Fast and destructive density currents created by ocean-entering volcanic eruptions.

Volcanic eruptions on land create hot and fast pyroclastic density currents, triggering tsunamis or surges that travel over water where they reach the ocean. However, no field study has documented what happens when large volumes of erupted volcanic material are instead delivered directly into the ocean. We show how the rapid emplacement of large volumes of erupted material onto steep submerged slopes triggered extremely fast (122 kilometers per hour) and long-runout (>100 kilometers) seafloor currents. These density currents were faster than those triggered by earthquakes, floods, or storms, and they broke seafloor cables, cutting off a nation from the rest of the world. The deep scours excavated by these currents are similar to those around many submerged volcanoes, providing evidence of large eruptions at other sites worldwide.

Optical polarization-based seismic and water wave sensing on transoceanic cables.

Seafloor geophysical instrumentation is challenging to deploy and maintain but critical for studying submarine earthquakes and Earth's interior. Emerging fiber-optic sensing technologies that can leverage submarine telecommunication cables present an opportunity to fill the data gap. We successfully sensed seismic and water waves over a 10,000-kilometer-long submarine cable connecting Los Angeles, California, and Valparaiso, Chile, by monitoring the polarization of regular optical telecommunication channels. We detected multiple moderate-to-large earthquakes along the cable in the 10-millihertz to 5-hertz band. We also recorded pressure signals from ocean swells in the primary microseism band, implying the potential for tsunami sensing. Our method, because it does not require specialized equipment, laser sources, or dedicated fibers, is highly scalable for converting global submarine cables into continuous real-time earthquake and tsunami observatories.

Field and lab experimental demonstration of nonlinear impairment compensation using neural networks.

Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. Nonlinear impairments are determined by the signal pattern and the transmission system parameters. Deterministic algorithms based on approximating the nonlinear Schrodinger equation through digital back propagation, or a single step approach based on perturbation methods have been demonstrated, however, their implementation demands excessive signal processing resources, and accurate knowledge of the transmission system. A completely different approach uses machine learning algorithms to learn from the received data itself to figure out the nonlinear impairment. In this work, a single-step, system agnostic nonlinearity compensation algorithm based on a neural network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6 dB Q improvement after 2800 km standard single-mode fiber transmission using 32 Gbaud signal. Without prior knowledge of the transmission system, the neural network tensor weights are constructed from training data thanks to the intra-channel cross-phase modulation and intra-channel four-wave mixing triplets used as input features.