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  • 1
    Online Resource
    Online Resource
    Machine Learning, Optimization, and Data Science : 6th International Conference, LOD 2020, Siena, Italy, July 19-23, 2020, Revised Selected Papers, Part I. (2021)
    Cham :Springer International Publishing AG,
    Details
    Keywords: Computing Milieux. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (777 pages)
    Edition: 1st ed.
    ISBN: 9783030645830
    Series Statement: Lecture Notes in Computer Science Series ; v.12565
    URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=6452467
    DDC: 006.31
    Language: English
    Note: Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Revisiting Clustering as Matrix Factorisation on the Stiefel Manifold -- 1 Introduction -- 1.1 Historical Background -- 1.2 Our Contribution -- 1.3 Notation -- 2 Non-negative Factorisation of the Stiefel Manifold -- 2.1 Model -- 2.2 Ideal Solution -- 2.3 The Latent Variable Model -- 2.4 Generalized Posterior and Estimator -- 2.5 A PAC-Bayesian-Flavored Error Bound -- 3 A Langevin Sampler -- 3.1 Computing the Gradient on the Stiefel Manifold -- 3.2 The Alternating Langevin Sampler -- 4 Numerical Experiment -- References -- A Generalized Quadratic Loss for SVM and Deep Neural Networks -- 1 Introduction -- 2 Related Works -- 3 The Modified Loss SVM Problem -- 4 Notation -- 5 Algorithms -- 5.1 The SMOS Optimization Algorithm -- 5.2 The RTS Optimization Algorithm -- 5.3 The Deep Learning Framework -- 6 Results -- 7 Conclusions -- References -- Machine Learning Application to Family Business Status Classification -- 1 Introduction -- 2 Description of the Dataset -- 3 Data Pre-processing -- 4 Application of Machine Learning Techniques -- 5 Conclusions and Possible Future Developments -- References -- Using Hessians as a Regularization Technique -- 1 Introduction -- 2 Proposed Method -- 3 Experiment -- 4 Results -- 5 Conclusion -- References -- Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1 -- 1 Introduction -- 2 Sampled Limited-Memory SR1 (S-LSR1) -- 2.1 Naive Distributed Implementation of S-LSR1 -- 3 Efficient Distributed S-LSR1 (DS-LSR1) -- 3.1 Reducing the Amount of Information Communicated -- 3.2 Balancing the Computation Across the Nodes -- 3.3 The Distributed S-LSR1 (DS-LSR1) Algorithm -- 3.4 Complexity Analysis - Comparison of Methods -- 4 Numerical Experiments -- 4.1 Scaling -- 4.2 Performance of DS-LSR1 -- 5 Final Remarks -- References. , Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning? -- 1 Introduction -- 2 Theoretical Analysis -- References -- An Application of Machine Learning to Study Utilities Expenses in the Brazilian Navy -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data -- 3.2 Methods -- 4 Experimental Study -- 4.1 Experiments for the Model Selection -- 4.2 Performance Results -- 4.3 Feature Importance -- 4.4 Reduced Dataset Analysis -- 4.5 Potential Uses -- 5 Conclusions -- References -- The Role of Animal Spirit in Monitoring Location Shifts with SVM:Novelties Versus Outliers -- 1 Motivation -- 2 The Basic Idea -- 2.1 Novelty Versus Outlier Detection -- 3 Assessing Survey Data -- 3.1 Structural Breaks -- 4 Detecting Novelties with SVM -- 4.1 Choosing a Training Sample -- 4.2 Novelties Since June 2019 -- 5 Rare Event in March-April-May 2020 -- 6 Concluding Remarks -- References -- Long-Term Prediction of Physical Interactions: A Challenge for Deep Generative Models -- 1 Introduction -- 2 Temporal Deep Generative Models -- 3 Training and Testing Methods -- 4 Results -- 5 Conclusion -- References -- Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data -- 1 Introduction and Related Work -- 2 Methodology -- 3 Evaluation and Results -- 4 Conclusions -- References -- Methods for Hyperparameters Optimization in Learning Approaches: An Overview -- 1 Introduction -- 2 Hyperparameter Optimization -- 3 Overview on Existing Methods -- 4 Gradient-Based Methods -- 5 Discussion -- References -- Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images -- 1 Introduction -- 2 Reconstruction Error Measures -- 2.1 Mathematical Properties -- 3 Structure of the Algorithm -- 3.1 NMF -- 3.2 Clustering Stage -- 4 Experimental Results. , 4.1 HS-SOD Dataset -- 4.2 Hermiston Dataset -- 5 Conclusions -- References -- Sparse Consensus Classification for Discovering