Kinematics and source zone properties of the 2004 Sumatra‐Andaman earthquake and tsunami: Nonlinear joint inversion of tide gauge, satellite altimetry, and GPS data

Abstract

[1] We (re)analyzed the source of the 26 December 2004 Sumatra-Andaman earthquake and tsunami through a nonlinear joint inversion of an inhomogeneous data set made up of tide gauges, satellite altimetry, and far-field GPS recordings. The purpose is twofold: (1) the retrieval of the main kinematics rupture parameters (slip, rake, and rupture velocity) and (2) the inference of the rigidity of the source zone. We independently estimate the slip from tsunami data and the seismic moment from geodetic data to derive the rigidity. Our results confirm that the source of the 2004 Sumatra-Andaman earthquake has a complex geometry, constituted by three main slip patches, with slip peaking at $30 m in the southern part of the source. The rake direction rotates counterclockwise at the northern part of the source, according to the direction of convergence along the trench. The rupture velocity is higher in the deeper than in the shallower part of the source, consistent with the expected increase of rigidity with depth. It is also lower in the northern part, consistent with known variations of the incoming plate properties and shear velocity. Our model features a rigidity (20-30 GPa) that is lower than the preliminary reference Earth model (PREM) average for the seismogenic volume. The source rigidity is one of the factors controlling the tsunami genesis: for a given seismic moment, the lower the rigidity, the higher the induced seafloor displacement. The general consistence between our source model and previous studies supports the effectiveness of our approach to the joint inversion of geodetic and tsunami data for the rigidity estimation.

Teleseismic inversion of the 2004 Sumatra-Andaman earthquake rupture process using complete Green’s functions

“…Our results indicate that the rupture occurred in both shallow and deep parts of the Nicobar region (areas B and C); this result was further obtained by Lorito et al (2010). Two large left lateral fault mechanism aftershocks (Mw7.2, Mw7.5) occurred in the Nicobar region.…”

“…These areas may form a barrier region (velocity-strengthening behavior), and the barriers may cause the rupture velocities to be slow. The slow rupture initiation in the shallow Nicobar region was further supported by a recent joint inversion of tide gauge, satellite altimetry, and GPS data by Lorito et al (2010). The slow rupture velocity of the Andaman region has been indicated in many studies (e.g., de Groot-Hedlin 2005; Guilbert et al 2005;Tolstoy and Bohnenstiehl 2005;Tsai et al 2005).…”

A unified GPS‐based earthquake catalog for the Sumatran plate boundary between 2002 and 2013

“…According to coseismic vertical motions derived from satellite images and microatoll measurements, the 2004 M w 9.2 Sumatra‐Andaman earthquake extended ∼1600 km along the trench from under northern Simeulue at ∼2.5°N to Preparis Island in Myanmar at ∼14.9°N [ Meltzner et al , ]. Along this long rupture, coseismic slip was heterogeneous with distinct localized patches of high slip at different latitudes, and the largest slip exceeded more than 20 m offshore northwestern Sumatra in the south [e.g., Subarya et al , ; Chlieh et al , ; Fujii and Satake , ; Rhie et al , ; Sladen and Hébert , ; Hoechner et al , ; Lorito et al , ]. Such long rupture and large slip generated widespread deformation on an unprecedentedly large scale, with very clear coseismic offsets (several millimeters) detected more than 3000 km away from the epicenter [ Banerjee et al , ; Vigny et al , ; Hashimoto et al , ; Kreemer et al , ].…”

Slip distribution of the 2003 Tokachi‐oki Mw 8.1 earthquake from joint inversion of tsunami waveforms and geodetic data

“…We solve the inverse problem using a particular implementation of the simulated annealing technique called the “heat bath algorithm” [ Rothman , 1986], following previous studies using tsunami, GPS, and strong motion data [e.g., Piatanesi et al , 2007; Lorito et al , 2010]. For tsunami time series, we use a cost function sensitive to both amplitude and phase matching [ Spudich and Miller , 1990; Sen and Stoffa , 1991]: In equation (1) u O and u S are the observed and synthetic waveforms, respectively, t i and t f are the lower and upper bounds of the time window, N is the number of records used in the inversion, and m indicates the single model.…”