SUMMARY A fraction of the acoustic wave energy (from the atmosphere) may couple into the ground, and it can thus be recorded as ground motion using seismometers. We have investigated this coupling, with two questions in mind, (i) how strong it is for small explosive sources and offsets up to a few tens of meters and (ii) what we can learn about the shallow subsurface from this coupling. 25 firecracker explosions and five rocket explosions were analysed using colocated seismic and infrasound sensors; we find that around 2 per cent of the acoustic energy is admitted into the ground (converted to seismic energy). Transfer coefficients are in the range of 2.85–4.06 nm Pa–1 for displacement, 1.99–2.74 μm s–1 Pa–1 for velocity, and 2.2–2.86 mm s−2 Pa–1 for acceleration. Recording dynamic air pressure together with ground motion at the same site allows identification of different waves propagating in the shallow underground, notably the seismic expression of the direct airwave, and the later air-coupled Rayleigh wave. We can reliably infer shallow ground properties from the direct airwave, in particular the two Lamé constants (λ and μ) and the Poisson ratio. Firecrackers as pressure sources allow constraining elastic parameters in the top-most layer. In this study, they provide frequency-dependent values of λ decreasing from 119 MPa for low frequencies (48 Hz) to 4.2 MPa for high frequencies (341 Hz), and μ values decreasing from 33 to 1.8 MPa. Frequency-dependent Poisson ratios ν are in the range of 0.336–0.366.

Seismologists have to deal with overlapping and noisy signals. Techniques such as source separation can be used to solve this problem. Over the past few decades, signal processing techniques used for source separation have advanced significantly for multi‐station settings. But not so many options are available when it comes to single‐station data. Using Machine Learning, we demonstrate the possibility of separating signals for single‐station, one‐component seismic recordings. The technique that we use for seismic signal separation is based on a dual‐path recurrent neural network which is applied directly to the time domain data. Such source separation may find applications in most tasks of seismology, including earthquake analysis, aftershocks, nuclear verification, seismo‐acoustics, and ambient‐noise tomography. We train the network on seismic data from STanford EArthquake Dataset and demonstrate that our approach is (a) capable of denoising seismic data and (b) capable of separating two earthquake signals from one another. In this work, we show that Machine Learning is useful for earthquake‐induced source separation. We provide a reproducible research repository with the algorithms here: https://github.com/crimeacs/source-separation.

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We obtain a large dataset of seismic data from the temporary seismic network AlpArray in Europe and a large dataset of lightning data from the lightning location system Austrian Lightning Detection and Information System and focus on the investigation of thunder signals recorded with seismic stations in a frequency range of 10–49 Hz if no other frequency band is specified. We try to establish whether important information about a lightning flash can be determined independent of optical and electrical measurements through the means of seismic analysis. Seismic data provide useful information on thunder and lightning, and we observe a correlation between lightning peak current and maximum ground displacement induced by the thunder for positive cloud-to-ground flashes of lightning.

<p>Lightning strokes create powerful wavefields of seismoacoustic nature, which we refer to as thunder. Unfortunately, even though bolts of lightning received much attention in such fields as physics of plasma and meteorology, less research was conducted to investigate the thunder itself.<br><br>A radio tower on the top of the Gaisberg mountain in Salzburg is permanently instrumented with electrical sensors able to record the current of lightning strokes hitting the tower’s top. In October 2020, observations of 5 thunder signals have been made using several one-component seismic sensors. At the same time, this tower is instrumented with a meteorological station, which allows us to model precisely the propagation of seismo-acoustic thunder signals from the above-mentioned lightnings.<br><br>These observations and modeling give insight into how thunder is created during the lightning stroke, which is an important milestone for seismo-acoustic observations of atmospheric events.</p>