Utilizing existing telecommunication cables for Distributed Acoustic Sensing (DAS) experiments has eased the collection of seismological data in previously difficult-to-access areas such as the ocean bottom. To assess the potential of submarine DAS for monitoring seismic activity, we conducted an experiment from mid-October to mid-December 2021 using a 45 km long dark fiber extending from the Greek island of Santorini along the ocean bottom to the neighboring island of Ios. This region is of great geophysical and public interest because of its historical and recent seismic and volcanic activity, especially along the Kolumbo volcanic chain. Besides recording anthropogenic noise and around 1000 seismic events, we observe the primary and secondary microseisms in the submarine section, the latter inducing Scholte waves in a sediment layer where the cable is well-coupled. By using the spectral element wave propagation solver Salvus, we compute synthetic strains for earthquakes with varying degrees of model complexity. Despite including topography, a water layer, and a heterogeneous velocity model, we are unable to reproduce the lack of coherence in our observed earthquake waveforms. Backpropagation simulations for four observed earthquakes indicate that clear convergence of the wavefield, and thus the ability to constrain a source region, is only possible when all model complexities are considered. We conclude that, despite the promising emergence of DAS, monitoring capabilities are limited by often unfavorable cable geometries, cable coupling, and the complexity of the medium. Interrogating multiple cables simultaneously or jointly analyzing DAS and seismometer data could help improve future monitoring experiments.

To improve methods in full-waveform ambient noise tomography and monitoring it is important to have knowledge of the spatio-temporal variations of the noise source distribution. Without this knowledge, an uneven distribution of sources may bias observations, and a changing source distribution may be falsely interpreted as subsurface velocity changes. By combining two methods to locate noise sources and decreasing the computational cost, we are able to invert for the global noise source distribution of the secondary microseisms on a daily basis. Additionally, we present a web framework where the Seismic Ambient Noise Source (SANS) maps are made available to the public. Many different methods to locate ambient noise sources have been developed. Bowden et al. (2021) show how a more data-driven Matched-Field Processing (MFP) approach and a more rigorous finite-frequency sensitivity kernel method can be derived from one another. Igel at al. (2021) implement spatially variable grids and pre-computed wavefields to make the finite-frequency inversion more efficient. This has made daily inversions on a regional to global scale feasible for secondary microseismic noise sources in a frequency range from 0.1 to 0.2 Hz. Since the inversion approach allows for prior information to be implemented, we use the more efficient MFP method to create an initial model and steer the inversion in the right direction. In collaboration with the Swiss National Supercomputing Centre (CSCS) we are able to run this workflow on a daily basis. The resulting noise source maps are subsequently made available to the public through our web framework SANS. A user can look through all iterations of the inversions, download all model and inversion files, and implement them in their own methods. Additionally, code is provided to help the user create plots and simplify the implementation in other studies. We are looking for collaboration with ambient noise tomography studies to investigate how the implementation of noise source maps could potentially improve the resulting structure models.

As observed elsewhere on a global scale, permafrost at Mt. Zugspitze (German/Austrian Alps) is warming in response to climate change. To monitor permafrost warming and thawing, which affect the rock slope stability and thus the hazard potential, borehole temperature logging and electrical resistivity tomography (ERT) have been employed at Mt. Zugspitze for more than a decade. Furthermore, a recent study shows that the ambient seismic noise recordings of a single seismometer at the same site can be utilized to track permafrost changes over the past 15 years. This passive seismic approach is non-invasive, labour- and cost-effective and provides high temporal resolution. Together with recent advances in instrumentation allowing the measurement of seismic vibrations on a meter scale along a fiber-optic cable (known as distributed acoustic sensing), passive seismology provides unprecedented spatio-temporal resolution for monitoring applications. Starting in July 2021, we extended the single-station deployment on Mt. Zugspitze with three small seismic arrays (six stations each, aperture ~25 m) along the permafrost-affected ridge. The stations are partly installed in a tunnel beneath the surface, which intersects a permafrost body, thus allowing in-situ observations of the frozen rock. We equipped the tunnel facilities with a fiber-optic cable, which we will interrogate on a regular basis, about once per quarter year, to resolve seasonal permafrost dynamics. A first 10-day data set of this monitoring element with seismic channel spacing of 2 m along a cable exceeding 1 km in length is already available and shows that artificial avalanche triggering explosions were successfully recorded. We present data and first results dedicated to permafrost monitoring along the fiber-optic cable and between pairs of seismic stations through cross-correlation of ambient seismic noise. In addition, the seismic arrays are designed to derive rotational ground motions, which we expect to be more sensitive to local subsurface/permafrost changes compared to the classical translational motion measurements. The experiment aims to explore the permafrost monitoring capabilities of passive seismology compared to more classical and established methods as ERT.

