Full waveform modeling and inversion of seismic attenuation and application to characterizing near-surface fractures at Susquehanna Shale Hills Critical Zone Observatory

地震衰减的全波形建模和反演以及用于表征萨斯奎哈纳页岩山关键区域观测站近地表裂缝的应用

基本信息

  • 批准号:
    1919650
  • 负责人:
  • 金额:
    $ 12.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-12-01 至 2022-06-30
  • 项目状态:
    已结题

项目摘要

Seismologists often use seismic waves to study the Earth's interior. This is because seismic wave properties are very sensitive to the physical and chemical state of the Earth, from the shallow to the deep Earth. This project explores one such property, seismic attenuation, which is considered to be a direct indicator for temperature of the Earth with depth, as well as the presence of fluids. Because of its multi-purpose use, a map of this property in Earth structure can improve our understanding of how the Earth's interior works. The main goal of this project is to develop a method to image seismic attenuation using seismic waves. Broader impacts include the mentoring of a postdoctoral researcher at Penn State University and the development of new methods for the scientific community to use.Measurements of seismic wave attenuation potentially provide valuable source of information about the physical and chemical state of the Earth's interior. Seismic full waveform inversion (FWI) as a promising measurement tool for inverting Q still has several key issues to be investigated: 1) whether or not the existing fractional anelastic wave equation, that separates amplitude absorption and phase dispersion, can facilitate the development of FWI; 2) explicit Q embedded in fractional anelastic wave equations will simplify the computations of gradient; 3) how cross-talks between velocity and Q in FWI could be alleviated. To address these issues, this project will conduct three main tasks. First, starting from the newly developed fractional anelastic wave equation that parameterize an explicit Q, a Q-FWI method will be developed using adjoint-state approaches. Second, a suitable inversion strategy for multi-parameters (velocity and Q) will be sought. Third, the Q-FWI will be validated using synthetic near-surface geological models.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.
地震学家经常使用地震波来研究地球内部。这是因为地震波的性质对地球的物理和化学状态非常敏感,从浅层到深层都是如此。该项目探索了这样一个特性,即地震衰减,它被认为是地球深度温度以及流体存在的直接指标。由于它的多用途,地球结构中这种属性的地图可以提高我们对地球内部工作方式的理解。该项目的主要目标是开发一种利用地震波成像地震衰减的方法。更广泛的影响包括宾夕法尼亚州立大学的一名博士后研究员的指导,以及科学界使用的新方法的开发。对地震波衰减的测量可能提供有关地球内部物理和化学状态的宝贵信息来源。地震全波形反演(FWI)作为一种很有前途的反演Q的测量工具,仍有几个关键问题需要研究:1)现有的分离振幅吸收和相位色散的分数阶非弹性波方程是否有利于FWI的发展;2)将显式Q嵌入分数阶非弹性波动方程,简化了梯度的计算;3)如何缓解FWI中速度与Q之间的串扰。为了解决这些问题,本项目将进行三项主要任务。首先,从新建立的参数化显式Q的分数阶非弹性波动方程出发,利用伴随态方法建立Q- fwi方法。其次,寻找适合多参数(速度和Q)的反演策略。第三,Q-FWI将使用合成近地表地质模型进行验证。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A viscoelastic model for seismic attenuation using fractal mechanical networks
Modeling Frequency‐Independent Q Viscoacoustic Wave Propagation in Heterogeneous Media
Critical Zone Structure by Elastic Full Waveform Inversion of Seismic Refractions in a Sandstone Catchment, Central Pennsylvania, USA
Decoupled Fréchet kernels based on a fractional viscoacoustic wave equation
基于分数粘声波方程的解耦 Fréchet 核
  • DOI:
    10.1190/geo2021-0248.1
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Xing, Guangchi;Zhu, Tieyuan
  • 通讯作者:
    Zhu, Tieyuan
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Tieyuan Zhu其他文献

