Abstract We compare earthquake wavefield measurements from a ∼95 km urban dark fiber distributed acoustic sensing (DAS) array and a collocated 16 km aperture broadband seismic array in Dallas, Texas. Using data from a regional M 5.2 earthquake, we assess the ability of DAS to recover ground-motion amplitudes and wavefield direction of arrival (DOA). Surface-wave (Rayleigh) amplitude ratios between broadband and DAS range from 0.25 to 22.15, revealing significantly more heterogeneous DAS coupling than previously reported. Although broadband sensors provide consistent amplitude and high-quality DOA estimates across all phases (P, S, and Rayleigh), DAS data show large amplitude variability unrelated to fiber orientation, suggesting coupling and emplacement differences as dominant factors. Signal coherence in DAS is also highly variable, with some segments showing stronger correlations during pre-event noise than during the earthquake signal. Nonetheless, by applying hierarchical clustering to interchannel cross-correlations, we identify a 36-channel DAS subgroup and obtain a DOA estimate for the Rayleigh wave within 4.5° of that derived from the broadband array. These results highlight both the promise and limitations of using urban dark fiber DAS for earthquake monitoring and underscore the need for careful characterization of fiber coupling and geometry to enable reliable wavefield reconstruction.

The stratosphere and mesosphere are important regions for the prediction of weather at the Earth's surface for medium- and long-range forecasts. The availability of observations in these layers is lower than that of the troposphere, especially for the dynamics. While seeking new observational sources is important, there are existing infrasound data sets that provide indirect observations of these layers to be exploited. Infrasound waves generated at the Earth's surface travel horizontally and vertically through the atmosphere, and can be detected by sensor arrays at ranges of hundreds or thousands of kilometers. These waves are affected by the atmospheric conditions they encounter during propagation, and the integrated contributions can be observed in the recorded observations. Inverse problem techniques can be readily used to extract information from these integrated observations and provide valuable data related to the atmospheric conditions. We exploit observations from controlled ammunition explosions in Oklahoma, which generate waves traveling to 30-50 km altitude before being refracted back to the surface and detected 256 km from the explosion site. As model background we use the 10-member European Reanalysis product, valid 1 hr before the explosions. We use the Modulated Ensemble Transform Kalman Filter to combine these two sources of information and obtain updated atmospheric profiles. The assimilated observations bring the atmospheric profiles closer to those obtained by solely interpolating the reanalysis product to the time of the explosions. The most benefited altitudes are those close to the refraction heights of the infrasound waves, 35-55 km.

Infrasound phase identification is challenging because atmospheric variability strongly influences signal propagation on short timescales that are not resolved by standard atmospheric models. While traditional approaches rely on propagation modeling and array-derived parameters, recent work suggests that waveform structure itself encodes information about atmospheric conditions. Here, we investigate whether explosion-generated infrasound propagation phases can be identified directly from waveform data using an unsupervised deep representation learning framework. We analyze a densely sampled data set of direct, tropospheric, and stratospheric arrivals and decompose each waveform into complementary representations of energy, amplitude modulation, and wavelet phase. A convolutional-Transformer autoencoder learns compact latent representations that are fused into a joint embedding and refined through a combined reconstruction and clustering objective. Arrival groupings are identified using a Gaussian Mixture Model applied to the learned latent space. The resulting representations clearly separate the three propagation regimes with high accuracy. Attention analysis shows that the model focuses on physically interpretable temporal features associated with each arrival type. These results demonstrate that infrasound waveforms alone contain sufficient information for phase identification and highlight the potential of data-driven approaches for atmospheric and geophysical monitoring.