Imaging the spatio-temporal variations of ambient seismic noise sources can provide important information to improve near real-time monitoring and noise tomography. Various methods have been developed to tackle this problem. For example, Matched-Field Processing (MFP) offers an efficient data-driven approach by testing different noise source locations and subsequently correlating and stacking. A more rigorous approach is treating it as a finite-frequency full-waveform inversion problem. In contrast to the MFP technique, an inversion framework allows for the incorporation of prior information and subsequent iterative updates of the noise source distribution by numerically modelling correlations and source sensitivity kernels. Bowden et al. (2020) discuss the similarities between these two methods and how one can be derived from the other. We aim to compare and contrast the two methods using real data from a regional to a global scale to locate the secondary microseismic sources in the ocean. Igel et al. (2021, in prep) use a logarithmic energy ratio as measurement for the sensitivity kernels, which is chosen due to its robustness with respect to unknown 3D Earth structures. However, some disadvantages of this type of measurement are not considering absolute amplitudes and discarding information outside of the expected surface wave arrival time window. By combining the two methods and first using MFP to create an initial model for the inversion, we are able to steer the inversion in the right direction, allowing us to use a more elaborate full-waveform measurement in the inversion and hence increasing the resolution and quality of the final model. Results for noise source inversions in the ocean on a daily basis using the combination of the two methods will be presented. This work paves the way for publicly available, daily, multi-scale ambient noise source maps.

Common assumptions in ambient noise seismology such as Green’s function retrieval and equipartitioned wavefields are often not met in the Earth. Full waveform ambient noise tomography methods are free of such assumptions, as they implement knowledge of the time- and space-dependent ambient noise source distribution, whilst also taking finite-frequency effects into account. Such methods would greatly simplify near real-time monitoring of the sub-surface. Additionally, the distribution of the secondary microseisms could act as a new observable of the ocean state since its mechanism is well understood (e.g. Ardhuin et al., 2011). To efficiently forward-model global noise cross-correlations we implement (1) pre-computed high-frequency wavefields obtained using, for example, AxiSEM (Nissen-Meyer et al., 2014), and (2) spatially variable grids, both of which greatly reduce the computational cost. Global cross-correlations for any source distribution can be computed within a few seconds in the microseismic frequency range (up to 0.2 Hz). Similarly, we can compute the finite-frequency sensitivity kernels which are then used to perform a gradient-based iterative inversion of the power-spectral density of the noise source distribution. We take a windowed logarithmic energy ratio of the causal and acausal branches of the cross-correlations as measurement, which is largely insensitive to unknown 3D Earth structures. Due to its parallelisation on a cluster, our inversion tool is able to rapidly invert for the global microseismic noise source distribution with minimal required user interaction. Synthetic and real data inversions show promising results for noise sources in the North Atlantic with the structure and spatial distribution resolved at scales of a few hundred kilometres. Finally, daily noise sources maps could be computed by combining our inversion tool with a daily data download and processing toolkit.

Interest in measuring displacement gradients, such as rotation and strain, is growing in many areas of geophysical research. This results in an urgent demand for reliable and field-deployable instruments measuring these quantities. In order to further establish a high-quality standard for rotation and strain measurements in seismology, we organized a comparative sensor test experiment that took place in November 2019 at the Geophysical Observatory of the Ludwig-Maximilians University Munich in Fürstenfeldbruck, Germany. More than 24 different sensors, including three-component and single-component broadband rotational seismometers, six-component strong-motion sensors and Rotaphone systems, as well as the large ring laser gyroscopes ROMY and a Distributed Acoustic Sensing system, were involved in addition to 14 classical broadband seismometers and a 160 channel, 4.5 Hz geophone chain. The experiment consisted of two parts: during the first part, the sensors were co-located in a huddle test recording self-noise and signals from small, nearby explosions. In a second part, the sensors were distributed into the field in various array configurations recording seismic signals that were generated by small amounts of explosive and a Vibroseis truck. This paper presents details on the experimental setup and a first sensor performance comparison focusing on sensor self-noise, signal-to-noise ratios, and waveform similarities for the rotation rate sensors. Most of the sensors show a high level of coherency and waveform similarity within a narrow frequency range between 10 Hz and 20 Hz for recordings from a nearby explosion signal. Sensor as well as experiment design are critically accessed revealing the great need for reliable reference sensors.

