Abstract: Compressive sensing provides a novel approach to address the prohibitively expensive computational cost in the least-squares imaging of seismic data.By down-sampling the source and receiver wavefields, the proposed method greatly reduces the number of wave equations solved in the imaging process.However, down-sampling also leads to ill-posedness of the imaging system.By introducing sparsity constraints, compressive sensing stabilizes the inversion process.In this article, we review the principles of least-squares imaging using compressive sensing and discuss methods for extending this technique to address the issue of unknown source wavelets and to eliminate interferences from strong surface-related multiples.In addition, we review synthetic and field data examples that demonstrate the efficacy of the method.