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.

这项研究旨在改进地表地貌测年的方法，从而改进确定断层滑移率的方法。准确的滑动速率对于构造学和地震灾害的研究是至关重要的，而且往往需要大量的地表年龄。由于缺乏可确定日期的材料、成本问题或现场可访问性，这样的测年工作可能具有挑战性。最近的调查将特定的遥感观测与地表地貌的绝对年龄直接联系起来。利用大量的遥感数据，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结合起来，开发一个校准的年龄模型，并将其应用于加利福尼亚州东南部的断层地貌。该方法将应用于该地区的主要走滑断层--加洛克东部断层，并有助于回答有关该断层在南加州构造中的作用的长期问题。这项研究将对加州东南部许多活动断裂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。改进的地震风险评估对联邦、州和地方机构和监管机构、广泛的行业和公众至关重要。这项研究产生的模型也可以为未来世界各地的表面年龄研究形成一个框架。此外，这项研究将通过促进对一名女研究生和至少两名本科生的教育和培训，以及促进包括一名女助理教授在内的两名职业早期研究人员的专业发展，促进STEM工作队伍的发展。地质滑移率是地震危险性分析的重要组成部分，对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，晚新生代和现今滑移率的差异仍然存在争议，特别是在滑移率估计跨越不同活动时间尺度的情况下。要区分真实的滑移率差异和观测偏差/限制，需要对滑移率及其时间和空间变异性有一个准确(和自洽)的看法。然而，由于缺乏可确定日期的材料、成本问题或外地地点的可达性，获得可靠的滑点率仍然具有挑战性。为了应对这些挑战，最近的调查将具体的遥感指数与地貌的绝对年龄直接联系起来。利用过去20年遥感数据的广泛组合存储库以及数据科学和机器学习的最新进展，调查人员将扩大这些努力，并将几种类型的遥感数据与已公布的地质年代学数据相结合，以开发一个校准的地表特性-年龄模型，该模型将应用于加利福尼亚州东南部剪切带/南沃克巷的断层地貌。总体模型将包含感应值和地表年龄之间的不同模拟响应。集成建模使用各种统计和计算模型来融合一组单变量模型来解决分类和回归问题。有了各种可用的远程传感器、波段和空间尺度，可以将大量数据整合到一个连贯、健壮的模型中。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著改善结果年龄的不确定性。一旦实施，拟议的工作将产生一种利用遥感数据和东部加洛克断层的新滑移率来估计地表年龄的校准方法。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。