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: 2210203
• 负责人: Tandis Bidgoli
• 金额: $ 17.52万
• 依托单位: University of Missouri-Columbia
• 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: 2022-01-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210203&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.

这项研究旨在改善表面地面约会周围的方法，从而确定断层滑移速率的方法。准确的滑动速率对于构造和地震危害研究至关重要，通常需要大量的表面年龄。由于缺乏数据材料，成本问题或现场站点的可访问性，这种约会工作可能会具有挑战性。最近的调查已将特定的遥感观察结果与表面地面的绝对年龄相关联。使用遥感数据的大量存储库以及数据科学和机器学习的最新进展，本研究将将多种不同类型的远程感知数据与已发表的年龄数据集成在一起，以开发校准的年龄模型，该模型将应用于加利福尼亚州东南部的故障地面。该方法将应用于东部Garlock断层，这是该地区的主要滑移断层，并有助于回答有关南加州构造中断层作用的长期问题。这项研究将对加利福尼亚州东南部的许多主动断层的地震危害分析做出重大贡献，该地区面临着损害地震的威胁，人口超过300万人。改善地震危险评估对于联邦，州和地方机构以及监管机构，各种各样的工业和公众至关重要。这项研究产生的模型还可以为世界各地未来的地表年龄研究构成一个框架。此外，这项研究将通过推进女性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，从而为STEM劳动力的发展做出贡献。 地质滑移率是地震危害分析的重要组成部分，对于在构造和地震学研究的最前沿解决许多紧迫问题至关重要。然而，晚期和当今滑移率的差异仍在争论中，尤其是当滑移速率估计范围不同时。区分真实滑移率差异与观察性偏见 /局限性需要准确（且自洽）的滑动率及其时间和空间变异性。 但是，由于缺乏可数据的材料，成本问题或现场站点的可访问性，获得可靠的滑动率仍然具有挑战性。在解决这些挑战时，最近的调查将特定的遥感指数与地貌的绝对年龄直接相关。利用过去20年中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，研究人员将扩大这些努力，并将几种类型的远程感知数据与已发表的地球体学数据整合在一起，以开发校准的表面财产与年模型，这些模型将应用于东加州Shorear shear shear shear sherear sherear sherear sherear sherear sherear Lane lane lane lane lane lane lane lane lane lane lane lane lane lane lane lane california calitiatia的故障。整体模型将在感应的值和表面年龄之间结合不同的模型响应。合奏建模使用各种统计和计算模型来融合一系列单个变量模型来解决分类和回归问题。借助可用的各种遥控传感器，频段和空间尺度，可以将大量数据合并为凝聚力，可靠的模型。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导致年龄的不确定性。实施后，提议的努力将产生校准的手段，使用遥感数据和东部Garlock故障的新滑移速率估算表面年龄。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来通过评估来支持的。