Reviewerly/exHarmony huggingface dataset:
Conductivity and Temperature as indicators of hydrothermal activity: A comparison of two submarine volcanoes (Greece)

The presence of active hydrothermal vent fields near residential areas and their possible link to volcanic activity pose a potential natural hazard to the environment, to society, and to the economy. Despite the importance of risk assessment and mitigation, the monitoring of volcanic activity is hindered by the remoteness and extreme conditions of underwater volcanoes. By developing a mathematical model for geological and physical processes in these environments we shed light on the underlying dynamics of chemical products emitted from the vents and point to the underlying mechanisms that govern potentially hazardous, underwater volcanic environments. Santorini and Nisyros both belong to the Hellenic Volcanic Arc but appear to have different underlying mechanisms. The Generalized Moments Method (GMM) was applied to data gathered from the Northern Caldera of Santorini and the Nisyros caldera, Avyssos, for the purpose of this work, where we focus on the high-frequency recorded CTD data (Conductivity, Temperature, Depth) in the water column over the hydrothermal vents. The data from Santorini were collected in 2017 using an Autonomous Underwater Vehicle (AUV) during the POS510 mission led by GEOMAR, while the data from Nisyros were gathered with the help of a Remotely Operated Vehicle (ROV) in 2010, during the Nautilus expedition.
Optimizing the performance of EQTransformer by parameter tuning and comparison to handpicked benchmark datasets in a subduction setting

As the number of seismic stations and experiments greatly increases due to ever greater availability of instrumentation, automated data processing becomes more and more necessary and important. Machine Learning (ML)methods are becoming widespread in seismology, with programsthat identify signals and patterns or extract features that can eventually improve our understanding of ongoing physical processes.We here focus on critically evaluating the performance of a popular machine-learning-based seismic event detection and arrival time picking program, EQ-Transformer, forseismic data from the IPOC deployment in Northern Chile, using handpicked benchmark datasets.By using the open-source framework SeisBench, we can test the effect of using different pre-trained models as well as modify critical parameters such as probability thresholds.By performing this evaluation, we want to decide whether it is necessary to retrain EQTransformer with local data, or if its performance with one of the supplied pre-trained sets is sufficiently good for our purposes.We prepared alarge handpicked benchmark dataset for Northern Chile, which we use to find the optimal configuration of EQTransformer. For this benchmark dataset, we select a total of 35 days distributed throughout the 15 years covered by the IPOC deployment. Our goal was to pick all of the many small events in the dataset, even when they are only visible at one or two stations, with high accuracy. We found around three hundred events per day, which highlights the very high seismic activity of the region. We then ran EQTransformer for the same days, using a wide range of parameter choices and pre-trained models.We need to find if our data is similar to seisbench benchmark dataset or if we should use our data to calibrate the EQTransformer for picking in subduction zones.We use our handpicked benchmark dataset to evaluate the detection rate (missed events, false detections) as well as the picking accuracy (residuals to handpicks) achieved with EQTransformerin the various tested configurations. We present results of choosing different event detection thresholds, showingtrue positive rate vs. false positive rate plots in order to find optimal thresholds, and evaluate the pick accuracy of obtained arrival time picks by comparing to the handpicked benchmark. This comparison of picking times (P &amp; S) is visualized with residual histograms. Lastly,we also show examples for a visual comparison of picks fromEQTransformer with manual picks.The present study is the first step towards the design of an automated workflow that comprises event detection and phase picking, phase association and event location and will be used to evaluate subduction zone microseismicity in different locations.
Learning fluid dynamical statistics using stochastic neural networks

