Volcanic eruptions that occur without warning can be deadly in touristic and populated areas. Even with real-time geophysical monitoring, forecasting sudden eruptions is difficult, because their precursors are hard to recognize and can vary between volcanoes. Here, we describe a general seismic precursor signal for gas-driven eruptions, identified through correlation analysis of 18 well-recorded eruptions in New Zealand, Alaska, and Kamchatka. The precursor manifests in the displacement seismic amplitude ratio between medium (4.5–8 Hz) and high (8–16 Hz) frequency tremor bands, exhibiting a characteristic rise in the days prior to eruptions. We interpret this as formation of a hydrothermal seal that enables rapid pressurization of shallow groundwater. Applying this model to the 2019 eruption at Whakaari (New Zealand), we describe pressurization of the system in the week before the eruption, and cascading seal failure in the 16 h prior to the explosion. Real-time monitoring for this precursor may improve short-term eruption warning systems at certain volcanoes.

Phreatic explosions at volcanoes are difficult to forecast but can be locally devastating, as illustrated by the deadly 2019 Whakaari (New Zealand) eruption. Quantifying eruption likelihood is essential for risk calculations that underpin volcano access decisions and disaster response. But estimating eruption probabilities is notoriously difficult for sudden onset eruptions. Here, we describe two retrospectively developed models for short-term (48 h) probabilistic forecasting of phreatic eruptions at Whakaari. The models are based on a pseudo-prospective analysis of seven Whakaari eruptions whose precursors were identified by time series feature engineering of continuous seismic data. The first model, an optimized warning system, could anticipate six out of seven eruptions at the cost of 14 warning days each year. While a warning is in effect, the probability of eruption is about 8% in 48 h, which is about 126 times higher than outside the warning. The second model used isotonic calibration to translate the output of the forecast model onto a probability scale. When applied pseudo-prospectively in the 48 h prior to the December 2019 eruption, it indicated an eruption probability up to 400 times higher than the background. Finally, we quantified the accuracy of these seismic data-driven forecasts, alongside an observatory expert elicitation that used multiple data sources. To do this, we used a forecast skill score that was benchmarked against the average rate of eruptions at Whakaari between 2011 and 2019. This exercise highlights the conditions under which the three different forecasting approaches perform well and where potential improvements could be made.

Broadband seismic data were recorded on the ground surface around an exceptionally regular eruptive system, geyser El Jefe, in the El Tatio geyser field, Chile. We identify two stages in the eruption, recharge and discharge, characterized by a radial expansion and contraction, respectively, of the surface around the geyser. We model the deformation with spherical sources that vary in size, location, and pressure, constrained by pressure observations inside the conduit that are highly correlated with deformation signals. We find that in order to fit the data, the subsurface pressure sources must be laterally offset from the geyser vent during the recharge phase and that they must migrate upward toward the vent during the eruption phase. This pattern is consistent with models in which ascending fluids accumulate and then are released from a bubble trap that is horizontally offset from the shallow conduit of the geyser.

In geothermal exploration, magnetotelluric (MT) data and inversion models are commonly used to image shallow conductors typically associated with the presence of an electrically conductive clay cap that overlies the main reservoir. However, these inversion models suffer from non-uniqueness and uncertainty, and the inclusion of useful geological information is still limited. We develop a Bayesian inversion method that integrates the electrical resistivity distribution from MT surveys with borehole methylene blue data (MeB), an indicator of conductive clay content. MeB data is used to inform structural priors for the MT Bayesian inversion that focus on inferring with uncertainty the shallow conductor boundary in geothermal fields. By incorporating borehole information, our inversion reduces non-uniqueness and then explicitly represents the irreducible uncertainty as estimated depth intervals for the conductor boundary. We use Markov chain Monte Carlo (McMC) and a one-dimensional three-layer resistivity model to accelerate the Bayesian inversion of the MT signal beneath each station. Then, inferred conductor boundary distributions are interpolated to construct pseudo-2D/3D models of the uncertain conductor geometry. We compared our approach against a deterministic MT inversion software on synthetic and field examples and showed good performance in estimating the depth to the bottom of the conductor, a valuable target in geothermal reservoir exploration.

A geothermal system is a convective groundwater circulation system with three primary components: an underlying heat source, a fluid reservoir with sufficient permeability to support convective heat transport, and an overlying low permeability confining structure that is often formed from hydrothermally altered clays, commonly termed the clay cap (or caprock). Given the clay cap's primary role to separate hot reservoir fluids from cold groundwater at shallow depths, constraining its geometry is a key component to developing conceptual models of geothermal systems that guide exploration, resource estimation, and operational decision making. Although surface hydrothermal features are often associated with geothermal systems, subsurface geothermal reservoirs are obscured and their characterization (and sometimes discovery) relies on multidisciplinary surface exploration to reveal their location, size, and temperature distribution (Cumming, 2009). In electromagnetic geothermal prospecting, shallow conductive clay formations are synonymous with the shallow "conductor." Magnetotelluric methods (MT) have emerged as the preferred technology for imaging this resistivity structure (Johnston et al., 1992), particularly for identifying drilling targets and improving the conceptual understanding of the deep geothermal system (