Capturing Volcanic Lamb Waves and Blast Yields with Netatmo Weather Networks
Year: 2026
Type: preprint
Abstract: Explosive volcanic eruptions excite atmospheric acoustic-gravity waves and nondispersive Lamb waves that propagate over regional to planetary distances, serving as critical diagnostic signatures of eruption dynamics and explosive energy release. However, conventional sparse infrasound networks (station spacing > 100 km) suffer from spatial aliasing and severe wind noise contamination. In this study, we present the Volcanic-Lamb-IoT Framework, the first comprehensive geophysical framework that harnesses continuous, ultra-dense crowdsourced weather observation big data (29,334 authentic records across 691+ IoT stations) from the Netatmo network across Japan to capture volcanic atmospheric wave dynamics. We investigate two contrasting major Japanese volcanic eruptions in 2018: (1) the January 23, 2018 phreatic explosion of Mt. Kusatsu-Shirane (Moto-Shirane), and (2) the March 6, 2018 explosive magmatic eruption of Mt. Shinmoedake (Kirishima), spanning continuous ±12 hour observational windows. By coupling two-dimensional compressible Lamb wave equations, Time-Difference-of-Arrival (TDOA) reduced travel-time regression, slowness delay-andsum beamforming, and acoustic energy flux integrals, our framework quantitatively reconstructs apparent wave propagation velocities (capp = 304.38 ± 4.73m/s, R2 = 0.9796 for Kusatsu-Shirane; capp = 313.27 ± 4.20m/s, R2 = 0.9898 for Shinmoedake) with exceptional consistency to theoretical acoustic speeds (310–320m/s). Slowness beamforming accurately inverts the volcanic crater back-azimuth with sub-degree angular errors (< 1.78◦, Semblance > 0.87). Furthermore, acoustic energy inversion successfully isolates total radiated atmospheric energy (Eacoustic = 7.48× 1011 J ≈ 178.8 tons TNT for phreatic vs. 4.36× 1012 J ≈ 1041.1 tons TNT for magmatic rupture). Simultaneously, downwind thermal shadowing from volcanic umbrella clouds is captured (∆T = −6.13◦C and −4.10◦C). Strict negative control testing against quiet winter baselines and severe synoptic windstorms achieved a 0.0% false positive rate, proving that crowdsourced IoT sensor meshes provide an unprecedented, planetary-scale diagnostic laboratory for volcanology and geophysical fluid dynamics.
Author: Kanshi Tanaike
Language: English
Topic: Seismic Waves and Analysis
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7204765277