Добірка наукової літератури з теми "Sangeang Api"

Penelitian dengan menggunakan metode seismik pantul saluran ganda (multichannel) dan geomagnet mengindikasikan adanya gunungapi bawah laut. Dari penampang rekaman seismik dapat ditafsirkan bahwa Gunungapi bawah laut ditandai dengan bentuk tonjolan atau terobosan menembus dasar laut. Dari data megnetik kelautan diperoleh bahwa pada lokasi gunungapi bawah laut diketahui nilai anomali intensitas magnet total cukup tinggi yaitu sekitar 124 nT. Umumnya anomali intensitas magnet tinggi terdapat di bagian selatan daerah penelitian yang ditafsirkan juga sebagai penipisan kerak atau adanya Gunungapi bawah laut. Bagian selatan memang banyak didapat Gunungapi seperti gunungapi Sangeang Api yang terdapat diujung timur dan rangkaian Gunungapi lainnya yang terdapat di pulau Sumbawa (Gunungapi Tambora dan lainnya).Kata kunci metode seismik dan geomagnet, gunungapi bawah laut, Perairan Sangiang The study is equipped by using multi-channel seismic reflection and marine geomagnetic method and it indicates a submarine volcano. The seismic reflection profile can be interpreted that the submarine volcano is characterized by the bulge or break shape penetrate the seabed. From the data obtained of marine geomagnetic, the location of submarine volcanoes known value of the total magnetic intensity anomalies is quite high which is about 124 nT. Generally, the intensity of high magnetic anomaly is located in the southern part of the study area. This anomaly is interpreted as a thinning crust or the presence of submarine volcanoes. The southern part is the area where volcanoes are found such as Sangeang Api volcano located at the eastern tip and other volcanoes series on the island of Sumbawa (volcano Tambora and others). Keywords: seismic and geomagnetic methods, submarine volcanoes, Sangiang waters

The use of ensemble models to forecast the dispersion and transport of airborne volcanic ash in operational contexts is increasingly being explored. The ensemble members are usually constructed to represent a priori uncertainty estimates in meteorological fields and volcanic ash source parameters. Satellite data can be used to further filter ensemble members within an analysis time window by rejecting poorly performing members, leading to improved forecasts. In this study, the ensemble filtering technique is used to improve the representation of temporal source variations. Ensemble members are initially created by representing the source time variations as random functions of time that are modulated by crude initial estimates of the variations estimated from satellite imagery. Ensemble filtering is then used to remove members whose fields match poorly with observations within a specified analysis time window that are represented by satellite retrievals of volcanic ash properties such as mass load, effective radius, and cloud top height. The filtering process leads to an ensemble with statistics in closer agreement with the observations. It is shown in the context of the 30 May 2014 Sangeang Api eruption case study that this method leads to significantly enhanced forecasting skill beyond the analysis time window—about 20% improvement on average—when compared to a system that assumes constant emission rates for the duration of the eruption, as is the case in many operational volcanic ash forecasting systems.

Abstract. Due to the climate effects and aviation threats of volcanic eruptions, it is important to accurately locate ash in the atmosphere. This study aims to explore the accuracy and reliability of training a neural network to identify cases of ash using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Satellite images were obtained for the following eruptions: Kasatochi, Aleutian Islands, 2008; Okmok, Aleutian Islands, 2008; Grímsvötn, northeastern Iceland, 2011; Chaitén, southern Chile, 2008; Puyehue-Cordón Caulle, central Chile, 2011; Sangeang Api, Indonesia, 2014; and Kelut, Indonesia, 2014. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to obtain ash concentrations for the same archived eruptions. Two back-propagation neural networks were then trained using brightness temperature differences as inputs obtained via the following band combinations: 12–11, 11–8.6, 11–7.3, and 11 μm. Using the ash concentrations determined via HYSPLIT, flags were created to differentiate between ash (1) and no ash (0) and SO2-rich ash (1) and no SO2-rich ash (0) and used as output. When neural network output was compared to the test data set, 93 % of pixels containing ash were correctly identified and 7 % were missed. Nearly 100 % of pixels containing SO2-rich ash were correctly identified. The optimal thresholds, determined using Heidke skill scores, for ash retrieval and SO2-rich ash retrieval were 0.48 and 0.47, respectively. The networks show significantly less accuracy in the presence of high water vapor, liquid water, ice, or dust concentrations. Significant errors are also observed at the edge of the MODIS swath.

Abstract. Due to the climate effects and aviation threats of volcanic eruptions, it is important to accurately locate ash in the atmosphere. This study aims to explore the accuracy and reliability of training a neural network to identify cases of ash using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Satellite images were obtained for the following eruptions: Kasatochi, Aleutian Islands, 2008; Okmok, Aleutian Islands, 2008; Grímsvötn, northeastern Iceland, 2011; Chaiteìn, southern Chile, 2008; Puyehue-Cordoìn Caulle, central Chile, 2011; Sangeang Api, Indonesia, 2014; and Kelut, Indonesia, 2014. The Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was used to obtain ash concentrations for the same archived eruptions. Two back-propagation neural networks were then trained using brightness temperature differences as inputs obtained via the following band combinations: 12-11, 11-8.6, 11-7.3, and 11 μm. Using the ash concentrations determined via HYSPLIT, flags were created to differentiate between ash (1) and no ash (0) and SO2-rich ash (1) and no SO2-rich ash (0) and used as output. When neural network output was compared to the test dataset, 93 % of pixels containing ash were correctly identified and 7 % were missed. Nearly 100 % of pixels containing SO2-rich ash were correctly identified. The optimal thresholds, determined using Heidke skill scores, for ash retrieval and SO2-rich ash retrieval were 0.48 and 0.47, respectively. The networks show significantly less accuracy in the presence of high water vapor, liquid water, ice, or dust concentrations. Significant errors are also observed at the edge of the MODIS swath.

