Seismic waveforms are governed by the physical properties of the subsurface in which they propagate. Most modern conventional seismic imaging methods utilize only a small proportion of the seismic waveform, neglecting a wealth of additional information hidden within the full coda (Yilmaz, 2001). Full-waveform inversion (FWI) utilizes the entire seismic coda to produce accurate quantitative models of these governing properties (P-and S-wave velocity, attenuation, density, and anisotropy) (Tromp, 2020). First theorized in the 1980s, the FWI method looks to match the observed wavefield by iteratively updating a starting model, using linearized local inversion, to solve the full nonlinear inversion problem (Lailly & Bednar, 1983;Tarantola, 1984;Virieux & Operto, 2009). However, it took three decades for FWI to become widely utilized, particularly to higher frequencies because of its high computational requirements. Each doubling of the maximum FWI frequency requires the node spacing and sample interval to be halved, leading to an eight and 16 times increase in compute time in 2D and 3D, respectively, making high-frequency FWI prohibitively expensive. In the meantime, seismic reflection imaging has been the prime method for active-source subsurface imaging and exploration (Yilmaz, 2001).