NERC-NSTC SEIS-ELEC Slope Evaluation using Interferometric Seismology and Geoelectrical-Based Deep Learning for Instability Detection

Landslides represent a significant global hazard to infrastructure, ecosystems, and communities, a risk amplified by the increasing intensity of extreme weather patterns due to climate change. Conventional slope condition monitoring methods (walk-over or satellite/aerial surveys) rely heavily on surface observations. These methods are often inadequate for predicting the instability of critical slopes, as they cannot detect subsurface precursors to failure and typically identify issues once failure has begun. Often due to high cost of manual repeat surveying, conventional methods have low sampling frequency (months-years), making them insufficient for providing early warnings of failure. "NERC-NSTC SEIS-ELEC Slope Evaluation using Interferometric Seismology and Geoelectrical-Based Deep Learning for Instability Detection" addresses this issue by proposing a cost-effective long-term multi-physics monitoring strategy, using the Yorkshire Landslide Observatory (YOLO) at Hollin Hill as the primary test site. The slope stability of this crucial natural laboratory is primarily influenced by rainfall and moisture fluctuations, one of the major factors of slope failures. Previous research attempted different geophysical methods individually, such as electrical resistivity tomography (ERT), seismic refraction, and distributed acoustic sensing. A critical gap remains: a comprehensive multi-physics approach that integrates both ground resistivity and mechanical variations. SEIS-ELEC directly addresses this challenge by introducing seismic ambient noise interferometry (SANI) to complement the existing BGS 4D ERT monitoring system at YOLO. SANI is highly sensitive to changes in seismic wave velocity caused by hydrological variations. By combining these two methods, we hope to gain a deeper understanding of the hydro-mechanical coupling mechanisms leading to slope failure. The aim of SEIS-ELEC is to establish a globally relevant landslide hazard assessment framework using time-lapse geo-electrical monitoring and SANI, achieved through a long-term, sustainable international partnership. The project has several key objectives: To facilitate knowledge exchange between project partners and relevant stakeholders through workshops. To establish a mid-to-long term multi-physics monitoring programme through joint field campaigns. To demonstrate the feasibility of a deep learning framework to integrate measured physical properties into actionable intelligence for near-real-time slope condition estimation and identification of landslide precursors. To identify pathways for impact and sustain the partnership through constructive engagement. The project will develop a new international partnership between the British Geological Survey (BGS, UK) and the National Yang Ming Chiao Tung University (NYCU, TW). While BGS has extensive expertise in ERT monitoring at YOLO, NYCU brings unique expertise in seismic ambient noise interferometry (SANI) and a proven track record in developing deep learning frameworks for geohazard monitoring. The partnership will be developed through knowledge exchange workshops in both the UK and Taiwan, joint field campaigns, and regular project meetings. A Memorandum of Understanding will formalise the long-term collaboration, enabling future joint research, publications, and funding applications. By developing a transferable landslide monitoring framework, SEIS-ELEC can contribute to disaster prevention and critical infrastructure operations worldwide. Through integrating multi-physical observations with deep learning, SEIS-ELEC has the potential to enhance the accuracy and efficiency of early warning systems for rainfall-induced landslides. The near-real-time slope condition assessments could enable proactive management and mitigation, contributing to global efforts in disaster resilience and infrastructure protection. The establishment of this robust international partnership will foster knowledge exchange and build capacity in both the UK and Taiwan for advancing landslide hazard assessment. Research outcomes will be disseminated through publications and conferences, benefiting the wider research community and informing future monitoring efforts.