Updates in Volcanology - Transdisciplinary Nature of Volcano Science最新文献

{"title":"Seismic Geomorphology, Architecture and Stratigraphy of Volcanoes Buried in Sedimentary Basins","authors":"Alan Bischoff, S. Planke, S. Holford, A. Nicol","doi":"10.5772/INTECHOPEN.95282","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.95282","url":null,"abstract":"Our ability to investigate both the intrusive and extrusive parts of individual volcanoes has evolved with the increasing quality of seismic reflection datasets. Today, new seismic data and methods of seismic interpretation offer a unique opportunity to observe the entire architecture and stratigraphy of volcanic systems, with resolution down to tens of meters. This chapter summarises the methods used to extract the geomorphic aspects and spatio-temporal organisation of volcanic systems buried in sedimentary basins, with emphasis on the utility of 3D seismic reflection volumes. Based on descriptions and interpretations from key localities worldwide, we propose classification of buried volcanoes into three main geomorphic categories: (1) clusters of small-volume (<1 km3) craters and cones, (2) large (>5 km3) composite, shield and caldera volcanoes, and (3) voluminous lava fields (>10,000 km3). Our classification primarily describes the morphology, size and distribution of eruptive centres of buried volcanoes, and is independent of parameters such as the magma composition, tectonic setting, or eruption environment. The close correlation between the morphology of buried and modern volcanoes provides the basis for constructing realistic models for the facies distribution of igneous systems buried in sedimentary strata, establishing the principles for a new discipline of seismic-reflection volcanology.","PeriodicalId":435951,"journal":{"name":"Updates in Volcanology - Transdisciplinary Nature of Volcano Science","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124128921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Overview of the Mafic and Felsic Monogenetic Neogene to Quaternary Volcanism in the Central Andes, Northern Chile (18-28°Lat.S)","authors":"Gabriel Ureta, K. Németh, F. Aguilera, M. Vilches, M. Aguilera, Ivana Torres, J. Sepúlveda, Alexandra Scheinost, R. Gonzalez","doi":"10.5772/intechopen.93959","DOIUrl":"https://doi.org/10.5772/intechopen.93959","url":null,"abstract":"Monogenetic volcanism produces small eruptive volumes with short eruption history, different chemical compositions, and relatively simple conduit. The Central Volcanic Zone of the Andes is internationally known as a natural laboratory to study volcanism, where mafic and felsic products are present. In this contribution, the spectrum of architectures, range of eruptive styles, lithological features, and different magmatic processes of the mafic and felsic monogenetic Neogene to Quaternary volcanoes from the Central Volcanic Zone of the Andes in northern Chile (18°S-28°S) are described. The major volcanic activity occurred during the Pleistocene, where the most abundant activity corresponds to effusive and Strombolian eruptions. This volcanism is characterized by external (e.g., magma reservoirs or groundwater availability) and internal (e.g., magma ascent rate or interaction en-route to the surface) conditions, which determine the changes in eruptive style, lithofacies, and magmatic processes involved in the formation of monogenetic volcanoes.","PeriodicalId":435951,"journal":{"name":"Updates in Volcanology - Transdisciplinary Nature of Volcano Science","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128481728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning in Volcanology: A Review","authors":"R. Carniel, S. Guzmán","doi":"10.5772/intechopen.94217","DOIUrl":"https://doi.org/10.5772/intechopen.94217","url":null,"abstract":"A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches.","PeriodicalId":435951,"journal":{"name":"Updates in Volcanology - Transdisciplinary Nature of Volcano Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130599007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}