CMG: Collaborative Research: Multi-Scale (Wave Equation) Tomographic Imaging with USArray Waveform Data

CMG：协作研究：使用 USArray 波形数据进行多尺度（波方程）断层成像

• 批准号: 0723759
• 负责人: Ping Ma
• 金额: $ 2.13万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0723759&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Multi; Scale

Seismic tomography refers to a broad class of techniques that aim to estimate spatial variations in the propagation speed of seismic waves in Earth's interior using information gleaned from seismograms recorded at Earth's surface. The concept has many aspects in common with medical applications, such as CAT imaging. Over the past decades global tomography has made significant progress with the imaging of Earth's large-scale, deep-interior structure. However, currently available techniques do not do a very good job in constraining Earth's structure on a wide range of length scales, which is a prerequisite for understanding the relationship between near-surface and deeper-mantle processes. Indeed, uneven data coverage and heterogeneous data quality often render non-unique, fuzzy images, with substantial spatial variation in reliability. The approximate nature of the 'red and blue' images impedes quantitative interpretation and keeps tomography from reaching its full potential as a probe of Earth's deep interior. This is particularly important in the context of EarthScope, a nationwide, multi-year geosciences project funded by the National Science Foundation. Its seismology component, USArray, has begun to provide spectacular broad-band data from dense distributions of seismograph stations, but to make optimal use of such data, that is, to construct the best possible models of the crust and mantle beneath North America, one needs to use better tomographic imaging methods than are available today. Traditional tomographic techniques use only a small part of the recorded data, for instance the arrival time or (filtered) waveform of a particular seismic wave. We need to improve our ability to interpret the broad-band wavefield excited by earthquakes (or other sources). From a geosciences point of view, we wish to have better images of mantle heterogeneity (including anisotropy) beneath North America and better understanding of the causative physical and chemical processes. From a physical point of view, this requires full consideration of how the elastic waves propagate in media with strong (and often non-smooth) heterogeneity. From a mathematical point of view this requires the use of more accurate wave-propagation theory (based on the wave equation) in order to capture this complexity, clever wavefield representation and model parameterization (for instance by means of curvelet frames) to allow faster computation in view of the massive size of modern (academic and industrial) data sets, and new statistics to estimate realistic uncertainties in the resulting depictions of Earth's sub-surface. Indeed, we must consider full wave dynamics in all steps on the trajectory from 'data' to 'image', including data representation, wave theory, parameterization, regularization, and uncertainty analysis.

地震层析成像指的是一大类技术，其目的是利用从地球表面记录的地震图中收集的信息来估计地震波在地球内部传播速度的空间变化。该概念在许多方面与医学应用有共同之处，例如CAT成像。在过去的几十年里，全球层析成像技术在地球大规模深层内部结构成像方面取得了重大进展。然而，目前可用的技术并不能很好地在大范围的长度尺度上限制地球的结构，而这是理解近地表和更深地幔过程之间关系的先决条件。事实上，不均匀的数据覆盖和异构的数据质量往往导致非唯一的、模糊的图像，在可靠性方面存在很大的空间差异。“红色和蓝色”图像的近似性质阻碍了定量解释，并使断层扫描无法充分发挥其作为地球深层内部探测的潜力。这在EarthScope的背景下尤为重要，这是一个由美国国家科学基金会资助的全国性多年地球科学项目。它的地震学组成部分USArray已经开始从密集分布的地震仪站中提供惊人的宽带数据，但是为了最佳地利用这些数据，也就是说，为了构建北美地壳和地幔的最佳模型，人们需要使用比现在更好的层析成像方法。传统的层析成像技术只使用记录数据的一小部分，例如到达时间或特定地震波的（过滤）波形。我们需要提高解释地震（或其他来源）激发的宽带波场的能力。从地球科学的角度来看，我们希望获得北美地下地幔非均质性（包括各向异性）的更好图像，并更好地了解其成因的物理和化学过程。从物理的角度来看，这需要充分考虑弹性波如何在具有强非均质性（通常是非光滑的）介质中传播。从数学的角度来看，这需要使用更精确的波传播理论（基于波动方程）来捕捉这种复杂性，聪明的波场表示和模型参数化（例如通过曲线形框架），以便考虑到现代（学术和工业）数据集的庞大规模，允许更快的计算，以及新的统计数据来估计地球地下的实际不确定性。事实上，我们必须在从“数据”到“图像”的轨迹的所有步骤中考虑完整的波动动力学，包括数据表示、波动理论、参数化、正则化和不确定性分析。