Rootless tephra stratigraphy and emplacement processes

Hamilton, C. W.; Fitch, Erin P.; Fagents, S. A.; Thordarson, T.

doi:10.1007/s00445-016-1086-4

Cited by 18 publications

(11 citation statements)

References 51 publications

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“…We use the Volcanic Advanced Radar Retrieval (VARR, previously termed as volcanic ash radar retrieval, e.g., [12,13]) to analyze the time series of X-band radar data in order to quantitatively estimate the tephra particle category, mass concentration, MERs and plume height every ten minutes within eruptive columns [13]. The VARR methodology, supported by numerical simulations of plume coupled with lava fountain [10], has been improved in order to consider the strong heterogeneity of the volcanic jet, e.g., composition in microlite, vesicle content, or spindle composition [37].…”

Section: Microwave Radar Algorithmsmentioning

confidence: 99%

Multisensor Characterization of the Incandescent Jet Region of Lava Fountain-Fed Tephra Plumes

et al. 2020

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Explosive basaltic eruptions eject a great amount of pyroclastic material into the atmosphere, forming columns rising to several kilometers above the eruptive vent and causing significant disruption to both proximal and distal communities. Here, we analyze data, collected by an X-band polarimetric weather radar and an L-band Doppler fixed-pointing radar, as well as by a thermal infrared (TIR) camera, in relation to lava fountain-fed tephra plumes at the Etna volcano in Italy. We clearly identify a jet, mainly composed of lapilli and bombs mixed with hot gas in the first portion of these volcanic plumes and here called the incandescent jet region (IJR). At Etna and due to the TIR camera configuration, the IJR typically corresponds to the region that saturates thermal images. We find that the IJR is correlated to a unique signature in polarimetric radar data as it represents a zone with a relatively high reflectivity and a low copolar correlation coefficient. Analyzing five recent Etna eruptions occurring in 2013 and 2015, we propose a jet region radar retrieval algorithm (JR3A), based on a decision-tree combining polarimetric X-band observables with L-band radar constraints, aiming at the IJR height detection during the explosive eruptions. The height of the IJR does not exactly correspond to the height of the lava fountain due to a different altitude, potentially reached by lapilli and blocks detected by the X-band weather radar. Nonetheless, it can be used as a proxy of the lava fountain height in order to obtain a first approximation of the exit velocity of the mixture and, therefore, of the mass eruption rate. The comparisons between the JR3A estimates of IJR heights with the corresponding values recovered from TIR imagery, show a fairly good agreement with differences of less than 20% in clear air conditions, whereas the difference between JR3A estimates of IJR height values and those derived from L-band radar data only are greater than 40%. The advantage of using an X-band polarimetric weather radar in an early warning system is that it provides information in all weather conditions. As a matter of fact, we show that JR3A retrievals can also be obtained in cloudy conditions when the TIR camera data cannot be processed.

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Section: Microwave Radar Algorithmsmentioning

confidence: 99%

Multisensor Characterization of the Incandescent Jet Region of Lava Fountain-Fed Tephra Plumes

et al. 2020

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“…MY13091305 was collected from the lower part of a rootless cone (Fig. 1 ), and contains information about the early stage of lava-water interaction, which is considered to possibly be as energetic as a phreatomagmatic explosion 35 . MY13091402 was collected in Hagi, where the lava flowed down approximately 45 km from the fissure vent (Fig.…”

Section: Resultsmentioning

confidence: 99%

Classification of volcanic ash particles using a convolutional neural network and probability

Dake

Noguchi

Otsuki

et al. 2018

Sci Rep

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Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.

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“…Cone morphology depends on the dynamics of the lava-water interaction during formation (Greeley and Fagents, 2001;Fagents et al, 2002;Fagents and Thordarson, 2007;Stevenson et al, 2012;Hamilton et al, 2017;Fitch et al, 2017;Boreham et al, 2018). Repeated high-intensity explosions at a single site build large, layered edifices (≤450 m basal diameter; Fagents and Hamilton et al, 2017). Some of these have multiple craters or contain smaller inner cones built by discrete explosive episodes (Noguchi et al, 2016).…”

Section: Rootless Conesmentioning

confidence: 99%

Hazards from lava–river interactions during the 1783–1784 Laki fissure eruption

2020

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Interactions between lava flows and surface water are not always considered in hazard assessments, despite abundant historical and geological evidence that they can create significant secondary hazards (e.g., floods and steam explosions). We combine contemporary accounts of the 1783−1784 Laki fissure eruption in southern Iceland with morphological analysis of the geological deposits to reconstruct the lava−water interactions and assess their impact on residents. We find that lava disrupted the local river systems, impounded water that flooded farms and impeded travel, and drove steam explosions that created at least 2979 rootless cones on the lava flow. Using aerial photographs and satellite-derived digital terrain models, we mapped and measured 12 of the 15 rootless cone groups on the Laki lava field. We have identified one new rootless cone group and provide data that suggest another cone group previously attributed to the 939−940 CE Eldgjá eruption was created by the Laki eruption. We then use contemporary accounts to estimate formation dates and environments for each cone group, which formed in wetland/lake areas, on riverbeds, and near areas of impounded water. Furthermore, comparison with previous field studies shows that assessments using remote sensing can be used to identify and map meter-scale and larger features on a lava flow, although remote mapping lacks the detail of field observations. Our findings highlight the different ways in which lava can interact with surface water, threatening people, property, water supplies, and infrastructure. For these reasons, anticipation of such interactions is important in lava flow hazard assessment in regions with abundant surface water; we further demonstrate that remote sensing can be an effective tool for identifying lava−water interactions in past lava flows.

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Rootless tephra stratigraphy and emplacement processes

Cited by 18 publications

References 51 publications

Multisensor Characterization of the Incandescent Jet Region of Lava Fountain-Fed Tephra Plumes

Multisensor Characterization of the Incandescent Jet Region of Lava Fountain-Fed Tephra Plumes

Classification of volcanic ash particles using a convolutional neural network and probability

Hazards from lava–river interactions during the 1783–1784 Laki fissure eruption

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