Researchers in China say that they are the first to use cosmic-ray muography to monitor the region surrounding a tunnel. Described as a lightweight, robust and affordable scintillator setup, the technology was developed by Kim Siang Khaw at Shanghai Jiao Tong University and colleagues. They hope that their approach could provide a reliable and non-invasive method for the real-time monitoring of subterranean infrastructure.

Monitoring the structural health of tunnels and other underground infrastructure is challenging because of the lack of access. Inspection often relies on techniques such as borehole drilling, sonar scanning, and multibeam echo sounders to determine when maintenance is needed. These methods can be invasive, low resolution and involve costly and disruptive shutdowns. As a result there is often a trade-off between the quality of inspections and the frequency at which they are done.

This applies to the Shanghai Outer Ring Tunnel: a major travel artery in China’s largest city, which runs for almost 3 km beneath the Huangpu River. Completed in 2023, the submerged section of the tunnel is immersed in water-saturated sediment, creating a unique set of challenges for structural inspection.

Time-varying stresses

In particular, different layers of sediment surrounding the tunnel can vary widely in their density, permeability, and cohesion. As they build up above the tunnel, they can impart uneven, time-varying stresses, making it incredibly challenging for existing techniques to accurately assess when maintenance is needed.

To address these challenges, a multi-disciplinary team was formed to explore possible solutions. “During these talks, the [Shanghai Municipal Bureau of Planning and Natural Resources] emphasized the practical challenges of monitoring sediment build-up around critical infrastructure, such as the Shanghai Outer Ring Tunnel, without causing disruptive and costly shutdowns,” Khaw describes.

Among the most promising solutions they discussed was muography, which involves detecting the muons created when high-energy cosmic rays interact with Earth’s upper atmosphere. These muons can penetrate deep beneath Earth’s surface and are absorbed at highly predictable rates depending on the density of the material they pass through.

A simple version of muography involves placing a muon detector on the surface of an object and another detector beneath the object. By comparing the muon fluxes in the two detectors, the density of the object can be determined. By measuring the flux attenuation along different paths through the object, an image of the interior density of the object can be obtained.

Muography has been used for several decades in areas as diverse as archaeology, volcanology and monitoring riverbanks. So far, however, its potential for monitoring underground infrastructure has gone largely untapped.

“We took this ‘old-school’ technique and pioneered its use in a completely new scenario: dynamically monitoring low-density, watery sediment build-up above a submerged, operational tunnel,” Khaw explains. “Our approach was not just in the hardware, but in integrating the detector data with a simplified tunnel model and validating it against environmental factors like river tides.”

With its durable, lightweight, and affordable design, the scintillator features a dual-layer configuration that suppresses background noise while capturing cosmic muons over a broad range of angles. Crucially, it is portable and could be discreetly positioned inside an underground tunnel to carry out real-time measurements, even as traffic flows.

Sediment profiles

To test the design, Khaw’s team took measurements along the full length of the Shanghai Outer Ring Tunnel while it was undergoing maintenance; allowing them to map out a profile of the sediment surrounding the tunnel. They then compared their muon flux measurements with model predictions based on sediment profiles for the Huangpu River measured in previous years. They were pleased to obtain results that were better than anticipated.

“We didn’t know the actual tidal height until we completed the measurement and checked tidal gauge data,” Khaw describes. “The most surprising and exciting discovery was a clear anti-correlation between muon flux and the tidal height of the Huangpu River.” Unexpectedly, the detector was also highly effective at measuring the real-time height of water above the tunnel, with its detected flux closely following the ebb and flow of the tides.

Reassuringly, the team’s measurements confirmed that there are no as-yet unmapped obstructions or gaps in the sediment above the tunnel thereby confirming the structure’s safety.

“Additionally, we have effectively shown a dual-purpose technology: it offers a reliable, non-invasive method for sediment monitoring and also reveals a new technique for tidal monitoring,” says Khaw. “This opens the possibility of using muon detectors as multi-functional sensors for comprehensive urban infrastructure and environmental oversight.”