Novel Biomarkers in Rheumatoid Arthritis -- 1 Introduction -- 2 Methods -- 2.1 Rheumatologic Transcriptomic Data -- 2.2 Sparse Logistic Regression -- 2.3 Bayesian Networks -- 3 Results and Discussion -- 3.1 Identification of Response Biomarkers -- 3.2 Identification of Protein-Protein Interactions -- 4 Conclusions -- References -- Learning More Expressive Joint Distributions in Multimodal Variational Methods -- 1 Introduction -- 2 Related Work -- 3 Variational Inference -- 4 Learning Flexible and Complex Distributions in Multimodal Variational Methods -- 5 Evaluation -- 5.1 Evaluation of the Model and Generated Sample Quality -- 5.2 Image Transformation Tasks -- 6 Conclusion and Future Work -- References -- Estimating the F1 Score for Learning from Positive and Unlabeled Examples -- 1 Introduction -- 2 Problem Formulation -- 3 Literature Review -- 3.1 PU Learning Algorithms: Two-Step Strategy -- 3.2 Performance Estimation -- 4 Estimating the F1 Score -- 4.1 Approach to Estimate F1-score -- 4.2 Estimating -- 4.3 Behavior Under Noisy -- 5 Experimental Setup -- 5.1 Datasets and Setup -- 5.2 Experimental Conditions and Performance Metrics -- 6 Results -- 6.1 Checking the Assumptions -- 6.2 Correct -- 6.3 Noisy -- 7 Conclusion -- References -- Dynamic Industry-Specific Lexicon Generation for Stock Market Forecast -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 The Proposed Approach -- 4.1 Generation of Industry-Specific Lexicons -- 4.2 Class Prediction Algorithm -- 5 Experimental Framework -- 5.1 Datasets -- 5.2 Methodology -- 5.3 Results -- 6 Conclusions and Future Work -- References -- A Stochastic Optimization Model for Frequency Control and Energy Management in a Microgrid -- 1 Introduction. , 2 Context of the Problem -- 2.1 Frequency Containment Reserve - FCR -- 2.2 Related Work -- 3 A Stochastic Day-Ahead Optimization Model -- 4 MPC Controller and Simulation -- 5 Numerical Assessments -- 5.1 Sensitivity Analysis and Out-of-Sample Simulations -- 5.2 Closed-Loop Simulations -- 6 Conclusion -- References -- Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market -- 1 Introduction -- 2 Related Work -- 3 Data -- 3.1 About GDELT -- 3.2 Yield Spread -- 4 Methods -- 4.1 Big Data Management -- 4.2 Feature Engineering -- 4.3 Big Data Analytics -- 4.4 Experimental Analysis -- 5 Conclusions -- References -- Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features -- 1 Introduction -- 2 Concepts and Methods -- 2.1 Feature Selection Stability -- 2.2 Adjusted Stability Measures -- 3 Experiments and Results -- 3.1 Experimental Results on Artificial Feature Sets -- 3.2 Experimental Results on Real Feature Sets -- 4 Conclusions -- References -- Reliable Solution of Multidimensional Stochastic Problems Using Metamodels -- 1 Introduction -- 2 Concepts and Methods -- 2.1 Kriging -- 2.2 Pareto Set and Pareto Frontier -- 2.3 Attainment Function -- 3 Estimating Expectation and Standard Deviation with Metamodels -- 3.1 General Idea -- 3.2 Estimation of Expectation and Standard Deviation -- 4 Experiments -- 4.1 Design of Experiments -- 4.2 Computational Aspects -- 5 Evaluation of the Experiments -- 6 Conclusion -- References -- Understanding Production Process Productivity in the Glass Container Industry: A Big Data Approach -- 1 Introduction -- 2 The Glass Container Production Process -- 3 Methodology -- 4 On Going Work -- References -- Random Forest Parameterization for Earthquake Catalog Generation -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Data Processing -- 3.2 Random Forest -- 4 Experiments -- 5 Conclusions. , References -- Convolutional Neural Network and Stochastic Variational Gaussian Process for Heating Load Forecasting -- 1 Introduction -- 2 Materials and Methods -- 2.1 Problem Definition and System Overview -- 2.2 Dataset -- 2.3 Convolutional Neural Network Model -- 2.4 Stochastic Variational Gaussian Process Model -- 2.5 Performance Measure -- 3 Results -- 3.1 CNN Model -- 3.2 SVGP Model -- 3.3 Model Comparison -- 4 Conclusion and Ongoing Work -- References -- Explainable AI as a Social Microscope: A Case Study on Academic Performance -- 1 Introduction -- 2 Data and Pre-processing -- 2.1 FFC Dataset -- 2.2 Pre-processing and Feature Selection -- 3 Comparative Analysis -- 3.1 General Indicators -- 3.2 Proposed Methodology: Targeted Indicators -- 3.3 