Accelerating Full-Waveform Inversion by Stochastic and Adaptive Event Subsampling

Session: Full-Waveform Inversion: Recent Advances and Applications [Poster]
Type: Poster
Date: 4/29/2020
Time: 08:00 AM
Room: Ballroom
Description: 

We present a full-waveform inversion based on dynamic mini-batch optimization, which naturally exploits redundancies in observed data from different sources. This reduces computational costs and enables the flexible integration of new data.

Quasi-random subsets (mini-batches) of sources are used to approximate the misfit and gradient of the complete dataset. The size of the mini-batch is dynamically controlled by the desired quality of the approximation of the full gradient. Within each mini-batch, redundancy is minimized by selecting sources with the largest angular differences between their respective gradients, and spatial coverage is maximized by selecting candidate events with Mitchell’s best-candidate algorithm. Information from sources not included in a specific mini-batch is incorporated into each gradient calculation through a quasi-Newton approximation of the Hessian, and a consistent misfit measure is achieved through the inclusion of a control group of sources.

By design, the dynamic mini-batch approach has several main advantages: (1) The use of mini-batches with adaptive size ensures that an optimally small number of sources is used in each iteration, thus potentially leading to significant computational savings. (2) Curvature information is accumulated and preserved during the inversion, using a stochastic quasi-Newton method. (3) Data from new events or different time windows can seamlessly be incorporated during the iterations, thereby enabling an evolutionary mode of full-waveform inversion.

To illustrate our method, we start an inversion for upper-mantle structure beneath the African plate. Starting from a smooth 1-D background model for a dataset recorded in the years 1990 to 1995, we then sequentially add more and more recent data into the inversion and show how the method allows a model to evolve as a function of data coverage. The mini-batch sampling approach allows us to incorporate data from several hundred earthquakes without increasing the computational burden, thereby going significantly beyond previous regional-scale full-waveform inversions.

Presenting Author: Dirk Philip van Herwaarden

Authors