The isle of Vulcano is a part of the central volcanic ridge of the Aeolian archipelago on the Tyrrhenian Sea in southern Italy. Over the course of its history, Vulcano has undergone multiple explosive eruptions, with the last one thought to have occurred around 1888–1890. However, there is an active hydrothermal system under Vulcano that has shown evidence of intermittent magma and gas flows since 2021 – a sign that the volcano has been in a state of unrest.

During unrest, the volcanic risk increases significantly – and the summer months on the island currently attract a lot of tourists that might be at risk, even from minor eruptive events or episodes of increased degassing. To examine why this unrest has occurred, researchers from the University of Geneva have collaborated with the National Institute of Geophysics and Volcanology (INGV) in Italy to recreate a 3D model of the interior of the volcano on Vulcano, using a combination of nodal seismic networks and artificial intelligence (AI).

Until now, few studies have examined the deep underground details of volcanoes, instead relying on looking at the outline of their internal structure. This is because the geological domains where eruptions nucleate are often inaccessible using airborne geophysical techniques, and onshore studies don’t penetrate far enough into the volcanic plumbing system to look at how the magma and hydrothermal fluids mix. Recent studies have shown the outline of the plumbing systems, but they’ve not had sufficient resolution to distinguish the magma from the hydrothermal system.

3D modelling of the volcano

To better understand what could have caused the 2021 Vulcano unrest, the researchers deployed a nodal network of 196 seismic sensors across Vulcano and Lipari (another island in the archipelago) to measure secondary seismic waves (S-waves) using a technique called seismic ambient noise tomography. S-waves propagate slowly as they pass through fluid-rich zones, which allows magma to be identified.

The researchers captured the S-wave data using the nodal sensor network and processed it with AI – using a deep neural network. This allowed the extensive seismic dispersion data to be quickly and automatically recovered, enabling generation of a 3D S-wave velocity model. The data were captured during the volcano’s early unrest’s phase, and the sensors recorded the natural ground vibrations over a period of one month. The model revealed the high-resolution tomography of the shallow part of a volcanic system in unrest, with the approach compared to taking an “X-ray” of the volcano.

“Our study shows that our end-to-end ambient noise tomography method works with an unprecedented resolution due to using dense nodal seismic networks,” says lead author Douglas Stumpp from the University of Geneva. “The use of deep neural networks allowed us to quickly and accurately measure enormous seismic dispersion data to provide near-real time monitoring.”

The model showed that there was no new magma body between Lipari and Vulcano within the first 2 km of the Earth’s crust, but it did reveal regions that could host cooling melts at the base of the hydrothermal system. These melts were proposed to be degassing melts that could easily release gas and brines if disturbed by an Earthquake – suggesting that tectonic fault dynamics may trigger volcanic unrest. It’s thought that the volcano might have released trapped fluids at depth after being perturbed by fault activity during the 2021 unrest.

Improving risk management

While this method doesn’t enable the researchers to predict when the eruption will happen, it provides a significant understanding into how the internal dynamics of volcanoes work during periods of unrest. The use of AI enables rapid processing of large amounts of data, so in the future, the approach could be used as an early warning system by analysing the behaviour of the volcano as it unfolds.

In theory, this could help to design dynamic evacuation plans based on the direct real-time behaviour of the volcano, which would potentially save lives. The researchers state that this could take some time to develop due to the technical challenge of processing such massive volumes of data in real time – but they note that this is now more feasible thanks to machine learning and deep learning.

When asked about how the researchers plan to further develop the research, Stumpp concludes that “our study paves the ground for 4D ambient noise tomography monitoring – three dimensions of space and one dimension of time. However, I believe permanent and maintained seismic nodal networks with telemetric access to the data need to be implemented to achieve this goal”.

The research is published in Nature Communications.

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A new high-speed multifocus microscope could facilitate discoveries in developmental biology and neuroscience thanks to its ability to image rapid biological processes over the entire volume of tiny living organisms in real time.

The pictures from many 3D microscopes are obtained sequentially by scanning through different depths, making them too slow for accurate live imaging of fast-moving natural functions in individual cells and microscopic animals. Even current multifocus microscopes that capture 3D images simultaneously have either relatively poor image resolution or can only image to shallow depths.

In contrast, the new 25-camera “M25” microscope – developed during his doctorate by Eduardo Hirata-Miyasaki and his supervisor Sara Abrahamsson, both then at the University of California Santa Cruz, together with collaborators at the Marine Biological Laboratory in Massachusetts and the New Jersey Institute of Technology – enables high-resolution 3D imaging over a large field-of-view, with each camera capturing 180 × 180 × 50 µm volumes at a rate of 100 per second.

“Because the M25 microscope is geared towards advancing biomedical imaging we wanted to push the boundaries for speed, high resolution and looking at large volumes with a high signal-to-noise ratio,” says Hirata-Miyasaki, who is now based in the Chan Zuckerberg Biohub in San Francisco.

The M25, detailed in Optica, builds on previous diffractive-based multifocus microscopy work by Abrahamsson, explains Hirata-Miyasaki. In order to capture multiple focal planes simultaneously, the researchers devised a multifocus grating (MFG) for the M25. This diffraction grating splits the image beam coming from the microscope into a 5 × 5 grid of evenly illuminated 2D focal planes, each of which is recorded on one of the 25 synchronized machine vision cameras, such that every camera in the array captures a 3D volume focused on a different depth. To avoid blurred images, a custom-designed blazed grating in front of each camera lens corrects for the chromatic dispersion (which spreads out light of different wavelengths) introduced by the MFG.

The team used computer simulations to reveal the optimal designs for the diffractive optics, before creating them at the University of California Santa Barbara nanofabrication facility by etching nanometre-scale patterns into glass. To encourage widespread use of the M25, the researchers have published the fabrication recipes for their diffraction gratings and made the bespoke software for acquiring the microscope images open source. In addition, the M25 mounts to the side port of a standard microscope, and uses off-the-shelf cameras and camera lenses.

The M25 can image a range of biological systems, since it can be used for fluorescence microscopy – in which fluorescent dyes or proteins are used to tag structures or processes within cells – and can also work in transmission mode, in which light is shone through transparent samples. The latter allows small organisms like C. elegans larvae, which are commonly used for biological research, to be studied without disrupting them.

The researchers performed various imaging tests using the prototype M25, including observations of the natural swimming motion of entire C. elegans larvae. This ability to study cellular-level behaviour in microscopic organisms over their whole volume may pave the way for more detailed investigations into how the nervous system of C. elegans controls its movement, and how genetic mutations, diseases or medicinal drugs affect that behaviour, Hirata-Miyasaki tells Physics World. He adds that such studies could further our understanding of human neurodegenerative and neuromuscular diseases.

“We live in a 3D world that is also very dynamic. So with this microscope I really hope that we can keep pushing the boundaries of acquiring live volumetric information from small biological organisms, so that we can capture interactions between them and also [see] what is happening inside cells to help us understand the biology,” he continues.

As part of his work at the Chan Zuckerberg Biohub, Hirata-Miyasaki is now developing deep-learning models for analysing dynamic cell and organism multichannel dynamic live datasets, like those acquired by the M25, “so that we can extract as much information as possible and learn from their dynamics”.

Meanwhile Abrahamsson, who is currently working in industry, hopes that other microscopy development labs will make their own M25 systems.  She is also considering commercializing the instrument to help ensure its widespread use.

The post High-speed 3D microscope improves live imaging of fast biological processes appeared first on Physics World.

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