Development of innovative methodology for seismological analysis with applications to the studies of tectonic structures and natural hazards

开发地震分析创新方法并应用于构造结构和自然灾害研究

• 批准号: RGPIN-2019-04148
• 负责人: Kao, Honn
• 金额: $ 2.19万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714665
• 关键词: Development; innovative; methodology; seismological; analysis

Seismology is a branch of observational science that depends critically on innovative analysis of seismic data. High-resolution results come from accurate and comprehensive analysis of available data with minimum artifacts. One of the most effective ways to make scientific breakthroughs is to develop innovative methodologies capable of extracting subtle, yet important information that were underexplored in the past. The purpose of this proposal is to seek modest funds for partial support of two graduate students to conduct innovative development of seismological methodologies that will significantly improve the quality of seismic data analysis. Specifically, the proposed research consists of three (3) major activities. The first one is to develop an artificial intelligence (AI)-based method to accurately detect weak earthquake signals. Recent development of convolutional neural networks has greatly improved the ability and efficiency of extracting systematic information from large datasets. We plan to focus our research on the issue of induced seismicity that are possibly related to hydraulic fracturing and/or wastewater disposal in western Canada. The ultimate goal is to establish a physical model that can not only explain the observed pattern of injection-induced seismicity but also provide a certain level of predictability on the spatiotemporal distribution of induced events based on the identified factors and variables. The second activity is to develop an earthquake location algorithm that is automatic, comprehensive, accurate and versatile. Determination of precise source locations is a very time-consuming process that requires skillful analysts with consistent performance of seismic phase-picking. Existing automatic earthquake location algorithms all have specific advantages and drawbacks and the final results still require human verification. We propose to take a totally new cocktail approach by taking advantage of the merit of each algorithm to achieve the optimal performance. The newly developed method will be capable of generating the most accurate and comprehensive seismic catalogue without any human intervention. Finally, we plan to develop a systematic method to accurately determine the shallow and deep velocity structures simultaneously, especially for regions without frequent local seismicity such as the continental interior of North America. Surface wave tomography is an ideal tool if the dispersion curve spans a broad period range. However, the dispersion curve derived from conventional processing of ambient seismic noise (between 2 and 50 s) is not always consistent with that from earthquake surface waves (between 20 and 150 s). We propose to study this inconsistency in detail and develop a totally automatic algorithm to process seismic data such that a broadband (from 1 s to >100 s) dispersion curve can be reliably constructed and used for velocity structure inversion at all depths.

地震学是观测科学的一个分支，它严重依赖于对地震数据的创新分析。高分辨率的结果来自对可用数据的准确和全面的分析，而伪影最少。取得科学突破的最有效方法之一是开发创新的方法，能够提取过去未被探索的微妙但重要的信息。这项建议的目的是寻求适度的资金，用于部分支持两名研究生进行地震学方法的创新开发，以显着提高地震数据分析的质量。具体地说，拟议的研究包括三(3)项主要活动。 第一个是开发一种基于人工智能(AI)的方法来准确检测微弱地震信号。卷积神经网络的最新发展极大地提高了从大数据集中提取系统信息的能力和效率。我们计划将我们的研究重点放在可能与加拿大西部的水力压裂和/或废水处理有关的诱发地震活动问题上。最终目标是建立一个物理模型，该模型不仅可以解释观测到的注入诱发地震活动的模式，而且还可以根据确定的因素和变量对诱发事件的时空分布提供一定程度的可预测性。 第二项活动是开发一种自动、全面、准确和通用的地震定位算法。确定精确的震源位置是一个非常耗时的过程，需要技术娴熟的分析人员具有一致的地震拾相性能。现有的地震自动定位算法都有各自的优缺点，最终的定位结果还需要人工验证。我们建议采取一种全新的鸡尾酒方法，利用每种算法的优点来实现最优性能。新开发的方法将能够在没有任何人为干预的情况下生成最准确和最全面的地震目录。 最后，我们计划开发一种系统的方法，以准确地同时确定浅层和深层的速度结构，特别是对于北美大陆内陆等局部地震活动不频繁的地区。如果频散曲线跨越较宽的周期范围，则面波层析成像是一种理想的工具。然而，由环境地震噪声常规处理得到的频散曲线(在2-50 S之间)与由地震面波得到的频散曲线(在20-150 S之间)并不总是一致的。我们建议对这种不一致性进行详细的研究，并开发一种处理地震数据的全自动算法，以便可靠地建立宽频带(从1 S到100 S)频散曲线，并用于所有深度的速度结构反演。