Chapter Machine Learning in Volcanology: A Review

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

Unraveling the presence of multiple plagioclase populations and identification of representative two-dimensional sections using a statistical and numerical approach

Many plagioclase phenocrysts from volcanic and plutonic rocks display quite complex chemical and textural zoning patterns. Understanding the zoning patterns and variety of crystal populations holds clues to the processes and timescales that lead to the formation of the igneous rocks. However, in addition to a "true" natural complexity of the crystal population, the large variety of plagioclase types can be partly artifacts of the use of two-dimensional (2D) petrographic thin sections and random cuts of three-dimensional (3D) plagioclase crystals. Thus, the identification of the true number of plagioclase populations, and the decision of which are "representative" crystal sections to be used for detailed trace element and isotope analysis is not obvious and tends to be subjective. Here we approach this problem with a series of numerical simulations and statistical analyses of a variety of plagioclase crystals zoned in 3D. We analyze the effect of increasing complexity of zoning based on 2D chemical maps (e.g., backscattered electron images, BSE). We first analyze the random sections of single crystals, and then study the effect of mixing of different crystal populations in the samples. By quantifying the similarity of the compositional histogram of about a hundred 2D plagioclase sections it is possible to identify the so-called reference and ideal sections that are representative of the real 3D crystal populations. These section types allow filtering out the random-cut effects and explain more than 90% of the plagioclase compositional data of a given sample. Our method allows the identification of the main crystal populations and representative crystals that can then be used for a more robust interpretation of magmatic processes and timescales

Machine Learning in Volcanology: A Review

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

Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears Project

Recognizing the mechanisms underlying seismic activity and tracking temporal and spatial patterns of earthquakes represent primary inputs to monitor active volcanoes and forecast eruptions. To quantify this seismicity, catalogs are established to summarize the history of the observed types and number of volcano-seismic events. In volcano observatories the detection and posterior classification or labeling of the events is manually performed by technicians, often suffering a lack of unified criteria and eventually resulting in poorly reliable labeled databases. State-of-the-art automatic Volcano-Seismic Recognition (VSR) systems allow real-time monitoring and consistent catalogs. VSR systems are generally designed to monitor one station of one volcano, decreasing their efficiency when used to recognize events from another station, in a different eruptive scenario or at different volcanoes. We propose a Volcano-Independent VSR (VI.VSR) solution for creating an exportable VSR system, whose aim is to generate labeled catalogs for observatories which do not have the resources for deploying their own systems. VI.VSR trains universal recognition models with data of several volcanoes to obtain portable and robust characteristics. We have designed the VULCAN.ears ecosystem to facilitate the VI.VSR application in observatories, including the pyVERSO tool to perform VSR tasks in an intuitive way, its graphical interface, geoStudio, and liveVSR for real-time monitoring. Case studies are presented at Deception, Colima, Popocatépetl and Arenal volcanoes testing VI.VSR models in challenging scenarios, obtaining encouraging recognition results in the 70–80% accuracy range. VI.VSR technology represents a major breakthrough to monitor volcanoes with minimal effort, providing reliable seismic catalogs to characterise real-time changes.European Union'sHorizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 74924

A review of wildlife ecotourism in Manaus, Brazil

The Amazon’s ability to draw tourists is thought to be strongly associated with the opportunity to have sight of and interact with iconic wild animals. Tourism leaders are calling for the private and public sectors to develop wildlife focused ecotourism in this region. However, specific information regarding current practice and their impact on wildlife is lacking. Although wildlife ecotourism here remains in its relative infancy, our study demonstrates that a wide variety of wildlife-focused activities are already being promoted and provided to tourists who visit the city of Manaus in Brazil. Issues of potential wildlife conservation and animal welfare concern include wildlife-baiting, swim-with free-ranging pink river dolphin activity, the use of captive wild animals as photo props and the sale of wildlife body parts as souvenirs. We found that tour guides actively promoted these activities on 77% of excursions attended, which involved a range of different wild animals, representing at least 10 different species from three different taxonomic classes. From a legal perspective, despite the potential risks imposed to wildlife and tourist well-being, there are still no specific laws regulating feeding, touching and swimming with pink river dolphins in Brazil. However, the illegality of advertising and providing direct physical contact wildlife ‘photo prop’ tourism is demonstrated by enforcement action taken by wildlife authorities during our study. We suggest that tourist focused human behavior change initiatives should become a critical component of a wider holistic approach to effectively balance wildlife protection goals and any expansion of wildlife ecotourism in the Amazon

