DEVELOPING TECHNIQUES FOR REGIONAL STRUCTURAL INTERPRETATION USING GIS AND DEM-BASED TERRAIN ANALYSIS

Traditional structural mapping techniques rely heavily on field data correlated with topographic maps and aerial imagery, especially when working on regional-scale projects in complex terrain. Geographic Information Systems (GIS) enable combination of geologic, map and image data in a seamless environment. Visualization of shape and location information relevant to structural interpretation is facilitated by use of digital elevation models (DEMs) of suitable resolution, without distraction from the shapes, textures, tones and colors that are artifacts of human activities or vegetation. DEMs also facilitate mathematical analysis of ground shape, including identification of drainage lineaments and gradient changes in slopes, ridges and along drainage channels that may be structurally induced. The ability to drape georegistered aerial imagery, digitized geological and geophysical maps, epicenter locations, and other types of spatial data in selectable layers over the DEM provides an interpretive environment of unparalleled richness. An example from the central Santa Monica Mountains (SMM) of southern California illustrates the utility of an integrated GIS-based approach to reconnaissance mapping. Geologic mapping in the SMM is challenging because of high relief, dense chaparral vegetation, common large landslides and soils/sediment mantling bedrock, private property restrictions and the fact that the southern base of the range is below sea level. Active faults along the SMM trend may pose a significant seismic risk to residents of southern California, so comprehensive characterization of potentially active faults is necessary. We show that DEM-based analysis yields important results in regional terrain interpretation. Many known faults in the SMM are resolvable through DEM analysis. The geomorphic signatures associated with known faults are also observed in areas where faults have not (yet) been mapped. Analysis of available data on a GIS base promotes development of specific hypotheses for field evaluation.