Full‐waveform inversion (FWI) is today a standard process for the inverse problem of
seismic imaging. PDE‐constrained optimization is used to determine unknown parameters …

Mean-field games arise in various fields, including economics, engineering, and machine
learning. They study strategic decision-making in large populations where the individuals …

The Wasserstein distance serves as a loss function for unsupervised learning which
depends on the choice of a ground metric on sample space. We propose to use the …

Conventional full-waveform seismic inversion (FWI) tries to estimate a subsurface model that
can accurately predict surface records by minimizing an-norm misfit between observed and …

Seismic full waveform inversion (FWI) represents a state-of-the-art technique for estimating
the parameter model. Conventional FWI faces the challenge of cycle skip**, because it …

Full waveform inversion (FWI) is an important and popular technique in subsurface Earth
property estimation. In this paper, several improvements to the FWI methodology are …

We present a Bayesian framework based on a new exponential likelihood function driven by
the quadratic Wasserstein metric. Compared to conventional Bayesian models based on …

In this paper, we apply the Wasserstein-Fisher-Rao (WFR) metric from the unbalanced
optimal transport theory to the earthquake location problem. Compared with the quadratic …

The quadratic Wasserstein metric has shown its power in measuring the difference between
probability densities, which benefits optimization objective function with better convexity and …

In seismic exploration, sources and measurements of seismic waves on the surface are
used to determine model parameters representing geophysical properties of the earth. Full …