Many practical problems in fluid dynamics demand an empirical approach, where statistics estimated from data inform understanding and modelling. In this context data-driven probabilistic modelling offers an elegant alternative to ad hoc estimation procedures. Probabilistic models are useful as emulators, but also offer an attractive means of estimating particular statistics of interest. In this paradigm one can rely on proper scoring rules for model comparison and validation, and invoke Bayesian statistics to obtain rigorous uncertainty quantification. Stochastic neural networks provide a particularly rich class of probabilistic models, which, when paired with modern optimisation algorithms and GPUs, can be remarkably efficient. We demonstrate this approach by learning the single particle transition density of ocean surface drifters from decades of Global Drifter Program observations using a Bayesian mixture density network. From this we derive maps of various displacement statistics and corresponding uncertainty maps. Our model also offers a means of simulating drifter trajectories as a discrete-time Markov process, which could be used to study the transport of plankton or plastic in the upper ocean.
pointcloudset - A Python package to analyze large datasets of point clouds recorded over time

Point clouds can be acquired by different sensor types and methods, such as lidar (light detection and ranging), radar (radio detection and ranging), RGB-D (red, green, blue, depth) cameras, SfM (structure from motion), etc.&amp;#160;In many cases multiple point clouds are recorded over time, sometimes also referred to as 4D point clouds. For example, automotive lidars from Ouster or Velodyne record point clouds at around 10-20Hz resulting in millions of points per second.&amp;#160;In addition, monitoring with terrestrial laser scanners is becoming used more often.&amp;#160;Producing similar datasets than the automotive lidars, although with larger individual point clouds at a lower frame rate.Analyzing such a large collection of point clouds is a big challenge due the size and unstructured nature of the data. The Python package "pointcloudset" provides a way to store, analyze, and visualize&amp;#160;large datasets consisting of multiple point clouds recorded over time. Pointcloudset features lazy evaluation, parallel processing and is designed to enable&amp;#160;the&amp;#160;development of new point cloud algorithms and their application on big datasets.&amp;#160;The package is based on the Python packages pandas, pytncloud, dask and open3D. Its API is easy to use and high level and the package is open source and available on GitHub.&amp;#160;
Mineralization potential from vein statistics at regional scale – an example from the Dharwar Craton (Southern India)

Hydrothermal veins can form when pre-existing anisotropy gets dilated by increase in fluid pressure (Pf). In addition fluctuation in Pf can result in the deposition of economically important deposits, such as gold in veins. Therefore, statistical analysis of dilational quartz veins can help to identify the mineralization potential of an area. This present study is focused on this aspect. The metavolcanic rocks of Dharwar Craton (Southern India) are replete with quartz veins, but mineralization is restricted to certain domains in the vicinity of Gadag, Hutti, and Kolar. To study the topological importance in mineral exploration, an experiment is carried out in different parts (e.g., Gadag, Hutti, Raichur, Gadwal) of Dharwar Craton. Orientation of dilational quartz veins, thickness, and spacing data are collected from different transects of the above-mentioned areas. Variations in driving pressure ratio (&amp;#916;R&amp;#697;), fractal dimension (Dc), Weibull modulus (&amp;#945;), vein intensity (Vi), and coefficient of vein spacing (Cv(S)) are calculated from vein data of each transect. These parameters are integrated to plot a three-axes &amp;#8220;mineralization potential plot&amp;#8221;, also known as P-D-F plot (Lahiri et al., 2020). The distance between the origin (0,0,0) and the point representing the transect in the P-D-F plot gives the &amp;#8220;mineralization potential parameter&amp;#8221; (Md). It is established that a transect in mineralized zone is represented by a point that lies closer to origin than non-mineralized zone. However, it is our observation that the points representing mineralized zone show more clustering on the P-D-F plot, whereas the points representing non-mineralized zone are dispersed. &amp;#160;We infer that in order to identify a zone as having high mineralization potential, the Md value should not only be lower but also the clustering should be higher.
Eerie sounds of Earth’s magnetic field

