DarkSeis: Seismic Imaging Of The Urban Subsurface Using Dark Fibre
Lead Research Organisation:
University of Bristol
Department Name: Earth Sciences
Abstract
Our cities are criss-crossed with fibre-optic telecommunications networks. For system redundancy and after technological improvements have reduced the required bandwidth, much of the installed fibre is unused. This unused fibre is known as "dark fibre". We can use dark fibre to investigate the properties of the ground below our cities. Distributed Acoustic Sensing (DAS) systems fire laser pulses along fibre-optic cables, and record the back-scattered light. If the cable is stretched or compressed then the back-scattered light will arrive slightly later or earlier. By recording and mapping these changes, we can turn any fibre-optic cable into a geophysical sensor, recording any movements or vibrations along the cable with very high resolution.
Seismic imaging is a well-established method to map the subsurface. The technique uses seismic sources such as a hammer strike or a special vibrating source to impart seismic energy into the ground. By recording the resulting vibrations that have reflected and/or refracted through the ground, we can build up an image of the subsurface. These images are useful for a broad range of applications, such as siting geothermal developments, understanding groundwater and drainage, assessing the likelihood of landslips, and detecting sinkholes. However, conventional seismic surveys rely on deploying a large array of geophone sensors across the survey area. This is often logistically challenging or impossible in urban areas. As a result, subsurface data under our towns and cities, perhaps the area where this data is needed most, is often lacking.
Seismic imaging using DAS provides an alternative with enormous potential. Dark fibre cables are already installed in buried telecommunication networks, meaning our sensor is already in place and available at minimal cost. We can access the fibre-optic network, install a DAS unit in a secure location, and record the resulting seismic data along the length of the cables, without any need to deploy geophone sensors across the area of interest. Hence, DAS seismic acquisition using dark fibre offers the potential to transform how we acquire images of the subsurface in urban areas.
To date, the enormous potential of this method is only just being realised. The objective of our research is to investigate the performance of dark fibre DAS for seismic imaging in urban settings, with the aim of working out how best to acquire and process this type of data. We will acquire data using the B-NET telecommunications network, which is a 250 km-long fibre-optic network running across the city of Bristol (owned by Bristol City Council). We will use this data to identify how to produce the best quality DAS seismic images - for example what types of seismic source are best, how best to set up the DAS acquisition unit, and how best to process the resulting data.
One of the major challenges of DAS seismic acquisition in urban areas is that background noise levels are likely to be high. This background noise could degrade the quality of our imaging. To address this, we will develop the use of state-of-the-art artificial intelligence algorithms to remove the background noise, improving the quality of our resulting images.
All of the data that we acquire and all of the machine learning algorithms that we develop will be posted to publicly available repositories. This will provide an extremely valuable resource for researchers and commercial geophysical companies, both in the UK and globally, who are working on the development of this technology.
Seismic imaging is a well-established method to map the subsurface. The technique uses seismic sources such as a hammer strike or a special vibrating source to impart seismic energy into the ground. By recording the resulting vibrations that have reflected and/or refracted through the ground, we can build up an image of the subsurface. These images are useful for a broad range of applications, such as siting geothermal developments, understanding groundwater and drainage, assessing the likelihood of landslips, and detecting sinkholes. However, conventional seismic surveys rely on deploying a large array of geophone sensors across the survey area. This is often logistically challenging or impossible in urban areas. As a result, subsurface data under our towns and cities, perhaps the area where this data is needed most, is often lacking.
Seismic imaging using DAS provides an alternative with enormous potential. Dark fibre cables are already installed in buried telecommunication networks, meaning our sensor is already in place and available at minimal cost. We can access the fibre-optic network, install a DAS unit in a secure location, and record the resulting seismic data along the length of the cables, without any need to deploy geophone sensors across the area of interest. Hence, DAS seismic acquisition using dark fibre offers the potential to transform how we acquire images of the subsurface in urban areas.
To date, the enormous potential of this method is only just being realised. The objective of our research is to investigate the performance of dark fibre DAS for seismic imaging in urban settings, with the aim of working out how best to acquire and process this type of data. We will acquire data using the B-NET telecommunications network, which is a 250 km-long fibre-optic network running across the city of Bristol (owned by Bristol City Council). We will use this data to identify how to produce the best quality DAS seismic images - for example what types of seismic source are best, how best to set up the DAS acquisition unit, and how best to process the resulting data.
One of the major challenges of DAS seismic acquisition in urban areas is that background noise levels are likely to be high. This background noise could degrade the quality of our imaging. To address this, we will develop the use of state-of-the-art artificial intelligence algorithms to remove the background noise, improving the quality of our resulting images.
All of the data that we acquire and all of the machine learning algorithms that we develop will be posted to publicly available repositories. This will provide an extremely valuable resource for researchers and commercial geophysical companies, both in the UK and globally, who are working on the development of this technology.
