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Abstract The 15 January 2022 eruption of Hunga volcano (Kingdom of Tonga) produced the most lightning ever documented during an explosive eruption to date. This study estimates the mass of erupted tephra that may be structurally or electromagnetically affected by the lightning, based upon lightning peak current, channel length, and ash plume particle concentration. The lightning channels totaled 1.67 million m³in volume and contained 548 kg of volcanic ash at a calculated plume concentration of 0.328 g/m³. From this total, 54.8 kg of ash may display physical evidence in the form of lightning‐induced textures, such as lightning‐induced volcanic spherules, but this is an insignificant fraction of the total airfall deposit (10⁻⁸%). However, the total mass of ash exposed to magnetic flux densities exceeding Earth's ambient surface values is 2.24 × 10¹⁴ g, corresponding to just over a third (34.9%) of the calculated mass of total airfall (5.21 × 10¹⁴ g). This study reveals that even though physical evidence of volcanic lightning may be limited, ash particles will still be affected by the electromagnetic fields generated by the lightning discharge. The extent of these effects will be a function of lightning properties, ash properties, and location of the ash in relation to the discharge channel. 

Abstract Geyser eruptions provide a test bed for using geophysical data to forecast eruptions and to understand heat and mass transport in hydrothermal systems. We used time series analyses of seismic data at Steamboat Geyser, Yellowstone National Park, to identify short‐term precursors that are recurrent, detectable in real time, distinctly identifiable, as well as being rare during non‐eruptive periods. We analyzed seismic data from March to December 2018 to identify patterns that occurred before 31 eruptions. Four seismic amplitude measures and 700 time‐series features were computed from the seismic data. A template matching analysis identified an optimal 18‐hr window for detecting precursors. We applied a random forest to classify pre‐eruptive and non‐eruptive data for out‐of‐sample eruptions (eruptions that were not included in the model's training data), showing ability to distinguish between the two. This model performed better than a simpler amplitude‐based approach. Seismic features with the most predictive power include autocorrelations, longest strike above the mean, and change quantiles, particularly within the 4.5–16 Hz frequency range. We applied isotonic regression, a method that converts raw model outputs into calibrated probabilities, to improve the interpretability of eruption forecasting outputs. The likelihood of an eruption reaches 12.6% within 18 hr prior to the event, representing a marginal increase over the static 8% probability derived solely from eruption intervals. Unlike the interval‐based approach, our model does not rely on the time since the last eruption, instead using real‐time seismic features to detect precursory signals. Our study advances Machine Learning methodologies in eruption forecasting by integrating calibrated probability estimation through isotonic regression, which has advantages over traditional approaches for geysers with highly irregular eruption intervals. 

Abstract On 15 January 2022, Hunga volcano erupted, creating an extensive and high-reaching umbrella cloud over the open ocean, hindering traditional isopach mapping and fallout volume estimation. In MODIS satellite imagery, ocean surface water was discolored around Hunga following the eruption, which we attribute to ash fallout from the umbrella cloud. By relating intensity of ocean discoloration to fall deposit thicknesses in the Kingdom of Tonga, we develop a methodology for estimating airfall volume over the open ocean. Ash thickness measurements from 41 locations are used to fit a linear relationship between ash thickness and ocean reflectance. This produces a minimum airfall volume estimate of$${1.8}_{-0.4}^{+0.3}$$ ${1.8}_{-0.4}^{+0.3}$km³. The whole eruption produced > 6.3 km³of uncompacted pyroclastic material on the seafloor and a caldera volume change of 6 km³DRE. Our fall estimates are consistent with the interpretation that most of the seafloor deposits were emplaced by gravity currents rather than fall deposits. Our proposed method does not account for the largest grain sizes, so is thus a minimum estimate. However, this new ocean-discoloration method provides an airfall volume estimate consistent with other independent measures of the plume and is thus effective for rapidly estimating fallout volumes in future volcanic eruptions over oceans. 

Abstract Most volcanic eruptions on Earth take place below the ocean surface and remain largely unobserved. Reconstruction of past submerged eruptions has thus primarily been based on the study of seafloor deposits. Rarely before the 15 January 2022 eruption of Hunga volcano (Kingdom of Tonga) have we been able to categorically link deep‐sea deposits to a specific volcanic source. This eruption was the largest in the modern satellite era, producing a 58‐km‐tall plume, a 20‐m high tsunami, and a pressure wave that propagated around the world. The eruption induced the fastest submarine density currents ever measured, which destroyed submarine telecommunication cables and traveled at least 85 km to the west to the neighboring Lau Basin. Here we report findings from a series of remotely operated vehicle dives conducted 4 months after the eruption along the Eastern Lau Spreading Center‐Valu Fa Ridge. Hunga‐sourced volcaniclastic deposits 7–150 cm in thickness were found at nine sites, and collected. Study of the internal structure, grain size, componentry, glass chemistry, and microfossil assemblages of the cores show that these deposits are the distal portions of at least two ∼100‐km‐runout submarine density currents. We identify distinct physical characteristics of entrained microfossils that demonstrate the dynamics and pathways of the density currents. Microfossil evidence suggests that even the distal parts of the currents were erosive, remobilizing microfossil‐concentrated sediments across the Lau Basin. Remobilization by volcaniclastic submarine density currents may thus play a greater role in carbon transport into deep sea basins than previously thought.