2004 Denver Annual Meeting (November 7–10, 2004)

Paper No. 10
Presentation Time: 3:45 PM


MATTHEWS, Jeffrey M., Division of Natural Sciences, Lewis-Clark State College, 500 8th Ave, Lewiston, ID 83501, matthews@lcsc.edu

Traditional lineament analysis techniques group lineaments into a single generalized spatial domain coincident with the area of study or rely on subjective, qualitative methods to divide lineaments into geologically significant sub-domains. Current semi-automated lineament detection techniques produce large numbers of spatially distributed lineaments that are readily treated with spatial statistical methods in a Geographic Information System (GIS) yielding a unique perspective for regional structural interpretation.

A regional analysis from the Blue Mountains and Hell’s Canyon areas of Washington, Idaho, and Oregon serves to illustrate the spatial statistical treatment of lineaments. This rugged and remote area is structurally complex and includes a suture zone, various accreted terranes, significant deep canyons and alpine mountains. Multiple lineament detection algorithms are applied to regional Digital Elevation Models (DEMs) to produce a large set of lineaments. Lineament detection is facilitated by use of DEMs as they are free from distracting textures, tones, and colors that are artifacts of vegetation, snow, or human activity. The lineament set is analyzed at three different scales (1:24,000, 1:100,000, and 1:250,000) and lineament attributes (length, orientation, location, density, and intersection angle) are clustered and classified to produce distinct lineament groups. The objective spatial lineament groups are qualitatively and statistically compared to various known geologic and geomorphic features and are utilized to enhance regional structural and tectonic interpretation. Analysis of these lineament groups in a feature rich, robust GIS suggests various specific hypotheses that are appropriate for field evaluation and further study.