Segmentation of the Sumatran fault

Abstract

Segmentation of the Sumatran fault is discerned using an analytical approach in which a k-means algorithm partitions earthquakes into clusters of seismicity along the fault. Clusters are tessellated into segment zones from which segment lengths and maximum credible magnitude are estimated. Decreasing the depth of seismicity sampled from 70 to 60 to 50 km reduces interaction with deeper seismicity, and results from the k-means algorithm initially suggest that the fault has K = 14, 16, and 16 clusters, respectively. After inspection, it becomes clear that the optimum number of clusters is 16. The 16 cluster model developed into zones generates segment lengths ranging from 22 to 196 km and maximum earthquake potentials in the range of M w 6.5-7.8 . The Sumatran fault is dominated by eight great central segments distributed approximately symmetrically about Lake Maninjau. These central fault segments dominate the hazard, which is less in the far north because segments are shorter.

Earthquake Clustering and Energy Release of the African–Arabian Rift System

“…Indeed, it is possible to partition the seismicity on a long and specific fault into clusters of seismicity that are comparable to the geometry of a geological segmentation. This has been done for the 1800-km-long Sumatran fault (Burton and Hall, 2014) where the partitioned seismicity along the fault (or earthquake clusters), hence fault segments, complements the geological segmentation of Sieh and Natawidjaja (2000). Additionally, in regions where deformation occurs on more distributed fault networks, such as the Aegean, rather than on a single fault strand, the method successfully partitions seismicity in line with the structural segmentation (Weatherill and Burton, 2009).…”

“…Additionally, in regions where deformation occurs on more distributed fault networks, such as the Aegean, rather than on a single fault strand, the method successfully partitions seismicity in line with the structural segmentation (Weatherill and Burton, 2009). The procedure to partition seismicity into earthquake clusters is described in detail by Weatherill and Burton (2009) and Burton and Hall (2014), but the procedure is as follows. A cluster is envisaged with a centroid and related surrounding earthquakes.…”

“…To identify a value of K that warrants inspection, we focus on any K that is more than 3 standard deviations away from the mean KL value achieved across all cluster models. We use the same implementation and identification techniques as Burton and Hall (2014) to identify optimum K but increase the amount of repetitions the algorithm goes through from 100 to 1000, given the larger dataset in this study.…”

“…However, the characterization of seismic sources is often inconsistent, resulting in disagreement between zonation models of the same area. An emerging method of hazard zonation is to delineate source zones based on the hypocentral distribution of seismicity using a K-means cluster analysis (Weatherill and Burton, 2009;Burton and Hall, 2014). A clustering analysis of instrumentally observed and historical seismicity is particularly useful in areas where physical characteristics (i.e., structural observations) of seismic sources are absent.…”

“…The K-means cluster analysis has already been successfully applied to the Aegean region (Weatherill and Burton, 2009), Pakistan (Rehman et al, 2013(Rehman et al, , 2017, and the Sumatran fault (Burton and Hall, 2014) where the optimum cluster model correlates well with known structural segmentation of the fault. Here, we apply it to the entire East African rift (EAR) system where there are variations in the extent and quality of structural observations throughout the region.…”