Time-lapse full-waveform inversion (FWI) has become a powerful tool for characterizing and monitoring subsurface changes in various geophysical applications. However, non-repeatability (NR) issues caused, for instance, by GPS inaccuracies, often make it difficult to obtain unbiased time-lapse models. In this work we explore the portability of combining a receiver-extension FWI approach and Bayesian analysis to mitigate time-lapse noises arising from NR issues. The receiver-extension scheme introduces an artificial degree of freedom in positioning receivers, intending to minimize kinematic mismatches between modeled and observed data. Bayesian analysis systematically explores several potential solutions to mitigate time-lapse changes not associated with reservoir responses, assigning probabilities to each scenario based on prior information and available evidence. We consider two different subsurface models to demonstrate the potential of proposed approaches. First, using the Marmousi model, we investigate two NR scenarios associated with background noise in seismic data. Second, using a challenging deep-water Brazilian pre-salt setting, we investigate several NR scenarios to simulate real-world challenges. Our results demonstrate that combining Bayesian analysis with the receiver-extension FWI strategy can mitigate adverse NR effects successfully, producing cleaner and more reliable time-lapse models than conventional approaches. The results also reveal that the proposed Bayesian weighted procedure is a valuable tool for determining time-lapse estimates through statistical analysis of pre-existing models, allowing its application in ongoing time-lapse (4D) projects.

翻译：时移全波形反演已成为表征和监测地下变化的重要工具，广泛应用于各类地球物理应用。然而，由GPS误差等因素引起的非重复性问题常导致难以获得无偏的时移模型。本研究探讨了结合接收器扩展全波形反演方法与贝叶斯分析来缓解非重复性问题引起的时移噪声的可行性。接收器扩展方案通过引入接收器定位的人工自由度，旨在最小化模拟数据与观测数据之间的运动学失配。贝叶斯分析系统性地探索多种潜在解决方案，以消除与储层响应无关的时移变化，并依据先验信息和现有证据为每种情景分配概率。我们采用两种不同的地下模型来验证所提方法的潜力。首先，基于Marmousi模型，我们研究了与地震数据背景噪声相关的两种非重复性情景。其次，通过具有挑战性的巴西深水盐下地质场景，我们探究了多种非重复性情景以模拟实际挑战。结果表明，贝叶斯分析与接收器扩展全波形反演策略的结合能有效缓解非重复性效应，相比传统方法可生成更清晰可靠的时移模型。研究还表明，所提出的贝叶斯加权方法能通过对现有模型的统计分析确定时移估计量，为正在进行的时移（四维）项目提供了有价值的分析工具。