Results and Discussion -- 4 Future Work -- 5 Concluding Remarks -- References -- Policy Feedback in Deep Reinforcement Learning to Exploit Expert Knowledge -- 1 Introduction -- 2 Design -- 3 Experiments and Results -- 4 Discussion -- 5 Conclusions -- References -- Gradient Bias to Solve the Generalization Limit of Genetic Algorithms Through Hybridization with Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 The Generalization Limit of Genetic Algorithms -- 3.1 Experiments -- 3.2 Discussion -- 4 X-DDPG -- 4.1 Motivations -- 4.2 The Algorithm -- 4.3 Experiments and Results -- 5 Conclusions -- 5.1 Limits and Future Directions -- References -- Relational Bayesian Model Averaging for Sentiment Analysis in Social Networks -- 1 Introduction -- 2 Literature Review -- 3 Background Concept and Model Training -- 4 RBMA: The Predictive Model -- 5 Computational Results -- 6 Conclusions -- References -- Variance Loss in Variational Autoencoders -- 1 Introduction -- 2 Variational Autoencoders -- 2.1 KL Divergence in Closed Form -- 3 The Variance Loss Issue -- 3.1 General Case. , 4 Addressing the Variance Loss.
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  • 2
    Online Resource
    Online Resource
    Machine Learning, Optimization, and Data Science : 7th International Conference, LOD 2021, Grasmere, UK, October 4-8, 2021, Revised Selected Papers, Part II. (2022)
    Cham :Springer International Publishing AG,
    Details
    Keywords: Big data. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (571 pages)
    Edition: 1st ed.
    ISBN: 9783030954703
    Series Statement: Lecture Notes in Computer Science Series ; v.13164
    URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=6882567
    DDC: 006.31
    Language: English
    Note: Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Boosted Embeddings for Time-Series Forecasting -- 1 Introduction -- 2 Gradient Boosting -- 3 DeepGB Algorithm -- 3.1 Gradient Boosting, Forward Stagewise Additive Models, and Structural Time Series Analysis -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Model Setup -- 4.3 Results -- 5 Conclusion -- References -- Deep Reinforcement Learning for Optimal Energy Management of Multi-energy Smart Grids -- 1 Introduction -- 1.1 Context of the Problem -- 1.2 Deep Reinforcement Learning in Smart Grids: Related Work -- 2 The Multi-energy Smart Grid Model and Optimal Control Mechanism -- 3 The Proposed Deep Reinforcement Learning-Based Approach -- 4 Implementation Details, Simulations and Results -- 5 Conclusion -- References -- A k-mer Based Sequence Similarity for Pangenomic Analyses -- 1 Introduction -- 2 Background: Notations and PanDelos -- 3 A Computationally Efficient Approach -- 4 Experimental Results -- 5 Conclusions -- References -- A Machine Learning Approach to Daily Capacity Planning in E-Commerce Logistics -- 1 Introduction -- 2 Proposed Approach -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- Explainable AI for Financial Forecasting -- 1 Introduction -- 2 Related Work -- 3 Standard XAI Methods -- 4 The Proposed Strategies -- 5 Experimental Setup -- 5.1 Dataset -- 5.2 Forecasting and Feature Selection -- 5.3 Backtesting -- 5.4 Baselines -- 5.5 Evaluation Metrics -- 6 Results -- 6.1 Discussion -- 7 Conclusions -- References -- Online Semi-supervised Learning from Evolving Data Streams with Meta-features and Deep Reinforcement Learning -- 1 Introduction -- 2 Background and Related Work -- 2.1 Meta-learning -- 2.2 Online Semi-supervised Learning -- 3 Online Reinforce Algorithm -- 3.1 Meta-features. , 3.2 Pseudo-labelling with Meta-reinforcement Learning -- 3.3 Training of the Meta-reinforcement Learning Model -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 SSL Algorithms -- 4.3 Experimental Results -- 4.4 Discussion -- 5 Conclusion and Future Work -- References -- Dissecting FLOPs Along Input Dimensions for GreenAI Cost Estimations -- 1 Introduction -- 2 Measures of Efficiency -- 3 Computation of FLOPs for Basic Layers -- 4 The Problem of Convolutions -- 5 -FLOPs -- 5.1 Main Properties of the -correction -- 5.2 Rationale -- 6 Additional Experimental Results -- 6.1 Dense Layers vs Batchsize -- 7 Conclusions -- References -- Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction -- 1 Introduction -- 2 Hybrid Modeling: The Framework -- 2.1 Parametric Modeling -- 2.2 