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1. Seismic Response of Cook Inlet Sedimentary Basin, Southern Alaska

   https://doi.org/10.1785/0220190205  

   Smith, Kyle; Tape, Carl (October 2019, Seismological Research Letters)

   null (Ed.)

   Abstract Cook Inlet fore‐arc basin in south‐central Alaska is a large, deep (7.6 km) sedimentary basin with the Anchorage metropolitan region on its margins. From 2015 to 2017, a set of 28 broadband seismic stations was deployed in the region as part of the Southern Alaska Lithosphere and Mantle Observation Network (SALMON) project. The SALMON stations, which also cover the remote western portion of Cook Inlet basin and the back‐arc region, form the basis for our observational study of the seismic response of Cook Inlet basin. We quantify the influence of Cook Inlet basin on the seismic wavefield using three data sets: (1) ambient‐noise amplitudes of 18 basin stations relative to a nonbasin reference station, (2) earthquake ground‐motion metrics for 34 crustal and intraslab earthquakes, and (3) spectral ratios (SRs) between basin stations and nonbasin stations for the same earthquakes. For all analyses, we examine how quantities vary with the frequency content of the seismic signal and with the basin depth at each station. Seismic waves from earthquakes and from ambient noise are amplified within Cook Inlet basin. At low frequencies (0.1–0.5 Hz), ambient‐noise ratios and earthquake SRs are in a general agreement with power amplification of 6–14 dB, corresponding to amplitude amplification factors of 2.0–5.0. At high frequencies (0.5–4.0 Hz), the basin amplifies the earthquake wavefield by similar factors. Our results indicate stronger amplification for the deeper basin stations such as near Nikiski on the Kenai Peninsula and weaker amplification near the margins of the basin. Future work devoted to 3D wavefield simulations and treatment of source and propagation effects should improve the characterization of the frequency‐dependent response of Cook Inlet basin to recorded and scenario earthquakes in the region. 

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2. Tracking Multiyear Sea‐Ice Variation in the Arctic Ocean Over Decades With Microseism

   https://doi.org/10.1029/2024GL111159  

   Chen, Jui‐Chun_Freya; Park, Sunyoung; MacAyeal, Douglas_R (January 2025, Geophysical Research Letters)

   Abstract We construct a linear model of microseism power as a function of sea‐ice concentration and ocean‐wave activity with a seismic station located on northern Ellesmere Island. The influence of wind‐ice‐ocean interactions on microseism has been taken into account. We find the increase in microseism power over the last 32 years reflects the long‐term loss of sea ice and increasing ocean‐wave activity in the Arctic Ocean likely associated with climate change. We further assess model performance to determine a representative region over which sea‐ice concentration and ocean‐wave activity most directly influence the microseism power. The seismological methods developed here suggest that there is the potential to augment or refine observations of sea‐ice conditions obtained from satellites and fromin‐situobservations. Seismological methods may thus help determine properties such as sea‐ice thickness, which are less amenable to conventional observations, under a changing climate, particularly in remote areas like the High Arctic. 

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3. Broad-band ocean bottom seismometer noise properties

   https://doi.org/10.1093/gji/ggac450  

   Janiszewski, Helen A.; Eilon, Z.; Russell, J. B.; Brunsvik, B.; Gaherty, J. B.; Mosher, S. G.; Hawley, W. B.; Coats, S. (November 2022, Geophysical Journal International)

   SUMMARY We present a new compilation and analysis of broad-band ocean bottom seismometer noise properties from 15 yr of seismic deployments. We compile a comprehensive data set of representative four-component (seismometer and pressure gauge) noise spectra and cross-spectral properties (coherence, phase and admittance) for 551 unique stations spanning 18 U.S.-led experiments. This is matched with a comprehensive compilation of metadata parameters related to instrumentation and environmental properties for each station. We systematically investigate the similarity of noise spectra by grouping them according to these metadata parameters to determine which factors are the most important in determining noise characteristics. We find evidence for improvements in similarity of noise properties when grouped across parameters, with groupings by seismometer type and deployment water depth yielding the most significant and interpretable results. Instrument design, that is the entire deployed package, also plays an important role, although it strongly covaries with seismometer and water depth. We assess the presence of traditional sources of tilt, compliance, and microseismic noise to characterize their relative role across a variety of commonly used seismic frequency bands. We find that the presence of tilt noise is primarily dependent on the type of seismometer used (covariant with a particular subset of instrument design), that compliance noise follows anticipated relationships with water depth, and that shallow, oceanic shelf environments have systematically different microseism noise properties (which are, in turn, different from instruments deployed in shallow lake environments). These observations have important implications for the viability of commonly used seismic analysis techniques. Finally, we compare spectra and coherences before and after vertical channel tilt and compliance noise removal to evaluate the efficacy and limitations of these now standard processing techniques. These findings may assist in future experiment planning and instrument development, and our newly compiled noise data set serves as a building block for more targeted future investigations by the marine seismology community. 

