Rupture process and coseismic deformations of the 27 February 2010 Maule earthquake, Chile
We estimated the spatial and temporal slip distribution for the 27 February 2010 Maule earthquake from teleseismic body wave data. To obtain a stable inversion solution, we used the data covariance matrix from the observation and modeling errors, incorporated smoothing constraints and determined their optimal values by using the Akaike Bayesian Information Criterion (ABIC). The fault rupture can be divided into three stages. For the first 30 s the rupture started as an elliptical crack elongated in the in-plane direction along the dip. After 30 s the rupture propagated bi-laterally along strike reaching the maximum moment release rate at around 50 s near the hypocenter. Finally the rupture propagated mainly to the north reaching another peak of moment release rate at 80 s and 130 km north-east from the hypocenter. Main rupture lasted for about 110 s. In order to evaluate our source model, we calculated the predicted coseismic vertical displacements and compare them with observed uplift/subsidence values measured along the coastline, as well as displacements obtained from strong ground motion and high-sampling GPS records in Concepción. Our model provides good estimations of the static displacements in the northern source region, but under-estimates the coseismic uplifts in the southern region.
Key words2010 Chile earthquake source process permanent displacement strong motion
We retrieved teleseismic body-wave data (38 P waves) with a good azimuthal coverage from the IRIS-DMC web site (http://www.iris.edu). These teleseismic body waves were integrated into ground displacements, and decimated with a sampling period of 1.5 seconds. We applied an antialiasing Butterworth low pass filter before the re-sampling. Surface waves may produce large off-diagonal components in the data covariance matrix, which can not be treated in the inversion approach of Yagi and Fukahata (2011).
To evaluate our model, we used 28 observed uplift/subsidence data along the coast and estuarine valleys (Farías et al., 2010). Coastal uplift was estimated by measurements of a white fringe formed by dead coralline crustose algae raised above the lower intertidal zone, and subsidence was measured based on inundated constructions and vegetation (Farías et al., 2010). We also used a strong motion digital recording at Concepción (CCSP station), belonging to the strong motion network of the Seismological Service (SSN), Universidad de Chile (Barrientos, 2010). Using this record we obtained the permanent displacement at Concepcioin. Finally we compare the displacement estimated from the strong ground motion record with those from a high-sampling GPS record at station CONZ located 9 km west of CCSP in the Transportable Integrated Geodetic Observatory (TIGO) (Sierk and Hase, 2010), which is jointly operated by the Universidad de Concepcioin and the German Federal Agency for Cartography and Geodesy (BKG).
3. Teleseismic Inversion and Fault Model
To investigate the rupture process of the Maule earthquake, we used a multitime window inversion scheme that incorporates the data covariance matrix from the observation and modeling errors (Yagi and Fukahata, 2011), and the Akaike Bayesian Information Criteria (ABIC) for optimum estimations of smoothness constraint parameters (Yagi and Fukahata, 2008). The Yagi and Fukahata (2011) inversion approach incorporates correlated errors that originate from uncertainty of Green’s functions, to reduce the bias of inversion results. In this technique slip rate distribution on the assumed fault plane is represented as a linear combination of basis functions in space and time. For the Maule earthquake the basis functions of slip rate in space were obtained using B-splines, and 27×10 knots distributed with an uniform interval of 18 km across the fault plane. The slip time history of each knot was represented by overlapping triangle functions interpolated with linear B-splines with an interval of 1.5 s, allowing a maximum rise time of 60 s. In order to obtain a stable solution, we applied smoothness constraints with respect to time, the spatial distribution of slip, and the rake angle. The optimum smoothness parameters were estimated by finding the minimum value of ABIC.
In Table 1 we show the 1-D structural velocity model used for calculating the Green functions (Bohm et al., 2002). We added an ocean layer with 1-km thickness to model water reverberations in teleseismic waveforms. The thickness of this layer was estimated by trial and error to optimize the fits to inverted data.
Density (x103 kg/m3)
Our fault model has a length of 486 km along strike and a width of 180 km along dip. We used the epicenter estimated by SSN (73.239W, 36.290S), which is located about 62 km south-west of the USGS hypocenter. The fault geometry for inversion (strike 15° and dip 18°) was estimated by slightly modifying the gCMT solution (strike 18° and dip 18°) based on fit to teleseismic waveforms. The starting time of rupture at each space knot was evaluated by its distance to the hypocenter and the rupture front velocity. Based on our preliminary analysis, we determined the rupture front velocity and the depth of hypocenter to be 2.8 km/s and 34 km, respectively.
4. Source Rupture Process
5. Coseismic Permanent Displacements
The main slip in our source model is located in a region near the coastline, around the hypocenter. This feature is in good agreement with the source model of Delouis et al. (2010), but differs from the source model of Lay et al. (2010), in which the main moment release is located near the trench. A recent tomographic study conducted in the source area of the Maule earthquake suggests the updip limit of the rupture zone to be located 30–40 km away from the trench (Contreras-Reyes et al., 2010). This limit is defined by the presence of a 20–40 km wide sedimentary wedge above the subducting plate, which would behave aseismically due to the presence of high-porosity fluid-rich sediments (Contreras-Reyes et al., 2010). The source model of Lay et al. (2010) would imply that the majority of slip occurred in this aseismic zone. On the other hand, the high-frequency radiated seismic energy suggests that the Maule earthquake may be identified as a normal megathrust event, namely an earthquake whose rupture does not extend to the near-trench region (Newman and Convers, 2010), supporting the rupture near the coast as indicated by our source model.
The underestimation of uplift in the Arauco Peninsula may suggest that the southern rupture is not well resolved by our model. The source models of Lay et al. (2010) and Delouis et al. (2010) also display small slip in this region, suggesting a lack of the resolution for the southern rupture in teleseismic waveform inversions in general. A possible explanation for the large observed uplift values at the Arauco Peninsula as well as the absence of coseismic fault slip obtained from teleseismic inversions beneath the peninsula, could be the occurrence of large post-seismic deformation in the region. However the large concentration of early aftershocks of the Maule earthquake beneath the Peninsula suggests a coseismic fault rupture in this region (Moreno et al., 2010). On the other hand a recently published slip model of the Maule earthquake based on the inversion of geodetic and tsunami data indicates larger slip in the southern region as compared to those from seismological models (Lorito et al., 2011). This difference may indicate different rupture characteristics in the southern and northern regions in which the northern region radiated strong seismic waves, while the southern rupture was characterized by slower rupture processes, inefficient in seismic radiation. This would explain why geodetic data better resolved the southern rupture. Further studies are required to investigate the characteristics of the southern rupture beneath the Arauco Peninsula.
We would like to thank Sergio Barrientos and Jaime Campos of the Universidad de Chile for sharing the strong motion data used in this study. The high-sampling GPS data were obtained from the TIGO observatory homepage. This study was partly supported by the SATREPS project “Enhancement of Earthquake and Tsunami Mitigation Technology in Peru”. We thank the editor-in-chief Prof. Yomogida as well as two anonymous referees for helpful comments. Teleseismic data was downloaded from the IRIS-DMC web site (http://www.iris.edu), and the gCMT mechanism was obtained from the global CMT project web site (http://www.globalcmt.org).
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