Non-parametric Modeling -- 2.3 Hybrid Modeling -- 3 Proposed Hybrid Methodology for Oscillating Systems -- 3.1 Approach -- 3.2 Training -- 3.3 Prediction -- 3.4 Challenges in Real Experiments -- 4 Validation: Double Pendulum -- 4.1 Synthetic Data -- 4.2 Measurements -- 5 Discussion -- 6 Summary -- References -- Numerical Issues in Maximum Likelihood Parameter Estimation for Gaussian Process Interpolation -- 1 Introduction -- 2 Background -- 2.1 Gaussian Processes -- 2.2 Maximum Likelihood Estimation -- 3 Numerical Noise -- 4 Strategies for Improving Likelihood Maximization -- 4.1 Initialization Strategies -- 4.2 Stopping Condition -- 4.3 Restart and Multi-start Strategies -- 4.4 Parameterization of the Covariance Function -- 5 Numerical Study -- 5.1 Methodology -- 5.2 Optimization Schemes -- 5.3 Data Sets -- 5.4 Results and Findings -- 6 Conclusions and Recommendations -- References -- KAFE: Knowledge and Frequency Adapted Embeddings -- 1 Introduction -- 2 Background -- 3 Method -- 3.1 Notations -- 3.2 Knowledge Injection into word2vec. , 3.3 Frequency Expulsion -- 3.4 KAFE -- 4 Experiments and Results -- 4.1 Data -- 4.2 Reproducibility -- 4.3 Quantitative Tasks -- 4.4 Qualitative Tasks -- 5 Conclusion and Future Work -- References -- Improved Update Rule and Sampling of Stochastic Gradient Descent with Extreme Early Stopping for Support Vector Machines -- 1 Problem -- 2 Sampling with Full Replacement -- 3 New Update Rule -- 4 Improvement of Speed of Tuning -- 5 Theoretical Analysis -- 6 Methods -- 7 Experiments -- 8 Summary -- A Appendix -- References -- A Hybrid Surrogate-Assisted Accelerated Random Search and Trust Region Approach for Constrained Black-Box Optimization -- 1 Introduction -- 2 Global and Local Constrained Black-Box Optimization Using Radial Basis Functions -- 2.1 RBF-Assisted Constrained Accelerated Random Search -- 2.2 CONORBIT Trust Region Method -- 2.3 Radial Basis Function Interpolation -- 3 A Hybrid Surrogate-Based Algorithm for Constrained Black-Box Optimization -- 4 Numerical Experiments -- 4.1 Experimental Setup -- 4.2 Comparison Using Data Profiles -- 4.3 Results and Discussion -- 5 Summary and Future Work -- References -- Health Change Detection Using Temporal Transductive Learning -- 1 Introduction -- 2 Notation and Background -- 3 Our Approach -- 3.1 Analysis -- 4 Experiments -- 4.1 Baselines -- 4.2 Experimental Setup -- 4.3 Datasets -- 4.4 Turbofan Engine Degradation -- 4.5 Controlled Experiments -- 4.6 Results and Observations -- 4.7 Need for a Balancing Constraint -- 5 Conclusion -- References -- A Large Visual Question Answering Dataset for Cultural Heritage -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusions and Future Work -- References -- Expressive Graph Informer Networks -- 1 Introduction -- 2 Proposed Approach -- 2.1 Setup -- 2.2 Dot-Product Self-attention -- 2.3 Route-Based Dot-Product Self-attention. , 2.4 Locality-Constrained Attention -- 2.5 Implementation Details -- 3 Architecture of the Network -- 4 Expressiveness of Graph Informer -- 4.1 The Weisfeiler-Lehman Test -- 4.2 Beyond the Weisfeiler-Lehman Test -- 5 Related Research -- 6 Evaluation -- 6.1 Model Selection -- 6.2 Node-Level Task: NMR 13C Spectrum Prediction -- 6.3 Results for Graph-Level Tasks -- 7 Conclusion -- References -- Zero-Shot Learning-Based Detection of Electric Insulators in the Wild -- 1 Introduction -- 2 Related Work -- 3 Dataset Details -- 4 Methodology -- 5 Experimental Results -- 6 Discussion -- 7 Conclusion -- References -- Randomized Iterative Methods for Matrix Approximation -- 1 Introduction and Motivation from Optimization -- 2 Fundamental Problem, Samples, and Terminology -- 3 Randomized One-Sided Quasi-Newton Algorithms -- 4 Randomized Two-Sided Quasi-Newton Algorithms -- 4.1 General Two-Sided Sampled Update -- 4.2 Symmetric Update -- 4.3 Multi-step Symmetric Updates -- 5 Convergence Analysis -- 6 Numerical Results -- 7 Heuristic Accelerated Schemes -- 8 Conclusions and Future Work -- References -- Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem with Lot-Streaming of Random Breakdown -- 1 Introduction -- 2 Problem Statement -- 3 The IMBO Algorithm for RBHLFS -- 3.1 Population Initialization -- 3.2 Neighborhood Structure -- 3.3 Local Search and Reset Mechanism -- 3.4 The Proposed Algorithm -- 4 Experimental Results -- 5 Conclusions -- References -- Building Knowledge Base for the Domain of Economic Mobility of Older Workers -- 1 Introduction -- 2 Building Domain Lexicon -- 2.1 Domain Specificity Score -- 2.2 Phrase Extraction and Term Recognition -- 2.3 Relation Extraction -- 3 Description Guided Topic Modeling -- 3.1 Algorithm Details -- 3.2 Experimentation Settings and Results -- 4 Constructing Domain Ontology. , 5 Case Study on the Issue of Broadband Access -- 6 Conclusions and Future Work -- References -- Optimisation of a Workpiece Clamping Position with Reinforcement Learning for Complex Milling Applications -- 1 Introduction -- 2 ML Applications in Mechanical Engineering -- 3 Problem Statement -- 4 RL Experiment Setup -- 4.1 State Space, Action Space and Reward Function -- 4.2 Search Efficiency Modifications -- 4.3 RL Agent Training and Validation -- 4.4 Data Generation and Approximation of the Simulation with Machine Learning -- 5 Experimental Results -- 6 Conclusion and Future Work -- References -- Thresholding Procedure via Barzilai-Borwein Rules for the Steplength Selection in Stochastic Gradient Methods -- 1 Introduction -- 2 Novel Contribution in Steplength Selection via Ritz-like Values -- 3 Numerical Experiments -- 4 Conclusions and Future Works -- References -- Learning Beam Search: Utilizing Machine Learning to Guide Beam Search for Solving Combinatorial Optimization Problems -- 1 Introduction -- 2 Related Work -- 3 Learning Beam Search -- 4 Case Studies -- 5 State Graphs for the LCS and CLCS Problems -- 6 ML Models for the LCS and CLCS Problems -- 7 Experimental Evaluation -- 7.1 LCS Experiments -- 7.2 CLCS Experiments -- 8 Conclusions and Future Work -- References -- Modular Networks Prevent Catastrophic Interference in Model-Based Multi-task Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Method Description -- 3.1 Vector-Quantized Variational Autoencoder -- 3.2 Recurrent Dynamics Models -- 3.3 Context Detection -- 3.4 Planning -- 3.5 Training -- 4 Experiments -- 4.1 Evaluation -- 5 Conclusion -- References -- A New Nash-Probit Model for Binary Classification -- 1 Introduction -- 1.1 The Nash-Probit Game -- 2 Covariance Matrix Adaptation - Nash - Evolution Strategy -- 3 Numerical Examples -- 4 Conclusions -- References. , An Optimization Method for Accurate Nonparametric Regressions on Stiefel Manifolds.
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  • 3
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    Plume composition and volatile flux of Nyamulagira volcano, Democratic Republic of Congo, during birth and evolution of the lava lake, 2014–2015 (2017)
    Details
    Publication Date: 2021-06-07
    Description: Very little is known about the volatile element makeup of the gaseous emissions of Nyamulagira volcano. This paper tries to fill this gap by reporting the first gas composition measurements of Nyamulagira’s volcanic plume since the onset of its lava lake activity at the end of 2014. Two field surveys were carried out on 1 November 2014, and 13–15 October 2015. We applied a broad toolbox of volcanic gas composition measurement techniques in order to geochemically characterize Nyamulagira’s plume. Nyamulagira is a significant emitter of SO2, and our measurements confirm this, as we recorded SO2 emissions of up to ~ 14 kt/d during the studied period. In contrast to neighbouring Nyiragongo volcano, however, Nyamulagira exhibits relatively low CO2/SO2 molar ratios (〈 4) and a highH2O content (〉 92%of total gas emissions). Strong variations in the volatile composition, in particular for the CO2/SO2 ratio, were measured between 2014 and 2015, which appear to reflect the simultaneous variations in volcanic activity.We also determined the molar ratios for Cl/S, F/S and Br/S in the plume gas, finding values of 0.13 and 0.17, 0.06 and 0.11, and 2.3·10−4 and 1·10−4, in 2014 and 2015, respectively. A total gas emission flux of 48 kt/ d was estimated for 2014. The I/S ratio in 2015 was found to be 3.6·10−6. In addition, we were able to distinguish between hydrogen halides and non-hydrogen halides in the volcanic plume. Considerable amounts of bromine (18–35% of total bromine) and iodine (8–18%of total iodine) were found in compounds other than hydrogen halides. However, only a negligible fraction of chlorine was found as compounds other than hydrogen chloride.
