Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sounds.

Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior

Subglacial volcano monitoring with fibre-optic sensing: Grímsvötn, Iceland

We present a distributed acoustic sensing (DAS) experiment at Grímsvötn, Iceland. This is intended to investigate volcano-microseismicity at Grímsvötn specifically, and to assess the suitability of DAS as a subglacial volcano monitoring tool in general. In spring 2021, we trenched a 12 km long fiber-optic cable into the ice sheet around and within the caldera, followed by nearly one month of continuous recording. An image processing algorithm that exploits spatial coherence in DAS data detects on average ~100 events per day, almost 2 orders of magnitude more than in the regional earthquake catalog. A nonlinear Bayesian inversion reveals the presence of pronounced seismicity clusters, containing events with magnitudes between −3.4 and 1.7. Their close proximity to surface volcanic features suggests a geothermal origin. In addition to painting a fine-scale picture of seismic activity at Grímsvötn, this work confirms the potential of DAS in subglacial volcano monitoring

The western painted turtle genome, a model for the evolution of extreme physiological adaptations in a slowly evolving lineage

Background: We describe the genome of the western painted turtle, Chrysemys picta bellii, one of the most widespread, abundant, and well-studied turtles. We place the genome into a comparative evolutionary context, and focus on genomic features associated with tooth loss, immune function, longevity, sex differentiation and determination, and the species' physiological capacities to withstand extreme anoxia and tissue freezing.Results: Our phylogenetic analyses confirm that turtles are the sister group to living archosaurs, and demonstrate an extraordinarily slow rate of sequence evolution in the painted turtle. The ability of the painted turtle to withstand complete anoxia and partial freezing appears to be associated with common vertebrate gene networks, and we identify candidate genes for future functional analyses. Tooth loss shares a common pattern of pseudogenization and degradation of tooth-specific genes with birds, although the rate of accumulation of mutations is much slower in the painted turtle. Genes associated with sex differentiation generally reflect phylogeny rather than convergence in sex determination functionality. Among gene families that demonstrate exceptional expansions or show signatures of strong natural selection, immune function and musculoskeletal patterning genes are consistently over-represented.Conclusions: Our comparative genomic analyses indicate that common vertebrate regulatory networks, some of which have analogs in human diseases, are often involved in the western painted turtle's extraordinary physiological capacities. As these regulatory pathways are analyzed at the functional level, the painted turtle may offer important insights into the management of a number of human health disorders

Potentials of Distributed Acoustic Sensing in Seismic Imaging

This thesis investigates the potentials of Distributed Acoustic Sensing in seismology. Distributed Acoustic Sensing (DAS) is an emerging method to measure strain along optical fibers. DAS systems are capable of measuring acoustic and elastic waves propagating along the fiber, and hence can be used as a seismological receiver, where its measurements are related to the symmetric part of the displacement gradient tensor. To investigate the potentials of DAS in seismic imaging, this thesis first establishes the properties of DAS measurements in an extensive instrument response study utilizing data from a wide range of experiments conducted as part of this thesis. Confirming the suitability of DAS measurements for seismological applications from the instrument response study, this thesis then connects strain to rotational measurements, related to the anti-symmetric part of the displacement gradient tensor, and introduces the concept of obtaining rotational observations from DAS recordings. This thesis then extends the existing theory of generalized ambient noise interferometry from displacements to gradient measurements. We show the potential to combine different seismic observables within the framework of interferometry, accounting for the observational effect on the interferometric wavefield due to spatial gradients. With the extended formulation of interferometry, we use adjoint-based methods to incorporate spatial gradient observations into full waveform ambient noise inversion. Based on theoretical investigations, numerical simulations and real-world examples, this thesis ultimately aims to incorporate gradient observations into existing geophysical workflows and to develop new methods utilizing such gradient observations, expanding the fields of theoretical, numerical and observational seismology

