Search

F² – Fibre for the Future

F² (Fibre for the Future) develops fibre optic sensing into a reliable tool for monitoring the ground. A single fibre optic cable can measure strain, vibration and temperature along its whole length. The project combines laboratory tests, modelling, machine learning and two field studies at mine tailings sites.

Glowing blue fibre optic strands with the F² Fibre for the Future logo
Submenu

Why fibre optic sensing?

Fibre optic sensing (FOS) turns a fibre optic cable into a continuous line of sensors. It measures strain (how much the cable is stretched), vibration and temperature at high resolution over long distances.

Two main variants are used in geotechnical monitoring: Distributed Acoustic Sensing (DAS), which records vibrations along the cable, and Distributed Strain Sensing (DSS), which measures strain along the cable. FOS has been used to detect early slope movement, study landslides, follow ground movement during tunnelling, monitor pipelines, foundations and embankments, and track ground subsidence and uplift, underground CO₂ storage and laboratory soil tests.

What do we still not know?

In geotechnical applications, FOS is still new, especially in Norway, and important knowledge gaps remain. One of the biggest is how well the fibre is coupled to the surrounding soil. In granular materials such as sand and gravel, the coupling can vary and add considerable uncertainty to the measurements. We also need to understand better how external factors affect data quality, how the data should be interpreted, and how to calibrate and validate FOS against traditional geotechnical measurements.

Cost is another factor. DAS in particular is expensive, and its cost-benefit needs to be better understood.

What does F² do?

F² is an applied research project. Its goal is to raise the technology readiness level (TRL) of FOS, a measure of how mature a technology is, so that it can be used widely in geotechnical projects.

The project works from the laboratory to the field, using multi-scale modelling, AI and machine learning for data analysis, new sensor technologies, laboratory characterisation and field studies. It aims to establish best practices for fibre selection and installation, calibration, and data validation and interpretation, particularly in granular materials.

What is new?

F² aims to break new ground in five areas:

  • models of how fibre optic sensors interact with granular soil (WP1);
  • 3D shape sensing in the ground, which measures how the ground deforms in three dimensions (WP2);
  • machine learning that detects anomalies and predicts failures by recognising subtle precursory signals in FOS data (WP3);
  • low-frequency DAS at an active tailings facility, combined with other monitoring techniques (WP4);
  • a novel pH-sensitive distributed chemical sensing system to monitor possible contaminant migration in groundwater (WP5).

Who benefits?

For mining companies, continuous and high-resolution FOS monitoring of a tailings dam's condition (such as deformation and water content) and of leakage (groundwater pH) can support better-informed risk assessment and decisions on design, operation and closure. This can help reduce risks to people, infrastructure and the environment, and benefits communities downstream.

The knowledge from F² can also be applied to other monitoring tasks, including landslides, tunnels, excavations and onshore and offshore infrastructure.

Research questions

F² is organised around three research questions:

  • What geotechnical parameters can be accurately inferred from FOS, for example ground deformation, stiffness, moisture, environmental pollution or fluid leakage? What the fibre measures depends on the soil, the cable and how it is installed, so the project designs experiments that isolate and quantify the interaction between soil and fibre.
  • How can these parameters be used in predictive geotechnical models to improve safety? This requires methods for FOS data analysis, machine learning and data fusion that can handle the large data volumes from DAS. In the field studies, changes in water content and pore water pressure will be inferred from changes in seismic velocity or low-frequency DAS strain. The project also compares the cost and benefit of FOS with traditional monitoring.
  • How can new technology make FOS even more useful for geotechnical monitoring? FOS is sensitive to strain, so the project looks for new ways to "strain the fibre" in response to the surrounding material. One example is groundwater pH sensing, where polymer materials relay a change in the chemical environment to the fibre (WP5).

Work packages

The project has six work packages.

WP1 – Soil–fibre physical and numerical models
Lead: Stefan Ritter, NGI

Develops physical and numerical models of how fibre optic cables and soil interact. Laboratory coupling tests vary conditions such as effective stress, saturation, cable type and orientation, and soil type, and the results are compared with the models. The work package also develops tools to calibrate the models and physically models tailings dam failure to produce controlled test data for the machine learning in WP3.

WP2 – Short-range shape sensing
Lead: Henrik Meland, NGI

Develops 3D shape sensing in the ground together with Nerve-Sensors. The work package investigates how the spatial resolution of the strain measurements affects how accurately the deformation shape can be reconstructed, in the laboratory and in the field, and develops processing and interpretation methods for 3D monitoring of ground deformation.

WP3 – Data analysis and interpretation
Lead: Ingeborg Gjerde, NGI

Uses machine learning, in addition to classical analysis methods, to extract meaningful information from FOS data. Develops and validates data processing pipelines and machine learning models for anomaly detection, and collects the tools in a Python repository.

