Full-Waveform Inversion (FWI) is a nonlinear iterative seismic imaging
technique that, by reducing the misfit between recorded and predicted seismic
waveforms, can produce detailed estimates of subsurface geophysical properties.
Nevertheless, the strong nonlinearity of FWI can trap the optimization in local
minima. This issue arises due to factors such as improper initial values, the
absence of low frequencies in the measurements, noise, and other related
considerations. To address this challenge and with the advent of advanced
machine-learning techniques, data-driven methods, such as deep learning, have
attracted significantly increasing attention in the geophysical community.
Furthermore, the elastic wave equation should be included in FWI to represent
elastic effects accurately. The intersection of data-driven techniques and
elastic scattering theories presents opportunities and challenges. In this
paper, by using the knowledge of elastic scattering (Physics of problem) and
integrating it with deep learning techniques, we propose methods for the
solution of time-harmonic FWI to enhance accuracy compared to pure data-driven
approaches. Moreover, by modifying the structure of the Variational
Autoencoder, we introduce a probabilistic deep learning method based on the
physics of the problem that enables us to explore the uncertainties of the
solution. According to the limited availability of datasets in this field and
to assess the performance and accuracy of the proposed methods, we create a
comprehensive dataset close to reality and conduct a comparative analysis of
the presented approaches to it.

基于弹性散射理论和深度学习技术，本文提出了一种基于物理问题的时间谐波全波形反演方法，以增强解的准确性，并通过修改变分自编码器的结构引入了一种基于物理问题的概率深度学习方法，能够探索解的不确定性。为了评估提出的方法的性能和准确性，并鉴于该领域数据集的有限可用性，我们创建了一个逼近实际的综合数据集，并对所提出的方法进行了比较分析。

弹性全波形反演：问题的物理学如何改善数据驱动技术？

Elastic Full-Waveform Inversion : How the physics of problem improves  data-driven techniques?

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

Seismic image analysis plays a crucial role in a wide range of industrial
applications and has been receiving significant attention. One of the essential
challenges of seismic imaging is detecting subsurface salt structure which is
indispensable for identification of hydrocarbon reservoirs and drill path
planning. Unfortunately, exact identification of large salt deposits is
notoriously difficult and professional seismic imaging often requires expert
human interpretation of salt bodies. Convolutional neural networks (CNNs) have
been successfully applied in many fields, and several attempts have been made
in the field of seismic imaging. But the high cost of manual annotations by
geophysics experts and scarce publicly available labeled datasets hinder the
performance of the existing CNN-based methods. In this work, we propose a
semi-supervised method for segmentation (delineation) of salt bodies in seismic
images which utilizes unlabeled data for multi-round self-training. To reduce
error amplification during self-training we propose a scheme which uses an
ensemble of CNNs. We show that our approach outperforms state-of-the-art on the
TGS Salt Identification Challenge dataset and is ranked the first among the
3234 competing methods.

该研究提出了一种半监督方法，利用未标记的数据进行多轮自我训练，使用卷积神经网络以及 CNNs 的集合来减少自我训练过程中的误差放大，以检测地震图像中的盐体，实验证明该方法在 TGS Salt Identification Challenge 数据集上性能优于现有方法，位列 3234 种竞争方法的第一位。