Reviewerly/exHarmony huggingface dataset:
Can Seismicity Declustering be solved by Unsupervised Artificial Intelligence ?

Due to the complexity and high dimensionality of seismic catalogues, the dimensional reduction of raw seismic data and the feature selections needed to decluster these catalogues into crisis and non-crisis events remain a challenge. To address this problem, we propose a two-level analysis.&amp;#160;First, an unsupervised approach based on an artificial neural network called self-organising map (SOM) is applied. The SOM is a machine learning model that performs a non-linear mapping of large input spaces into a two-dimensional grid, which preserves the topological and metric relationships of the data. It therefore facilitates visualisation and interpretation of the results obtained. Then, agglomerative clustering is used to classify the different clusters obtained by the SOM method as containing background events, aftershocks and/or swarms. To estimate the classification uncertainty and confidence level of our declustering results, we developed a probabilistic function based on the feature representation learned by the SOM (spatiotemporal distances between events, magnitude variations and event density).&amp;#160;We tested the two-level analysis on synthetic data and applied it to real data: three seismic catalogues (Corithn Rift, Taiwan and Central Italy) that differ in area size, tectonic regime, magnitude of completeness, duration and detection methods. We show that our unsupervised machine learning approach can accurately distinguish between crisis and non-crisis events without the need for preliminary assumptions and that it is applicable to catalogues of various sizes in time and space without threshold selection.
On the potential of offshore sensors and array processing for improving seismic event detection and locations in the North Sea

CCS (Carbon Capture and Storage) is becoming an increasingly important technology to reduce global greenhouse gas emissions. The Norwegian part of the North Sea is an ideal place to conduct such projects as much of the infrastructure and experience is already in place due to current oil and gas operations. For safe CO2 storage an improved understanding of the natural background seismicity in the North Sea is still required. Currently, earthquakes in the region are monitored using onshore stations deployed on mainland Norway, resulting in low azimuthal sensor coverage and poorly constrained earthquake locations. However, permanent reservoir monitoring systems (PRMs), which are deployed offshore to surveil oil and gas fields, have the potential to reduce these station gaps and thus improve earthquake locations.In this study we test the potential of incorporating offshore sensors at the Grane oil field for earthquake locations and detection utilizing array processing techniques. The advantage of array processing is that it can enhance seismic signals, decrease the detection threshold, and put additional constraints on direction and apparent velocities. Out of the 3400 sensors deployed at the Grane field, we have access to two subsets of data: i) Continuous data from 10 sensors spread along the boundaries of the field and, ii) segments of data from 30 sensors optimized for the purpose of array processing. Since the distances between the 10 sensors are large (6 km), traditional array processing methods are not applicable, and we therefore test and develop a new method for incoherent array processing using the kurtosis characteristic function. The kurtosis function is applied to the seismic signal prior to FK-analysis to make the signal more coherent. &amp;#160;The method showed great potential and worked for the majority of earthquakes analyzed in this study. The 30 sensor array was superior to the 10 sensor array and could potentially decrease the detection threshold of seismic events if continuous data are available. We conclude that the Grane sensors could be implemented as a part of a system for passive seismic monitoring in the North Sea. We recommend using the 30-sensors for this purpose. However, as we only have access to continuous data from 10 sensors, we found that these sensors are an appropriate replacement when the 30 sensors are not accessible.&amp;#160;
B-AMA: a new Python protocol for hydrological predictions using data-driven models

The objective of this interactive poster session is to show the main features of B-AMA (Basic dAta-driven Models for All), an easy, flexible, fully coded Python-written protocol for the application of data-driven models (DDM) in hydrology. The protocol is specifically tailored for early career scientists with little background in coding, to foster them through the development of DDMs for hydrological forecasting while ensuring that none of the fundamental methodological steps is overlooked.While a Jupyter notebook is already available online to guide the users through the protocol employment, during the session the interested audience can learn the main features of the software (data splitting, feature selection, hyperparameter optimization, and performance metrics) by running several practical hydrological workflows. The session will couple the visual representation B-AMA&amp;#8217;s methodology with some laptop-based experiments, including rainfall-runoff, hydropower, and groundwater forecasts. We also allow loading customized csv data to deliver a first-hand experience of the protocol forecasting ability on the user&amp;#8217;s specific case study, thanks also to the embedded visualization tools, which facilitate the efficient investigation and communication of results.&amp;#160;
Machine-learned interatomic potentials for modelling nanoscale fracturing in silica and basalt

