EAGER: Forecasting Subsequent Seismicity using Double-Difference Tomography

EAGER:使用双差层析成像预测后续地震活动

基本信息

项目摘要

Imaging technologies are routinely used within the medical field yet have not been adapted for use in the study of the earth. Of particular consequence is the study of seismicity, either naturally occurring or triggered by human activity. The rock mechanics community lacks a well-founded tool for imaging and assessing the potential for seismicity within a rock mass. Seismicity is a result of unstable equilibrium due to increased depth of excavation and/or increased extraction ratio. Both of these phenomena result in high-stress concentrations. Double-difference tomography, a recently developed technology, has been used by the PI to image stress redistribution within a rock mass more clearly than ever before. It is hypothesized that by combining an understanding of expected stress redistribution (through lithologic mapping, lab testing, and numerical modeling) with observed stress redistribution (from double-difference tomography) a forecast of subsequent seismicity can be made. This project will test this hypothesis using data from a deep, underground nickel mine. Because this project is based upon a very novel hypothesis that has potential to transform existing abilities, the proposal is particularly well-suited for an NSF Early-concept Grant for Exploratory Research (EAGER). One graduate student will be supported by project funding and results will be disseminated through journal papers and conference proceedings.
成像技术通常用于医学领域,但尚未被用于地球研究。特别重要的是对地震活动的研究,要么是自然发生的,要么是由人类活动引发的。岩石力学团体缺乏一个有充分依据的工具来成像和评估岩体内的地震活动潜力。地震活动是由于开挖深度增加和/或采掘比增加而导致的不稳定平衡的结果。这两种现象都会导致高度的应力集中。双差层析成像是最近发展起来的一项技术,PI已经使用它来比以往任何时候都更清楚地成像岩石内的应力重新分布。假设通过对预期应力重分布的了解(通过岩性填图、实验室测试和数值模拟)与观测到的应力重分布(来自双差层析成像)相结合,可以预测后续的地震活动。该项目将使用一座深埋在地下的镍矿的数据来验证这一假设。因为这个项目是基于一个非常新颖的假设,有可能改变现有的能力,所以这个提议特别适合美国国家科学基金会探索性研究早期概念资助(EARGER)。一名研究生将得到项目资金的支持,结果将通过期刊论文和会议记录传播。

项目成果

期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)

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Erik Westman其他文献

A Machine Learning Approach to Lithology Classification in Mining Using Measurement While Drilling and Exploration Data
  • DOI:
    10.1007/s42461-025-01286-1
  • 发表时间:
    2025-05-29
  • 期刊:
  • 影响因子:
    2.000
  • 作者:
    Gbétoglo Charles Komadja;Erik Westman;Aditya Rana;Anye Vitalis
  • 通讯作者:
    Anye Vitalis
Predicting rock mass strength from drilling data using synergistic unsupervised and supervised machine learning approaches
  • DOI:
    10.1007/s12145-025-01837-6
  • 发表时间:
    2025-03-12
  • 期刊:
  • 影响因子:
    3.000
  • 作者:
    Gbétoglo Charles Komadja;Erik Westman;Aditya Rana;Anye Vitalis
  • 通讯作者:
    Anye Vitalis
New time-lapse seismic tomographic scheme based on double-difference tomography and its application in monitoring temporal velocity variations caused by underground coal mining
基于双差层析成像的新型时移地震层析成像方案及其在地下采煤引起的时间速度变化监测中的应用
  • DOI:
    10.1093/gji/ggy404
  • 发表时间:
    2018-10
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Jiawei Qian;Haijiang Zhang;Erik Westman
  • 通讯作者:
    Erik Westman
An integrated relational database for tracking rock mass data during tunneling
  • DOI:
    10.1016/j.tust.2005.12.071
  • 发表时间:
    2006-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Jeramy B. Decker;Alfred Antony;Andrew Ray;Sotirios Vardakos;Michael M. Murphy;Matthew Mauldon;Joseph E. Dove;Marte Gutierrez;Doug Bowman;Erik Westman
  • 通讯作者:
    Erik Westman

Erik Westman的其他文献

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{{ truncateString('Erik Westman', 18)}}的其他基金

IUCRC Phase II+ Virginia Tech: Center to Advance the Science of Exploration to Reclamation in Mining (CASERM)
IUCRC 第二阶段弗吉尼亚理工大学:推进采矿复垦勘探科学中心 (CASERM)
  • 批准号:
    2310948
  • 财政年份:
    2023
  • 资助金额:
    $ 4.99万
  • 项目类别:
    Continuing Grant
Phase I IUCRC at Virginia Tech: Center for Advanced Subsurface Earth Resource Models (CASERM)
弗吉尼亚理工大学 IUCCRC 第一阶段:高级地下地球资源模型中心 (CASERM)
  • 批准号:
    1822108
  • 财政年份:
    2018
  • 资助金额:
    $ 4.99万
  • 项目类别:
    Continuing Grant
CAREER: Stress Redistribution Imaging for Rock Failure Prediction
职业:用于岩石破坏预测的应力重新分布成像
  • 批准号:
    0134034
  • 财政年份:
    2002
  • 资助金额:
    $ 4.99万
  • 项目类别:
    Standard Grant

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