Local Explosion Detection and Infrasound Localization by Reverse Time Migration Using 3-D Finite-Difference Wave Propagation

Fee, David; Toney, Liam; Kim, Kee Hoon; Sanderson, R. W.; Iezzi, Alexandra M.; Matoza, Robin S.; Angelis, Silvio De; Jolly, Arthur D.; Lyons, J. J.; Haney, Matthew M.

doi:10.3389/feart.2021.620813

Cited by 23 publications

(27 citation statements)

References 35 publications

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“…Variations on the RTM method include back projection, stacking, source scanning, and time-reversal (e.g., Walker et al 2011;De Angelis et al 2012;Jolly et al 2014;Sanderson et al 2020). This approach is originally used to locate seismic (Ishii et al 2005;Kiser and Ishii 2011) and acoustic (Jolly et al 2014;Sanderson et al 2020;Fee et al 2021) sources related to volcanic eruptions, while here we identified the dominant propagation velocity across a seismic network. For the Ambae eruptions, the dominant frequency band of acoustic waves significantly differs from that of seismic tremor (Fig.…”

Section: Methods To Check For Air-to-ground-coupled Waves In Seismogramsmentioning

confidence: 99%

Seismo-acoustic characterisation of the 2018 Ambae (Manaro Voui) eruption, Vanuatu

et al. 2021

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A new episode of unrest and phreatic/phreatomagmatic/magmatic eruptions occurred at Ambae volcano, Vanuatu, in 2017–2018. We installed a multi-station seismo-acoustic network consisting of seven 3-component broadband seismic stations and four 3-element (26–62 m maximum inter-element separation) infrasound arrays during the last phase of the 2018 eruption episode, capturing at least six reported major explosions towards the end of the eruption episode. The observed volcanic seismic signals are generally in the passband 0.5–10 Hz during the eruptive activity, but the corresponding acoustic signals have relatively low frequencies (< 1 Hz). Apparent very-long-period (< 0.2 Hz) seismic signals are also observed during the eruptive episode, but we show that they are generated as ground-coupled airwaves and propagate with atmospheric acoustic velocity. We observe strongly coherent infrasound waves at all acoustic arrays during the eruptions. Using waveform similarity of the acoustic signals, we detect previously unreported volcanic explosions at the summit vent region based on constant-celerity reverse-time-migration (RTM) analysis. The detected acoustic bursts are temporally related to shallow seismic volcanic tremor (frequency content of 5–10 Hz), which we characterise using a simplified amplitude ratio method at a seismic station pair with different distances from the vent. The amplitude ratio increased at the onset of large explosions and then decreased, which is interpreted as the seismic source ascent and descent. The ratio change is potentially useful to recognise volcanic unrest using only two seismic stations quickly. This study reiterates the value of joint seismo-acoustic data for improving interpretation of volcanic activity and reducing ambiguity in geophysical monitoring.

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Section: Methods To Check For Air-to-ground-coupled Waves In Seismogramsmentioning

confidence: 99%

Seismo-acoustic characterisation of the 2018 Ambae (Manaro Voui) eruption, Vanuatu

et al. 2021

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“…Unlike ground-or satellite-based optical sensing, infrasound recording is not impacted by poor visibility, and data latency for local installations is usually less than a few tens of seconds. In its most basic application, infrasound can be used to detect and locate explosions (e.g., Matoza et al 2011Matoza et al , 2017De Angelis et al 2012), while more advanced data processing can characterize diverse eruptive activity (Anderson et al 2018a), discriminate between closely spaced vents (Ripepe et al 2007;Fee et al 2021), help forecast eruptions (Garcés et al 1999;Ulivieri et al 2013;Johnson et al 2018;Ripepe et al 2018), and provide quantitative eruption source parameters (e.g., Vergniolle & Caplan-Auerbach 2006;Ripepe et al 2013;). Near-real-time eruption monitoring with infrasound has been useful at well-monitored volcanoes, including at Tungurahua (Fee et al 2010), Etna (Ripepe et al 2018), Stromboli (Le Pichon et al 2021, Kilauea (Patrick et al 2019), and Sakurajima (Yokoo et al 2013).…”

Section: Eruption Monitoringmentioning

confidence: 99%

“…Incorporating topography can result in improved source localizations (Fig. 1; Fee et al 2021), and improved estimations of volumetric and mass flow rates (Kim et al 2015;.…”

Section: Infrasound Propagationmentioning

confidence: 99%

“…Elevation contour units are meters. Modified from Fee et al (2021) demonstrated that neglecting nonlinear effects may result in inaccurate infrasound-derived estimates of eruption properties (Anderson 2018;Brogi et al 2018;Dragoni and Santoro 2020;Maher et al 2020;Watson et al 2021a). For example, aeroacoustic simulations by Watson et al (2021a) show that changes to waveform shape due to nonlinear effects can lead to underestimation of total erupted volume (Fig.…”

