Characterizing the oceanic ambient noise as recorded by the dense seismo-acoustic Kazakh network

Abstract

Abstract. In this study, the dense seismo-acoustic network of the Institute of Geophysical Research (IGR), National Nuclear Centre of the Republic of Kazakhstan, is used to characterize the global ocean ambient noise. As the monitoring facilities are collocated, this allows for a joint seismo-acoustic analysis of oceanic ambient noise. Infrasonic and seismic data are processed using a correlation-based method to characterize the temporal variability of microbarom and microseism signals from 2014 to 2017. The measurements are compared with microbarom and microseism source model output that are distributed by the French Research Institute for Exploitation of the Sea (IFREMER). The microbarom attenuation is calculated using a semi-empirical propagation law in a range-independent atmosphere. The attenuation of microseisms is calculated taking into account seismic attenuation and bathymetry effect. Comparisons between the observed and predicted infrasonic and seismic signals confirm a common source mechanism for both microbaroms and microseisms. Multi-year and intra-seasonal parameter variations are analyzed, revealing the strong influence of long-range atmospheric propagation on microbarom predictions. In winter, dominating sources of microbaroms are located in the North Atlantic and in the North Pacific during sudden stratospheric warming events, while signals observed in summer could originate from sources located in the Southern Hemisphere; however, additional analyses are required to consolidate this hypothesis. These results reveal the strengths and weaknesses of seismic and acoustic methods and lead to the conclusion that a fusion of two techniques brought the investigation to a new level of findings. Summarized findings also provide a perspective for a better description of the source (localization, intensity, spectral distribution) and bonding mechanisms of the ocean–atmosphere–land interfaces.

Benchmarking microbarom radiation and propagation model against infrasound recordings: a vespagram-based approach

“…Recently, De Carlo et al (2021) provided a multi-station comparison between progressive multi-channel correlation (PMCC)processed microbarom data and the microbarom model by De Carlo et al (2020), which is also used in the current study. Results for 45 IMS stations in (De Carlo et al, 2021) demonstrate that integrating microbarom sources at distances up to 5000 km provides realistic spectra. Thus, all sources that are more than 5000 km away from the IS37 station are excluded from this study.…”

“…The next step is to account for the atmospheric influence on the microbarom propagation and ducting. For example, 3-D ray tracing or full-waveform approaches would provide an accurate simulation of the infrasound propagation; however, these methods involve a large computational burden (De Carlo et al, 2021). Instead, the semi-empirical attenuation law by Le Pichon et al ( 2012) is used in the current study.…”

“…The distance limitation comes from the attenuation law definition. Although this attenuation law is widely used for propagation over very long distances (Smirnov et al, 2021;Pilger et al, 2019;Hupe et al, 2019;De Carlo et al, 2019), it was designed for distances up to 3000 km only. The choice of the maximum distance depends on the location of the station and the main sources, as well as on how realistic spectrum is needed for a specific task.…”

“…The low-frequency microbaroms can be ducted over long distances due to the weak attenuation, which is proportional to the frequency squared. Hence, there is a potential to exploit microbaroms to probe the dynamics of the stratosphere, where the representation of the atmospheric dynamics in model products is often poorly constrained (Polavarapu et al, 2005;Rienecker et al, 2011;Smith, 2012;Amezcua et al, 2020).…”

“…It might be further refined for applications such as near-real-time diagnostics of ocean wave and atmospheric models and for a long-term assessment of model product uncertainties, particularly when applied to data from a global network of infrasound stations. A key aspect of this approach is that benchmarking between model and infrasound vespagrams considers all back azimuth directions rather than just the direction of the dominant microbarom source, as done in several previous studies (Garcés et al, 2004;Hupe et al, 2019;De Carlo et al, 2019, 2021Smirnov et al, 2021). The microbarom soundscape at a station is typically a sum of components stemming from a wide spatial distribution of ocean regions, and recently, den Ouden et al (2020) demonstrated that an iterative decomposition of the array spatial covariance matrix, using the CLEAN algorithm (Högbom, 1974), can be exploited to resolve the back azimuth and trace velocity of the most coherent wavefront arrivals.…”