The development of the International Monitoring System infrasound network, combined with additional deployments of regional infrasound arrays, has led to enhancements in our understanding of acoustic signals and noise in the 0.1–5 Hz range over the last two decades. The goal of this study is to explore the properties of signals and noise at higher frequencies: from high-frequency (HF) infrasound to low-frequency (LF) audible (1–30 Hz). By embedding two new HF infrasound arrays within existing regional arrays in Nevada and South Korea, we are exploring the signals observed at these frequencies and assessing how such HF arrays can complement traditional regional infrasound arrays. In addition to observing regional infrasound signals that vary seasonally due to the stratospheric winds, repeating local sources are identified at each array, including mining explosions and operational signals such as machinery. Notable detections from the OSIRIS-REx reentry, missile and satellite launches, and the 2024 MW 7.5 Noto earthquake are highlighted. In addition to cataloging signals, we extend noise models to the HF infrasound/LF audio range, assessing coherent and incoherent noise contributions. We find that the new HF arrays extend the capability of traditional regional arrays by detecting new local signals and providing new information on regional signals, and they open the understanding to study HF infrasound propagation at local distances.

The Earth’s atmosphere is a highly dynamic system influenced by periodic and transient phenomena, such as diurnal cycles, seasonal changes, thunderstorms, and solar eclipses. This variability is especially pronounced in the Atmospheric Boundary Layer (ABL), where rapid fluctuations in temperature and wind speed/direction significantly affect the propagation of infrasound waves. Traditional numerical models often fail to fully capture this complexity due to the ABL’s inherent spatiotemporal variability. In this study, we employed cross-correlation functions (CCFs) to analyze the atmospheric response to infrasound waves. A 24-h experiment conducted at Southern Methodist University, Dallas, Texas, from April 8 to 9, 2024, during diverse atmospheric conditions—including a total solar eclipse—generated a unique dataset. Chirp signals emitted by a fixed infrasound source were recorded by an array of sensors at various distances, allowing detailed CCF analysis of source-receiver signals. The results revealed significant changes in signal travel time and amplitude, particularly during the solar eclipse, highlighting the ABL's transient response. These findings demonstrate that CCFs effectively capture the dynamic evolution of the atmosphere, providing insights that surpass the capabilities of numerical models. This study underscores the importance of experimental techniques in advancing infrasound propagation research and atmospheric studies.

On 4 March 2020, an accidental explosion occurred at a chemical plant in South Korea, generating both seismic and infrasound signals, which were recorded by multiple sensors at distances as great as 400 km across the southern Korean Peninsula. We used seismic and infrasound data recorded at 5 seismoacoustic arrays, 4 infrasound arrays, 13 single infrasound stations, the KSRS array, and single seismometers from several networks to quantify the explosion characteristics. Seismic, infrasound, and air-to-ground coupled acoustic arrivals from the explosion and coda-like signals from secondary sources are identified based on array processing and analyst review. We estimated the explosion origin time and location using combined seismic Lg and infrasound backprojection methods, demonstrating the importance of the dense network data using both wavefield types. We also found that dense network data can reduce the uncertainty in the location estimation using P arrivals and infrasound back azimuths. Yield estimates using infrasound amplitudes provide a 95% confidence from 1.3 to 3.2 tons with a maximum a posteriori of 1.9 tons, determined using propagation-based, stochastic path geometry models. The methods in this study document a seismoacoustic examination procedure that can be used in forensic investigations to detect, locate, and characterize anthropogenic sources.

This study introduces an earthquake detection and location technique that exploits the spatial coherence of the seismic wavefield. The method leverages the signal coherence across clusters of seismic stations to generate characteristic functions that are backprojected (migrated) to detect and locate seismic events. The effectiveness of the technique is assessed using a limited set of stations from the Oklahoma wavefield experiment, with minimal tuning of processing parameters. The technique is then applied to one day of continuous data, leading to the detection of new seismic events compared with an analyst-based catalog. Noteworthy advantages of this method include its independence from prior information or assumptions (such as template waveform) and its ability to operate effectively with a network design for which seismometers are deployed in a relatively small number of clusters rather than distributed throughout a region.