Achieving long-term monitoring CO2 and geothermal fluids using deep learning
利用深度学习实现二氧化碳和地热流体的长期监测
Fractional Laplacians viscoacoustic wavefield modeling with k-space-based time-stepping error compensating scheme
基于k空间的时间步进误差补偿方案的分数拉普拉斯粘声波场建模
  • DOI:
    10.1190/geo2019-0151.1
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Ning Wang;Tieyuan Zhu;Hui Zhou;Hanming Chen;Xuebin Zhao;Yukun Tian
  • 通讯作者:
    Yukun Tian
Constraining water dynamics through unsaturated and saturated zones using fiber-optic seismic sensing data
利用光纤地震传感数据通过非饱和区和饱和区来约束水动力学
  • DOI:
    10.1016/j.epsl.2025.119507
  • 发表时间:
    2025-09-15
  • 期刊:
  • 影响因子:
    5.100
  • 作者:
    Junzhu Shen;Tieyuan Zhu
  • 通讯作者:
    Tieyuan Zhu
Deep learning based microearthquake location prediction at Newberry EGS using physics-informed synthetic dataset
使用物理信息合成数据集基于深度学习的 Newberry EGS 微地震位置预测
Optimal surgical timing for lung cancer following SARS-CoV-2 infection: a prospective multicenter cohort study
  • DOI:
    10.1186/s12885-024-13020-z
  • 发表时间:
    2024-10-09
  • 期刊:
  • 影响因子:
    3.400
  • 作者:
    Ziyun Shen;Zhihua Huang;Tieyuan Zhu;Jing Zhang;Meixin Teng;Yang Qing;Shiqi Hu;Yang Li;Yanzheng Xiong;Jie Shen;Yiwen Huang;Lele Zhang;Huansha Yu;Jian Chen;Dongchun Ma;Qing Geng;Yan Luo;Gening Jiang;Peng Zhang
  • 通讯作者:
    Peng Zhang

Tieyuan Zhu的其他文献

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{{ truncateString('Tieyuan Zhu', 18)}}的其他基金

CIVIC-FA Track A: Leveraging existing fiber-optic cables to identify and manage urban environmental hazards
CIVIC-FA 轨道 A:利用现有光纤电缆识别和管理城市环境危害
  • 批准号:
    2322198
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant
CIVIC-PG Track A: Leveraging Existing Fiber-Optic Cables to Identify and Manage Urban Environmental Hazards
CIVIC-PG 轨道 A:利用现有光纤电缆识别和管理城市环境危害
  • 批准号:
    2228314
  • 财政年份:
    2022
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant

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  • 批准号:
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  • 批准年份:
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Collaborative Research: NSFGEO-NERC: Advancing capabilities to model ultra-low velocity zone properties through full waveform Bayesian inversion and geodynamic modeling
合作研究:NSFGEO-NERC:通过全波形贝叶斯反演和地球动力学建模提高超低速带特性建模能力
  • 批准号:
    2341238
  • 财政年份:
    2024
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant
Collaborative Research: NSFGEO-NERC: Advancing capabilities to model ultra-low velocity zone properties through full waveform Bayesian inversion and geodynamic modeling
合作研究:NSFGEO-NERC:通过全波形贝叶斯反演和地球动力学建模提高超低速带特性建模能力
  • 批准号:
    2341237
  • 财政年份:
    2024
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Speech privacy protection by high-quality, invertible, and extendable speech anonymization and de-anonymization
通过高质量、可逆、可扩展的语音匿名化和去匿名化保护语音隐私
  • 批准号:
    21K17775
  • 财政年份:
    2021
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Use of Machine Learning on Integrated Electronic Medical Record, Genetic and Waveform Data to Predict Perioperative Cardiorespiratory Instability
使用机器学习集成电子病历、遗传和波形数据来预测围手术期心肺不稳定性
  • 批准号:
    10247089
  • 财政年份:
    2020
  • 资助金额:
    $ 12.5万
  • 项目类别:
Use of Machine Learning on Integrated Electronic Medical Record, Genetic and Waveform Data to Predict Perioperative Cardiorespiratory Instability
使用机器学习集成电子病历、遗传和波形数据来预测围手术期心肺不稳定性
  • 批准号:
    10055690
  • 财政年份:
    2020
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    $ 12.5万
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Venous Waveform Analysis to Predict Volume Overload
静脉波形分析可预测容量超载
  • 批准号:
    10238742
  • 财政年份:
    2019
  • 资助金额:
    $ 12.5万
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Venous Waveform Analysis to Predict Volume Overload
静脉波形分析可预测容量超载
  • 批准号:
    10460999
  • 财政年份:
    2019
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    $ 12.5万
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One model for all sounds: fast and high-quality neural source-filter model for speech and non-speech waveform modeling
适用于所有声音的一种模型:用于语音和非语音波形建模的快速且高质量的神经源滤波器模型
  • 批准号:
    19K24371
  • 财政年份:
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    $ 12.5万
  • 项目类别:
    Grant-in-Aid for Research Activity Start-up
Venous Waveform Analysis to Predict Volume Overload
静脉波形分析可预测容量超载
  • 批准号:
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A Real-Time Computational System for Detecting ARDS Using Ventilator Waveform Data
使用呼吸机波形数据检测 ARDS 的实时计算系统
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  • 财政年份:
    2018
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