Lateral migration of explosive hazards during maar eruptions constrained from crater shapes

Maar volcanoes are produced by subsurface phreatomagmatic explosions that can move vertically and laterally during an eruption. Constraining the distances that maar-forming explosions move laterally, and the number of relocations common to these eruptions, is vital for informing hazard scenarios and numerical simulations. This study uses 241 intact Quaternary maar crater shapes to establish global trends in size and spacing of explosion position relocations. Maar craters are sorted into shape classes based on the presence of uniquely identifiable combinations of overlapping circular components in their geometry. These components are used to recognize the minimum number of explosion locations responsible for observed crater shapes. Craters with unique solutions are then used to measure the size and spacing of the explosion footprints, the circular area of the largest crater produced by a single explosion of a given energy, that produce the crater shape. Thus, even in the absence of abundant observations of maar-type eruptions, the typical range, size and spacing of explosion positions are derived from maar crater shapes. This analysis indicates that most Quaternary maar eruptions involved at least three different explosion locations spanning distances of 200–600 m that did not always follow the trend of the dike feeding the eruption. Additional evaluation of larger maars, consistent with stratigraphic studies, indicates that centers of explosive activity, and thus the origin of ballistic and density current hazards, can move as many as twenty times during a maar-forming eruption. These results provide the first quantitative constraints on the scale and frequency of lateral migration in maar eruptions and these values can directly contribute to hazard models and eruption event trees in advance of future maar-type eruptions.

Centroid migration on an impacted granular slope due to asymmetric ejecta deposition and landsliding

For a fundamental understanding of terrain relaxation occurring on sloped surfaces of terrestrial bodies, we analyze the crater shape produced by an impact on an inclined granular (dry-sand) layer. Owing to asymmetric ejecta deposition followed by landsliding, the slope of the impacted inclined surface can be relaxed. Using the experimental results of a solid projectile impact on an inclined dry-sand layer, we measure the distance of centroid migration induced by asymmetric cratering. We find that the centroid migration distance xmig normalized to the crater minor-axis diameter Dcy can be expressed as a function of the initial inclination of the target tan θ, the effective friction coefficient μ, and two parameters K and c that characterize the asymmetric ejecta deposition and oblique impact effect: xmig/Dcy = Ktan θ/(1 − (tan θ/μ)2) + c, where K = 0.6, μ = 0.8, and c = −0.1 to 0.3. This result is consistent with a previous study that considered the effect of asymmetric ejecta deposition. The obtained results provide fundamental information for analyzing the degradation of sloped terrain on planetary surfaces, such as crater-shape degradation due to the accumulation of micro-impacts.

Influence of polycrystalline material on crater shape optimization and roughness using low-power/low-pressure direct-current glow discharge mass spectrometry

It is likely that observation of roughness at crater bottom upon GDMS sputtering is due to differential sputtering of grains.

A Machine Learning Approach to Crater Classification from Topographic Data

Craters contain important information on geological history and have been widely used for dating absolute age and reconstructing impact history. The impact process results in a lot of ejected fragments and these fragments may form secondary craters. Studies on distinguishing primary craters from secondary craters are helpful in improving the accuracy of crater dating. However, previous studies about distinguishing primary craters from secondary craters were either conducted by manual identification or used approaches mainly concerning crater spatial distribution, which are time-consuming or have low accuracy. This paper presents a machine learning approach to distinguish primary craters from secondary craters. First, samples used for training and testing were identified and unified. The whole dataset contained 1032 primary craters and 4041 secondary craters. Then, considering the differences between primary and secondary craters, features mainly related to crater shape, depth, and density were calculated. Finally, a random forest classifier was trained and tested. This approach showed a favorable performance. The accuracy and F1-score for fivefold cross-validation were 0.939 and 0.839, respectively. The proposed machine learning approach enables an automated method of distinguishing primary craters from secondary craters, which results in better performance.