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

地震成像是一种基于穿越地球内部的地震波绘制地下结构的技术，它生成了地壳和地幔的迷人图像，这些图像对理解地球动力学过程和地质事件至关重要，这些过程和地质事件塑造了地球的构造演化。另一方面，地震学致力于了解地震这一对人类最具破坏性的自然灾害之一，包括地震的位置、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灾害评估和减轻风险提供了重要指导，特别是对地震易发地区而言。基于精确数值方法模拟地震波传播的能力，如过去二十年发展起来的谱元法（SEM），使创新的、基于数值模拟的全波形反演（FWI）技术应用于高分辨率成像，以及基于三维格林函数的复杂背景模型的震源反演成为可能。我们的团队继续致力于开发基于最先进的开源SEM软件包的FWI功能，并通过提高其数值效率和探索新的反演策略，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，如环境噪声和远震体波，用于阵列成像。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上地幔生成第一个高分辨率各向同性和径向各向异性模型。为了利用环境噪声和远震波数据集的互补性，我们建议开发联合环境噪声和远震波FWI方法，并将联合FWI应用于加利福尼亚中部板块边界区域的高分辨率成像。我们还探索了基于格林函数的源反演方法，利用SEM求解器计算改进的三维区域模型。我们提出了基于三维数值应变-格林张量（SGT）数据库存储的近实时震源机制反演的高效工作流程，该流程可作为常规地震台网操作。这些新开发的结构成像和震源反演能力将加强我们作为领先的计算地震学研究小组之一的实力，并有助于培养下一代hqp，这些hqp具备并行计算、大型地震数据集处理、先进成像和震源表征方法以及机器学习等关键技能。

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.