Reviewerly/exHarmony huggingface dataset:
Tailoring tsunami Digital-Twins for future Destination Earth integration

Probabilistic Tsunami Forecasting (PTF) combines early estimates of earthquake parameters with large ensembles of urgent shallow water tsunami propagation simulations using the GPU based Tsunami-HySEA model (Selva et al. 2021, Nature Commun.). The present version of the PTF is initialised by the earthquake information, but not updated further with new data. In the recently started Horizon Europe project DT-GEO, this PTF is presently being upgraded to a Digital Twin. The first and essential upgrade to realise the Digital Twin is continuous data assimilation enabling a close to real time synthesis of data products and a set of numerical models that allow an updating of the model forecast as new data are continuously assimilated into the model. In DT-GEO, an extended set of data sources, including improved earthquake solutions, sea level tsunami data, and GNSS, will be integrated. A second objective of the PTF is to implement a modularised Digital Twin Component that allows for the inclusion of improved wave and source physics through dispersion, non-hydrostatic tsunami generation, inundation, improved earthquake physics, and cascading earthquake triggered landslide tsunamis. The model will be tested at site demonstrators, in the Mediterranean Sea for eastern Sicily and Samos, and in the Pacific Ocean for Chile and eastern Japan. The presentation will explain how the PTF as it works today, followed by an outline of the design of the components in the Digital Twin, as well as briefly describing initial improvements and plans for further development, including potential integration into Destination Earth. This work is supported by the European Union&amp;#8217;s Horizon Europe Research and Innovation Program under grant agreement No 101058129 (DT-GEO, https://dtgeo.eu/).
Deriving the triggering potential in the Lower Rhine Embayment using background seismicity near Weisweiler, Germany

The Earth&amp;#8217;s crust is permeated with faults and fractures due to its long tectonic history. Faults and fractures not only act as zones of weakness but can increase permeability and act as conduits to circulate fluids in the underground, making them ideally suited for geothermal energy production. However, human-induced changes to in-situ pressure fields have a documented history of leading to fault (re-)activation in the form of earthquakes (seismic slip) or aseismic slip that does not generate measurable ground motion.This study aims to quantify the current background earthquake activity, the spatiotemporal relationship to the seismotectonic setting, and the remote earthquake-earthquake triggering propensity in the Lower Rhine Embayment (LRE) in western North-Rhine Westphalia. The study area is targeted for extensive exploration activities focused on geothermal energy production. While regional mean slip rates do not exceed 0.1 mm/yr, paleo-seismic studies suggest that the normal faulting system has hosted a series of ~14 earthquakes of Mw &gt; 5.0 since the 14th century, including the 1992 Mw 5.3 Roermond earthquake. Therefore, estimating background seismicity rates independent of anthropogenic stress perturbation is one important element in developing strategies to minimize the probability of felt earthquakes caused by industrial activity.&amp;#160;We evaluate waveform data from a temporary deployment of 48 seismic stations operating between July 2021 and May 2022 in a radius of roughly 10 km around a future exploration well drilling site in Weisweiler. We implement a machine learning-based earthquake detection algorithm, SeisBench, to detect earthquakes and denoise the continuous seismic waveforms (DeepDenoiser), estimate P- and S-phase arrivals (PhaseNet and Generalised Phase Detection; GPD), and associate earthquake phases using a Bayesian Gaussian mixture model (GaMMA). We locate 81 local earthquakes to complement the 14 recorded in the Earthquake Observatory Bensberg (BNS) catalog for a total of 95 seismic events recorded between July 2021 and December 2021 with magnitudes ranging from 0 &lt; ML &lt; 1.3. In addition, we use the continuous BNS catalog to evaluate the dynamic triggering potential in the LRE starting in 1990. We select 20 teleseismic mainshocks with M &gt; 6 (1990 &amp;#8211; 2015) and M &gt; 7 (2016 &amp;#8211; present), as well as the 1992 Roermond Mw 5.3 due to the high amplitude shaking it caused within the study area. Preliminary remote dynamic triggering results suggest that the passing surface waves of the July 2021 M 8.2 Chignik, Alaska earthquake may have triggered a seismic sequence of about 16 locatable earthquakes. The migrating aftershock sequence of the Roermond earthquake also suggests the mainshock caused limited dynamic triggering within the study area, which lies outside of the classical aftershock zone of ~2-3 fault lengths.
Can learning regression features by computer vision improve the generalisation of geostastistical interpolators?