    Description: Published
    Description: 90
    Description: 5V. Dinamica dei processi eruttivi e post-eruttivi
    Description: JCR Journal
    Keywords: Nyamulagira ; Plume composition ; Total gas flux ; 04.08. Volcanology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 4
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    The first observations of CO2 and CO2/SO2 degassing variations recorded at Mt. Etna during the 2018 eruptions followed by three strong earthquakes (2021)
    SGI
    Details
    Publication Date: 2021-02-23
    Description: Mount Etna volcano is well-known for its frequent eruptions and high degassing rates from its summit craters and flanks. The geochemical monitoring network on Mt. Etna that measures soil CO 2 flux and in-plume CO 2 /SO 2 ratio recorded very important degassing variations from the flank and the summit craters during the second half of 2018. In this area several significant volcanic events occurred in October and December 2018 and in January 2019. Past observations have distinguished a tendency for wide variations in degassing rates, marked by a sharp increase preceding the onset of volcanic activity. However, this is the first time that three earthquakes of magnitude M〉4 have been registered since the inception of the geochemical network in January 2001. Of particular interest is the CO 2 /SO 2 ratio in plumes recorded by the monitoring station sited at the summit crater of Voragine showed very significant degassing variations, which were comparable with those recorded for the soil CO 2 flux. This paper focuses on the combination of events occurring on Mt. Etna and their relationship with degassing rates. The most remarkable results can be summarized as follow: i) the networks recorded high variations of soil CO 2 flux and CO 2 /SO 2 ratio, which assisted in identifying distinctive phases of pressurization of Mt. Etna plumbing system and ii) all earthquakes occurred during phases of minimum gas rate, which in turn followed stages of pressurization involving different portions of the plumbing system. The 2018 period of high volcanic activity and the corresponding seismic episodes provided an invaluable case study for Mt. Etna, which allowed to combine seismic events and geochemical signal variations.
    Description: Published
    Description: 95-106
    Description: 4V. Processi pre-eruttivi
    Description: 6SR VULCANI – Servizi e ricerca per la società
    Description: 1IT. Reti di monitoraggio e sorveglianza
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 5
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    Il Monitoraggio dei Vulcani Campani - Primo semestre 2019 (2021)
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    Publication Date: 2021-05-12
    Description: L'Istituto Nazionale di Geofisica e Vulcanologia (INGV) è componente del Servizio Nazionale di Protezione Civile, ex articolo 6 della legge 24 febbraio 1992 n. 225 ed è Centro di Competenza per i fenomeni sismici, vulcanici e i maremoti per il Dipartimento della Protezione Civile Nazionale (DPC). L’Osservatorio Vesuviano, Sezione di Napoli dell’INGV, ha nei suoi compiti il monitoraggio e la sorveglianza H24/7 delle aree vulcaniche attive campane (Vesuvio, Campi Flegrei e Ischia). Tali attività sono disciplinate dall’Accordo-Quadro (AQ) sottoscritto tra il DPC e l’INGV per il decennio 2012-2021 e sono dettagliate negli Allegati A e B del suddetto AQ. Il presente Rapporto sul Monitoraggio dei Vulcani Campani rappresenta l’attività svolta dall’Osservatorio Vesuviano e dalle altre Sezioni INGV impegnate nel monitoraggio dell’area vulcanica campana nel primo semestre 2019.
    Description: Istituto Nazionale di Geofisica e Vulcanologia
    Description: Unpublished
    Description: 4V. Processi pre-eruttivi
    Description: 6SR VULCANI – Servizi e ricerca per la società
    Description: 1IT. Reti di monitoraggio e sorveglianza
    Keywords: Campi Flegrei ; Vesuvio ; Ischia ; Volcano Monitoring ; 04.06. Seismology ; 04.03. Geodesy ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: report
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  • 6
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    Characterization of seismic signals recorded in Tethys Bay, Victoria Land (Antarctica): data from atmosphere-cryosphere-hydrosphere interaction (2018)
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    Publication Date: 2021-06-15
    Description: In this paper, we analysed 3-component seismic signals recorded during 27 November 2016 - 10 January 2017 by two stations installed in Tethys Bay (Victoria Land, Antarctica), close to Mario Zucchelli Station. Due to the low noise levels, it was possible to identify three different kinds of signals: teleseismic earthquakes, microseisms, and icequakes. We focus on the latter two. A statistically significant relationship was found between microseism amplitude and both wind speed and sea swell. Thus, we suggest that the recorded microseism data are caused by waves at the shore close to the seismic stations rather than in the deep ocean during storms. In addition, we detected three icequakes, with dominant low frequencies (below 2 Hz), located in the David Glacier area with local magnitude of 2.4-2.6. These events were likely to have been generated at the rock-ice interface under the glacier. This work shows how seismic signals recorded in Antarctica provide insights on the interactions between the atmosphere-cryosphere- hydrosphere. Since climate patterns drive these interactions, investigations on Antarctic seismic signals could serve as a proxy indicator for estimating climate changes.
