5. Data misfit 3. Full Bayesian Analysis of the Final Slip Distribution 3.1 Used data > InSAR: RadarSAT-2, ascending and descending orbit > GPS networks: TO, PBO. > Vertical offsets from Tide gauges > Tsunami: DART stations 3.1 The forward problem > Solution: 3.2 Calculation of Cp based on the physics of the problem > A perturbation approach 2. The 2014 Pisagua earthquake sequence 2.1 Tectonic setting The 2014 Pisagua earthquake occurred in the northern portion of the north Chilean seismic gap which last ruptured in 1877 megathrust earthquake (Mw=8.8). Before 1877, it is unclear whether the region failed in huge single ruptures or in sequences of smaller ruptures The 2014 mainshock was preceded by two weeks of intense foreshock activity and followed by a large Mw=7.7 aftershock. 2.2 W-phase moment tensor inversion We compute a point-source moment tensor inversion of 85 manually selected low frequency (1–5 mHz) W-phase waveforms within an epicentral distance of 85°.This yields an almost purely double-couple solution consistent with Global CMT and the local slab geometry (Green mechanisms in Fig. 1). The Mw=8.1 Pisagua Earthquake of 1 April 2014 Z. Duputel, J. Jiang, R. Jolivet, M. Simons, L. Rivera, B. Riel, J.-P. Ampuero, S. Minson, H. Zhang, N. Cotte, E. Fielding, J. Klotz, A. W. Moore, E. O. Norabuena, S. E. Owen, S. Samsonov and A. Socquet 1. Abstract We investigate the rupture process of the 2014 Mw=8.1 Pisagua earthquake using ALTAR: an innovative Bayesian algorithm that is now implemented to run on GPU and allows sampling of posterior PDFs for high-dimensional problems. Our observations include InSAR, GPS and Tsunami data. We include a full data covariance matrix to account for measurement and prediction uncertainties. This covariance model provides a physical rationale for the relative weighting between available datasets and provides more realistic estimates of uncertainty on the inferred parameters. Our results indicate a relatively compact slip zone located down dip of the hypocenter. The absence of shallow rupture is mainly controlled by tsunami observations. In the same way, the 1995 Antofagasta and the 2007 Tocopilla earthquakes did not involve any slip near the trench. Using land-based geodetic data, it is fundamentally difficult to know if shallow portions of the north Chilean subduction zone are seismogenic or not. Therefore the occurrence of a major event rupturing the southern portion of the seismic gap and driving slip at shallow depth is plausible. However, the possibility of multiple smaller events that would progressively release the accumulated strain cannot be ruled out. Exact theoryStochastic (non-deterministic) theory p(d|m) = δ(d – g(,m))p(d|m) = N(d | g(,m), C p ) δg = K µ. δln C p = K µ. C µ. K µ T Observational errorPrediction uncertainty Partial derivatives w.r.t. the elastic parameters (sensitivity kernel) Covariance matrix describing uncertainty in the Earth model parameters Figure 2. Preliminary posterior mean slip model for the Mw=8.1 Pisagua earthquake. The color of each subfault patch indicates the slip amplitude. Red star is the hypocenter and Blue star is the W-phase centroid location. Figure 3. Model performance for InSAR data. Observations, synthetics and residuals are shown (top) for descending data, (bottom) for ascending data 6. Conclusion > The slip inversion is conducted in a Bayesian framework accounting for uncertainty in the elastic structure used to compute our predictions. > Our slip inversion results indicate a somewhat compact slip zone located south of the hypocenter. > This model can explain InSAR, GPS, tide gauges offsets and DART data reasonably well. > The Mw=8.1 Pisagua earthquake ruptured relatively small portion of the north Chilean seismic gap. > A Shallower slip zone leads to precursory tsunami arrivals that cannot be explained by DART data. > The remaining unbroken region is limited to the south by the 1995 Antofagasta (Mw=8.1) and the 2007 Tocopilla (Mw=7.7) events. > These events did not involve any shallow rupture, which brings interesting questions regarding the possibility of large slip close to the trench. Figure 4. Model performance for GPS, tide gauges and DART data. (left) GPS and tide gauges. (right) DART data. Arrows are GPS displacements and circles are tide gauges offsets. For GPS and DART data, observations are shown in black and synthetics are shown in red. For tide gauges, outer circles are observations and inner circles are predictions. Figure 1. The 2014 Pisagua earthquake sequence. Green focal mechanisms are W-phase solutions for the mainshock and the largest aftershock. Foreshocks from until are presented in Blue. Aftershocks until are shown in red. GCMT solutions are presented for events with Mw>6. Red contours are ruptures of the 1995, Mw 8.1, Antofagasta, the 2001, Mw 8.4, Arequipa, and the 2007, Mw 7.7 Tocopilla earthquakes. 4. Bayesian estimation of slip distribution We assume a curved fault derived from the distribution of seismicity and focal mechanisms. The slip is parameterized with piece-wise linear “tent” functions using a triangular mesh. This type of parameterization allows continuity of slip on the fault plane. The slip is inverted using a large number of transitional Markov Chains exploring the model space in parallel. This massive exploration of the parameter space is done using ALTAR, which exploit the capability of multiple GPUs on large computing clusters.