Recent approaches for large-scale mapping of continuous environmental variables by combining ground observations, remote sensing and machine learning have proposed incorporating computer vision capabilities into the model, so that potentially-complex regression features may be learned automatically from covariate datasets, such as of terrain elevation and other satellite imagery (e.g. see Kirkwood et al 2022; 'Bayesian deep learning for spatial interpolation in the presence of auxiliary information').Here we present new research using national-scale land-surface geochemical data to explore and compare how the incorporation of computer vision for automatic feature learning affects the predictive performance of geostastistical interpolators both within and beyond the spatial extents of the study areas in which ground observations are collected. We attempt to characterise empirically how well the predictive performance of different models is preserved with increasing distance from training observations in order to provide insights into the value of incorporating computer vision capabilities into geostatistical models, compared to more traditional approaches.
Animasi Motion Graphic Sebagai Media Pembelajaran Sistem Tata Surya Untuk Siswa Kelas 6 Sekolah Dasar

Along with the rapid development of information technology today, multimedia has an important role in the field of education. Studying the solar system is very important, especially for elementary school children because it is very closely related to other natural sciences. In learning activities, the delivery of solar system material in schools is only conventional and theoretical which is equipped with several static examples, so that most students have difficulty and do not even like the lesson. Therefore, it is necessary to have an application that explains the characteristics of the Solar System with animations and visualizations that can support elementary school students in understanding the solar system easily and not boringly. One alternative media that can attract children's attention is video motion graphics. The method in this study applies the 4D model which includes four stages: (1) define, (2) design, (3) develop and (4) disseminate. The evaluation used is alpha testing and usability questionnaires for 6th grade elementary school students. The results of this research are 2D animated videos about the solar system which are made according to the needs and help learning activities of the solar system.
DIVEnet, an example of drilling monitoring

&amp;lt;p&amp;gt;The DIVEnet is the ICDP DIVE project dedicated seismographic network. The ICDP DIVE project (www.dive2ivrea.org) is aimed at addressing fundamental questions on the nature of the lower continental crust and its transition to the mantle through two drillings in the Ivrea Verbano zone (IVZ), considered the world&amp;#039;s best outcrop of lower crustal continental rocks. Between September and December 2022 the first drilling has been done in Ornavasso. To be ready for its monitoring the DIVEnet has started to record continuously since November 2021.&amp;amp;#160;&amp;lt;br /&amp;gt;The network includes 11 seismic stations located within a 17.7 km radius from the drilling site. Additionally, the stations from the CH (Switzerland), GU (University of Genova) and IV (INGV) networks are used to locate the detected earthquakes.&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Before the beginning of the drilling the acquired data have been analyzed checking for daily and sub-daily continuity and, when needed, recovery acquisition gaps. After the data recovery, the total loss in the period span between November 2021 and 20 December 2022, date when the drilling stopped, is less than 2% and 5% in daily and sub-daily missing data, respectively.&amp;amp;#160;&amp;lt;br /&amp;gt;Furthermore, probabilistic power spectral densities (PPSD) analysis has been conducted monthly and for the entire period (11-2021 / 12-2022).&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;The drilling started on the 6 of October, showing no significant changes in the noise spectrum at each station, included those close to the drilling site. Real time analysis has been conducted from the beginning of the drilling with the automatic detection of local earthquakes counting less than 20 small events in the period between 1 October and 20 December.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Several automated codes have been developed to daily analyze, localize and eventually plot on map the events falling inside the network. Currently the workflow include a set of Python scripts followed by SeisComP calls for automatic earthquakes detection and localization; &amp;amp;#160;the set of scripts comprises codes to: daily checks for earthquakes, weekly checks for gaps and, if necessary, download lost data, monthly analysis of PPSD and HTML-report creation with map location earthquakes.&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;This routine would make future analysis faster and cleaner during the second drilling planned in Megolo in spring 2023 as well as other real time seismic analysis.&amp;lt;/p&amp;gt;
Connecting the Long Tail: sharing and describing heterogeneous data via common metadata standards