    Description: Published
    Description: S0555
    Description: 2T. Sorgente Sismica
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 7
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    Remote sensing of volcanic CO2, HF, HCl, SO2, and BrO in the downwind plume of Mt. Etna (2018)
    Details
    Publication Date: 2019-02-19
    Description: Remote sensing of the gaseous composition of non-eruptive, passively degassing volcanic plumes can be a tool to gain insight into volcano interior processes. Here, we report on a field study in September 2015 that demon- strates the feasibility of remotely measuring the volcanic enhancements of carbon dioxide (CO2), hydrogen fluoride (HF), hydrogen chloride (HCl), sulfur dioxide (SO2), and bromine monoxide (BrO) in the downwind plume of Mt. Etna using portable and rugged spectroscopic instrumenta- tion. To this end, we operated the Fourier transform spec- trometer EM27/SUN for the shortwave-infrared (SWIR) spectral range together with a co-mounted UV spectrome- ter on a mobile platform in direct-sun view at 5 to 10 km distance from the summit craters. The 3 days reported here cover several plume traverses and a sunrise measurement. For all days, intra-plume HF, HCl, SO2, and BrO vertical column densities (VCDs) were reliably measured exceeding 5 ×1016, 2 ×1017, 5 ×1017, and 1 ×1014 molec cm−2, with an estimated precision of 2.2 ×1015, 1.3 ×1016, 3.6 ×1016, and 1.3 ×1013 molec cm−2, respectively. Given that CO2, unlike the other measured gases, has a large and well- mixed atmospheric background, derivation of volcanic CO2 VCD enhancements (􏰅CO2) required compensating for changes in altitude of the observing platform and for background concentration variability. The first challenge was met by simultaneously measuring the overhead oxy- gen (O2) columns and assuming covariation of O2 and CO2 with altitude. The atmospheric CO2 background was found by identifying background soundings via the co- emitted volcanic gases. The inferred 􏰅CO2 occasionally exceeded 2 × 1019 molec cm−2 with an estimated precision of 3.7 × 1018 molec cm−2 given typical atmospheric back- ground VCDs of 7 to 8 × 1021 molec cm−2. While the cor- relations of 􏰅CO2 with the other measured volcanic gases confirm the detection of volcanic CO2 enhancements, cor- relations were found of variable significance (R2 rang- ing between 0.88 and 0.00). The intra-plume VCD ratios 􏰅CO2 /SO2, SO2 /HF, SO2 /HCl, and SO2 /BrO were in the range 7.1 to 35.4, 5.02 to 21.2, 1.54 to 3.43, and 2.9 × 103 to 12.5 × 103, respectively, showing pronounced day-to-day and intra-day variability.
