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Uncertainties in the 2004 Sumatra-Andaman source through nonlinear stochastic inversion of tsunami waves
D Gopinathan1, M Venugopal2, D Roy3
1Department of Statistical Science, University College London, London WC1E 6BT, UK.
Proceedings. Mathematical, Physical, and Engineering Sciences
|October 10, 2017
Summary
This study uses satellite data to invert earthquake source parameters for the 2004 Sumatra tsunami. Large uncertainties highlight the need for advanced physical models to improve tsunami early warning systems.
Area of Science:
- * Geophysics
- * Tsunami Science
- * Seismology
Background:
- * Traditional tsunami source inversions rely on subjective methods and suffer from data instability.
- * Deterministic inversions often face non-uniqueness and instability issues due to noisy or insufficient data.
- * Existing methods inadequately address the complexities of tsunami generation and propagation.
Purpose of the Study:
- * To apply satellite altimetry data for inverting earthquake source parameters of the 2004 Sumatra-Andaman tsunami.
- * To incorporate kinematic parameters for a more accurate description of tsunami generation and propagation.
- * To perform a novel nonlinear joint inversion with minimal prior constraints.
Main Methods:
- * Employed satellite altimetry data from the 2004 Sumatra-Andaman tsunami event.
- * Utilized a finite fault model incorporating trench geometry and rupture extent.
- * Conducted a nonlinear joint inversion of slips, rupture velocities, and rise times.
Main Results:
- * Achieved persistently good waveform fits during the inversion process.
- * Identified significant uncertainties in the joint parameter distribution as a key outcome.
- * Demonstrated the potential of kinematic parameters in refining tsunami source descriptions.
Conclusions:
- * Large uncertainties in inversion parameters indicate limitations of current deterministic approaches.
- * Advanced physical models of seabed deformation are crucial for enhancing tsunami early warning systems.
- * Objective inversion strategies require more sophisticated modeling to overcome data limitations and improve reliability.
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