Detection of high-silica lava flows and lava morphology at the Alarcon Rise, Gulf of California, Mexico using automated classification of the morphological-compositional relationship in AUV multibeam bathymetry and sonar backscatter

Abstract:

An automated compositional classification by adaptive neuro-fuzzy inference system (ANFIS) was developed to study volcanic processes that create high-silica lava at oceanic ridges. The objective of this research is to determine the existence of a relationship between lava morphology and composition. Researchers from the Monterey Bay Aquarium Research Institute (MBARI) recorded morphologic observations and collected samples for geochemical analysis during ROV dives at the Alarcon Rise in 2012 and 2015. The Alarcon Rise is a unique spreading ridge environment where composition ranges from basaltic to rhyolitic, making it an ideal location to examine the compositional-morphologic relationship of lava flows. Preliminary interpretation of field data indicates that high-silica lavas are typically associated with 3-5 m, blocky pillows at the heavily faulted north end of the Alarcon. Visual analysis of multibeam bathymetry and side-scan sonar backscatter from MBARI AUV D. Allen B. and gridded at 1 m suggests that lava flow morphology (pillow, lobate, sheet) can be distinguished by seafloor roughness. Bathymetric products used by ANFIS to quantify the morphologic-compositional relationship were slope, aspect, and bathymetric position index (BPI, a measure of local height relative to the adjacent terrain). Sonar backscatter intensity is influenced by surface roughness and previously used to distinguish lava morphology. Gray-level co-occurrence matrices (GLCM) were applied to backscatter to create edge-detection filters that recognized faults and fissures. Input data are slope, aspect, bathymetric value, BPI at 100 m scale, BPI at 500 m scale, backscatter intensity, and the first principle component of backscatter GLCM. After lava morphology was classified on the Alarcon Rise map, another classification was completed to detect locations of high-silica lava. Application of an expert classifier like ANFIS to distinguish lava composition may become an important tool in oceanic ridge exploration.