In October 2022 an audio installation using Data Sonification methods premiered in Copenhagen. The project was a sonic representation of Earth&amp;#8217;s magnetic field played on a 32-channel speaker system dug into the ground of a public square. The project was a massive success &amp;#8211; particularly online with close to a million plays on ESA&amp;#8217;s SoundCloud and thus proved a great opportunity to showcase the important work in Earth Observation done within the Swarm community.The audio installation is an artistic representation controlled by magnetic field data through a sonification method called parameter mapping. The output of the 32 speakers was controlled individually by fluctuations in the magnetic field strength at 32 locations on the globe. Data was taken from the GGF100k &amp;#8211; a global field model going back 100.000 years &amp;#8211; and condensed into a 5-minute sequence composed of 32 different tracks running simultaneously. Six different parameters were mapped to various qualities of the audio playback (volume, pitch, filter, sample playback speed); these included the three components of the magnetic field at the core mantle boundary, the rate of change, and the field average at surface level.The soundtrack was meant to give the listener an impression of a living planet in constant flux and is build up from recordings of natural sounds in various combinations. This presentation will describe the approach and give a demonstration of the results. In addition, it will briefly discuss data sonification as a method for inclusion and science outreach.
Hydroacoustic Monitoring of Earthquake Sequences on the Blanco Oceanic Transform Fault

Understanding how earthquakes initiate lies at the heart of earthquake physics and analyzing foreshock sequences is one way to probe the initiation process of large earthquakes. A few oceanic transform fault (OTF) earthquakes have been observed to have more foreshocks compared to continental earthquakes. It has also been proposed that OTFs accommodate plate motion primarily by slow creep instead of rapid seismic slip, though with significant along-fault variability. These characteristics make OTFs unique laboratories for probing the physical processes underlying foreshocks and their relations with slow slip events. However, in the past, detailed studies at OTFs have been limited due to their distance from land-based seismic stations. Since 2015, small arrays of ocean-bottom seismometers and hydrophones have been permanently deployed on cabled seafloor observatories in the northeast Pacific Ocean, allowing for monitoring of seismicity on the Blanco Transform Fault (BTF) using the earthquake&amp;#8217;s radiated hydroacoustic energy (T-phase). T-phases propagate through the SOFAR channel in the world&amp;#8217;s oceans with little attenuation, allowing for the detection of low-magnitude earthquakes at large distances. In this study, we apply a suite of techniques to detect, associate, and locate foreshocks and aftershocks of large mainshocks occurring along the BTF since 2015. We define a mainshock as the largest event occurring within two weeks and a radius of 50 km. 19 Mw &amp;#160;5.0 mainshocks are selected from the GCMT catalogue. However, we are only able to analyze 12 mainshocks due to data availability issues. We use the STA/LTA algorithm to detect T-phase arrivals one week before and after each mainshock through the continuous waveforms recorded on both OBSs and hydrophones. For each detection, we then use the relative station arrival times compared to the mainshock to make sure we only retain events close to the mainshock, i.e., its foreshocks and aftershocks. We then employ the GLOBAL mode Non-Linear Location (NLLoc) program for event localization using a 3D ocean sound velocity model. Compared to the IRIS catalogue which only has a minimum detection level of magnitude 3, lower-magnitude T-phase events are successfully detected by our method. Using our T-phase catalogue, we quantify how the BTF sequences compare with earthquake sequences observed at continental transform faults and test the various proposed models to explain foreshock spatiotemporal behavior and the partitioning of seismic and slow slips along the BTF.&amp;#160;&amp;#160;
Comparison of seismic phase association algorithms and their performance.