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

Atmospheric variability at short time-scales (seconds to minutes) is challenging to detect, quantify, and include in numerical models of atmospheric circulation. Infrasound can be generated by natural and anthropogenic sources, and due to the low frequency of the signal, it can travel relatively long distances (hundreds to thousands of kilometers) and be detected by acoustic arrays. When detected, the observed wavefront properties quantities (travel time, backazimuth angle, apparent velocity) contain integrated effects of the atmospheric slab through which the wave traveled. We use data assimilation, in particular an ensemble Kalman filter, to invert these observations to atmospheric quantities. As observations, we use three days of daily infrasonic signals originating from 52 explosions. The signals propagated through the stratospheric waveguide and were recorded at a distance of 256 km. The assimilation background field is provided by the 10-member ERA ensemble reanalysis product, which is valid every 3 hours. The departures with respect to the background shed light to the atmospheric variability in very short time-scales (minutes).

The Korean infrasound catalogue (KIC) covers 1999-2022 and characterizes a rich variety of source types as well as document the effects of the time-varying atmosphere on event detection and location across the Korean Peninsula. The KIC is produced using data from six South Korean infrasound arrays that are cooperatively operated by Southern Methodist University and Korea Institute of Geoscience and Mineral Resources. Signal detection relies on an Adaptive F-Detector that estimates arrival time and backazimuth, which draws a distinction between detection and parameter estimation. Detections and associated parameters are input into a Bayesian Infrasonic Source Location procedure. The resulting KIC contains 38 455 infrasound events and documents repeated events from several locations. The catalogue includes many anthropogenic sources such as an industrial chemical explosion, explosions at limestone open-pit mines and quarries, North Korean underground nuclear explosions and other atmospheric or underwater events of unknown origin. Most events in the KIC occur during working hours and days, suggesting a dominance of human-related signals. The expansion of infrasound arrays over the years in South Korea and the inclusion of data from the International Monitoring System infrasound stations in Russia and Japan increase the number of infrasound events and improve location accuracy because of the increase in azimuthal station coverage. A review of selected events and associated signals at multiple arrays provides a location quality assessment. We quantify infrasound events that have accompanying seismic arrivals (seismoacoustic events) to support the source type assessment. Ray tracing using the Ground-to-Space (G2S) atmospheric model generally predicts observed arrivals when strong stratospheric winds exist, although the predicted arrival times have significant discrepancies. In some cases, local atmospheric data better captures small-scale variations in the wind velocity of the shallow atmosphere and can improve arrival time predictions that are not well matched by the G2S model. The analysis of selected events also illustrates the importance of topographic effects on tropospheric infrasound propagation at local distances. The KIC is the first infrasound catalogue compiled in this region, and it can serve as a valuable data set in developing more robust infrasound source localization and characterization methods.

Sample return capsules (SRCs) entering Earth’s atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 2023 September 24 arrival of the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing, and Global Positioning System technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the data set collected during this campaign will improve entry and propagation models and augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources.

Abstract Recent geophysical studies have highlighted the potential utility of integrating both seismic and infrasound data to improve source characterization and event discrimination efforts. However, the influence of each of these data types within an integrated framework is not yet well‐understood by the geophysical community. To help elucidate the role of each data type within a merged structure, we develop a neural network which fuses seismic and infrasound array data via a gated multimodal unit for earthquake‐explosion discrimination within the Korean Peninsula. Model performance is compared before and after adding the infrasound branch. We find that the seismoacoustic model outperforms the seismic model, with the majority of the improvements stemming from the explosions class. The influence of infrasound is quantified by analyzing gated multimodal activations. Results indicate that the model relies comparatively more on the infrasound branch to correct seismic predictions.

