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.

中文翻译：

---

随机轨迹估计的超低记忆地震反演

受近期关于扩展图像量的研究的启发，该研究为超大型地震波场矩阵的随机探测奠定了基础，我们提出了一种使用自由线性代数技术的存储节俭和计算有效的反演方法。通过精心选择的现实综合示例，我们证明了我们能够以有限的计算开销，以传统全波形反演的存储成本的一小部分获得有竞争力的反演结果。通过将内存交换为可忽略的计算开销，我们使用提出的技术打开了使用低内存加速器（例如GPU）的大门。