Reexamining the Potential to Classify Lava Flows From the Fractality of Their Margins

Can fractal analysis of a lava flow's margin enable classification of the lava's morphologic type (e.g., pahoehoe)? Such classifications would provide insights into the rheology and dynamics of the flow when it was emplaced. The potential to classify lava flows from remotely-sensed data would particularly benefit the analysis of flows that are inaccessible, including flows on other planetary bodies. The technique's current interpretive framework depends on three assumptions: (1) measured margin fractality is scale-invariant; (2) morphologic types can be uniquely distinguished based on measured margin fractality; and (3) modification of margin fractality by topography, including substrate slope and confinement, would be minimal or independently recognizable. We critically evaluate these assumptions at meter scales (1-10 m) using 15 field-collected flow margin intervals from a wide variety of morphologic types in HawaiModified Letter Turned Commai, Iceland, and Idaho. Among the 12 margin intervals that satisfy the current framework's suitability criteria (e.g., geomorphic freshness, shallowly-sloped substrates), we show that five exhibit notably scale-dependent fractality and all five from lava types other than 'a'a or pahoehoe would be classified as one or both of those types at some scales. Additionally, an 'a'a flow on a 15 degrees slope (Mauna Ulu, HawaiModified Letter Turned Commai) and a spiny pahoehoe flow confined by a stream bank (Holuhraun, Iceland) exhibit significantly depressed fractalities but lack diagnostic signatures for these modifications. We therefore conclude that all three assumptions of the current framework are invalid at meter scales and propose a new framework to leverage the potential of the underlying fractal technique while acknowledging these complexities.

Automated summary

The study evaluates the use of fractal analysis for classifying lava flows, finding that the current framework's assumptions are invalid at meter scales. A new framework is proposed to leverage the potential of the underlying fractal technique while acknowledging these complexities.