Research Article| July 07, 2023 Introduction to the Special Section on Seismoacoustics and Seismoacoustic Data Fusion Fransiska K. Dannemann Dugick; Fransiska K. Dannemann Dugick * 1Sandia National Laboratories, Geophysical Detection Programs, Albuquerque, New Mexico, U.S.A. *Corresponding author: fkdanne@sandia.gov https://orcid.org/0000-0001-8328-4835 Search for other works by this author on: GSW Google Scholar Jordan W. Bishop; Jordan W. Bishop 2Wilson Alaska Technical Center & Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska, U.S.A. https://orcid.org/0000-0002-8940-9827 Search for other works by this author on: GSW Google Scholar Léo Martire; Léo Martire 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, U.S.A. https://orcid.org/0000-0002-9402-6150 Search for other works by this author on: GSW Google Scholar Alexandra M. Iezzi; Alexandra M. Iezzi 4U.S. Geological Survey, Cascades Volcano Observatory, Vancouver, Washington, U.S.A. Search for other works by this author on: GSW Google Scholar Jelle D. Assink; Jelle D. Assink 5R&D Seismology and Acoustics, Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands https://orcid.org/0000-0002-4990-6845 Search for other works by this author on: GSW Google Scholar Quentin Brissaud; Quentin Brissaud 6Norwegian Seismic Array (NORSAR), Kjeller, Norway https://orcid.org/0000-0001-8189-4699 Search for other works by this author on: GSW Google Scholar Stephen Arrowsmith Stephen Arrowsmith 7Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, Texas, U.S.A. https://orcid.org/0000-0002-9150-0363 Search for other works by this author on: GSW Google Scholar Author and Article Information Fransiska K. Dannemann Dugick https://orcid.org/0000-0001-8328-4835 * 1Sandia National Laboratories, Geophysical Detection Programs, Albuquerque, New Mexico, U.S.A. Jordan W. Bishop https://orcid.org/0000-0002-8940-9827 2Wilson Alaska Technical Center & Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska, U.S.A. Léo Martire https://orcid.org/0000-0002-9402-6150 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, U.S.A. Alexandra M. Iezzi 4U.S. Geological Survey, Cascades Volcano Observatory, Vancouver, Washington, U.S.A. Jelle D. Assink https://orcid.org/0000-0002-4990-6845 5R&D Seismology and Acoustics, Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands Quentin Brissaud https://orcid.org/0000-0001-8189-4699 6Norwegian Seismic Array (NORSAR), Kjeller, Norway Stephen Arrowsmith https://orcid.org/0000-0002-9150-0363 7Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, Texas, U.S.A. *Corresponding author: fkdanne@sandia.gov Publisher: Seismological Society of America First Online: 07 Jul 2023 Online ISSN: 1943-3573 Print ISSN: 0037-1106 © Seismological Society of America Bulletin of the Seismological Society of America (2023) 113 (4): 1383–1389. https://doi.org/10.1785/0120230049 Article history First Online: 07 Jul 2023 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Fransiska K. Dannemann Dugick, Jordan W. Bishop, Léo Martire, Alexandra M. Iezzi, Jelle D. Assink, Quentin Brissaud, Stephen Arrowsmith; Introduction to the Special Section on Seismoacoustics and Seismoacoustic Data Fusion. Bulletin of the Seismological Society of America 2023;; 113 (4): 1383–1389. doi: https://doi.org/10.1785/0120230049 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search A variety of geophysical hazards (e.g., volcanic activity, earthquakes, mass movements, marine storms, and bolides) and anthropogenic sources (e.g., chemical and nuclear explosions, mining blasts, rocket launches, and military activity) can release energy as mechanical waves in the ground, ocean, and atmosphere (Campus and Christie, 2009; Arrowsmith et al., 2010). Because of the mechanical coupling between a planetary body, its ocean, and its atmosphere, waves propagate across these interfaces (Ben‐Menahem and Singh, 1981) and carry information about the source and the media they propagated through. The field of seismoacoustics, driven by geophysical observations of... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Several sources of interest often generate both low-frequency acoustic and seismic signals due to energy propagation through the atmosphere and the solid Earth. Seismic and acoustic observations are associated with a wide range of sources, including earthquakes, volcanoes, bolides, chemical and nuclear explosions, ocean noise, and others. The fusion of seismic and acoustic observations contributes to a better understanding of the source, both in terms of constraining source location and physics, as well as the seismic to acoustic coupling of energy. In this review, we summarize progress in seismoacoustic data processing, including recent developments in open-source data availability, low-cost seismic and acoustic sensors, and large-scale deployments of collocated sensors from 2010 to 2022. Similarly, we outline the recent advancements in modeling efforts for both source characteristics and propagation dynamics. Finally, we highlight the advantages of fusing multiphenomenological signals, focusing on current and future techniques to improve source detection, localization, and characterization efforts. This review aims to serve as a reference for seismologists, acousticians, and others within the growing field of seismoacoustics and multiphenomenology research.

