Researchers suggest that the early detection of earthquakes could see significant enhancements by utilizing the global internet network through an innovative new algorithm.
Advancements in technology have allowed fiber optic cables, commonly used for cable TV, phone systems, and the internet, to potentially measure seismic activity. However, effectively harnessing this innovation presents challenges.
A recent study published in Geophysical Journal International aims to tackle these obstacles by modifying a straightforward physics-based algorithm to incorporate fiber optic data, which can complement traditional seismometer readings.
This exciting development could not only be integrated into current earthquake early warning systems but may also aid in detecting seismic events linked to volcanic eruptions, geothermal sites, and icequakes in glaciers.
“The capability to transform fiber optic cables into thousands of seismic sensors has inspired various methods for using fiber in earthquake detection. Yet, solving the challenges of fiber optic earthquake detection is not straightforward,” stated Dr. Thomas Hudson, the lead researcher and senior research scientist at ETH Zurich.
“Our approach leverages the advantage of employing thousands of sensors along with a simple physics-based method to detect earthquakes using any fiber optic cable, anywhere,” he explained.
Distributed acoustic sensing (DAS) is an emerging technology that utilizes fiber optic cables to capture acoustic signals and vibrations, useful for monitoring diverse installations like pipelines and railways, as well as subsurface activity.
This technology shows promise in transforming fiber optic networks, which transmit data rapidly, into tools for measuring seismic activity and thus identifying earthquakes.
This potential is fascinating, as fiber optic networks are widespread in densely populated areas and even extend across oceans, enabling the development of detailed and effective seismic monitoring systems beyond what currently exists.
However, realizing this potential is more challenging than it seems.
Real-world fiber network designs tend to be intricate, and seismologists lack control over their configurations. Additionally, these fiber optic cables are frequently situated in noisy urban areas, complicating the task of distinguishing earthquake signals from other noise, something traditional seismometers do more effectively.
Another aspect to consider is that DAS measurements primarily detect strain along the fiber’s axis, while seismometers monitor three-dimensional ground motion. Consequently, surface fiber optic cables are more attuned to slower S-waves (which travel solely through solids and are the second set of waves to reach during an earthquake) than to faster P-waves (which travel through both liquids and solids), making earthquake detection and localization more challenging.
A possible way to address this issue is to integrate data from both traditional seismometers and fiber optic cables to identify earthquakes, although this is complicated by differing sensitivities and measurement units of each instrument.
Additionally, converting a fiber optic cable into thousands of sensors produces a vast amount of data. Developing efficient processing algorithms is crucial for real-time earthquake monitoring.
The new algorithm focuses on the energy recorded by either fiber optic channels or seismometers and traces that energy back through space and time to identify a significant peak in energy that matches a potential earthquake’s signature.
This technique has also proven effective in detecting earthquakes related to volcanic activity, geothermal boreholes, and glacier-induced icequakes.
“One of the main advantages of this physics-based method is its efficacy in noisy environments because noise is usually less coherent than the actual earthquake signal,” noted Dr. Hudson.
“Moreover, it can be utilized immediately on any fiber network.”
He added: “While we don’t claim to have completely resolved the issue of handling large data volumes, we offer practical solutions, and our algorithm operates in real time for the datasets we have tested.”
“This method is made available as open-source, so the broader seismology community can start benefiting from it right away.”
