Seismic imaging is the numerical process of creating a volumetric representation of the subsurface geological structures from elastic waves recorded at the surface of the Earth. As such, it is widely utilized in the energy and construction sectors for applications ranging from oil and gas prospection, to geothermal production and carbon capture and storage monitoring, to geotechnical assessment of infrastructures. Extracting quantitative information from seismic recordings, such as an acoustic impedance model, is however a highly ill-posed inverse problem, due to the band-limited and noisy nature of the data. This paper introduces IntraSeismic, a novel hybrid seismic inversion method that seamlessly combines coordinate-based learning with the physics of the post-stack modeling operator. Key features of IntraSeismic are i) unparalleled performance in 2D and 3D post-stack seismic inversion, ii) rapid convergence rates, iii) ability to seamlessly include hard constraints (i.e., well data) and perform uncertainty quantification, and iv) potential data compression and fast randomized access to portions of the inverted model. Synthetic and field data applications of IntraSeismic are presented to validate the effectiveness of the proposed method.

翻译：地震成像是利用地表记录的弹性波对地下地质结构进行三维立体建模的数值过程，在能源与建筑领域具有广泛应用，涵盖油气勘探、地热开发、碳捕集与封存监测，以及基础设施岩土工程评估等场景。然而，由于地震数据带限且包含噪声，从中提取声阻抗模型等定量信息构成了一个高度病态的反演问题。本文提出IntraSeismic——一种新型混合地震反演方法，该方法将坐标驱动学习与叠后模型化算子的物理过程无缝融合。IntraSeismic的关键特性包括：i）在二维和三维叠后地震反演中展现卓越性能，ii）快速收敛速率，iii）可无缝集成硬约束（如测井数据）并实现不确定性量化，iv）具备数据压缩能力及对反演模型区域的快速随机访问。通过合成数据与野外数据应用验证了该方法的有效性。