At the nanoscale, fracturing creates surface area and flow pathways, which control the rates of fluid-rock interactions. However, how fractures form at the nanoscale remains enigmatic. Here, we implement molecular dynamics simulations to reproduce fracture propagation in quartz and basalt. These simulations require large systems and long simulation times and are therefore currently depending on interatomic potentials. In the recent years, machine learning approaches have been established as a way to fit interatomic potentials, where the potentials are trained with quantum-mechanical data obtained from ab initio molecular dynamics simulations. We have developed machine-learned interatomic potentials for silica and basalt that allow using molecular dynamics simulations to simulate fracture propagation at the nanoscale. The interatomic potentials reproduce the mechanical properties of bulk silica and basalt sand have also been trained to account for fracture propagation. First, we trained a potential on silica to verify the fitting procedure, and then we used the same procedure to train an interatomic potential for basalt. By training the potential with water and carbon dioxide as fluids, we aim to study how a dynamic fracture damage basaltic glass and how the water and carbon dioxide enter these fractures in the wake of rupture. Our results are relevant for carbon mineralization where a coupling between dissolution of the basalt and precipitation of carbonate minerals can lead to nanofracturing of the rock.
Earthquake Early Warning with 3 seconds of records on a single station

We introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning algorithms designed to detect, locate and estimate the magnitude of an earthquake using 3 seconds (or more) of P waves recorded by a single station. The system is made of 6 Ensemble Machine Learning algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral and cepstral domains. The training set comprises datasets from Peru, Chile, Japan, and the STEAD global dataset. E3WS consists of three sequential stages: detection, P-phase picking and source characterization. The latter involves magnitude, epicentral distance, depth and back-azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%. For P-phase picking, the Mean Absolute Error (MAE) is 0.14s. For source characterization, the MAEs for magnitude, distance, depth and back-azimuth are 0.34 magnitude units, 27 km, 15.7 km and 45.2&amp;#176;, respectively. By updating estimates every second, the approach gives time-dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems, providing additional valuable seconds for potential protective actions.
The Aspire campaign: Assessing the effects of aerosols on solar radiation and energy in SE Europe.

In recent years, solar power applications are growing rapidly worldwide, to meet the increasing power demand and the sustainable development planning. Estimation of solar radiation availability at surface level, its characteristics and various factors that affect it, play a key role in designing and achieving the optimal performance of systems employing solar energy. Various solar -PV related - applications are using radiative transfer modeling to characterize the radiation field, since accurate surface solar irradiance measurements are not always available, especially in remote regions. Understanding the effect of aerosols to the solar energy potential is highly important for the energy sector as well as for a variety of fields. &amp;#160;&amp;#160;&amp;#160;In areas and periods where cloudiness is limited and they are in the proximity of particle sources, the significance of aerosol effect is very high.The objective of this study is to assess the impact of the variability of aerosols on the solar Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI) and solar energy, using spectral solar measurements and aerosol optical properties retrievals, in the framework of the one-year experimental campaign (December 2020-December 2021) of the ASPIRE (Atmospheric parameters affecting SPectral solar IRradiance and solar Energy, https://aspire.geol.uoa.gr) project, which was held in Athens, Greece.Main findings include an assessment of differences among different PV technology and their calculated outputs using actual and standard spectra, linking the differences with aerosol optical properties (optical depth, spectral dependence, absorption). Aerosol optical depth is the major factor of such differences for all PV technologies. Spectral aerosol characteristics affect differently PV technologies as a consequence of different spectral responsivities.Finally, aerosol effect on solar nowcasting models have been investigated by comparing spectral solar measurements and aerosol properties with model inputs and outputs.
The Functional Machine Calculus

This paper presents the Functional Machine Calculus (FMC) as a simple model of higher-order computation with "reader/writer" effects: higher-order mutable store, input/output, and probabilistic and non-deterministic computation. The FMC derives from the lambda-calculus by taking the standard operational perspective of a call-by-name stack machine as primary, and introducing two natural generalizations. One, "locations", introduces multiple stacks, which each may represent an effect and so enable effect operators to be encoded into the abstraction and application constructs of the calculus. The second, "sequencing", is known from kappa-calculus and concatenative programming languages, and introduces the imperative notions of "skip" and "sequence". This enables the encoding of reduction strategies, including call-by-value lambda-calculus and monadic constructs. The encoding of effects into generalized abstraction and application means that standard results from the lambda-calculus may carry over to effects. The main result is confluence, which is possible because encoded effects reduce algebraically rather than operationally. Reduction generates the familiar algebraic laws for state, and unlike in the monadic setting, reader/writer effects combine seamlessly. A system of simple types confers termination of the machine.
Machine Translation of Selected Ghazals of Hafiz from Persian into English

