation between genetic distance and hybrid fertility demonstrated here suggests that this proxy can be employed to predict whether the hybrid offspring of two mammalian species will follow Haldane's Rule. 

Abstract Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction^1,2offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10⁻⁶logical error per cycle floor set by a single high-energy event (1.6 × 10⁻⁷excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.