Critical technology elements (WP1)

The overall objective of the DigiMon project is to “accelerate the implementation of CCS by developing and demonstrating an affordable, flexible, societally embedded and smart Digital Monitoring early warning system”, for monitoring any CO2 storage reservoir and subsurface barrier system. Within the project the objective of WP1 was to develop individual technologies, data acquisition, analysis techniques and workflows in preparation for inclusion in the DigiMon system. The technologies and data processing techniques developed as part of WP1 include distributed fibre-optic sensing (DFOS) for seismic surveys and chemical sensing, 4D gravity and seafloor deformation measurements, a new seismic source and seismic monitoring survey design. For these technologies the key targets for WP1 were • Develop individual components of the system to raise individual technology readiness levels (TRLs), • Validate and optimise processing software for individual system components, • Develop an effective Distributed Acoustic Sensing (DAS) data interpretation workflow. This work was performed with the expected outcomes of • Raising the DAS TRL for passive seismic monitoring, • An assessment the feasibility of using Distributed Chemical Sensing (DCS) for CO2 detection, • Reducing the cost of 4D gravity and seafloor deformation measurements

Emerging Technologies and Advanced Analyses for Non-Invasive Near-Surface Site Characterization

This dissertation introduces novel techniques for estimating the soil small-strain shear modulus (Gmax) and damping ratio (D), crucial for modeling soil behavior in various geotechnical engineering problems. For Gmax estimation, a machine learning approach is proposed, capable of generating two-dimensional (2D) images of the subsurface shear wave velocity, which is directly related to Gmax. The dissertation also presents a method for estimating frequency dependent attenuation coefficients from ambient vibrations collected using 2D arrays of seismic sensors deployed across the ground surface. These attenuation coefficients can then be used in an inversion process to estimate D. The developed techniques for Gmax and D estimation have undergone rigorous validation and testing through synthetic simulations and field experiments, demonstrating their effectiveness. Furthermore, the dissertation presents a comprehensive dataset collected using cutting-edge seismic sensing technologies, including distributed acoustic sensing, three-component seismometers, and a large mobile shaker truck. This dataset has been archived and made publicly available, aiding researchers worldwide in developing and testing new non-invasive imaging techniques. Finally, the dissertation concludes with a review and comparison of recent advancements in non-invasive subsurface imaging techniques and their application at the same site

Project report on WP1 outcomes relevant to other WPs

This report summaries some of the key technologies that have been studied and developed through WP1 with the purpose of transferring these finding to other WPs in the DigiMon project. The objective of the DigiMon project is to develop an early-warning system for Carbon Capture and Storage (CCS) which utilises a broad range of sensor technologies including Distributed Acoustic Sensing (DAS). While the system is primarily focused on the CCS projects located in the shallow offshore environment of the North Sea, it is also intended to be adaptable to onshore settings. Some of the key areas that the systems will monitor include the movement of the plume within the reservoir, well integrity and CO2 leakage into the overburden. A combination of different methods will be adopted to monitor these key areas, which include active and passive seismics, gravimetry, temperature and chemical sensing. This report focuses on technology and methods which have been developed by the DigiMon project and is not intended as a technology review, which is instead the focus of the DigiMon deliverable 2.3 Technology Readiness Assessment

Tier-scalable reconnaissance: the challenge of sensor optimization, sensor deployment, sensor fusion, and sensor interoperability

Robotic reconnaissance operations are called for in extreme environments, not only those such as space, including planetary atmospheres, surfaces, and subsurfaces, but also in potentially hazardous or inaccessible operational areas on Earth, such as mine fields, battlefield environments, enemy occupied territories, terrorist infiltrated environments, or areas that have been exposed to biochemical agents or radiation. Real time reconnaissance enables the identification and characterization of transient events. A fundamentally new mission concept for tier-scalable reconnaissance of operational areas, originated by Fink et al., is aimed at replacing the engineering and safety constrained mission designs of the past. The tier-scalable paradigm integrates multi-tier (orbit atmosphere surface/subsurface) and multi-agent (satellite UAV/blimp surface/subsurface sensing platforms) hierarchical mission architectures, introducing not only mission redundancy and safety, but also enabling and optimizing intelligent, less constrained, and distributed reconnaissance in real time. Given the mass, size, and power constraints faced by such a multi-platform approach, this is an ideal application scenario for a diverse set of MEMS sensors. To support such mission architectures, a high degree of operational autonomy is required. Essential elements of such operational autonomy are: (1) automatic mapping of an operational area from different vantage points (including vehicle health monitoring); (2) automatic feature extraction and target/region-of-interest identification within the mapped operational area; and (3) automatic target prioritization for close-up examination. These requirements imply the optimal deployment of MEMS sensors and sensor platforms, sensor fusion, and sensor interoperability