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation with the potential to overcome many weaknesses in conventional phrase-based translation systems. However, there are still grey areas that need attention in the field of machine translation, mainly in the context of literary translation and poetic language. This research aims to identify the shortcomings of the NMT in addressing ambiguity and the semantic complexities of poetic expressions that are important in translating literature in a cross-cultural and multilingual context. Furthermore, while human translators struggle to retain naturalness and accuracy in translation, this research aims to identify to what extent machine translation could convey the message in translating literary-based texts from Persian into English. The corpus of this study consists of Forty ghazals from the Divan of Hafiz, which is translated by a computer-assisted translation software, memoQ and then compared to the authentic translation of Hafiz by Henry Wilberforce Clarke. This comparative descriptive study portrayed the challenges and issues related to Hafiz’s poems, which demonstrate that the literary touch is lost in MT products.
Simple Melodic Patterns

Abstract This unit addresses features of pitch, the “highness” or “lowness” of musical sounds. It introduces the hand signs for the steps of the scale, solfège syllables, and expression markings found in musical scores. The image of part of the piano keyboard is used to help students learn to identify half steps, whole steps, larger intervals, and accidentals. Sight-reading exercises are offered to reinforce the unit’s concepts.
Secure, Dynamic and Uncomplicated Licensing of Movies on a Blockchain Infrastructure

Nowadays, the consumption of media content has been growing rapidly and consistently, driven by an easy access to Video on Demand platforms. In this context, licensing is needed to ensure that filmmakers receive rightful payment for their content and ensure that their rights as content owners are respected. The traditional licensing process, which is heavily dependent on third parties (legal entities) to mediate the transaction, is very long, costly, and complex, which is a barrier to smaller independent filmmakers.The solution proposed in this work, to address this problem, is to create a business-to-business marketplace platform supported by a Blockchain licensing module. This module takes advantage of Blockchain technology to ensure the licensing requirements and to provide a secure, practical and straightforward way to license media in a decentralised paradigm.The result of this work was validated though a prototype, and a global assessment of the system’s usability was performed using the System Usability Scale, where it got the best possible grade.
Interoperability of Electronic Health Records for Dyslipidemia using Knowledge graphs

Clinical information of a patient is voluminous, varied and critically important for decision making process to the medical experts. The data is complicated and is not organized in a single common platform that leads to complexity. The information is stored in varied frameworks using different formats. The data is structured at varied levels which leads to the difficult management of the information and its interoperability in cases that integrate and reuse the data. This paper presents an alternative to cope with those difficulties using semantic technologies. The data is structured and transformed into information that clinches interoperability and reuse of the information by different platforms/clinicians
Heimdall: Blockchain-Based Consent Management Framework

The healthcare sector has undergone drastic changes with the adoption of information technology. One of the main changes in healthcare is the creation of the Electronic Health Record (EHR), which records all the patient’s medical data in a digital medium. However, with creating the General Data Protection Regulation (GDPR), the data owner must grant consent for third parties to collect, process, and store user data. In this context, the present work proposes Heimdall, a blockchain-based consent management framework that meets the requirements set by GDPR. We used a queuing system to assist in receiving requests, an object storage system to store patient data, and a memory database to record the transaction status. In our tests, Heimdall managed consents satisfactorily, where we were able to achieve a consistent amount of reading and writing transactions before saturating our test environment.
Object Classification by Spectrogram Analysis of Impact Force Response using Convolution Neural Network

The object classification is widely used for many industrial application areas. There are many researchers try to develop a new classification method. A signal can carry data from sound picture video and text data. For example, sound is converted into electrical signals by using microphone. However, sound signal contains a lot of noise interference. To solve this problem, we proposed to use impact force response for object classification. In this research, impact force response signal is estimated from the interaction force between object and robot system by using Disturbance Observer (DOB). It will be depended on size, material type stiffness and flexibility of the object. The characteristic of signal is applied to classify object by using the Convolution Neural Network (CNN). There are two step for our proposed algorithm. The first step is to create spectrogram images from impact force response to improve performance and accuracy of classification. The second step is to use CNN to classify objects. As shown in the experimental results, it can be achieved accuracy rate about 96%, precision rate 93%, and recall rate 84%.
From Fieldwork to Publication with AusGeochem: An Open end-to-end Solution for managing FAIR Sample Based Geoscience Data