The study of ambient seismic noise sources and their time- and space-dependent distribution is becoming a crucial component of the real-time monitoring of various geosystems, including active fault zones and volcanoes, as well as geothermal and hydrocarbon reservoirs. In this context, we have previously implemented a combined cloud - HPC infrastructure for production of ambient source maps with high temporal resolution. It covers the entire European continent and the North Atlantic, and is based on seismic data provided by the ORFEUS infrastructure. The solution is based on the Application-as-a-Service concept and includes (1) acquisition of data from distributed ORFEUS data archives, (2) noise source mapping, (3) workflow management, and (4) front-end Web interface to end users.We present the new results of this ongoing project conducted with support of the Swiss National Supercomputing Centre (CSCS). Our recent goal has been transitioning from mapping the seismic noise sources towards modeling them based on our new method for near real-time finite-frequency ambient seismic noise source inversion. To invert for the power spectral density of the noise source distribution of the secondary microseisms we efficiently forward model global cross-correlation wavefields for any noise distribution. Subsequently, a gradient-based iterative inversion method employing finite-frequency sensitivity kernels is implemented to reduce the misfit between synthetic and observed cross correlations.During this research we encountered substantial challenges related to the large data volumes and high computational complexity of involved algorithms. We handle these problems by using the CSCS massively parallel heterogeneous supercomputer "Piz Daint". We also apply various specialized numeric techniques which include: (1) using precomputed Green's functions databases generated offline with Axisem and efficiently extracted with Instaseis package and (2) our previously developed high performance package for massive cross correlation of seismograms using GPU accelerators. Furthermore, due to the inherent restrictions of supercomputers, some crucial components of the processing pipeline including the data acquisition and workflow management are deployed on the OpenStack cloud environment. The resulting solution combines the specific advantages of the supercomputer and cloud platforms thus providing a viable distributed platform for the large-scale modeling of seismic noise sources.

We introduce the open-source tool noisi for the forward and inverse modeling of ambient seismic cross-correlations with spatially varying source spectra. It utilizes pre-computed databases of Green's functions to represent seismic wave propagation between ambient seismic sources and seismic receivers, which can be obtained from existing repositories or imported from the output of wave propagation solvers. The tool was built with the aim of studying ambient seismic sources while accounting for realistic wave propagation effects. Furthermore, it may be used to guide the interpretation of ambient seismic auto- and cross-correlations, which have become preeminent seismological observables, in light of nonuniform ambient seismic sources. Written in the Python language, it is accessible for both usage and further development and efficient enough to conduct ambient seismic source inversions for realistic scenarios. Here, we introduce the concept and implementation of the tool, compare its model output to cross-correlations computed with SPECFEM3D_globe, and demonstrate its capabilities on selected use cases: a comparison of observed cross-correlations of the Earth's hum to a forward model based on hum sources from oceanographic models and a synthetic noise source inversion using full waveforms and signal energy asymmetry.

The study of ambient seismic noise sources and their timeand space-dependent distribution is becoming a crucial component of the real-time monitoring of various geosystems, including active fault zones and volcanoes, as well as geothermal and hydrocarbon reservoirs. In this context, we aim to produce ambient noise source maps with high temporal resolution. For this we use advanced source imaging methods based on the massive cross correlation of seismic noise data in the region of interest. We have previously implemented a pilot solution covering Switzerland and based on processing of seismic data provided by the Swiss Seismological Service (SED). The solution architecture is based on the Application-as-a-Service concept and includes the following subsystems: (1) acquisition of data from seismic networks, (2) noise source mapping, (3) workflow management framework for coordination of various tasks and computations, and (4) front-end Web interface providing the service to end users.