Quantifying temporal and spatial causalities between climate change and slope failures

量化气候变化与边坡破坏之间的时空因果关系

• 批准号: EP/X01777X/1
• 负责人: Lina Stankovic
• 金额: $ 25.77万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX01777X%2F1
• 关键词: Quantifying; temporal; spatial; causalities; between

The goal of this high-risk research project is to develop data-driven methodological tools to identify influencing or triggering factors and indicator signals that characterise the early stages of slope instabilities, which escalate to landslide displacements, debris flow and rockfalls. These geological hazards negatively impact the economy and public, through disruption, damage of infrastructure and even loss of life. The intended project outcome is feasibility of timely landslide displacement prediction from particular continuous monitoring data streams, providing the basis for landslide early warning systems. Conventional approaches to slope monitoring rely heavily on surface observations (aerial, UAV and satellite image and GPS data). There is a large volume of work on detecting landslides once they have happened and there are early attempts at identifying locations prone to landslides, i.e., susceptibility assessments, from multi-scale spatial information from field surveys and aerial/satellite data at the catchment-to-regional-scale. However, timely prediction of imminent landslides at the slope-scale remains a challenging problem because precursory signals from subsurface recordings are as yet not fully understood or quantified.The generation and recording of seismic signals from a detached soft soil mass that is moving downwards a mountain slope has been documented, but the presence of precursory signals for such failures has only been shown in the lab. The presence of precursory signals in the field has been documented for rock failure, i.e., in the shape of the formation and propagation of cracks. We know of no publicly available catalogues/labels of such events for soft soils. We hypothesise that soft soil failure does generate seismic signals that can be recorded by seismometers and identified through advanced signal processing but the evidence to fully support this statement is yet to be found.To determine to what extent early detection and characterisation of slope instabilities is possible, this project will investigate the precursors to a landslide and the underlying subsurface processes. We will quantify the instrumentation/sensor modalities, density/granularity and geographic area around a hill slope, in conjunction with advanced signal information processing and machine learning (instrumentation and advanced analysis are traditionally treated in isolation), to determine the feasibility of an effective real-time warning system. This approach will radically transform our very limited understanding of temporal and spatial causalities between precipitation, temperature, and landslide induced seismicity. Current climate modelling (e.g., UKCIP) is predicting wetter winters and higher intensity of rainfall due to climate change, and the Met Office with BGS have demonstrated a marked increase in the number of landslides at times of heavy rainfall. Understanding these causalities will enable the development of new fields of research into data-driven engineering solutions to (i) accurately extract seismic predictor signals from large, noisy and continuous recordings, (ii) make linkages between instrumentation that make surface observations of landslides, measures seismicity at subsurface and geophysical approaches that interrogate the subsurface, (iii) augment climate impact programme (e.g., UKCIP) to include effect on landslides, (iv) predict an impending landslide and its scale. Ultimately, these will enable us mitigate the devastating effect of slope instabilities on humans and the economy.

这一高风险研究项目的目标是开发数据驱动的方法工具，以确定影响或触发因素和指示信号，这些因素和信号是斜坡不稳定早期阶段的特征，这些阶段会升级为滑坡位移、泥石流和落石。这些地质灾害通过破坏、破坏基础设施甚至造成生命损失，对经济和公众产生负面影响。项目预期成果是通过特定连续监测数据流及时预测滑坡位移的可行性，为滑坡预警系统提供依据。传统的边坡监测方法严重依赖于地面观测（航空、无人机、卫星图像和GPS数据）。有大量的工作是在发生滑坡时进行探测，也有早期尝试确定易发生滑坡的地点，即易感性评估，从实地调查的多尺度空间信息和集水区的航空/卫星数据到区域尺度。然而，在斜坡尺度上及时预测即将发生的山体滑坡仍然是一个具有挑战性的问题，因为来自地下记录的前兆信号尚未完全理解或量化。地震信号的产生和记录是有文献记载的，这些地震信号来自一个分离的软土块，它正沿着山坡向下移动，但这种故障的前兆信号的存在只在实验室里显示过。现场已经记录了岩石破坏的前兆信号，即裂缝形成和扩展的形状。据我们所知，没有公开的此类软土事件的目录/标签。我们假设软土破坏确实会产生地震信号，这些信号可以被地震仪记录下来，并通过先进的信号处理来识别，但完全支持这一说法的证据尚未找到。为了确定斜坡不稳定的早期检测和表征在多大程度上是可能的，这个项目将调查滑坡的前兆和潜在的地下过程。我们将结合先进的信号信息处理和机器学习（传统上，仪器和高级分析是分开处理的），量化山坡周围的仪器/传感器模式、密度/粒度和地理区域，以确定有效的实时预警系统的可行性。这种方法将从根本上改变我们对降水、温度和滑坡引起的地震活动之间的时空因果关系的非常有限的理解。目前的气候模型（例如，英国气候中心）预测，由于气候变化，冬季会更潮湿，降雨强度会更高。英国气象局和英国地质调查局已经证明，在强降雨时期，山体滑坡的数量会显著增加。了解这些因果关系将有助于开发数据驱动的工程解决方案的新研究领域，以(i)从大量、嘈杂和连续的记录中准确提取地震预报信号，（ii）在地表观测滑坡的仪器之间建立联系，测量地下地震活动性和地球物理方法之间的联系，（iii）增加气候影响计划（例如UKCIP），以包括对滑坡的影响，（iv）预测即将发生的滑坡及其规模。最终，这些将使我们能够减轻斜坡不稳定对人类和经济的破坏性影响。

项目成果

Semi-supervised seismic event detection using Siamese Networks

使用 Siamese Networks 进行半监督地震事件检测

• DOI: 10.5194/egusphere-egu23-14184
• 发表时间: 2023
• 作者: Murray D