Here we introduce the AusGeochem platform enabling geoscientists to manage and publish sample based geoscience data in a consistent and FAIR way.&amp;#160;The AusGeochem data platform has been built by the AuScope Geochemistry Network (AGN) and Lithodat Pty Ltd as part of a national digital infrastructure project to facilitate the sharing of data produced by geochemistry laboratories across Australia.&amp;#160;In order to improve national geochemical facility data management, the AusGeochem platform uses detailed structured and standardized method specific data models for collating, preserving, and disseminating geochronology and isotopic data.&amp;#160;AusGeochem provides researchers with a solution to record and utilize all of this information, while streamlining their workflow from collection to publication. Here we will give an overview of a typical sample workflow.&amp;#160;The demonstrated workflow contains the following steps: Storing all sample details on-the-fly during sample collection with the field app. Managing the data by logging into the AusGeochem website. Adding subsequent geochemical analyses to the sample. Visualizing the data using analytic dashboards and graphs. Sharing the data with a team of collaborators. And finally, making the data referenceable by minting DOIs and IGSNs.Manifold data is produced and captured along this process of collecting, analyzing and publishing. This is not confined to just the analytical results, but also a lot of meta information, such as involved people, instruments, funding sources, grant numbers, laboratories and institutions. Being rich in such metadata opens up the path to interesting new functionality, e.g. in the space of structured quantification and quality assessment of research projects.The AusGeochem platform is of great help for a single researcher or a team of researchers by providing them with the means and tools to make their work more efficient and productive. However, the real benefit comes by having an access control layer, so data from multiple institutions can be&amp;#160; stored on one single platform. Since the data is now all stored in one detailed model, it gives researchers the possibility to apply analysis across data from all contributing institutions on the fly.&amp;#160; This means, no downloading of data from multiple sources required. And most importantly, no difficult and often impossible data preprocessing required.How to cite: Theile, M., Noble, W., Beucher, R., McMillan, M., Boone, S., and Kohlmann, F.: From Field Application to Publication: An end-to-end Solution for FAIR Geoscience Data, EGU General Assembly 2022, Vienna, Austria, 23&amp;#8211;27 May 2022, EGU22-11980, https://doi.org/10.5194/egusphere-egu22-11980, 2022.
Primary versus secondary degassing during basaltic eruptions

Volcanic gases are a major concern, especially when eruptions take place in inhabited or touristic areas. Several studies have revealed that during basaltic eruptions, toxic metals such as Pb, Cd, As and Zn are efficiently outgassed, carried by the major gas species, mainly sulfur and halogens. However, part of the degassing occurs after the eruption, while the lava flow is solidifying, and the composition of this secondary gas is virtually unknown.After the primary (syn-eruptive) degassing, the lava is depleted in sulfur, leading to relative enrichment in halogens in secondary (post-eruptive) gas emission. This change in major species concentration could impact the volatility of metals and thus the toxicity of the gas emitted.To investigate this subject, we collected, using filter packs, gas samples of both the primary and the secondary gas phases of the Geldingadalir and Meradalir eruptions. The filters were then leached in diluted acid and the resulting solution analyzed for trace element composition.Results show syn-eruptive gas samples with very homogeneous trace volatile element composition and distinct from all the post-eruptive gas. Conversely, the secondary gas is more diverse, with distinct composition in samples collected around the main Geldingadalir crater and those collected on the lava flow.To compare our gas samples (having different air dilution factors), we normalized each element to Cu (well measured and moderately volatile). Overall, the lava flow post-eruptive gas appears enriched in Zn, Sb and Pb with respect to syn-eruptive (10-100 times higher normalized enrichment factor). These elements are known to form chloride species and could thus have an enhanced volatility due to higher Cl concentration in the secondary gas phase. The Sulfur-loving (chalcophile) element Te has, on the other hand, a 10 times lower normalized enrichment factor in the lava flow gas, which is consistent with a sulfur depletion.It thus seems that volcanic gas emission changes radically between primary and secondary degassing. Increase volatility of some metals such as Lead or Zinc might lead to higher toxicity, with important hazard for the local population and environment.