ABSTRACT The ree-ont ontology formally represents the semantics of the complex interactions in the rare-earth elements (REE) mineral system from a self-organized criticality perspective. This ontology models various primarily heat-driven, melt-led, ore-forming processes that contribute to the formation of the REE-bearing alkaline intrusive bodies and related hydrothermal deposits. It also models the processes that form sediment-hosted concentrations of the REE from the weathering and erosion of the primary REE deposits. The ontology explicitly formalizes the interactions among the members of various magmatic, hydrothermal, basinal, regolith, and supergene subsystems. It represents the principal geodynamic, tectonic, and geological driver, trigger, carrier, conduit, and barrier roles that the components of the REE mineral system recursively play in enriching the REE during the subcritical and critical stages of the evolution of the complex system in and on the surface of the Earth’s crust. ree-ont establishes a logical machine processable basis for understanding the dynamic interactions among various components of the REE mineral system by extending the upper-level Basic Formal Ontology (BFO) and its underlying mid-level Common Core Ontologies (CCO).

ABSTRACT Kīlauea Volcano’s active summit lava lake posed hazards to downwind residents and over 1.6 million Hawai‘i Volcanoes National Park visitors each year during 2008–2018. The lava lake surface was dynamic; crustal plates separated by incandescent cracks moved across the lake as magma circulated below. We hypothesize that these dynamic thermal patterns were related to changes in other volcanic processes, such that sequences of thermal images may provide information about eruption parameters that are sometimes difficult to measure. The ability to learn about concurrent gas emissions and seismic activity from a remote thermal time-lapse camera would be beneficial when conditions are too hazardous for field measurements. We applied a machine learning algorithm called self-organizing maps (SOM) to thermal infrared time-lapse images of the lava lake collected hourly over 23 April–21 October 2013 ( n = 4354). The SOM algorithm can take thousands of seemingly different images, each representing the spatial distribution of relative temperature across the lava lake surface, and group them into clusters based on their similarities. We then related the resulting clusters to sulfur dioxide emissions and seismic tremor activity to characterizeties between the SOM classification and different emplacement conditions. The SOM classification results are highly sensitive to the normalization method applied to the input images. The standard pixel-by-pixel normalization method yields a cluster of images defined by the highest observed SO 2 emission levels, elevated surface temperatures, and a high proportion of cracks between crustal plates. When lava lake surface patterns are isolated by minimizing the effect of temperature variation between images, relationships with seismic tremor activity emerge, revealing an “intense spatter” cluster, characterized by unstable, broken-up crustal plate patterns on the lava lake surface. This proof of concept study provides a basis for extending the SOM classification method to hazard forecasting and real-time volcanic monitoring applications, as well as comparative studies at other lava lakes.

Abstract We applied a computational method to aid in clustering 41 alluvial fans along the southern coast of the Gulf of Corinth, Greece. The morphology of the fans and their catchments was quantitatively expressed through 12 morphometric parameters estimated using geographical information system techniques and the relationships among the geomorphometric features of the fans and their catchments were examined. Self-organizing maps were used to investigate the clustering tendency of fans based on morphometric variables describing both the fans and their corresponding catchments. The results of unsupervised classification through the self-organizing maps method revealed correlations among the morphometric parameters and five groups of alluvial fans were identified. These groups had a clear physical explanation, showed a preferred geographical distribution and reflected the processes related to the development of the fans. The geographical distribution of the fan catchment groups was partially controlled by variations in the relative tectonic uplift rate, which was the main control on the accommodation space for the development and accretion of the fans. The smaller fans were located in the central part of the study area, where the uplift rates were higher, whereas larger fluvial-dominated fan deltas formed to the east and west of the central group, where the uplift rates were lower.