The research is described in the Journal of Applied Physics.

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A machine learning-based approach that could help astronomers detect lower-frequency gravitational waves has been unveiled by researchers in the UK, US, and Italy. Dubbed deep loop shaping, the system would apply real-time corrections to the mirrors used in gravitational wave interferometers. This would dramatically reduce noise in the system, and could lead to a new wave of discoveries of black hole and neutron star mergers – according to the team.

In 2015, the two LIGO interferometers made the very first observation of a gravitational wave: attributing its origin to a merger of two black holes that were roughly 1.3 billion light–years from Earth.

Since then numerous gravitational waves have been observed with frequencies ranging from 30–2000 Hz. These are believed to be from the mergers of small black holes and neutron stars.

So far, however, the lower reaches of the gravitational wave frequency spectrum (corresponding to much larger black holes) have gone largely unexplored. Being able to detect gravitational waves at 10–30 Hz would allow us to observe the mergers of intermediate-mass black holes at 100–100,000 solar masses. We could also measure the eccentricities of binary black hole orbits. However, these detections are not currently possible because of vibrational noise in the mirrors at the end of each interferometer arm.

Subatomic precision

“As gravitational waves pass through LIGO’s two 4-km arms, they warp the space between them, changing the distance between the mirrors at either end,” explains Rana Adhikari at Caltech, who is part of the team that has developed the machine-learning technique. “These tiny differences in length need to be measured to an accuracy of 10^-19 m, which is 1/10,000^th the size of a proton. [Vibrational] noise has limited LIGO for decades.”

To minimize noise today, these mirrors are suspended by a multi-stage pendulum system to suppress seismic disturbances. The mirrors are also polished and coated to eliminate surface imperfections almost entirely. On top of this, a feedback control system corrects for many of the remaining vibrations and imperfections in the mirrors.

Yet for lower-frequency gravitational waves, even this subatomic level of precision and correction is not enough. As a laser beam impacts a mirror, the mirror can absorb minute amounts of energy – creating tiny thermal distortions that complicate mirror alignment. In addition, radiation pressure from the laser, combined with seismic motions that are not fully eliminated by the pendulum system, can introduce unwanted vibrations in the mirror.

The team proposed that this problem could finally be addressed with the help of artificial intelligence (AI). “Deep loop shaping is a new AI method that helps us to design and improve control systems, with less need for deep expertise in control engineering,” describes Jonas Buchli at Google DeepMind, who led the research. “While this is helping us to improve control over high precision devices, it can also be applied to many different control problems.”

Deep reinforcement learning

The team’s approach is based on deep reinforcement learning, whereby a system tests small adjustments to its controls and adapts its strategy over time through a feedback system of rewards and penalties.

With deep loop shaping, the team introduced smarter feedback controls for the pendulum system suspending the interferometer’s mirrors. This system can adapt in real time to keep the mirrors aligned with minimal control noise – counteracting thermal distortions, seismic vibrations, and forces induced by radiation pressure.

“We tested our controllers repeatedly on the LIGO system in Livingston, Louisiana,” Buchli continues. “We found that they worked as well on hardware as in simulation, confirming that our controller keeps the observatory’s system stable over prolonged periods.”

Based on these promising results, the team is now hopeful that deep loop shaping could help to boost the cosmological reach of LIGO and other existing detectors, along with future generations of gravitational-wave interferometers.

“We are opening a new frequency band, and we might see a different universe much like the different electromagnetic bands like radio, light, and X-rays tell complementary stories about the universe,” says team member Jan Harms at the Gran Sasso Science Institute in Italy. “We would gain the ability to observe larger black holes, and to provide early warnings for neutron star mergers. This would allow us to tell other astronomers where to point their telescopes before the explosion occurs.”

The research is described in Science.

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