Data assessment tools designed to improve data quality and real-time delivery of seismic and infrasound data produced by six seismoacoustic research arrays in South Korea are documented and illustrated. Three distinct types of tools are used including the following: (1) data quality monitoring; (2) real-time station state of health (SOH) monitoring; and (3) data telemetry and archive monitoring. The data quality tools quantify data gaps, seismometer orientation, infrasound polarity, digitizer timing errors, absolute noise levels, and coherence between co-located sensors and instrument-generated signals. Some of the tools take advantage of co-located or closely spaced instruments in the arrays. Digitizer timing errors are identified by continuous estimates of the relative orientation of closely spaced horizontal seismic components based on the root-mean-square error between a reference seismometer and each seismometer in the array. Noise level estimates for seismic and infrasound data are used to assess local environmental effects, seasonal noise variations, and instrumentation changes for maintenance purposes. The SOH monitoring system includes the status of individual ancillary equipment (battery, solar power, or components associated with communication) and provides the operator the capability to compare the current status to the historical data and possibly make remote changes to the system. Finally, monitoring data telemetry and overall data archival provide an assessment of network performance. This collection of tools enables array operators to assess operational issues in near real-time associated with individual instruments or components of the system in order to improve data quality of each seismoacoustic array.

In this study, we develop a method that assigns acoustic signals with Automatic Dependent Surveillance-Broadcast (ADS-B) data to build a labeled dataset of acoustic signals from aircraft without expensive ground-truth experiments. An exploration of the resultant labeled dataset enables an assessment of the acoustic characteristics from three types of aircraft. The fusion framework is evaluated using data from an acoustic sensor and collocated ADS-B receiver in the middle of a large urban area at Southern Methodist University in Dallas, Texas. Our results demonstrate the benefit of combining multiple types of data to generate a labeled dataset leveraging open-source aircraft surveillance data. By studying three classes of aircraft, we find that the smaller fixed wing single engine (FWSE) class is mostly detected within approximately 5000 m, while the larger fixed wing multi-engine (FWME) class is commonly detected out to greater distances above 7500 m. The FWSE class has a median source frequency at 100 Hz, compared to FWME class with median source frequency at 80 Hz, while rotorcraft has a source frequency falling into a lower range of 30-100 Hz.

SUMMARY This study focuses on the infrasound signals from the September 2017 North Korean underground nuclear explosion (UNE17) and subsequent collapse event (SCE17) that occurred close to the autumnal equinox when the atmospheric temperature structure undergoes rapid change. Multiple arrivals, including contributions from local, diffracted and epicentral infrasound, generated by UNE17, were observed at eight infrasound arrays in the Korean Peninsula and one IMS infrasound station (IS45) in Russia while at the closest five arrays for SCE17 only epicentral infrasound was observed. The UNE17 signals provide the opportunity to explore the utility of each distinct arrival in constraining atmospheric conditions during the change associated with the equinox. The observed characteristics of the multiple epicentral infrasonic phases (celerity, backazimuth, phase velocity and spectra) suggest propagation paths through the tropospheric, stratospheric and thermospheric waveguides, although geometric ray paths based on a global atmospheric model at the time of the explosion predict only thermospheric returns. The absence of predicted stratospheric returns may reflect errors in the atmospheric models due to the lack of predicted stratospheric winds which are weak and changing close to the autumnal equinox or the limited resolution of the fine-scale structure not captured by current atmospheric models. The differences between the model predictions and the observations suggest that the numerical weather forecast models need to be modified to fully explain the observations. In order to explore the model space that can explain the UNE17 data set, an inversion scheme is applied to atmospheric wind model parameters constrained by the multi-array observations. Zonal and meridional wind profiles are parametrized using empirical orthogonal functions (EOFs) estimated from 1-yr of Ground-to-Space atmospheric specifications. A best-fitting atmospheric model is estimated using a Bayesian approach that assesses the uncertainty in the inverse solution using a joint likelihood function combining components of azimuth deviation, traveltime and phase velocity. The updated atmospheric models from six different EOFs inversions have up to 20 m s–1 stronger zonal and meridional wind speeds in the stratosphere compared to the original model, and explain the stratospheric observations in the data set. This investigation illustrates that modest changes to atmospheric wind models at the time of autumnal equinox can improve the prediction of stratospheric returns.

