As demonstrated at Anak Krakatau on December 22nd, 2018, tsunamis generated by volcanic flank collapse are incompletely understood and can be devastating. Here, we present the first high-resolution characterisation of both subaerial and submarine components of the collapse. Combined Synthetic Aperture Radar data and aerial photographs reveal an extensive subaerial failure that bounds pre-event deformation and volcanic products. To the southwest of the volcano, bathymetric and seismic reflection data reveal a blocky landslide deposit (0.214 ± 0.036 km3) emplaced over 1.5 km into the adjacent basin. Our findings are consistent with en-masse lateral collapse with a volume ≥0.175 km3, resolving several ambiguities in previous reconstructions. Post-collapse eruptions produced an additional ~0.3 km3 of tephra, burying the scar and landslide deposit. The event provides a model for lateral collapse scenarios at other arc-volcanic islands showing that rapid island growth can lead to large-scale failure and that even faster rebuilding can obscure pre-existing collapse.
The flank failure and collapse of Anak Krakatau on December 22nd, 2018 triggered a destructive tsunami. Whether the prior activity of the volcano led to this collapse, or it was triggered by another means, remains a challenge to understand. This study seeks to investigate the recent volcano submarine mass-landslide deposit and emplacement processes, including the seafloor morphology of the flank collapse and the landslide deposit extent. Bathymetry and sparker seismic data were used during this study. Bathymetry data collected in August, 2019 shows the run-out area and the seafloor landslide deposit morphology. Bathymetry data acquired in May, 2017, is used as the base limit of the collapse to estimate the volume of the flank collapse. Comparisons between seismic data acquired in 2017 and 2019 provide an insight into the landslide emplacement processes, the deposit sequence, and structure below the seafloor. From these results we highlight two areas of the submarine-mass landslide deposit, one proximal to Anak Krakatau island (∼1.6 km) and one distal (∼1.4 km). The resulting analysis suggests that the submarine-mass landslide deposit might be produced by a frontally compressional, faulted, landslide, triggered by the critical stability slope, and due to the recent volcanic activity. Blocky seabed features clearly lie to the southwest of Anak Krakatau, and may represent the collapse blocks of the landslide. The seismic analysis of the data acquired in August, 2019 reveals that the blocky facies extends to ∼1.62 km in the width around Anak Krakatau, and the block thicknesses vary up to 70.4 m. The marine data provides a new insight into the landslide run out and extent, together with the landslide deposit morphology and structure that are not available from satellite imagery or subaerial surveys. We conclude that the landslide run out area southwest of the recent collapse, is ∼7.02 ± 0.21 km2.
Lake Maninjau is a lake formed by volcanic activity. Many human activities occur on the catchment area, but also in exploited waters. This study aims to mapping the depth of the waters in the Lake Maninjau and assess the effect of field sample distribution on the quality results of the image transformation. The data used are satellite imagery Sentinel 2A, results of point survey. The analysis technique uses the normalized difference water index algorithm, sun glint, empirical bathymetry method and linear regression. The result of the research which is has found that variations of distribution into the dispersion of the recording process of the depth of the object represented by cell. The depth of the water from the results of this transformation refers to the measurement sample in the field survey. The maximum depth of the waters is in the range of 107m. Shallow waters are predominantly distributed in the northern region which is the out late of Lake Maninjau. The southern area forms a deep basin. The distribution of this sample is in the form of an empirical bathymetry map and the relationship between the results of field measurements and the transformation with a regression value of 0.769, this indicates the consideration of total and distribution of survey sample is influence on quality of the results of the transformation.
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