Seismic phase association is a fundamental task for earthquake detection and location workflows, as it gathers individual seismic phases detected on multiple seismic stations and associates them to events.Current phase picking algorithms are capable of generating large phase datasets, and together with new improved phase association algorithms, they can create larger and more complete earthquake catalogs when applied to dense seismic networks (permanent or temporary).As part of project MILESTONE, which aims at the automatic creation of large microseismicity catalogs in subduction settings, the present study evaluates the performance of three different phase association algorithms, both by comparing their outputs with a handpicked benchmark dataset and by the retrieval of synthetic events.For this purpose, we used seismic data from the IPOC (Integrated Plate boundary Observatory Chile) permanent deployment of broadband stations in Northern Chile.We manually picked P and S phases of raw waveforms on randomly chosen days, with event rates in excess of 100-150 per day. All events that were visually recognizable were picked and located, leading to a dataset to be used as &amp;#8220;ground truth&amp;#8221;.We do the phase picking with EQTransformer (Mousavi et al. 2020) and evaluate the performance of three seismic phase associators: 1) PhaseLink (Ross et al. 2019), a deep learning based approach trained on millions of synthetic sequences of P and S arrival times, 2) REAL (Zhang et al. 2020), that combines the advantages of pick-based and waveform-based methods, primarily through counting the number of P and S picks and secondarily from travel-time residuals, and 3) GaMMA (Zhu et al. 2021), an associator that treats the association problem as an unsupervised clustering problem in a probabilistic framework.In the synthetic test we use NonLinLoc raytracer and add random noise, as well as false picks to simulate an automatic picker output.In both experiments we evaluate the number of correctly associated and lost events, and the number of constituent picks per event.
How does a denoising autoencoder improve earthquake detection and the estimation of magnitude in seismic networks?

Seismogram records always contain seismic noise from different sources. Previous studies have shown that denoising autoencoders can be used to suppress different types of disturbing noise at seismological stations, even when earthquake signal and noise share common frequency bands. A denoising autoencoder is a convolutional neural network that learns from a large training data set how to separate earthquake signal and noise. To train the denoising autoencoder, we used earthquake signals with high signal-to-noise ratio from the Stanford Earthquake Dataset and noise from single seismological stations. We used 160 seismological stations in Germany and surrounding countries and trained a denoising autoencoder model for each station. Afterwards, one year of continuous recorded data have been denoised.EQTransformer, a deep-learning model for earthquake detection and phase picking, was then applied to the raw and denoised data of each station. Working with denoised data leads to a massive increase of earthquake detections. First results show that in dense seismic networks more than 100% additional earthquakes can be detected compared to events detected in the raw data set. Moreover, the localization accuracy is increased as more stations can be used.However, like common filter techniques, denoising autoencoders decrease the waveform amplitude. Since earthquake magnitudes are often determined from these amplitudes, we expect a lower amplitude and thus a lower magnitude when using denoised data instead of raw data. So far, we did not find an empirical relation between the raw and denoised magnitude.
Probabilistic detection and tracking of icebergs in the Amundsen Sea embayment

Accurate estimates of iceberg populations, disintegration rates and iceberg movements are essential to understand ice sheet contributions to global sea level change and freshwater and heat balances. Knowledge and prediction of iceberg distributions is also important for the safety and efficiency of shipping operations in polar seas. The dynamics, persistence, fragmentation rates, melt rates and dispersal of icebergs are, however, poorly understood due to a lack of automated approaches for monitoring them. Better monitoring of icebergs would help parameterise the locations and quantities of freshwater and nutrient inputs within hydrographic and ecological models respectively and help mitigate collision hazards for navigation.Here we present a combination of Bayesian approaches to the identification of icebergs in synthetic aperture radar imagery and their subsequent tracking across multiple years. For detection we use a Dirichlet Process Mixture Model, while for tracking we adapt Bayesian Tracker, a probabilistic multi-object tracking algorithm originally developed for cell microscopy applications. We are able to reconstruct iceberg paths and lineages, which we validate against synthetic data and manual annotations. We demonstrate that icebergs across the size distribution can be tracked successfully from their point of calving in dense fields of objects, through dispersal and fragmentation, to distal locations.
DL-AISLE: A Deep Learning framework using Active Learning on Satellite imagery for Landslide identification 

