During this talk, Felix Herrmann will explain how ideas from compressive sensing and big data can be used to reduce costs of seismic data acquisition and wave-equation based inversion. The key idea is to explore structure within the data by deliberately breaking this structure with randomized sampling, e.g., by randomizing source/receiver positions or by source encoding, followed by an optimization procedure that restores the structure and therefore recovers the fully sampled data. These techniques not only underpin recent advances in missing trace interpolation and simultaneous acquisition but they are also responsible for significant improvements in full-waveform inversion and reverse-time migration. These concepts will be illustrated using a variety of compelling examples on real and field data.
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