Traditional seismic inversion (SI) maps the hundreds of terabytes of raw-field data to subsurface properties in gigabytes. This inversion process is expensive, requiring over a year of human and computational effort. Recently, data-driven approaches equipped with Deep learning (DL) are envisioned to improve the efficiency of SI. However, these improvements are restricted to data with highly reduced scale and complexity. To the best of our knowledge, this is the first successful demonstration of…