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.

介绍了 IntraSeismic，一种新颖的混合地震反演方法，具有 2D 和 3D 后处理地震反演中无与伦比的性能、快速收敛速率、无缝包含硬约束条件（即井数据）和执行不确定性量化的能力，同时还具备数据压缩和对反演模型部分的快速随机访问的潜力。通过合成和现场数据的应用验证了该方法的有效性。

地震反演的基于坐标的学习方法：IntraSeismic

IntraSeismic: a coordinate-based learning approach to seismic inversion

Imaging Earth structure or seismic sources from seismic data involves
minimizing a target misfit function, and is commonly solved through
gradient-based optimization. The adjoint-state method has been developed to
compute the gradient efficiently; however, its implementation can be
time-consuming and difficult. We develop a general seismic inversion framework
to calculate gradients using reverse-mode automatic differentiation. The
central idea is that adjoint-state methods and reverse-mode automatic
differentiation are mathematically equivalent. The mapping between numerical
PDE simulation and deep learning allows us to build a seismic inverse modeling
library, ADSeismic, based on deep learning frameworks, which supports high
performance reverse-mode automatic differentiation on CPUs and GPUs. We
demonstrate the performance of ADSeismic on inverse problems related to
velocity model estimation, rupture imaging, earthquake location, and source
time function retrieval. ADSeismic has the potential to solve a wide variety of
inverse modeling applications within a unified framework.

本研究提出了基于反向模式自动微分的地震反演算法框架，该框架可以通过深度学习框架支持高性能反向模式自动微分，可应用于包括速度模型估计、震源成像、地震定位和震源函数重构等广泛的反演建模应用。