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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750979
• 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.

地震学是观察科学的一个分支，严重取决于对地震数据的创新分析。高分辨率结果来自对最低文物的可用数据的准确和全面分析。实现科学突破的最有效方法之一是开发能够提取过去尚未忽视的微妙但重要信息的创新方法。该提案的目的是寻求适度的资金来部分支持两名研究生，以进行地震方法的创新发展，以显着提高地震数据分析的质量。具体而言，拟议的研究包括三（3）个主要活动。 第一个是开发一种基于人工智能（AI）的方法来准确检测弱地震信号。卷积神经网络的最新发展大大提高了从大数据集中提取系统信息的能力和效率。我们计划将研究重点放在加拿大西部的液压压裂和/或废水处置的诱发地震性问题上。最终目标是建立一个物理模型，该模型不仅可以解释注射诱导的地震性的模式，而且还可以根据确定的因素和变量为诱导事件的时空分布提供一定程度的可预测性。第二个活动是开发自动，全面，准确和通用的地震位置算法。确定精确源位置是一个非常耗时的过程，需要熟练的分析师，并具有一致的地震相挑选的性能。现有的自动地震位置算法都具有特定的优势和缺点，最终结果仍然需要人类验证。我们建议通过利用每种算法的优点来实现最佳性能，以采用全新的鸡尾酒方法。新开发的方法将能够在没有任何人类干预的情况下生成最准确，最全面的地震目录。最后，我们计划开发一种系统的方法，以同时准确地确定浅层和深度速度结构，尤其是对于没有频繁局部地震活动（例如北美大陆内部）的地区。如果分散曲线跨越广泛的时期范围，则表面波断层扫描是理想的工具。但是，从环境地震噪声（2至50 s之间）传统处理中得出的色散曲线并不总是与地震表面波（20至150 s）中的分散曲线。我们建议在详细研究这种不一致的情况下研究这种不一致的算法，以处理地震数据，以便可以可靠地构建宽带（从1 s到> 100 s）的色散曲线，并用于所有深度的速度结构反转。