We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning.

翻译：我们介绍了地震成像与模拟/监测（SLIM）开源软件框架，该框架专为计算地球物理学设计，更广泛而言适用于涉及波动方程（如地震与医学超声）的反问题、基于学习先验的正则化，以及多相流模拟的学习型神经替代模型。通过整合多层抽象架构，我们的软件兼具可读性与可扩展性，使研究者能够以抽象方式便捷地构建问题，同时充分利用高性能计算领域的最新进展。我们以时移井间地震数据渗透率反演可扩展原型构建为例，阐述并验证了设计原则及其优势——该过程除耦合波动物理与多相流动外，还涉及机器学习技术。