WP4 – Long-range sensing: tailings dam case
Lead: Luca Piciullo, NGI

Evaluates the performance of distributed FOS at an active tailings facility, in collaboration with Lumidas LLC. Includes repeated seismic imaging of the dam with DAS (time-lapse surface wave tomography), analysis of traditional geotechnical monitoring data, and correlation of FOS data with geotechnical and hydraulic parameters.

WP5 – Distributed chemical sensing: tailings case
Lead: Christian Totland, NGI

Develops and tests a novel pH-sensitive fibre optic sensor for groundwater, based on polyelectrolytes whose behaviour depends on pH. Includes laboratory testing, calibration and interpretation methods, and a planned field test at the old Båsmo tailings in Mo i Rana, Norway.

WP6 – Project management
Project Manager: Benjamin Lange, NGI. Technical Leader: Dylan Mikesell, NGI

Coordinates the project from administrative and scientific perspectives.

Why mine tailings?

Tailings are the fine-grained waste left after ore has been processed, and they are often stored behind dams. Each phase of a mine, from construction and deposition to closure, needs a holistic monitoring approach to ensure long-term dam stability and minimise environmental impact. The two field studies in F² focus on tailings and aim to show how FOS can be used for risk mitigation. Different FOS technologies are tested to demonstrate their potential.

Active tailings facility, Canada (WP4)

In collaboration with Lumidas LLC, the project evaluates low-frequency DAS and other FOS methods at an active tailings facility in Canada. The goal is to combine them with other monitoring techniques into a comprehensive understanding of how the dam behaves.

Old Båsmo tailings, Mo i Rana, Norway (WP5)

A pH-sensitive fibre optic sensor will be tested in the field at the old Båsmo tailings in Mo i Rana. The aim is to monitor potential contaminant migration in groundwater and to support long-term environmental safety.

Partners

Lumidas LLC (Calgary, Canada): A startup working to reduce the barriers to adopting FOS for geotechnical monitoring of critical infrastructure. Lumidas collaborates with NGI on the tailings dam case (WP4).

Nerve-Sensors (Kraków, Poland): An innovation company that develops and manufactures customised FOS cables for major infrastructure and geotechnical applications. Its cables are designed for optimal strain transfer from soil to fibre, and the company also develops shape sensing cables for field use. Nerve-Sensors collaborates with NGI on shape sensing (WP2).

Oslo Metropolitan University (OsloMet): Three project participants hold joint appointments at NGI and OsloMet.

Results and updates

F² will publish its results in peer-reviewed journals and present them at relevant conferences. The project also aims to contribute to new standards and guidelines for geotechnical testing with FOS. As the project progresses, you will find publications and news from the project on this page.

  • Why fibre optic sensing?

    Fibre optic sensing (FOS) turns a fibre optic cable into a continuous line of sensors. It measures strain (how much the cable is stretched), vibration and temperature at high resolution over long distances.

    Two main variants are used in geotechnical monitoring: Distributed Acoustic Sensing (DAS), which records vibrations along the cable, and Distributed Strain Sensing (DSS), which measures strain along the cable. FOS has been used to detect early slope movement, study landslides, follow ground movement during tunnelling, monitor pipelines, foundations and embankments, and track ground subsidence and uplift, underground CO₂ storage and laboratory soil tests.

    What do we still not know?

    In geotechnical applications, FOS is still new, especially in Norway, and important knowledge gaps remain. One of the biggest is how well the fibre is coupled to the surrounding soil. In granular materials such as sand and gravel, the coupling can vary and add considerable uncertainty to the measurements. We also need to understand better how external factors affect data quality, how the data should be interpreted, and how to calibrate and validate FOS against traditional geotechnical measurements.

    Cost is another factor. DAS in particular is expensive, and its cost-benefit needs to be better understood.

    What does F² do?

    F² is an applied research project. Its goal is to raise the technology readiness level (TRL) of FOS, a measure of how mature a technology is, so that it can be used widely in geotechnical projects.

    The project works from the laboratory to the field, using multi-scale modelling, AI and machine learning for data analysis, new sensor technologies, laboratory characterisation and field studies. It aims to establish best practices for fibre selection and installation, calibration, and data validation and interpretation, particularly in granular materials.

    What is new?

    F² aims to break new ground in five areas:

    • models of how fibre optic sensors interact with granular soil (WP1);
    • 3D shape sensing in the ground, which measures how the ground deforms in three dimensions (WP2);
    • machine learning that detects anomalies and predicts failures by recognising subtle precursory signals in FOS data (WP3);
    • low-frequency DAS at an active tailings facility, combined with other monitoring techniques (WP4);
    • a novel pH-sensitive distributed chemical sensing system to monitor possible contaminant migration in groundwater (WP5).

    Who benefits?

    For mining companies, continuous and high-resolution FOS monitoring of a tailings dam's condition (such as deformation and water content) and of leakage (groundwater pH) can support better-informed risk assessment and decisions on design, operation and closure. This can help reduce risks to people, infrastructure and the environment, and benefits communities downstream.

