Using machine learning as an aid to seismic geomorphology, which attributes are the best input?

Abstract

Volcanic rocks with intermediate magma composition indicate distinctive patterns in seismic amplitude data. Depending on the processes by which they were extruded to the surface, these patterns may be chaotic, moderate-amplitude reflectors (indicative of pyroclastic flows) or continuous high-amplitude reflectors (indicative of lava flows). We have identified appropriate seismic attributes that highlight the characteristics of such patterns and use them as input to self-organizing maps to isolate these volcanic facies from their clastic counterpart. Our analysis indicates that such clustering is possible when the patterns are approximately self-similar, such that the appearance of objects does not change at different scales of observation. We adopt a workflow that can help interpreters to decide what methods and what attributes to use as an input for machine learning algorithms, depending on the nature of the target pattern of interest, and we apply it to the Kora 3D seismic survey acquired offshore in the Taranaki Basin, New Zealand. The resulting clusters are then interpreted using the limited well control and principles of seismic geomorphology.