A landslide database is of utmost importance for hazard management as well as early warning systems. Historically landslides were manually identified by ground surveys or remote sensing data, but with development in satellite technology open-source satellite imagery has emerged as a preferred data source for landslide identification due to its cost effectiveness. On the other hand, an increase in computing power made computer vision methods especially deep learning popular for satellite image segmentation. Deep learning models require a large amount of data to reach operational performances, however there is very little labelled landslide data present. Labelling satellite imagery is costly and time consuming. Active learning remedies this by optimally selecting the data to label thereby maximizing the performance of the model given the limited data. In this study we present an active learning-based framework to train a segmentation model to identify landslides. The pre- and post-landslide images from sentinel 2 are merged with terrain features to create input data bands. The model is tested on a test database using metrics like IOU. The methodology has been developed with an application in India but can be applied globally.&amp;#160;
Investigation of 1-D and 3-D Crustal seismic velocity structure in the broader area of Crete (Greece)

The island of Crete is located in the Southern Aegean Sea, at the central part of the Hellenic Trench. The increased seismic activity in the region as a result of the convergence of the Eurasian and African tectonic plates highlights the importance of improving our knowledge on local seismotectonic features that play a key role in the seismic hazard. In this work, we used over than 1400 manually revised events from 2018 to 2022 in order to obtain both local and regional 1-D velocity models. The final output was put into the Seismic Inversion procedure, using body-wave (P, S) travel-time data in order to perform a detailed analysis and correlation of medium to large-scale (in km) anomalies with the activation of local and regional faults. Crete's central region exhibited complex shallow structures, whereas in deeper tomographic slices (&gt; 40 km depth) a smoother, more continuous image, was revealed. This result may have been caused by the upward migration of fluids due to the subduction of Tethys oceanic lithosphere. As a result of the final dataset analysis, and exploring the most recent aftershock sequences (e.g. 2021-2022 Arkalochorion earthquakes) the research area's prevailing seismotectonic processes were investigated in more detail, clarifying the relationship between shallow and intermediate-depth earthquake activity.&amp;#160;Acknowledgements:&amp;#160;This work was supported by the Hellenic Mediterranean University, contract 104/18-10-2022, in the frames of the &amp;#171;Study of GEophysicalsystems and their laboratory equivalents towards the understanding Of Earthquake Early Warning Systems (GEOWS)&amp;#187;.&amp;#160;
A Rugged, Portable and Intelligent Analogue Seismometer for Future and Pre-Existing Arrays – Güralp Certis

Seismic networks often face logistical and financial challenges that require portability, longevity and interoperability with existing equipment.G&amp;#252;ralp have combined proven ocean bottom, borehole and digitiser technology to produce an analogue seismometer with intelligence that benefits networks of all sizes. The G&amp;#252;ralp Certis is a broadband analogue instrument that incorporates specific aspects of its sister digital instrument (Certimus) while still remaining compatible with third-party digitisers.Each Certis stores its own serial number, calibration and response parameters internally and will automatically communicate these to a connected Minimus digitiser. This allows seismometer-digitiser pairings to be changed without manual entry of new parameters. If using GDI-link streaming protocol with the Minimus, these metadata parameters are transmitted within (and therefore inseparable from) the datastream itself. Therefore, this small piece of intelligence in the analogue sensor removes the need for any manual re-entry of response parameters anywhere along the sensor-digitiser-client chain.Certis enables users to install in locations with poor horizontal stability (e.g., glaciers, dynamic landslide scarps, water-saturated soils), without the need for cement bases or precise levelling, as the sensor can be deployed at any angle regardless of which model digitiser is connected. Due to its small size, low weight and ultra-low power consumption, Certis significantly reduces logistical efforts and makes short term temporary deployments far easier.Certis addresses many challenges of traditional seismometer deployments, including cost, but provides a flexible and simple solution for seismic monitoring applications across all disciplines.