The EPOS Multi-scale Laboratories (MSL) community includes a wide range of world-class solid Earth science laboratory infrastructures and as such it provides a multidisciplinary- and coherent platform for both virtual access to data and physical access to sophisticated research equipment. The MSL laboratories provide facilities for highly-specialized experimental research that results in experimental and analytical data underlying publications about phenomena ranging from the molecular to the continental scale.From the perspective of the intended FAIRness of these laboratory data, the challenge for the MSL community has been to develop a data management paradigm that on one hand acknowledges the uniqueness of many of the data collections involved, and on the other hand maximizes their findability through metadata dissemination via common standards into larger cross-disciplinary communities. Furthermore, besides provenance information about the data themselves, harmonized information about research groups and experimental assets must be considered as increasingly important for feeding the network relations that may help in making sense of scientific impact.&amp;#160;As part of the MSL Data Publication Chain, the MSL community has developed a standardised workflow that allows easy metadata exchange based on common formats (e.g., flavors of DCAT-AP, DataCite 4.x, and ISO19115), whereas at the same time it integrates dedicated ontologies to give access to the richness of specialized terminology with respect to the MSL subdomains (e.g., analogue modelling, paleomagnetism, rock physics, geochemistry). Community developed controlled vocabularies act as the binding agent between data, equipment, and the experiment itself, while at the same time processing tools like a user-friendly metadata editor and a CKAN-based MSL data publication portal provide the building blocks for the chain towards cross-disciplinary sustainable dissemination.We will demonstrate how the MSL data management paradigm exploits both the strength of controlled terminology and the availability of good agnostic common standards in an approach for managing heterogeneous data coming from long tail communities.
UCIS4EQ applied to the South Iceland region

Urgent Computing (UC) refers to the use of High-Performance Computing (HPC) and High-Performance Data Analytics (HPDA) during or immediately after emergency situations. It typically combines complex edge-to-end workflows with capacity computing, where multiple model realizations are required (to account for input and model uncertainties) under strict time-to-solution constraints. Enabling urgent HPC for emergency scenarios, such as earthquakes, can prove valuable towards resilience and relief. The temporal horizon for UC typically ranges from minutes to a few hours.A novel&amp;#160; HPC-based urgent seismic simulation workflow, the Urgent Computing Integrated Services for Earthquakes (UCIS4EQ),&amp;#160; focuses on short-time reports of the consequences of moderate to large earthquakes. UCIS4EQ automatically prepares and manages sets of physics-based deterministic simulations to rapidly obtain synthetic results. Based on pre-computed and on-the-fly simulations, UCIS4EQ delivers estimates of relevant ground motion parameters, such as peak ground velocity, peak ground acceleration, or shaking duration, with very high spatial resolution.&amp;#160; The physics-based engine includes pre-trained Machine Learning (ML) models fed with pre-computed simulation databases and r full 3D simulations on demand, providing results in minutes and hours, respectively.&amp;#160; The combined results, when well-calibrated, could complement GMPEs for rapid hazard assessmentTo demonstrate the potential of UC in seismology, we show the capability of the UCIS4EQ workflow both for the ML predictions and for deterministic simulations in the South Iceland Seismic Zone (SISZ) and the Reykjanes Peninsula Oblique Rift (RPOR). The largest historic earthquakes in Iceland have occurred within these zones and have exceeded magnitude 7. The study region&amp;#160; (63.5&amp;#176;-64.5&amp;#176;N, 20&amp;#176;-22&amp;#176;W) is where the largely sinistral East-West transform motion across the tectonic margin is taken up by a complex array of near-vertical and parallel North-South oriented dextral transform faults in SISZ-RPOR. The high seismic activity in the area widely affects the capital Reykjavik, the most populous city in Iceland.&amp;#160;This work has been supported by the ChEESE and eFlows4HPC projects.
FORESTER – Interactive visualization of tree-based machine learning

Visualizations are crucial for machine learning as they allow practitioners to understand, analyze, and communicate their models. They help interpret complex models by providing a graphical representation of both data and model performance. Visualizations can be used to understand the underlying patterns and trends in the data, identify biases and errors, and diagnose problems with the model. They also help in communicating the results of the model to a non-technical audience by providing an intuitive and interactive way to present the findings.Tree-based machine learning methods, such as Classification and Regression Trees or Random Forest, are well-established and widely used in the Earth Sciences. However, visualization tools provided by common machine-learning environments in Python, R, or Matlab often provide graphical representations that could be more visually appealing or helpful in conveying a clear message.Here we present FORESTER, a web-based and open-source software that produces visually appealing tree-based visualizations. Forester produces publication-ready plots that are, at the same time, interactive figures that can guide the user in interpreting the model. Visualizations can be streamlined to the user's requirements and offer a wide variety of insightful techniques. This makes Forester a promising alternative to currently used environments. Forester is open to collaborations, so we hope it will be extended within the Earth Science community and beyond, proving useful in other machine-learning-related fields.
Integration of Seismic and Quasi-Static Signals for Improved Volcanic Monitoring

