  Inspired by recent work on extended image volumes that lays the ground for
randomized probing of extremely large seismic wavefield matrices, we present a
memory frugal and computationally efficient inversion methodology that uses
techniques from randomized linear algebra. By means of a carefully selected
realistic synthetic example, we demonstrate that we are capable of achieving
competitive inversion results at a fraction of the memory cost of conventional
full-waveform inversion with limited computational overhead. By exchanging
memory for negligible computational overhead, we open with the presented
technology the door towards the use of low-memory accelerators such as GPUs.
