Assessing the seismic potential hazard of the Makran Subduction Zone
Purpose and Introduction
Long quiescent subduction zones like the Makran, Sunda , and Cascadia, which likely have long recurrence intervals for large (> Mw 8) earthquakes and poorly known seismic histories, are particularly vulnerable and often ill-prepared for seismic hazards. This is because the threat has either been long forgotten in human records or is unrecognized. Recent development of seismic and GPS data analysis tools have aided in enhancement of understanding the mechanics of stress and strain accumulation in subduction zones. With the expansion and modernization of global seismic and GPS networks, these recent developments can be used for reanalysis and evaluation of subduction zone hazards.
The Makran subduction zone is located off the coast of Pakistan and Iran (Figure 1). Subduction occurs along an approximately 900 km margin between the Arabian and Eurasian plates at a rate of about 4 cm/yr. (Kopp et al., 2000; Smith et al., 2012). This area is poorly understood tectonically and has not been studied extensively. The 1945 Mw 8.1 earthquake and its subsequent tsunami as well as more recent mid magnitude, intermediate depth (50-100 km) seismicity have demonstrated the active seismic nature of the region (Bryne et al., 1992; Rajendran et al., 2012). Recent increases in GPS and seismic monitoring now permit the geophysical modeling of the Makran subduction zone. [...]
I propose to use existing GPS data to assess the seismic potential and hazard of the western segment of the Makran (Figure 4). By modeling available GPS data to construct a fault coupling model, I will investigate whether the western part of the zone is locked and what type of earthquake magnitude and rupture could occur. Most of the previous studies in this zone have focused on using tsunami and seismicity data from the 1945 event to assess the seismic hazard (Heidarzadeh and Kijko, 2010; Pararas-Carayannis, 2006; Heidarzadeh, Pirooz, Zaker, and Synolakis, 2008), I anticipate that the inclusion of GPS data will provide better constraints. Since the GPS data observes elastic deformation as it occurs on a recent time scale it will allow me to establish better initial parameters and constraints on my models, which will help in the development of a model that best fits the actual GPS velocity vectors.
Full Thesis Proposal (PDF format)