Abstract. Tsunamis caused by large volcanic eruptions and flanks collapsing into the sea are major hazards for nearby coastal regions. They often occur with little precursory activity and are thus challenging to detect in a timely manner. This makes the pre-emptive identification of volcanoes prone to causing tsunamis particularly important, as it allows for better hazard assessment and denser monitoring in these areas. Here, we present a catalogue of potentially tsunamigenic volcanoes in Southeast Asia and rank these volcanoes by their tsunami hazard. The ranking is based on a multicriteria decision analysis (MCDA) composed of five individually weighted factors impacting flank stability and tsunami hazard. The data are sourced from geological databases, remote sensing data, historical volcano-induced tsunami records, and our topographic analyses, mainly considering the eruptive and tsunami history, elevation relative to the distance from the sea, flank steepness, hydrothermal alteration, and vegetation coverage. Out of 131 analysed volcanoes, we found 19 with particularly high tsunamigenic hazard potential in Indonesia (Anak Krakatau, Batu Tara, Iliwerung, Gamalama, Sangeang Api, Karangetang, Sirung, Wetar, Nila, Ruang, Serua) and Papua New Guinea (Kadovar, Ritter Island, Rabaul, Manam, Langila, Ulawun, Bam) but also in the Philippines (Didicas). While some of these volcanoes, such as Anak Krakatau, are well known for their deadly tsunamis, many others on this list are lesser known and monitored. We further performed tsunami travel time modelling on these high-hazard volcanoes, which indicates that future events could affect large coastal areas in a short time. This highlights the importance of individual tsunami hazard assessment for these volcanoes, the importance of dedicated volcanological monitoring, and the need for increased preparedness on the potentially affected coasts.

This thesis documents data from an active volcano (Sangeang Api) and previously unstudied, extinct volcanoes (Wai Sano and Doro Kota and Doro Kuta) from adjacent sectors of the eastern Sunda Arc, Indonesia. Sangeang Api erupts co-magmatic suites of lavas and cumulate xenoliths. Lavas are ne-normative, silica-undersaturated, volatile-rich, shoshonitic basalts to basaltic-trachyandesites. They have trace-element compositions typical of arc magmas; enrichment in LREE, alkali-earth elements and Sr, depletion in Nb, Ta, Zr and Ti and high U/Th. They are also enriched in fluid-mobile elements (Cl, Ba, Cs, etc.). The cumulate xenoliths are separated into two distinct groups; the cpx+mgt and cpx+ol pyroxenites and the cpx+mgt+plag±amph gabbros. These groups are compositionally distinct, with their chemistry reflecting their cumulate mineralogy and are shown to drive magma evolution in the system by fractional crystallisation. Oxidation state, water contents and pressures of crystallisation are the primary controls on the primary cumulate mineral assemblages of the xenoliths. Sangeang Api magmas degassed as they ascended through the crust, with degassing driving oxidation at depth and reduction more shallowly. Many of the cumulate xenoliths are variably contaminated by melts indicating percolation and incomplete compaction or post crystallisation intrusion. Fe-isotope studies of the Sangeang Api products shows that the lavas record δ⁵⁷Fe compositions (average = 0.099‰ ±0.051) typical of arc settings. Cumulate xenoliths record heavy Fe-isotope compositions compared to the lavas (mean δ⁵⁷Fegabbro = 0.166‰ ±0.051; mean δ⁵⁷Fepyroxenite = 0.109‰ ±0.066). Using published fractionation factors, it is shown that whilst magnetite mineral separates records equilibrium compositions (mean δ⁵⁷Fe = 0.142‰ ±0.072), Fe-Mg silicate mineral separates display significant disequilibrium in their compositions (mean δ⁵⁷Feoliv = -0.313‰ ±0.284, mean δ⁵⁷Feamph = 0.125‰ ±0.081 and δ⁵⁷Fecpx = 0.109‰ ±0.090). Iron isotope disequilibria highlights the pervasiveness of post-crystallisation contaminative processes. Lavas from the Quaternary (~2Ma) D. Kota and D. Kuta, E. Sumbawa, are geochemically and petrologically similar to those from nearby Sangeang Api. However, they are less potassic (high-K calc-alkaline) with smaller enrichments in Ba and Sr, higher Rb/Sr and lower U/Th, highlighting a role for residual amphibole in the mantle source. Wai Sano at the far western end of Flores, is unique in the Sunda Arc, erupting adakites with characteristically low MgO, high Al₂O₃, high SiO₂, low Y and high Sr/Y. However, the Sr-Nd isotope systematics of these lavas suggests that they cannot have been produced by slab melts as is often suggested for adakites. Trace element characteristics and thermodynamic modelling suggest that a possible source of these magmas is through partial melting (10-13%) of basaltic underplates. Ultrapotassic magmatism in the Sunda Arc is confined to the rear-arc and characterised by trace-element and isotopic evidence of mantle metasomatism and the influence of slab tear windows. Slab window development has changed the loci and composition of volcanism on Sumbawa. Thus, highlighting the effects of local tectonic setting on arc volcanoes.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Physical Sciences, 2018