    The knowledge from F² can also be applied to other monitoring tasks, including landslides, tunnels, excavations and onshore and offshore infrastructure.

  • Research questions

    F² is organised around three research questions:

    • What geotechnical parameters can be accurately inferred from FOS, for example ground deformation, stiffness, moisture, environmental pollution or fluid leakage? What the fibre measures depends on the soil, the cable and how it is installed, so the project designs experiments that isolate and quantify the interaction between soil and fibre.
    • How can these parameters be used in predictive geotechnical models to improve safety? This requires methods for FOS data analysis, machine learning and data fusion that can handle the large data volumes from DAS. In the field studies, changes in water content and pore water pressure will be inferred from changes in seismic velocity or low-frequency DAS strain. The project also compares the cost and benefit of FOS with traditional monitoring.
    • How can new technology make FOS even more useful for geotechnical monitoring? FOS is sensitive to strain, so the project looks for new ways to "strain the fibre" in response to the surrounding material. One example is groundwater pH sensing, where polymer materials relay a change in the chemical environment to the fibre (WP5).

    Work packages

    The project has six work packages.

    WP1 – Soil–fibre physical and numerical models
    Lead: Stefan Ritter, NGI

    Develops physical and numerical models of how fibre optic cables and soil interact. Laboratory coupling tests vary conditions such as effective stress, saturation, cable type and orientation, and soil type, and the results are compared with the models. The work package also develops tools to calibrate the models and physically models tailings dam failure to produce controlled test data for the machine learning in WP3.

    WP2 – Short-range shape sensing
    Lead: Henrik Meland, NGI

    Develops 3D shape sensing in the ground together with Nerve-Sensors. The work package investigates how the spatial resolution of the strain measurements affects how accurately the deformation shape can be reconstructed, in the laboratory and in the field, and develops processing and interpretation methods for 3D monitoring of ground deformation.

    WP3 – Data analysis and interpretation
    Lead: Ingeborg Gjerde, NGI

    Uses machine learning, in addition to classical analysis methods, to extract meaningful information from FOS data. Develops and validates data processing pipelines and machine learning models for anomaly detection, and collects the tools in a Python repository.

    WP4 – Long-range sensing: tailings dam case
    Lead: Luca Piciullo, NGI

    Evaluates the performance of distributed FOS at an active tailings facility, in collaboration with Lumidas LLC. Includes repeated seismic imaging of the dam with DAS (time-lapse surface wave tomography), analysis of traditional geotechnical monitoring data, and correlation of FOS data with geotechnical and hydraulic parameters.

    WP5 – Distributed chemical sensing: tailings case
    Lead: Christian Totland, NGI

    Develops and tests a novel pH-sensitive fibre optic sensor for groundwater, based on polyelectrolytes whose behaviour depends on pH. Includes laboratory testing, calibration and interpretation methods, and a planned field test at the old Båsmo tailings in Mo i Rana, Norway.

    WP6 – Project management
    Project Manager: Benjamin Lange, NGI. Technical Leader: Dylan Mikesell, NGI

    Coordinates the project from administrative and scientific perspectives.

  • Why mine tailings?

    Tailings are the fine-grained waste left after ore has been processed, and they are often stored behind dams. Each phase of a mine, from construction and deposition to closure, needs a holistic monitoring approach to ensure long-term dam stability and minimise environmental impact. The two field studies in F² focus on tailings and aim to show how FOS can be used for risk mitigation. Different FOS technologies are tested to demonstrate their potential.

    Active tailings facility, Canada (WP4)

    In collaboration with Lumidas LLC, the project evaluates low-frequency DAS and other FOS methods at an active tailings facility in Canada. The goal is to combine them with other monitoring techniques into a comprehensive understanding of how the dam behaves.

    Old Båsmo tailings, Mo i Rana, Norway (WP5)

    A pH-sensitive fibre optic sensor will be tested in the field at the old Båsmo tailings in Mo i Rana. The aim is to monitor potential contaminant migration in groundwater and to support long-term environmental safety.

  • Partners

    Lumidas LLC (Calgary, Canada): A startup working to reduce the barriers to adopting FOS for geotechnical monitoring of critical infrastructure. Lumidas collaborates with NGI on the tailings dam case (WP4).

    Nerve-Sensors (Kraków, Poland): An innovation company that develops and manufactures customised FOS cables for major infrastructure and geotechnical applications. Its cables are designed for optimal strain transfer from soil to fibre, and the company also develops shape sensing cables for field use. Nerve-Sensors collaborates with NGI on shape sensing (WP2).

    Oslo Metropolitan University (OsloMet): Three project participants hold joint appointments at NGI and OsloMet.

    Results and updates

    F² will publish its results in peer-reviewed journals and present them at relevant conferences. The project also aims to contribute to new standards and guidelines for geotechnical testing with FOS. As the project progresses, you will find publications and news from the project on this page.