Rotation and strain ambient noise interferometry

We propose a theory for rotational and strain ambient noise interferometry, motivated by the recent development of rotational ground motion sensors and distributed acoustic sensing (DAS) technology. In this context, we demonstrate that displacement, strain and rotation interferograms can be generically written in the form of a representation theorem, that is, as a solution to the seismic wave equation that we refer to as the interferometric wavefield. The physical quantity (displacement, strain or rotation) determines the distributed source of the interferometric wavefield, as well as an observational operator that extracts the correct type of noise correlation function. The proposed interferometric equations are free of assumptions on the distribution of noise sources or the equipartitioning of the ambient field, typically required for Green’s function retrieval. In addition to being valid for any kind of heterogeneous source and viscoelastic medium, they allow us to account for measurement details, such as the gauge length in DAS. We illustrate the practical feasibility of our approach with a series of numerical examples, based on regional-scale, spectral-element simulations of the interferometric wavefield. Specifically, we compare displacement and strain interferograms for homogeneous and heterogeneous earth models, and for homogeneous and heterogeneous noise sources. Ultimately, our developments are intended to enable adjoint-based waveform inversion with emerging measurement technologies that provide spatial gradient information in addition to conventional seismic displacement recordings.ISSN:0956-540XISSN:1365-246

A neural network for noise correlation classification

We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20  per cent or less can be achieved when the network training is performed with as little as 3.5  per cent and 16  per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35  per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.ISSN:0956-540XISSN:1365-246

Distributed Acoustic Sensing in Volcano-Glacial Environments - Mount Meager, British Columbia

We demonstrate the logistic feasibility and scientific potential of distributed acoustic sensing (DAS) in alpine volcano-glacial environments that are subject to a broad range of natural hazards. Our work considers the Mount Meager massif, an active volcanic complex in British Columbia, estimated to have the largest geothermal potential in Canada, and home of Canada's largest recorded landslide in 2010. From September to October 2019, we acquired continuous strain data, using a 3-km long fiber-optic cable, deployed on a ridge of Mount Meager and on the uppermost part of a glacier above 2,000 m altitude. The data analysis detected a broad range of unexpectedly intense, low-magnitude, local seismicity. The most prominent events include long-lasting, intermediate-frequency (0.01–1 Hz) tremor, and high-frequency (5–45 Hz) earthquakes that form distinct spatial clusters and often repeat with nearly identical waveforms. We conservatively estimate that the number of detectable high-frequency events varied between several tens and nearly 400 per day. We also develop a beamforming algorithm that uses the signal-to-noise ratio (SNR) of individual channels, and implicitly takes the direction-dependent sensitivity of DAS into account. Both the tremor and the high-frequency earthquakes are most likely related to fluid movement within Mount Meager's geothermal reservoir. Our work illustrates that DAS carries the potential to reveal previously undiscovered seismicity in challenging environments, where comparably dense arrays of conventional seismometers are difficult to install. We hope that the logistics and deployment details provided here may serve as a starting point for future DAS experiments.ISSN:2169-9313ISSN:0148-0227ISSN:2169-935

Development of a computing infrastructure for collaborative research on seismic noise sources

ISSN:1029-7006ISSN:1607-796

Towards multi-observational full-waveform inversion

ISSN:1029-7006ISSN:1607-796

Near-field observations of snow-avalanches propagating over a fiber-optic array

We present and evaluate array processing techniques and algorithms for the characterization of snow avalanche signals recorded with Distributed Acoustic Sensing (DAS). Avalanche observations rely on comprehensive measurements of sudden and rapid snow mass movement that is hard to predict. Conventional avalanche sensors are limited to observations on or above the surface. Recently, seismic sensors have increased in their popularity for avalanche monitoring and characterization due to their avalanche detection and characterization capabilities. To date, however, seismic instrumentation in avalanche terrain is sparse, thereby limiting the spatial resolution significantly. As an addition to conventional seismic instrumentation, we propose DAS to measure avalanche-induced ground motion. DAS is a technology using backscattered light along a fiber-optic cable to measure strain (-rate) along the fiber with unprecedented spatial and temporal resolution - in our example with 2 m spatial sampling and a sampling rate of 1kHz. We analyze DAS data recorded during winter 2020/2021 at the Valleé de la Sionne avalanche test site in the Swiss Alps, utilizing an existing 700 m long fiber-optic cable. Our observations include avalanches propagating on top of the buried cable, delivering near-field observations of avalanche-ground interactions. After analyzing the properties of near-field avalanche DAS recordings, we discuss and evaluate algorithms for (1) automatic avalanche detection, (2) avalanche surge propagation speed evaluation and (3) subsurface property estimation. Our analysis highlights the complexity of near-field DAS data, as well as the suitability of DAS-based monitoring of avalanches and other hazardous granular flows. Moreover, the clear detectability of avalanche signals using existing fiber-optic infrastructure of telecommunication networks opens the opportunity for unrivalled warning capabilities in Alpine environments