EAGER: Using machine learning to develop a calibrated, remote sensing-based age model to improve late Quaternary slip-rate estimates in arid environments

EAGER：利用机器学习开发基于遥感的校准年龄模型，以改善干旱环境中第四纪晚期滑移率的估计

• 批准号: 2233310
• 负责人: Tandis Bidgoli
• 金额: $ 17.52万
• 依托单位: University Enterprises Corporation at CSUSB
• 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: 2022-05-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233310&HistoricalAwards=false
• 关键词: EAGER; Using; machine; learning; develop

This study aims to improve the methods surrounding surface landform dating, and thus methods for determining rates of fault slip. Accurate slip rates are essential for tectonics and earthquake hazards research, and often require numerous surface ages. Such dating efforts can be challenging due to a lack of datable materials, cost concerns, or accessibility of field sites. Recent investigations have directly correlated specific remote sensing observations to the absolute age of surface landforms. Using a large repository of remote sensing data and recent advances in data science and machine learning, this study will integrate multiple, distinct types of remotely sensed data with published age data, to develop a calibrated age model that will be applied to faulted landforms in southeastern California. The methodology will be applied to the eastern Garlock fault, a major strike-slip fault in this region, and aid in answering longstanding questions about the role of the fault in southern California tectonics. This study will make a significant contribution to earthquake hazard analysis of many active faults in southeastern California, a region under threat of damaging earthquakes and with a population of more than 3 million people. Improved earthquake hazard assessments are critical for federal, state, and local agencies and regulatory bodies, a broad spectrum of industry, and the public. The model produced by this study can also form a framework for future surface age studies around the world. Additionally, this study will contribute to the development of the STEM workforce by advancing the education and training of a female graduate student and at least two undergraduate students, as well as the professional development of two early-career researchers, including a female assistant professor. Geologic slip rates are essential components of seismic hazard analysis and critical to addressing many pressing questions at the forefront of tectonics and seismological research. However, discrepancies of Late-Cenozoic and present-day slip rates continue to be debated, particularly when slip rate estimates span different timescales of activity. Discriminating true slip rate discrepancies from observational biases / limitations requires an accurate (and self-consistent) view of slip rates and their temporal and spatial variability. However, obtaining robust slip rates remains challenging, due to lack of dateable materials, cost concerns, or accessibility of field sites. Addressing these challenges, recent investigations have directly correlated specific remote sensing indices to the absolute age of landforms. Using the broad combined repository of remote sensing data from the past 20 years and recent advances in data science and machine learning, the investigators will expand on these efforts and integrate several types of remotely sensed data with published geochronology data to develop a calibrated surface property-age model that will be applied to faulted landforms in the Eastern California shear zone / southern Walker Lane of southeastern California. The ensemble model will incorporate different modeled responses between sensed values and surface age. Ensemble modeling uses a variety of statistical and computational models to fuse an array of single variate models to solve classification and regression problems. With the variety of remote sensors, bands, and spatial scales available, a wealth of data can be consolidated into a cohesive, robust model. The investigators believe such rigorous data treatment may yield significantly improved uncertainties on resulting ages compared with individual models. When implemented, the proposed effort will yield a calibrated means of estimating surface ages using remote sensing data and new slip rates for the eastern Garlock fault.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.

本研究旨在改进地表地貌测年方法，从而改进确定断层滑动速率的方法。精确的滑动速率对于构造和地震灾害研究是必不可少的，并且通常需要大量的地表年龄。由于缺乏可追溯的材料、成本问题或实地地点的可访问性，这种测年工作可能具有挑战性。最近的调查已将具体的遥感观测与地表地貌的绝对年龄直接联系起来。利用大型遥感数据库以及数据科学和机器学习的最新进展，这项研究将把多种不同类型的遥感数据与已公布的年龄数据相结合，开发一个校准的年龄模型，该模型将应用于加州东南部的断层地貌。该方法将被应用到东部Garlock断层，在这个地区的一个主要的走滑断层，并在回答长期存在的问题，在南加州构造断层的作用援助。这项研究将为加州东南部许多活动断层的地震危险性分析做出重大贡献，该地区受到破坏性地震的威胁，人口超过300万。改进地震灾害评估对联邦、州和地方机构和管理机构、广泛的行业和公众至关重要。这项研究所产生的模型也可以为世界各地未来的地表年龄研究提供一个框架。此外，这项研究将通过推进一名女研究生和至少两名本科生的教育和培训，以及两名早期职业研究人员的专业发展，包括一名女助理教授，为STEM劳动力的发展做出贡献。 地质滑动速率是地震危险性分析的重要组成部分，对于解决构造学和地震学研究前沿的许多紧迫问题至关重要。然而，晚新生代和现今滑动率的差异仍在争论中，特别是当滑动率估计跨越不同的活动时间尺度时。从观测偏差/限制中区分真实滑动率差异需要对滑动率及其时间和空间变异性进行准确（和自洽）的观察。 然而，由于缺乏可确定日期的材料、成本问题或现场的可访问性，获得稳健的滑动率仍然具有挑战性。为了应对这些挑战，最近的调查直接将具体的遥感指数与地貌的绝对年龄联系起来。利用过去20年来广泛的遥感数据组合库以及数据科学和机器学习的最新进展，研究人员将扩大这些努力，并将几种类型的遥感数据与已发布的地质年代学数据相结合，以开发一个校准的表面属性-年龄模型，该模型将应用于东加州剪切带/东南部加州步行者巷南部的断层地貌。集合模型将包含感测值和表面年龄之间的不同建模响应。包络建模使用各种统计和计算模型来融合一系列单变量模型，以解决分类和回归问题。随着遥感器、波段和空间尺度的多样性，大量的数据可以被整合成一个有凝聚力的、鲁棒的模型。研究人员认为，与单个模型相比，这种严格的数据处理可能会显着改善结果年龄的不确定性。当实施时，拟议的努力将产生一个校准的方法，估计表面年龄使用遥感数据和新的滑动率为东部Garlock future.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。