<p>Full waveform inversion (FWI) is an advanced velocity modeling method. FWI using active seismic data has high-resolution potential but often faces challenges dominated by cycle-skipping due to the insufficient low-frequency components. The ambient noise contains rich low-frequency components, but the inversion resolution is generally low. Joint FWI utilizing the complementary advantages of active and passive seismic data enables more accurate recovery of subsurface velocity structures. However, the conventional serial joint strategy is strongly dependent on the accuracy of passive source FWI, and complex interference from ambient noise introduces artifacts into inversion results, leading to instability of the serial strategy. To mitigate this problem, we develop a dynamically weighted parallel joint FWI. Firstly, an attenuated spatiotemporal window function combined with multidimensional deconvolution (MDD) seismic interferometry is designed to retrieve broadband body-wave Green’s functions from ambient noise. Subsequently, a weight function dynamically varying with iterations is established to integrate active and passive source data into a unified objective function framework, and the contributions of multi-source data are simultaneously utilized to construct a joint gradient for velocity reconstruction. Furthermore, we investigate the influence of parameter variations controlling the weight function on joint inversion performance, suggesting two parameter selection strategies, and verify their applicability and relative robustness. Numerical examples demonstrate that the proposed parallel joint FWI can effectively fuse the respective advantages of active seismic data and ambient noise, and robustly achieve high-resolution velocity modeling.</p>