基于卡尼亚达基斯-高斯分布的图形空间最佳运输方法,用于与波传播相关的反向问题
Sérgio Luiz E F da Silva1,2, João M de Araújo3, Erick de la Barra4
1Department of Applied Science and Technology, Politecnico di Torino, 10129 Torino, Italy.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种新的全波形反转 (FWI) 方法,使用卡尼亚达基 κ-高斯分布和最佳传输理论. 这种新的方法有效地解决了地震数据中的非高斯噪声和循环跳转问题.
科学领域:
- 地质物理学 地质物理学
- 反向问题 逆向问题
- 数据科学数据科学数据科学
背景情况:
- 全波形逆转 (FWI) 对于从地震数据中推断地下物质至关重要.
- 标准的FWI方法与非高斯噪声和跳转周期作斗争,限制了模型的准确性.
- 强大的目标功能对于克服这些FWI挑战至关重要.
研究的目的:
- 开发一种新的FWI目标函数,能够抵御非高斯噪声和相位模两可.
- 为了减轻传统FWI固有的跳转周期问题.
- 加强FWI模型的收和解决方案.
主要方法:
- 利用了卡尼亚达基斯 κ-高斯分布和最佳运输 (OT) 理论.
- 通过概率最大概率构建了一个k-目标函数.
- 在康托罗维奇-鲁宾斯坦度量 (一个正确的OT公式) 中集成了 κ-目标函数.
- 在图形空间中表示数据,以满足康托罗维奇-鲁宾斯坦框架的概率公理.
主要成果:
- 拟议的 κ-Graph-Space最佳运输FWI (κ-GSOT-FWI) 有效地规避了非高斯噪声和循环跳转问题.
- 卡尼亚达基的 κ-统计数据显著改善了 FWI 目标的功能趋同.
- 与经典的FWI技术相比,实现了更高分辨率的地下模型,特别是 κ=0.6.6.
结论:
- κ-GSOT-FWI为具有挑战性的FWI场景提供了一个强大的解决方案.
- 卡尼亚达基统计和OT理论的整合提高了FWI的表现.
- 这种方法导致更准确,更详细的地质物理模型.
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