Research Article| January 25, 2023 Introduction to the SRL Focus Section on the Hunga Tonga‐Hunga Ha’apai Eruption Margaret Hellweg; Margaret Hellweg * 1Berkeley Seismology Laboratory, University of California Berkeley, Berkeley, California, U.S.A. *Corresponding author: peggy@seismo.berkeley.edu Search for other works by this author on: GSW Google Scholar Stephen Arrowsmith; Stephen Arrowsmith 2Southern Methodist University, Dallas, Texas, U.S.A. https://orcid.org/0000-0002-9150-0363 Search for other works by this author on: GSW Google Scholar Hugo Delgado; Hugo Delgado 3Departamento de Vulcanologia, Instituto de Geofisica, Coyoacan, Mexico https://orcid.org/0000-0001-5263-7968 Search for other works by this author on: GSW Google Scholar James Gridley; James Gridley 4National Tsunami Warning Center, Palmer, Alaska, U.S.A. Search for other works by this author on: GSW Google Scholar Ronan Joseph Le Bras; Ronan Joseph Le Bras 5Comprehensive Test Ban Treaty Organization, Wien, Austria https://orcid.org/0000-0003-2439-6938 Search for other works by this author on: GSW Google Scholar Daniel McNamara; Daniel McNamara 6Ann Arbor, Michigan, U.S.A. Search for other works by this author on: GSW Google Scholar Steven Sherburn Steven Sherburn 7Data Science and Geohazards Monitoring Department, GNS Science, Taupo, New Zealand Search for other works by this author on: GSW Google Scholar Author and Article Information Margaret Hellweg * 1Berkeley Seismology Laboratory, University of California Berkeley, Berkeley, California, U.S.A. Stephen Arrowsmith https://orcid.org/0000-0002-9150-0363 2Southern Methodist University, Dallas, Texas, U.S.A. Hugo Delgado https://orcid.org/0000-0001-5263-7968 3Departamento de Vulcanologia, Instituto de Geofisica, Coyoacan, Mexico James Gridley 4National Tsunami Warning Center, Palmer, Alaska, U.S.A. Ronan Joseph Le Bras https://orcid.org/0000-0003-2439-6938 5Comprehensive Test Ban Treaty Organization, Wien, Austria Daniel McNamara 6Ann Arbor, Michigan, U.S.A. Steven Sherburn 7Data Science and Geohazards Monitoring Department, GNS Science, Taupo, New Zealand *Corresponding author: peggy@seismo.berkeley.edu Publisher: Seismological Society of America First Online: 25 Jan 2023 Online ISSN: 1938-2057 Print ISSN: 0895-0695 © Seismological Society of America Seismological Research Letters (2023) 94 (2A): 564–566. https://doi.org/10.1785/0220230001 Article history First Online: 25 Jan 2023 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Margaret Hellweg, Stephen Arrowsmith, Hugo Delgado, James Gridley, Ronan Joseph Le Bras, Daniel McNamara, Steven Sherburn; Introduction to the SRL Focus Section on the Hunga Tonga‐Hunga Ha’apai Eruption. Seismological Research Letters 2023;; 94 (2A): 564–566. doi: https://doi.org/10.1785/0220230001 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search Similar to the Roman god Janus, natural events often have two faces. For the people in their environment, they can be disasters, sometimes with truly terrible effects. For scientists, often geophysicists who observe the interactions on the planet, they may bring exciting new measurements and insights. The Hunga Tonga‐Hunga Ha’apai (HTHH) eruption was just such an event. It was the largest underwater eruption since 1883, when the Indonesian volcano Krakatoa produced a tsunami that killed more than 20,000 people, and the pressure wave that was heard (or measured) around the world. Similar to Krakatoa, the HTHH eruption was tremendous, with... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.