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

Improving the ability to detect and characterize long-duration volcanic tremor is crucial to understand the longterm dynamics and unrest of volcanic systems. We have applied data reduction methods (permutation entropy and polarization degree, among others) to characterize the seismic wave field near Copahue volcano (Southern Andes) between June 2012 and January 2013, when phreatomagmatic episodes occurred. During the selected period, a total of 52 long-duration events with energy above the background occurred. Among them, 32 were classified as volcanic tremors and the remaining as noise bursts. Characterizing each event by averaging its reduced parameters, allowed us to study the range of variability of the different events types. We found that, compared to noise burst, tremors have lower permutation entropies and higher dominant polarization degrees. This characterization is a suitable tool for detecting long-duration volcanic tremors in the ambient seismic wave field, even if the SNR is low.Project BRAVOSEIS of the Spanish Ministry of Science CTM2016.77315National University of Rio Negro PI40-A-54

Volcanic and volcano-tectonic activity forecasting: a review on seismic approaches

  Forecasting volcanic activity is a difficult problem that is being addressed worldwide from different perspectives. Significant advances have been made after the introduction of non-linear dynamical systems theory and the use of power-law distributions of different geophysical parameters in the Earth Sciences. In particular, frequency-magnitude power-law statistics evidences the scale-invariance and self-organization of seismicity, and brittle fracture models show that under certain conditions, a precursory causal evolution characterized by an accelerating strain rate culminates in a catastrophic failure of the system under stress. The precursory organization of the seismicity and the distinct characteristics of the seismic events have allowed the development of forecasting tools. In this work we present some examples of forecasting methods based on seismic observations at different volcanoes in the world, and how results and experiences has been used to improve both hardware and software tools developed for short-term forecasting of volcanic and seismo-volcanic activity

Oscillations in hydrothermal systems as a source of periodic unrest at caldera volcanoes: Multiparameter insights from Nisyros, Greece

Unrest at collapse calderas is generally thought to be triggered by the arrival of new magma at shallow depth. But few unrest periods at calderas over the past decades have culminated in volcanic eruptions and the role of hydrothermal processes during unrest is drawing more and more attention. Here we report joint and simultaneous continuous multi-parameter observations made at the restless Nisyros caldera (Greece), which reveal non-steady short-term oscillatory signals. The combined geodetic, gravimetric, seismic and electromagnetic records indicate that the oscillations are associated with thermohydromechanical disturbances of the hydrothermal system. The dominant period of oscillation (40–60 min) indicates short-term processes most likely associated with instabilities in the degassing process. Amplitudes of recorded geodetic and gravimetric signals are comparable to amplitudes observed at other periodically restless calderas. We conclude that shallow aqueous fluid migration can contribute significantly to periodic unrest, explaining the lack of eruptions in many cases of unrest

The December 22nd 2012 eruption of the Copahue volcano, Neuquén, Argentina: Characterization of the eruptive cycle and its products

