标题： 一种新的实用且有效的与源无关的全波形反演方法，该方法采用速度分布支持的深度图像先验：对两个真实数据集的应用 显示英文标题

标题： A new practical and effective source-independent full-waveform inversion with a velocity-distribution supported deep image prior: Applications to two real datasets

摘要： 全波形反演（FWI）是一种通过逐步减小观测数据和预测地震数据之间的差异来重建高分辨率地下物理参数的先进技术。 然而，传统的FWI在实际数据应用中面临挑战，主要是由于其传统目标是直接测量数据不匹配。 准确估计源波列对于有效的数据拟合至关重要，同时还需要低频数据和合理的初始模型以防止周期跳跃。 此外，在实际应用中，波动方程求解器往往难以准确模拟观测数据的振幅。 为了解决这些挑战，我们引入了一种基于相关性的源无关目标函数用于FWI，旨在减轻源不确定性与振幅依赖性，从而有效提高其在实际数据应用中的实用性。 我们开发了一个受此新目标函数约束的深度学习框架，该框架采用由速度分布支持的深度图像先验，将速度反演重新参数化为自动编码器内的可训练参数，从而降低了传统FWI目标函数中的非线性。 我们使用基准速度模型的合成数据以及更重要的是两个真实数据集来证明我们所提出方法的优越性。 这些例子表明，即使在缺失低频、初始速度模型粗糙和源波列错误等具有挑战性的条件下，该方法也表现出有效性和实用性。 显示英文摘要

摘要： Full-waveform inversion (FWI) is an advanced technique for reconstructing high-resolution subsurface physical parameters by progressively minimizing the discrepancy between observed and predicted seismic data. However, conventional FWI encounters challenges in real data applications, primarily due to its conventional objective of direct measurements of the data misfit. Accurate estimation of the source wavelet is essential for effective data fitting, alongside the need for low-frequency data and a reasonable initial model to prevent cycle skipping. Additionally, wave equation solvers often struggle to accurately simulate the amplitude of observed data in real applications. To address these challenges, we introduce a correlation-based source-independent objective function for FWI that aims to mitigate source uncertainty and amplitude dependency, which effectively enhances its practicality for real data applications. We develop a deep-learning framework constrained by this new objective function with a velocity-distribution supported deep image prior, which reparameterizes velocity inversion into trainable parameters within an autoencoder, thereby reducing the nonlinearity in the conventional FWI's objective function. We demonstrate the superiority of our proposed method using synthetic data from benchmark velocity models and, more importantly, two real datasets. These examples highlight its effectiveness and practicality even under challenging conditions, such as missing low frequencies, a crude initial velocity model, and an incorrect source wavelet.