Collaborative Research: Low-cost imaging and analysis of the August 24, 2014 M6.0 South Napa California earthquake surface rupture (RAPID)

非技术解释对重大地震后地球表面形状变化的测量，包括断层一直断裂到地表时引起的地面偏移，可以用来理解控制断层行为的物理学，包括地震和地壳的性质。这些特征很快就会被自然影响和人们修复受损的建筑、道路和其他基础设施所侵蚀。快速记录地震产生的脆弱特征对于科学地描述此类事件以及为应急反应和恢复提供信息至关重要。用于生成三维“点云”特征重建的新型激光扫描（LiDAR）和摄影测量（运动结构）技术有力地增强了传统的地质场方法。我们正致力于开发低成本的运动结构技术，用于2014年8月24日南纳帕6.0兆瓦地震的震后捕获、测量和地表偏移量存档。这些数据与震后机载激光扫描相结合，并与震前扫描相比较，以准确捕获地表破裂和邻近变形，并解释地震过程。技术描述地震后快速收集高分辨率图像是必不可少的，以便在退化或修复开始之前绘制地表破裂图，以及准确地反卷积同震和震后变形。同震滑动的分布以及后滑的存在和性质是理解断裂带摩擦和力学性质的基础。幸运的是，最近在低成本、航空地形成像和差分方面的进展，为地质学家提供了以前所未有的分辨率和创纪录的时间收集和解释亚米分辨率图像的工具。我们正在使用运动构造（SfM）制图和多时相机载光探测和测距（LiDAR）来绘制地表偏移量和2014年8月24日南纳帕6.0 Mw地震产生的非断层三维位移场。这是自2010年代初SfM测绘技术问世以来，美国境内发生的第一次地表破裂地震，也是第一次有事前激光雷达覆盖的地震。这项研究解决了三个关键的科学问题。(1)表面偏移测量的精度和可重复性如何？(2)早期震后形变偏差在多大程度上推断地表和深部同震滑动？(3)深部断层滑动的地表偏移有多大的代表性，浅层滑动亏缺的性质是什么？这些问题不仅对该断层的机制有影响，而且对在缺乏此类数据的其他事件中如何解释此类观察结果也有影响。该项目将展示收集、解释和存档此类数据的最佳做法。我们还将与美国地质调查局和加州地质调查局的同事们进行积极的响应活动。这些丰富的数据集将使我们能够在沉浸式环境中提供虚拟教育体验。

Non-Technical ExplanationMeasurements of the change in shape of Earth's surface following a significant earthquake, including ground offsets caused when faults break all the way to the surface, can be used to understand the physics governing fault behavior, including earthquakes, and the properties of the Earth's crust. These features can be quickly eroded by natural effects and people working to repair damaged structures, roads, and other infrastructure. Rapid documentation of fragile earthquake-generated features is essential for both the scientific characterization of such events as well as for informing emergency response and recovery. New laser scanning (LiDAR) and photogrammetric (Structure from Motion) techniques for generating three-dimensional, "point-cloud" feature reconstructions powerfully augment traditional geological field methods. We are working to develop low-cost Structure-from-Motion technology for post-earthquake capture, measurement, and archiving of surface offsets from the 24 August 2014 Mw 6.0 South Napa earthquake. These data are integrated with post-earthquake airborne laser scanning and compared with pre-earthquake scans to accurately capture the surface rupture and adjacent deformation, and to interpret earthquake processes.Technical DescriptionRapid collection of high-resolution imagery after an earthquake is essential in order to map surface ruptures before onset of degradation or repair, as well as to accurately deconvolve coseismic and post-seismic deformation. The distribution of coseismic slip and the presence and nature of after-slip are both fundamental to understanding the frictional and mechanical properties of faults zones. Fortunately, recent advances in low-cost, aerial topographic imaging and differencing have given geologists the tools to collect and interpret sub-meter resolution imagery at unparalleled resolution and in record time. We are using Structure from Motion (SfM) mapping and multi-temporal airborne Light Detection and Ranging (LiDAR) to map surface offsets and the off-fault, 3-dimensional (3-D) displacement field generated by from the 24 August 2014 Mw 6.0 South Napa earthquake. This is the first ground-rupturing earthquake within the US since the advent of SfM mapping in the early 2010s and also the first with pre-event LiDAR coverage. The study addresses three key scientific questions. (1) What is the accuracy and reproducibility of surface offset measurements? (2) To what extent does early post-seismic deformation bias inferences of coseismic slip at the surface, and at depth? (3) How representative are surface offsets of deeper fault slip and what is the nature of the shallow slip deficit? These questions have implications not only for the mechanics of this fault but also for how such observations are interpreted in other events where such data are lacking. The project will showcase best practice in collecting, interpreting and archiving such data. We will also have strong collaborations with active response activities undertaken with US Geological Survey and California Geological Survey colleagues. These rich datasets will enable us to provide virtual educational experiences in immersive environments.