Full waveform inversions and source characterization based on spectral-element simulations: innovative datasets and inversion strategies

基于谱元素模拟的全波形反演和源表征：创新数据集和反演策略

• 批准号: RGPIN-2021-03442
• 负责人: Liu, Qinya
• 金额: $ 2.19万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=762706
• 关键词: Full; waveform; inversions; source; characterization

Seismic imaging, the mapping of subsurface structures based on seismic waves traversing through the Earth's interior, has generated fascinating images of the Earth's crust and mantle which prove to be critical for understanding geodynamic processes and geological events that shaped the tectonic evolution of the Earth. On the other hand, seismology engages in understanding earthquakes, one of the most damaging natural disasters to mankind, including their locations, source mechanisms, seismic wave propagation and the intensity of induced ground shaking. The collection of this essential information offers crucial guidance on long-term seismic hazard assessment and risk mitigation, especially for earthquake-prone regions. The capability to simulate seismic wave propagation based on accurate numerical methods, such as the spectral-element method (SEM) developed in the past two decades, enabled the application of innovative, numerical-simulation-based full waveform inversion (FWI) techniques for high-resolution imaging, as well as seismic source inversions based on 3D Greens functions for complex background models. Our group continues to work at the forefront of developing capabilities for FWI based on open-source state-of-the-art SEM software packages and facilitate its adoption by the wider seismic community by improving its numerical efficiency and exploring new inversion strategies. We also apply FWI to innovative datasets, such as ambient-noise and teleseismic body waves, for array imaging. We plan to generate the first high-resolution isotropic and radially anisotropic models for the crust and upper-most mantle of the entire Alaska region based on multi-component ambient-noise FWI. we propose to develop joint ambient-noise and teleseismic-wave FWI method to take advantage of the complementary sensitivity of these two datasets, and apply the joint FWI to high-resolution imaging beneath central California plate boundary region. We also explore source inversion methods based on Greens functions computed for improved 3D regional models by SEM solvers. We propose to develop efficient workflows for near-realtime source mechanism inversions based on the storage of 3D numerical strain-Greens tensor (SGT) database that can be adopted as a regular seismic network operation. These newly developed structural imaging and source inversion capabilities will strength us as one of the leading computational seismology research groups and help train next-generation HQPs equipped with critical skills in parallel computing, large seismic dataset processing, advance imaging and source characterization methods, and machine learning.

地震成像是基于穿过地球内部的地震波绘制的地下结构图，它生成了令人着迷的地壳和地幔图像，事实证明，这些图像对于理解地球动力学过程和塑造地球构造演化的地质事件至关重要。另一方面，地震学致力于了解地震，这是对人类最具破坏性的自然灾害之一，包括其位置、震源机制、地震波传播和诱发地面震动的强度。这些基本信息的收集为长期地震危险评估和减轻风险提供了至关重要的指导，特别是对地震多发地区。基于精确的数值方法模拟地震波传播的能力，例如过去二十年发展起来的谱元素法(SEM)，使得创新的、基于数值模拟的全波形反演(FWI)技术能够用于高分辨率成像，以及用于复杂背景模型的基于3D格林函数的震源反演。我们的团队继续致力于基于开源最先进的扫描电子显微镜软件包开发FWI能力的前沿，并通过提高其数值效率和探索新的反演策略来促进其被更广泛的地震界采用。我们还将FWI应用于创新的数据集，例如环境噪声和远程地震体波，以进行阵列成像。我们计划基于多分量环境噪声FWI为整个阿拉斯加地区的地壳和最上地幔建立第一个高分辨率的各向同性和径向各向异性模型。我们建议开发环境噪声和远震波联合FWI方法，以利用这两个数据集的互补敏感性，并将联合FWI应用于加州中部板块边界区域下的高分辨率成像。我们还探索了基于改进的三维区域模型的格林函数的震源反演方法。我们建议基于三维数值应变-格林张量(SGT)数据库的存储来开发高效的近实时震源机制反演工作流程，该数据库可用作常规地震台网操作。这些新开发的构造成像和震源反演能力将增强我们作为领先计算地震学研究小组之一的实力，并帮助培训配备了并行计算、大型地震数据集处理、高级成像和震源表征方法以及机器学习方面关键技能的下一代HQP。