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

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

• 批准号: 0724808
• 负责人: Gunther Uhlmann
• 金额: $ 17.61万
• 依托单位: University of Washington
• 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=0724808&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.

地震层析成像是一种广泛的技术，旨在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。 该概念与医学应用有许多共同之处，例如CAT成像。 在过去的几十年里，全球层析成像技术取得了重大进展，对地球的大规模深层内部结构进行了成像。 然而，目前可用的技术并没有做一个很好的工作，约束地球的结构在一个广泛的长度尺度，这是一个先决条件，了解近地表和更深的地幔过程之间的关系。 事实上，不均匀的数据覆盖面和异构的数据质量往往呈现出非唯一的，模糊的图像，在可靠性方面有很大的空间差异。 “红色和蓝色”图像的近似性质阻碍了定量解释，并使层析成像技术无法充分发挥其作为地球深部探测器的潜力。 这一点在EarthScope这一由国家科学基金会资助的全国性多年期地球科学项目中尤为重要。 其地震学组成部分USAray已开始从密集分布的地震仪台站提供壮观的宽带数据，但要最佳利用这些数据，即构建北美地壳和地幔的最佳模型，需要使用比今天更好的层析成像方法。传统的层析成像技术仅使用所记录数据的一小部分，例如特定地震波的到达时间或（滤波）波形。 我们需要提高解释地震（或其他震源）激发的宽带波场的能力。 从地球科学的角度来看，我们希望有更好的图像地幔异质性（包括各向异性）在北美和更好地了解的物理和化学过程的成因。 从物理的角度来看，这需要充分考虑弹性波如何在具有强（通常是非光滑）非均匀性的介质中传播。 从数学的观点来看，这需要使用更精确的波传播理论（基于波动方程）为了捕捉这种复杂性，巧妙的波场表示和模型参数化（例如，通过曲波帧），以允许更快的计算，鉴于现代的巨大规模，（学术和工业）数据集和新的统计数据，以估计地球次表面的实际不确定性。 事实上，我们必须在从“数据”到“图像”的轨迹上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。