The time scale of ground displacement at volcanoes varies between short, sub second seismic events, to days, months or even years. This study is focused on data from seismic and GNSS stations located around Mount Etna. The GNSS and seismic stations operate at different time scales. Data from these different time scales is extracted and combined in order to better understand the subsurface dynamics. The overall aim of this research is to improve volcanic forecasting and monitoring. It does this in a novel way by applying signal processing and machine learning techniques to the rich dataset. Mount Etna offers an interesting case study as it is a widely monitored volcano with a variety of sensors and with a rich pool of data to analyse. Additionally the volcanic dynamics at Mount Etna are complex. This is a volcano where there is a variety of different sub-surface dynamics due to the movement of both deep and shallow magma. This allows for rich insights to be drawn through the combination of different signal types. This study looks at combining the information obtained from the seismic array at Mount Etna, with the information obtained from various GNSS stations on the volcano. The seismic array has been able to capture ground velocity data in the frequency range 0.025 Hz to 50 Hz from a range of stations at different locations across the volcano. The GNSS stations measure ground displacement with a sampling frequency of 1 Hz, and they allow for longer term ground dynamic analysis. We analyse different seismic events, and relate the type and number of the seismic events to the long term ground deformation that we see in the recorded GNSS data. Where links between the two signal types have been identified, research is ongoing to establish a direct connection with known volcanic activity on Mount Etna. This will help establish what the relationship that we are seeing signifies. This integration of data from different types of sensors is a significant step into bridging the gap between seismic and quasi-static ground displacement at active volcanoes and should open the path toward more in depth volcanic monitoring and forecasting.
PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning

Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled a large and labeled ocean bottom seismometer dataset from 15 deployments in different tectonic settings, comprising &amp;#8764;90,000 P and &amp;#8764;63,000 S manual picks from 13,190 events and 355 stations. We adapted two popular deep learning networks, EQTransformer and PhaseNet, to include hydrophone recordings, either in isolation or in combination with the three seismometer components, and trained them with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. Our final model, PickBlue, significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation (MAD) of 0.06 s for P waves and 0.10 s for S waves. We integrate our dataset and trained models into SeisBench to enable an easy and direct application in future deployments.
Learning causal drivers of PyroCb

Discovering causal relationships from purely observational data is often not possible. In this case, combining observational and experimental data can allow for the identifiability of the underlying causal structure. In Earth Systems sciences, carrying out interventional experiments is often impossible for ethical and practical reasons. However, &amp;#8220;natural interventions&amp;#8221;, are often present in the data, and these represent regime changes caused by changes to exogenous drivers. In [3,4], the Invariant Causal Prediction (ICP) methodology was presented to identify the causes of a target variable of interest from a set of candidate causes. This methodology takes advantage of natural interventions, resulting in different cause variables distributions across different environments.&amp;#160; In [2] this methodology is implemented in a geoscience problem, namely identifying the causes of Pyrocumulunimbus (pyroCb), and storm clouds resulting from extreme wildfires. Although a set of plausible causes is produced, certain heuristic adaptations to the original ICP methodology were implemented to overcome some of the practical. limitations of ICP: a large number of hypothesis tests required and a failure to identify causes when these have a high degree of interdependence. In this work, we try to circumvent these difficulties by taking a different approach. We use a learning paradigm similar to that presented in [3] to learn causal representations invariant across different environments. Since we often don&amp;#8217;t know exactly how to define the different environments best, we also propose to learn functions that describe their spatiotemporal extent. We apply the resulting algorithm to the pyroCb database in [1] and other Earth System sciences datasets to verify the plausibility of the causal representations found and the environments that describe the so-called natural interventions..&amp;#160;&amp;#160;[1] Tazi et al. 2022. https://arxiv.org/abs/2211.13052[2] D&amp;#237;az et al. 2022 .https://arxiv.org/abs/2211.08883[3] Arjovsky et al. 2019. https://arxiv.org/abs/1907.02893[4] Peters et al.2016.&amp;#160; https://www.jstor.org/stable/4482904[5] Heinze-Deml et al. 2018. https://doi.org/10.1515/jci-2017-
Staged Adaptive Blind Watermarking Scheme

In traditional digital image watermarking methods, the stren-gth factor is calculated from the content of the carrier image, which can find a balance between the robustness and imperceptibility of encoded images. However, traditional methods do not consider the feature of the message and it is also unrealistic to calculate the strength factor of each image separately when faced with a huge number of images. In recent years, digital image watermarking methods based on deep learning have also introduced the strength factor. They assign the strength factor of each image to a fixed value to better adjust the robustness and imperceptibility of the image. We hope that the network can choose the most appropriate strength factor for each image to achieve a better balance. Therefore, we propose a staged adaptive blind watermarking scheme. We designed a new component - the adaptor, and used two stages of training by training different components in different stages, and improved the robustness and imperceptibility of watermarked images. By comparing the experimental results, our algorithmic scheme shows better results compared to current advanced algorithms.