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.

地震层析成像是指旨在估算地震内部地震波传播速度的空间变化的广泛技术，该技术使用从地球表面记录的地震图中收集的信息收集的信息。 该概念与医学应用有许多共同点，例如猫成像。 在过去的几十年中，全球层析成像通过地球大规模深层结构的成像取得了重大进展。 但是，目前可用的技术在限制地球结构的长度范围范围很大方面做得不好，这是理解近地表和更深层面过程之间关系的先决条件。 实际上，数据覆盖率和异质数据质量通常会导致非唯一的模糊图像，并具有可靠性的显着空间变化。 “红色和蓝色”图像的近似性质阻碍了定量的解释，并阻止断层扫描作为对地球深内部探测的全部潜力。 这在全国范围内由国家科学基金会资助的全国性多年地球科学项目（这是一个全国性的，多年的地球科学项目）中尤其重要。 它的地震学组成部分USArray已开始从地震仪站的密集分布中提供壮观的宽带数据，但要最佳地使用此类数据，也就是说，构建了北美下的地壳和地幔的最佳模型，一个需要使用比当今可用的更好的断层成像方法。传统的断层扫描技术仅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。 我们需要提高我们解释通过地震（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，我们希望在北美下面拥有更好的地幔异质性（包括各向异性）的图像，并更好地了解因果关系。 从物理的角度来看，这需要充分考虑弹性波如何在具有强（且通常不太平滑）异质性的媒体中传播。 从数学的角度来看，这需要使用更准确的波传播理论（基于波动方程），以捕获这种复杂性，巧妙的波场表示和模型参数化（例如，通过curvelet框架）允许更快的计算鉴于现代（学术和工业）数据集的庞大规模以及在地球地面的描绘中估算现实不确定性的新统计数据。 实际上，我们必须考虑从“数据”到“图像”的轨迹的所有步骤中的全波动力学，包括数据表示，波浪理论，参数化，正则化和不确定性分析。