    Description: Published
    Description: 1–14
    Description: 3V. Proprietà dei magmi e dei prodotti vulcanici
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 8
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    Multi-component gas emission measurements of the active lava lake of Nyiragongo, DR Congo (2018)
    Details
    Publication Date: 2021-09-03
    Description: Between 2007 and 2011 four measurement campaigns (June 2007, July 2010, June 2011, and December 2011) were carried out at the crater rim of Nyiragongo volcano, DR Congo. Nyiragongo is one of the most active volcanoes in Africa. The ground-based remote sensing technique Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), which uses scattered sunlight, the in-situ Multi-Component Gas Analyzer System (Multi-GAS) and alkaline impregnated filter were simultaneously applied during all field trips. The bromine monoxide to sulfur dioxide (BrO/SO2) and carbon dioxide to sulfur dioxide (CO2/SO2) molar ratios were determined, among other ratios. During the different field trips variations of the level of the lava lake up to several tens of meters were observed during intervals of the order of minutes up to days and also between the years. The measured gas ratios presented covariations with the lava lake level changes. BrO/SO2 ratios and CO2/SO2 ratios showed similar behavior. Annual CO2/SO2 and BrO/SO2 average values are generally positively correlated. In June 2011 increased BrO/SO2 as well as increased CO2/SO2 ratios have been observed before a sudden decrease of the lava lake. Overall the Cl/S ratio, determined by filter-pack sampling, shows an increasing trend with time, which is accompanied by a decreasing sulfur dioxide flux, the later measured nearly continuously by automated MAX-DOAS instruments since 2004. Mean gas emission fluxes of CO2, Cl and ‘minimum-BrO’ fluxes are calculated using their ratio to SO2. The first two show an increase with time, in contrast to the SO2 fluxes. A simple conceptual model is proposed which can explain in particular the June 2011 data, but as well our entire data set. The proposed model takes up the idea of convective magma cells inside the conduit and the possible temporary interruption of part of the cycling. We propose than two alternatives to explain the observed gas emission variation: 1. It is assumed that the diffuse and fumarolic degassing could have significant influence on measured gas composition. The measured gas composition might rather represent a gas mixture of plume, diffuse and fumarolic degassing than only representing the volcanic plume. 2. It is proposed that the interruption of the convection has taken place in the upper part of the conduit and deep degassing of CO2 and bromine initially continues while mixing already with gas emissions from an ageing source, which is characterized by an already diminishing sulfur content. These complex process but as well as the gas mixing of different sources, could explain general features of our dataset, but can unfortunately neither be confirmed nor disproven by the data available today.
    Description: Published
    Description: 856-865
    Description: 5V. Processi eruttivi e post-eruttivi
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 9
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    Intense overpressurization at basaltic open-conduit volcanoes as inferred by geochemical signals: The case of the Mt. Etna December 2018 eruption (2021)
    AAAS
    Details
    Publication Date: 2021-09-06
    Description: The balance between the amount of gas coexisting with mantle-derived magmas at depth and that emitted during intereruptive phases may play a key role in the eruptive potential of volcanoes. Taking the December 2018 eruption at Mt. Etna volcano as a case study, we discuss the geochemical data streams observed. The signals indicate a long-lasting prelude stage to eruption, starting in 2017 and involving magma-fluid accumulation in the deep (〉7 km bsl) reservoir, followed by pressure buildup in the system at intermediate depth (5 to 2 km bsl), 6 to 7 months before the eruption. A brief preeruptive phase marks the pressurization at 2 to 3 km below the craters. By comparing the magma and fluid recharge at depth to the measured volcanic degassing from the plume, we provide evidence that Mt. Etna was in a state of extreme overpressurization in the weeks before the onset of the eruption.
    Description: Published
    Description: eabg6297
    Description: 6SR VULCANI – Servizi e ricerca per la società
    Description: JCR Journal
    Keywords: noble gas geochemistry ; degassing model ; magma recharge ; pressure buildup ; solid earth
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 10
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    Il Monitoraggio dei Vulcani Campani - Secondo semestre 2019 (2021)
    Details
    Publication Date: 2021-09-06
    Description: L'Istituto Nazionale di Geofisica e Vulcanologia (INGV) è componente del Servizio Nazionale di Protezione Civile, ex articolo 6 della legge 24 febbraio 1992 n. 225 ed è Centro di Competenza per i fenomeni sismici, vulcanici e i maremoti per il Dipartimento della Protezione Civile Nazionale (DPC). L’Osservatorio Vesuviano, Sezione di Napoli dell’INGV, ha nei suoi compiti il monitoraggio e la sorveglianza H24/7 delle aree vulcaniche attive campane (Vesuvio, Campi Flegrei e Ischia). Tali attività sono disciplinate dall’Accordo-Quadro (AQ) sottoscritto tra il DPC e l’INGV per il decennio 2012-2021 e sono dettagliate negli Allegati A e B del suddetto AQ. Il presente Rapporto sul Monitoraggio dei Vulcani Campani rappresenta l’attività svolta dall’Osservatorio Vesuviano e dalle altre Sezioni INGV impegnate nel monitoraggio dell’area vulcanica campana nel secondo semestre 2019.
    Description: Istituto Nazionale di Geofisica e Vulcanologia
    Description: Unpublished
    Description: 4V. Processi pre-eruttivi
    Description: 6SR VULCANI – Servizi e ricerca per la società
    Description: 1IT. Reti di monitoraggio e sorveglianza
    Description: 2IT. Laboratori analitici e sperimentali
    Description: 4IT. Banche dati
    Keywords: Campi Flegrei ; Vesuvio ; Ischia ; Volcano Monitoring ; 04.06. Seismology ; 04.03. Geodesy ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: report
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