Global Search for D" Discontinuity Structure

全局搜索 D" 不连续结构

• 批准号: 2132400
• 负责人: Michael Thorne
• 金额: $ 34.3万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132400&HistoricalAwards=false
• 关键词: Global; Search; Discontinuity; Structure

Approximately 1600 miles beneath the surface of our planet a sharp contrast in Earth properties, called the D" discontinuity, has been shown to exist in more than 100 studies. However, geoscientists still don’t have great knowledge of what this discontinuity physically represents. This is for two primary reasons: most seismic sensors used to detect this discontinuity are spatially restricted to those on land (only 30% of our planet is covered by land) and our current technology only allows us to detect this discontinuity from the deepest of earthquakes; extra noise in the seismic recordings have previously hindered our ability to use shallower earthquakes. Shallow earthquakes, however, are more numerous and occur in many more places on the Earth than deep earthquakes, so if shallow earthquakes could be utilized, they would greatly improve the coverage of the Earth in which we can search for the discontinuity. As such, this project seeks to develop new technology that allows geoscientists to sense the subtle seismic signals associated with this discontinuity using shallow earthquakes. In addition, the project will develop a new modeling capability that will allow a better understanding of what the materials making up this discontinuity are with greater accuracy. Being able to determine where and what the D” discontinuity represents is critical because it likely plays a large role in the ongoing processes inside the Earth. For example, depending on where and what the D" discontinuity is, could strongly affect the whole mantle convection process and control where we observe volcanic activity from hot spot volcanoes such as observed in Hawaii, Iceland, or Yellowstone. This research will be the focus of research for postdoctoral scholar at the University of Utah. This project will furthermore fund 2 years of undergraduate research experience for one student and will additionally support a research experience for one student as a part of the MS for Secondary School Teachers program at the University of Utah. All data collections and results from the extensive data analyses performed in this study will be shared openly on the University of Utah’s hive data repository and all software developed to carry out the array bootstrap technique described in this proposal will be made freely available through GitHub.Seismic array processing techniques are designed to enhance low amplitude seismic wave arrivals and are ideally suited for searching for laterally variable, low contrast seismic discontinuities. Yet only a limited number of studies have taken advantage of the vast amounts of seismic data available using these array processing methods. In addition, when using standard array processing approaches, one often observes potential arrivals that could be associated with known or unknown discontinuity structure but may also be due to correlated noise conditions on a subset of receivers. Here the investigators propose the development and application of a new array processing methodology. In particular, they group seismic stations into virtual subarrays, and compute velocity seismograms (vespagrams) for each subarray using a bootstrap resampling approach ultimately attaining multiple vespagrams for each subarray. For each subarray, the investigators automatically identify (1) seismic wave arrivals, (2) 95% confidence limits in travel-time and slowness, and (3) estimates on the likelihood that the arrival is not due to noise. They further demonstrate that they can image Dʺ discontinuity seismic arrivals using this methodology for shallow earthquakes which has the potential to increase data coverage of the lower mantle substantially. The investigators further develop a Bayesian inversion scheme to model the seismic velocity profile associated with their observations which provides further estimates of the errors, uncertainties and trade-offs associated with the observations. By combining the results from many subarrays, they further develop confidence constraints on discontinuity structures. With this approach the investigators hope to identify, catalog and map laterally variable yet consistent SH-wave seismic discontinuities in the bottommost 1,000 km of the mantle and to provide constraints on the depth range and seismic velocity structure at which these discontinuities exist based on error estimates and forward modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在地球表面以下大约1600英里处，地球性质的鲜明对比，称为D”不连续性，已经在100多项研究中证明存在。然而，地球科学家仍然没有很好的知识，这种不连续性的物理代表。这主要有两个原因：大多数用于检测这种不连续性的地震传感器在空间上局限于陆地上的传感器（地球上只有30%的土地被陆地覆盖），我们目前的技术只能让我们从最深的地震中检测到这种不连续性;地震记录中的额外噪音以前阻碍了我们使用浅层地震的能力。然而，浅层地震比深层地震数量更多，发生在地球上更多的地方，所以如果可以利用浅层地震，它们将大大改善我们可以搜索不连续性的地球覆盖范围。因此，该项目旨在开发新技术，使地球科学家能够利用浅层地震来感知与这种不连续性相关的微妙地震信号。此外，该项目将开发一种新的建模能力，使人们能够更好地了解构成这种不连续性的材料是什么。能够确定D”不连续性代表的位置和内容至关重要，因为它可能在地球内部正在进行的过程中发挥重要作用。例如，取决于D”不连续的位置和性质，它可能强烈影响整个地幔对流过程，并控制我们在哪里观察到热点火山的火山活动，如在夏威夷、冰岛或黄石公园观察到的火山活动。这项研究将成为犹他州大学博士后学者的研究重点。该项目还将资助一名学生2年的本科研究经验，并将额外支持一名学生的研究经验，作为犹他州大学中学教师MS计划的一部分。本研究中进行的广泛数据分析的所有数据收集和结果将在犹他州大学的蜂巢数据库上公开共享，并且为执行本提案中描述的阵列引导技术而开发的所有软件将通过GitHub免费提供。地震阵列处理技术旨在增强低振幅地震波到达，非常适合搜索横向可变，低对比度地震不连续面然而，只有有限数量的研究利用了大量的地震数据，使用这些阵列处理方法。此外，当使用标准阵列处理方法时，人们经常观察到可能与已知或未知的不连续结构相关联的潜在到达，但也可能是由于接收器子集上的相关噪声条件。在这里，研究人员提出了一种新的阵列处理方法的开发和应用。特别是，他们将地震台站分组到虚拟子阵列中，并使用自举响应方法计算每个子阵列的速度地震图（vespagrams），最终为每个子阵列获得多个vespagrams。对于每个子阵列，调查人员自动识别（1）地震波到达，（2）走时和慢度的95%置信限，以及（3）对到达不是由于噪声的可能性的估计。他们进一步证明，他们可以使用这种方法对浅源地震的D波不连续地震波至进行成像，这有可能大大增加下地幔的数据覆盖范围。研究人员进一步开发了一种贝叶斯反演方案，以对与他们的观测结果相关的地震速度剖面进行建模，从而提供对与观测结果相关的误差、不确定性和权衡的进一步估计。通过结合来自许多子阵列的结果，他们进一步发展不连续结构的置信约束。通过这种方法，研究人员希望在最底部的1，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。