Quantifying temporal and spatial causalities between climate change and slope failures

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，这些因素和指示信号会影响斜坡不稳定的早期阶段，进而升级为滑坡位移、泥石流和落石。这些地质灾害对经济和公众造成负面影响，破坏基础设施，甚至造成生命损失。预期的项目成果是从特定的连续监测数据流中及时预测滑坡位移的可行性，为滑坡预警系统提供基础。传统的斜坡监测方法主要依赖于地面观测（空中、无人机和卫星图像以及全球定位系统数据）。一旦发生山体滑坡，就有大量的工作要进行检测，并在早期尝试确定易发生山体滑坡的地点，即，敏感性评估，从多尺度的空间信息，从实地调查和航空/卫星数据，在集水区到区域规模。然而，及时预测即将发生的滑坡在斜坡规模仍然是一个具有挑战性的问题，因为从地下记录的地震信号尚未完全理解或quantitatives.The产生和记录的地震信号从一个分离的软土块，是向下移动的山坡已经记录，但存在的地震信号，这种故障只显示在实验室中。现场出现的地震信号已被记录为岩石破坏，即，在裂缝的形成和传播的形状。据我们所知，软土中没有此类事件的公开目录/标签。我们假设，软土破坏产生地震信号，可以记录的地震仪和识别通过先进的信号处理，但证据，以充分支持这一说法还有待发现。为了确定在何种程度上早期检测和表征的斜坡不稳定性是可能的，这个项目将调查的前兆滑坡和潜在的地下过程。我们将量化仪器/传感器模式，密度/粒度和山坡周围的地理区域，结合先进的信号信息处理和机器学习（仪器和先进的分析传统上被孤立对待），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非常有限的理解降水，温度和滑坡诱发地震活动之间的时间和空间的因果关系。目前的气候模型（例如，UKCIP）预测，由于气候变化，冬季将更加潮湿，降雨强度将更高，英国气象局和英国地质调查局已经证明，在强降雨期间，山体滑坡的数量将显著增加。了解这些因果关系将使新的研究领域能够发展成数据驱动的工程解决方案，以（i）从大的，嘈杂的和连续的记录中准确地提取地震预测信号，（ii）在进行滑坡地面观测的仪器之间建立联系，测量地下地震活动性和询问地下的地球物理方法，（iii）增强气候影响计划（例如，（iv）预测即将发生的山泥倾泻及其规模。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。

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.