Sparse sensing is a technique for finding sparse signal representations to underdetermined linear measurement equations. We use sparse sensing to locate seismic sources during the rupture of the 2011 Mw9.0 earthquake in Japan from teleseismic P waves recorded by a seismic sensor array of stations in the United States. The location estimates of the seismic sources are obtained by minimizing the square of ℓ2-norm of the difference between the observed and modeled waveforms penalized by the ℓ1-norm of the seismic source vector. The resulting minimization problem is convex and can be solved efficiently using LASSO type optimization. The potential to track the rupture sequentially is demonstrated.
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