Collaborative Research: Low-cost imaging and analysis of the August 24, 2014 M6.1 South Napa California earthquake surface rupture (RAPID)

非技术解释对大地震后地球表面形状变化的测量，包括断层断裂一直延伸到地表时造成的地面偏移，可以用来理解支配包括地震在内的断层行为的物理特性，以及地壳的性质。这些特征可能会很快被自然效应和修复受损结构、道路和其他基础设施的人们所侵蚀。对脆弱的地震特征进行快速记录，对于科学描述这类事件以及为应急和恢复提供信息都是至关重要的。新的激光扫描(LiDAR)和摄影测量(运动结构)技术可用于生成三维、“点-云”特征重建，有力地增强了传统的地质野外方法。我们正在努力开发低成本的运动结构技术，用于震后捕获、测量和存档2014年8月24日6.0兆瓦的南纳帕地震的地表偏移量。这些数据与震后机载激光扫描相结合，并与震前扫描进行比较，以准确地捕捉地表破裂和邻近变形，并解释地震过程。技术说明地震后快速收集高分辨率图像对于在退化或修复开始之前绘制地表破裂地图以及准确地解除同震和震后变形至关重要。同震滑动的分布和后滑动的存在和性质，都是了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无与伦比的分辨率和创纪录的时间收集和解释亚米级分辨率图像的工具。我们正在使用运动结构(SfM)映射和多时相机载光探测和测距(LiDAR)来绘制2014年8月24日6.0级南纳帕地震产生的地表偏移量和断层外三维(3-D)位移场。这是自2010年代初SfM地图出现以来，美国境内第一次地面破裂地震，也是第一次具有事前激光雷达覆盖范围的地震。这项研究解决了三个关键的科学问题。(1)地表错动测量的精度和重复性如何？(2)震后早期形变对地表和深部同震滑移量推论的偏差有多大？(3)深层断层错动的地表错动量有多大代表性？浅层错动赤字的性质是什么？这些问题不仅对这一断层的机制有影响，而且对在缺乏此类数据的其他事件中如何解释这些观察结果也有影响。该项目将展示收集、解释和存档这类数据的最佳做法。我们还将与美国地质调查局和加州地质调查局的同事进行强有力的合作，积极开展应对活动。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.