Section: Infrasound Propagationmentioning

confidence: 99%

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Volcano infrasound: progress and future directions

Watson¹,

Iezzi

Toney

et al. 2022

Bull Volcanol

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Over the past two decades (2000–2020), volcano infrasound (acoustic waves with frequencies less than 20 Hz propagating in the atmosphere) has evolved from an area of academic research to a useful monitoring tool. As a result, infrasound is routinely used by volcano observatories around the world to detect, locate, and characterize volcanic activity. It is particularly useful in confirming subaerial activity and monitoring remote eruptions, and it has shown promise in forecasting paroxysmal activity at open-vent systems. Fundamental research on volcano infrasound is providing substantial new insights on eruption dynamics and volcanic processes and will continue to do so over the next decade. The increased availability of infrasound sensors will expand observations of varied eruption styles, and the associated increase in data volume will make machine learning workflows more feasible. More sophisticated modeling will be applied to examine infrasound source and propagation effects from local to global distances, leading to improved infrasound-derived estimates of eruption properties. Future work will use infrasound to detect, locate, and characterize moving flows, such as pyroclastic density currents, lahars, rockfalls, lava flows, and avalanches. Infrasound observations will be further integrated with other data streams, such as seismic, ground- and satellite-based thermal and visual imagery, geodetic, lightning, and gas data. The volcano infrasound community should continue efforts to make data and codes accessible and to improve diversity, equity, and inclusion in the field. In summary, the next decade of volcano infrasound research will continue to advance our understanding of complex volcano processes through increased data availability, sensor technologies, enhanced modeling capabilities, and novel data analysis methods that will improve hazard detection and mitigation.

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“…Reverse time migration (RTM) was proposed in the 1980s (Baysal, 1983;McMechan, 1983;Whitmore, 1983), which is based on a two-way wave equation and applies zero-lag crosscorrelation imaging conditions to realize imaging. Theoretically, RTM can adapt to any complex velocity model without dip limitations and image nearly all kinds of waves, including refractions, prismatic waves, diffractions, and multiples, so it is considered to be the most accurate imaging algorithm and has been widely used in the field data processing (Sun et al, 2015;Oh et al, 2018;Qu et al, 2020;Fee et al, 2021). However, due to using the two-way wave equation to implement wavefield continuation, the backward reflection will occur when the seismic wave propagates to the reflection interface.…”

Section: Introductionmentioning

confidence: 99%

Angle-Weighted Reverse Time Migration With Wavefield Decomposition Based on the Optical Flow Vector

Xie

Song

et al. 2021

Front. Earth Sci.

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Reverse time migration (RTM) is based on the two-way wave equation, so its imaging results obtained by conventional zero-lag cross-correlation imaging conditions contain a lot of low-wavenumber noises. So far, the wavefield decomposition method based on the Poynting vector has been developed to suppress these noises; however, this method also has some problems, such as unstable calculation of the Poynting vector, low accuracy of wavefield decomposition, and poor effect of large-angle migration artifacts suppression. This article introduces the optical flow vector method to RTM to realize high-precision wavefield decomposition for both the source and receiver wavefields and obtains four directions of wavefields: up-, down-, left-, and right-going. Then, the cross-correlation imaging sections of one-way propagation components of forward- and back-propagated wavefields are optimized and stacked. On this basis, the reflection angle of each imaging point is calculated based on the optical flow vector, and an attenuation factor related to the reflection angle is introduced as the weight to generate the optimal stack images. The tests of theoretical model and field marine seismic data illustrate that compared with the conventional RTM with wavefield decomposition based on the Poynting vector, the angle-weighted RTM with wavefield decomposition based on the optical flow vector proposed in this article can achieve wavefield decomposition for both the source and receiver wavefields and calculate the reflection angle of each imaging point more accurately and stably. Moreover, the proposed method adopts angle weighting processing, which can further eliminate large-angle migration artifacts and effectively improve the imaging accuracy of RTM.

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Local Explosion Detection and Infrasound Localization by Reverse Time Migration Using 3-D Finite-Difference Wave Propagation

Cited by 23 publications

References 35 publications

Seismo-acoustic characterisation of the 2018 Ambae (Manaro Voui) eruption, Vanuatu

Seismo-acoustic characterisation of the 2018 Ambae (Manaro Voui) eruption, Vanuatu

Volcano infrasound: progress and future directions

Angle-Weighted Reverse Time Migration With Wavefield Decomposition Based on the Optical Flow Vector

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