Investigating polygonal fault topological variability: Structural causes vs image resolution

• Main Authors: C. K. Morley, H. Binazirnejad
• Format: Journal
• Published: 2020
• Subjects: Earth and Planetary Sciences
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074941502&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/68359
• Institution: Chiang Mai University

© 2019 Elsevier Ltd The topology of three polygonal fault sets is investigated to determine potential causes of variations in connectivity (as measured by nodes and branches) between, and vertically within the fault sets. Two faults sets (from the Great South Basin (GSB), New Zealand, and Southeast Asia (SEA)) are imaged by 3D seismic reflection data, a third data set (Al Farafra Oasis, Egypt) is imaged from satellite data. The values measuring connectivity for the GSB and SEA polygonal fault sets plot in ternary diagrams in similar areas to other complex normal fault sets from rifts. These data sets are dominated by faults with either isolated fault tips (I nodes) or with abutting or splaying geometries (Y nodes). The similarities in node and branch topology of polygonal faults, and complex fault patterns in rifts (associated with multi-phase rifting, complex local stress rotations, or inherited fabrics), which are imaged on 3D seismic, indicates that topology describes the complexity of fault connectivity, but in general, by itself, cannot discriminate the causes of normal fault pattern variations. The Al Farafra Oasis faults sets (areas 2 and 3) provide an end member example of fault patterns with a very high degree of connectivity associated with cross-cutting fault sets (X-nodes) that are more rarely present in other fault sets. Some faults forming X-nodes may be hybrid tensile-shear failure fractures, or even calcite vein-filled tensile fractures. Much of the difference between the seismic and satellite image data sets may be related resolution, indicating that comparisons of topology characteristics are best made between data sets of similar resolution.