Abstract Constraining magma storage and transport through the entire crust at a variety of arc volcanoes is essential to understand how subduction parameters may influence these processes. However, constraints throughout the crust are often limited to volcanoes that either fortuitously have properties that illuminate the entire crustal system, or volcanoes where high‐resolution geophysical imaging has been performed. Seismic receiver functions (RFs) offer sensitivity to the whole crust independent of local seismicity distribution using a few instruments. However, near volcanoes, they are often complex, requiring detailed analysis and potentially non‐unique velocity model solutions. Here, we introduce a novel, data‐driven method to identify and map RF variability using spectral clustering and kernel logistic regression. We apply it to 16 volcanoes in the Aleutian arc, enabling systematic comparisons of RF patterns without need for detailed velocity modeling. We observe that a subset of these volcanoes has RF variability that is spatially well‐separated in the crust, while others appear to have minimal spatial organization. Based on comparisons with previous results, we find that the former tends to occur at volcanoes with relatively simple magma storage systems, and the latter at volcanoes with complex, multi‐layer storage. This suggests that our technique reveals first‐order variability in crustal magmatic architecture. Furthermore, the ability to recover spatially coherent RF patterns shows no clear correlation with along‐arc subduction parameters but instead appears associated with edifice morphology. This work establishes a systematic framework for comparing arc volcanic structure and a foundation for future RF velocity modeling, extendable to other heterogeneous settings.
Abstract Array‐based surface wave tomography has significantly enhanced the resolution of crustal imaging; however, rigorous quantification of posterior uncertainty in these regularized inversions remains a critical challenge. Conventional statistical methods, such as raw bootstrap resampling, often underestimate uncertainties in data‐sparse regions due to the dominance of regularization terms. This underestimation creates an artificial precision that risks misinterpreting inversion artifacts as robust structural features. To address this, we propose a hybrid uncertainty quantification strategy tailored for regional‐scale dense arrays, integrating data‐driven bootstrap resampling with model‐based resolution matrix correction. By utilizing the resolution matrix to explicitly debias the bootstrap variance, our approach provides a first‐order uncertainty metric for identifying and down‐weighting poorly constrained model estimates in the inversion's null‐space while retaining sensitivity to data inconsistencies in well‐resolved regions. We applied this workflow to ambient noise data from the ChinArray‐Himalaya project, extracting multimodal dispersion curves via the frequency‐Bessel (F‐J) transform. The derived hybrid uncertainty metric serves as a quantitative validity mask, successfully delineating reliable imaging regions for both fundamental and higher‐order modes, thereby facilitating objective, automated quality control for large‐scale data sets. Semi‐synthetic robustness tests further demonstrate that, unlike analytical or raw bootstrap methods, the hybrid strategy reliably distinguishes among data gaps (null‐spaces) and data conflicts. Ultimately, incorporating this resolution‐corrected uncertainty to weight subsequent 1D shear wave velocity inversions significantly suppresses regularization artifacts, laying a robust statistical foundation for constructing physically reliable 3D structural models.
Abstract Explosive volcanic eruptions produce vast amounts of acoustic energy, generally in the infrasound range. For sustained eruptions, infrasound is often dominated by jet noise, a form of noise similar to the sound from human‐made jet engines. Jet noise largely results from the combination of two sound‐producing components: fine‐scale turbulence (FST) and large‐scale turbulence (LST), with distinctive frequency spectra. FST and LST spectra successfully reproduce single‐phase jet noise data (gas‐only), but little research has been directed toward multi‐phase jets, like volcanic jets (gas with solid particles). Here, we report novel experiments investigating the noise produced by particle‐laden jets. We produced both pure‐air and particle‐laden jets (nozzle diameters: 3.5–8 mm) and recorded the resulting sound at observation angles 42–159° (where 180° is the jet's propagation direction). Fitting FST and LST spectra to the particle‐laden noise spectra was less accurate than in the pure‐air case. The most prominent difference is the absence of LST‐dominated noise at high observation angles. We hypothesize that the particles break down large‐scale structures in the jet, thus favoring FST noise generation. Slower jet exit velocities due to the presence of particles may also explain some of the discrepancies observed and will require careful constraining in the future. The effect of particles on jet noise was most easily measured at high observation angles, which are also the most challenging to monitor at active volcanoes. The experiments presented here mark a first step toward integrating experimental, numerical, and observational approaches, to unravel the acoustic signatures of volcanic eruptions.
Abstract Seismic body waves provide valuable insights into the interiors of the Earth, the Moon, and Mars. By analyzing their waveform characteristics, we can effectively reveal deep seismic velocity structures and associated dynamic processes. For the Earth, incorporating various body waves helps improve the ray path coverage and therefore the resolution of seismic imaging. For other planets with limited data, identifying seismic phases beyond the main P and S arrivals can provide critical insights into the picture of the deep interior. PKPPcP and PcPPKP phases propagate along the major arc of the great circle path, and are theoretically degenerate—arriving simultaneously—in a 1‐D spherical Earth model. Their splitting offers a unique opportunity to resolve 3‐D mantle heterogeneity, yet this potential remains under‐explored. Here we compile a global data set of high‐quality PKPPcP waves from 94 large events. Using PKKP ab as a reference phase, we quantify the observed PKPPcP–PcPPKP splitting and evaluate it against predictions from 3‐D mantle tomography models. Mantle tomography models generally underestimate the observed splitting, but the spatial correlation suggests that the observed splitting is linked to large‐scale structures in the deep mantle. We further analyze the features of PKPPcP travel time sensitivity kernels and conduct numerical simulations with various heterogeneous models. The simulations indicate that large‐scale velocity anomalies, such as large low‐velocity provinces and high‐velocity subducted slabs, are the primary contributors to PKPPcP–PcPPKP splitting. Our results highlight both the potential and the limitations of the PKPPcP phase in imaging 3‐D mantle heterogeneities.
Abstract Hydrothermal sealing is a mechanism thought to be responsible for challenging‐to‐forecast phreatic and phreatomagmatic eruptions. To better understand this process, we present a study in which we assessed the textural, mineralogical, and physical properties (porosity, permeability, and tensile strength) of blocks of hydrothermal seal that were explosively expelled as ballistics during recent activity at Turrialba, Poás, and Rincón de la Vieja (Costa Rica). The hydrothermal seals consist of altered lavas and hydrothermal breccias comprising angular fragments cemented by hydrothermal minerals. The seal‐forming materials have high porosity (typically >0.2), low tensile strength (typically <4 MPa), and low permeability (as low as 10 −18 m 2 ). Mineralogical and petrophysical similarities between hydrothermal seals from five volcanoes (including Whakaari, New Zealand, and Tokachidake volcano, Japan) speaks to the commonality of the process of hydrothermal sealing, and the importance of alunite group minerals as a cementing material. We first present equations that provide permeability and tensile strength estimations for a seal of known porosity. Hydromechanical modeling of fluid injection into a shallow volcanic system, guided by our laboratory data, highlights that: (a) overpressures are often within the range of measured tensile strengths; and (b) the likelihood of an eruption increases if fluid injection rate increases, permeability decreases, and/or tensile strength decreases. The modeling predicts small‐magnitude changes to vertical displacement, but suggests that tiltmeters close to the conduit could detect shallow hydrothermal sealing and pressurization. Understanding hydrothermal sealing and forecasting eruptions requires a multidisciplinary approach in which monitoring signals are coupled with laboratory and modeling insights.