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4. A multitask encoder–decoder to separate earthquake and ambient noise signal in seismograms

   https://doi.org/10.1093/gji/ggac290  

   Yin, Jiuxun; Denolle, Marine A.; He, Bing (July 2022, Geophysical Journal International)

   SUMMARY Seismograms contain multiple sources of seismic waves, from distinct transient signals such as earthquakes to continuous ambient seismic vibrations such as microseism. Ambient vibrations contaminate the earthquake signals, while the earthquake signals pollute the ambient noise’s statistical properties necessary for ambient-noise seismology analysis. Separating ambient noise from earthquake signals would thus benefit multiple seismological analyses. This work develops a multitask encoder–decoder network named WaveDecompNet to separate transient signals from ambient signals directly in the time domain for 3-component seismograms. We choose the active-volcanic Big Island in Hawai’i as a natural laboratory given its richness in transients (tectonic and volcanic earthquakes) and diffuse ambient noise (strong microseism). The approach takes a noisy 3-component seismogram as input and independently predicts the 3-component earthquake and noise waveforms. The model is trained on earthquake and noise waveforms from the STandford EArthquake Dataset (STEAD) and on the local noise of seismic station IU.POHA. We estimate the network’s performance by using the explained variance metric on both earthquake and noise waveforms. We explore different neural network designs for WaveDecompNet and find that the model with long-short-term memory (LSTM) performs best over other structures. Overall, we find that WaveDecompNet provides satisfactory performance down to a signal-to-noise ratio (SNR) of 0.1. The potential of the method is (1) to improve broad-band SNR of transient (earthquake) waveforms and (2) to improve local ambient noise to monitor the Earth’s structure using ambient noise signals. To test this, we apply a short-time average to a long-time average filter and improve the number of detected events. We also measure single-station cross-correlation functions of the recovered ambient noise and establish their improved coherence through time and over different frequency bands. We conclude that WaveDecompNet is a promising tool for a broad range of seismological research. 

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5. Autocorrelation Infrasound Interferometry

   https://doi.org/10.1029/2020JB020513  

   Ortiz, Hugo D.; Matoza, Robin S.; Johnson, Jeffrey B.; Hernandez, Stephen; Anzieta, Juan C.; Ruiz, Mario C. (April 2021, Journal of Geophysical Research: Solid Earth)

   Abstract Seismic and infrasound multistation ambient‐noise interferometry has been widely used to infer ground and atmospheric properties, and single‐station and autocorrelation seismic interferometry has also shown potential for characterizing Earth structure at multiple scales. We extend autocorrelation seismic interferometry to ambient atmospheric infrasound recordings that contain persistent local noise from waterfalls and rivers. Across a range of geographic settings, we retrieve relative sound‐speed changes that exhibit clear diurnal oscillations consistent with temperature and wind variations. We estimate ambient air temperatures from variations in relative sound speeds. The frequency band from 1 to 2 Hz appears most suitable to retrieve weather parameters as nearby waterfalls and rivers may act as continuous and vigorous sources of infrasound that help achieve convergence of coherent phases in the autocorrelation codas. This frequency band is also appropriate for local sound‐speed variations because it has infrasound with wavelengths of ∼170–340 m, corresponding to a typical atmospheric boundary layer height. After applying array analysis to autocorrelation functions derived from a three‐element infrasound array, we find that autocorrelation codas are composed of waves reflected off nearby topographic features, such as caldera walls. Lastly, we demonstrate that autocorrelation infrasound interferometry offers the potential to study the atmosphere over at least several months and with a fine time resolution. 

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