A Two-Stage Strategy for Q Full-WaveformInversion in Viscoacoustic Media with Adam Optimization

Accurate characterization of subsurface attenuation hinges on the quality factor (Q), and high-fidelity Q models are indispensable for high-resolution imaging in exploration seismology. In principle, full-waveform inversion (FWI) offers the most accurate framework for parameter estimation, and decades of research have delivered substantial advances. Despite this, Q-FWI remains challenging in attenuating media, as velocity and Q exhibit strong crosstalk. Additionally, the sensitivity of data to Q is much weaker than to velocity, and absorption further diminishes signal strength. To address these issues, we introduce a staged FWI workflow that leverages the Adam optimization algorithm. To mitigate velocity–Q crosstalk in multiparameter inversion, we adopt a two-stage synchronous (joint–reset–joint) scheme: in stage one, velocity and Q are inverted jointly but the artifact-contaminated Q model is discarded; in stage two, velocity and Q are again inverted jointly using the updated velocity and the initial Q model. To avoid the additional trial forward simulations required by line-search step-length estimation, we use the Adam optimizer with parameter-specific adaptive updates. Numerical experiments on three synthetic models show that this workflow reduces velocity-related artifacts in the recovered Q models and improves the stability of dual-parameter viscoacoustic FWI.