Seismic inversion aims to obtain source properties or structural parameters by matching the predicted synthetics with the observed seismic data. It generally involves three aspects: (1) a good objective function that incorporates proper metric of mismatch between synthetics and data, prior knowledge, and appropriate parameterization of the problem, (2) an efficient and robust minimization technique, and (3) a comprehensive analysis of uncertainty that enables meaningful model comparison and interpretation. Developments in all three aspects can significantly improve the performance of seismic inversion.

Therefore, this session seeks presentations on approaches to construct objective functions, techniques to solve minimization problems and methods to quantify uncertainties in seismic inversion. We welcome advances in both source and structural inversion, both ray-based and full-waveform seismic inversion, and both deterministic and randomized inversion method. We also encourage contributions in deep learning techniques applied to solving seismic inversion problems.