Se describen las características y naturaleza de los productos emitidos durante la erupción del 22 de diciembre de 2012 del volcán Copahue. La erupción tuvo carácter pulsatorio, con máxima explosividad al inicio de la erupción (VEI 2) declinando rápidamente en aproximadamente 48 hs. La máxima altura de la columna eruptiva (1.500-2.000 m) fue registrada al inicio de la erupción y estuvo acompañada por una importante emisión de SO2. La pluma alcanzó 250 km en dirección ESE con anchos de 20 km (zonas proximales) y 35 km (zonas distales). Un segundo pulso eruptivo ocurrió por la tarde del 22 de diciembre, generando una pluma de menores dimensiones. Estos pulsos explosivos emitieron i) bloques y bombas balísticas y ii) piroclastos dispersados en suspensión, donde se destacan grandes fragmentos aplanados de escoria altamente vesiculada. El depósito de tefra se distribuyó en ambientes proximales alcanzando distancias de hasta unos 40 km desde el cráter activo y extendiéndose en un área de aproximadamente 200 km2. La tefra está constituida por componentes juveniles con diversos grados de vesiculación y componentes accesorios de composición subvolcánica alterados hidrotermalmente. Las bombas y bloques balísticos se dispersaron hasta 1.800-1.900 m alrededor de la boca eruptiva. Se estima un volumen total eruptado de aproximadamente 0,005 km3. La fase inicial de la erupción se asoció a una columna de magma en ascenso que al interactuar con el sistema hidrotermal superficial del volcán, gatilló una erupción hidromagmática. Una vez abierto el sistema, la descompresión condujo a la predominancia de una fragmentación magmática pulsatoria.This paper describes the characteristics, nature and distribution of the products of the 12/22/2012 eruption of the Copahue volcano (Neuquén Province, Argentina). The eruptive cycle was dominated by a pulsatory behavior, with a continuous decrease in the released energy since the onset of the eruption in the morning of December 22, when the eruptive column reached its maximum height (1,500-2,000 m). The volcanic plume extended for almost 250 km toward the ESE of the volcano and was characterized by a mean width of 20 km in proximal areas and 35 km in distal areas. This initial pulse was accompanied by significant SO2 emissions. A second, less intense, eruptive pulse occurred during the afternoon of December 22, leading to the development of a smaller volcanic plume. Eruption products can be classified as: i) ballistic bombs and blocks emitted from the eruptive center, and ii) fallout pyroclasts deposited directly from the volcanic plume, including large flattened juvenile fragments of highly vesiculated scoria. The characteristics of the eruptive event lead to the accumulation of tephra up to a distance of about 40 km from the crater, distributed along a surface of about 200 km2 to the SE of the volcano. Petrographic analysis performed on the fallout deposits showed the presence of juvenile fragments with different degrees of vesiculation and accessory fragments of hydrothermally altered subvolcanic rocks. Ballistic bombs and blocks were dispersed to a distance of 1,800-1,900 m from the eruptive center. We estimate a VEI 2 eruption intensity, with an emitted volume of about 0.005 km3 , which rapidly declined in about 48 hours after the onset of the eruption. The initial stages of the eruption were related to the interaction of an ascending column of magma with the shallow hydrothermal system of the volcano, triggering a hydromagmatic eruption. Once the conduit was open, the progressive decompression of the system led to an eruption dominated by a pulsatory magmatic fragmentation.Fil: Petrinovic, Ivan Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Ciencias de la Tierra; ArgentinaFil: Villarosa, Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; ArgentinaFil: D'elia, Leandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Geológicas (i); ArgentinaFil: Guzman, Silvina Raquel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Salta. Instituto de Bio y Geociencias del Noroeste Argentino; ArgentinaFil: Paez, Gerardo Nestor. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Instituto de Recursos Minerales; ArgentinaFil: Outes, Ana Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; ArgentinaFil: Manzoni, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; ArgentinaFil: Delménico, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; ArgentinaFil: Balbis, Catalina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaFil: Carniel, Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Ciencias de la Tierra; ArgentinaFil: Hernando, Irene Raquel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Geológicas (i); Argentin

A recent pyroclastic density current (1963-64 AD? - 1976 AD?) from the Copahue volcano (I): Field geological evidences and radiocarbon age

Se describe un depósito atribuido a una corriente piroclástica sobre los flancos NE, E y SE del volcán Copahue. La datación radiocarbónica (F = 1.3678 ±0.0075) permite asignarle dos edades eruptivas posibles 1963-64 AD y 1976 AD (1961 AD?). Estimamos su importancia en la asignación de peligro volcánico del Copahue.We describe a historical pyroclastic deposit attributed to a pyroclastic density current (PDC) in the NE, E and SE flanks from the Copahue volcano. The radiocarbon dating (F = 1.3678 ±0.0075) enable to correlate this pyroclastic deposit with two possible eruption times 1963-64 AD and 1976 AD (1961 AD?). We consider its importance to assess the volcanic hazard of the Copahue volcano.Facultad de Ciencias Naturales y MuseoInstituto de Recursos MineralesCentro de Investigaciones Geológica