Plowing, or tilling, is an age-old agricultural practice that readies the soil for planting by turning over the top layer to expose fresh earth. The method — intended to improve water and nutrient circulation — remains popular today, but concerns about soil degradation have prompted some to return to regenerative methods that disturb the soil less.

In a new study, a team led by 91̽�� researchers examined the impact of tilling on soil moisture and water retention using methods originally designed for monitoring earthquakes. Researchers placed fiber optic cables alongside fields at an experimental farm in the United Kingdom and recorded ground motion from plots receiving different amounts of tillage and compaction from tractor tires pulling farm equipment.

The study, , shows that tilling and compaction disrupt intricate capillary networks within the soil that give it a natural sponge-like quality.

“This study offers a clear explanation for why the process of tillage, one of humanity’s oldest agricultural activities, changes the structure of soil in ways that affect how it soaks up water,” said co-author , a 91̽��professor of Earth and space sciences.

The link between tilling and soil degradation has been established for quite some time, but the rationale is less robust.

“It’s counterintuitive,” Montgomery said.

Tilling is supposed to create holes for water to reach the roots of plants, but it breaks these small channels in the soil instead, causing rain to pool on the surface and form a muddy crust. Over time, this can increase erosion and flood risk. The researchers observed this phenomenon in detail using seismological methods.

For the past decade or so, physical scientists have been exploring ways to harness the fiber optic cable network to make remote observations. They use a technique called distributed acoustic sensing, or DAS, that records ground motion based on cable strain. Because the technology is so sensitive, it can also capture the speed at which sound waves pass through a substance, which is called seismic velocity.

When soil gets wet, seismic velocity changes. Sound moves slower through mud than dry dirt.

“We wanted to find out whether seismic tools could be used to understand how soil — under different treatment regimens — would respond to environmental variability,” said senior author , a 91̽��associate professor of Earth and space sciences.

An experimental farm near Newport in the United Kingdom, affiliated with Harper Adams University, turned out to be an ideal testing ground for their experiment.

The farm is split into rows that have received consistent cultivation for more than two decades.

There are no-till rows, rows tilled 10 centimeters deep and rows tilled 25 centimeters. Compaction is a byproduct of tilling caused by tractors. Different levels of compaction were tested by modulating tractor tire pressure.

“We took advantage of a natural experiment that had already been done, but just not yet measured,” Montgomery said.

The researchers lined their experimental plots with a fiber optic cable. They collected continuous ground motion data for 40 hours and combined it with weather data over the same period, which featured light to moderate rainfall and mild temperatures.

“We observed the natural vibration of the ground and found that it is really sensitive to environmental factors, including precipitation,” said , lead author and former 91̽��postdoctoral researcher of Earth and space sciences, now at the Chinese Academy of Sciences.

They determined how each cultivation strategy impacted the soil’s response to rainfall by comparing trends in seismic velocity across study sites. Shi developed various models to process the data and help the researchers understand seismic velocity in terms of soil moisture.

The method is straightforward, inexpensive and offers far better spatial and temporal resolution than previous monitoring tools.

The researchers believe it could help farmers understand how to manage their land, provide real time flooding alerts, improve earth systems models by refining estimates of atmospheric water content and better inform seismic hazard maps with data on liquefaction risk.

Additional co-authors include , a 91̽��professor of atmospheric and climate science, , a 91̽��research assistant professor of civil and environmental engineering, from the University of California Santa Cruz, formerly at Purdue University, , , and from Harper Adams University, from the University of Exeter 

This study was funded by The Pan Family Fund, the Murdock Charitable Trust, the 91̽��College of the Environment Seed Fund, the David and Lucile Packard Foundation, and a National Environmental Research Council cross-disciplinary research capability grant.��

For more information, contact Denolle at mdenolle@uw.edu.��

The Pacific Northwest boasts an extensive network of more than 600 seismic monitoring stations that help researchers track tectonic and volcanic phenomena, including earthquakes. This data provides key insights into regional faults and feeds into early warning systems, which can give a community precious moments to prepare before a natural disaster strikes. A significant threat to this region, however, sits miles offshore, where the Juan de Fuca plate is subducting beneath the North American plate, forming the Cascadia Subduction Zone.

Monitoring activity at ocean floor faults is challenging, and the existing methods don’t often yield enough data for detailed analyses. To overcome this hurdle, researchers are experimenting with a technique called Distributed Acoustic Sensing, or DAS, that involves measuring ocean bottom vibrations with fiber optic cables, which line the ocean floor for global telecommunications. Recent advances enable researchers to collect data from live cables and use artificial intelligence to capture distant earthquakes that would otherwise escape notice.

In a recent study, 91̽�� researchers tapped into the Ocean Observatory Initiative’s Regional Cabled Array, which spans the offshore plate boundary and transmits data via fiber optic cable. Unlike previous experiments that relied on offline or “dark fibers” for data collection, this new study demonstrates that DAS technology can operate without interfering with the OOI network.

The researchers February 28 in Seismological Research Letters.

“What we created is the starting point of any earthquake analysis,” said co-author , a 91̽��associate professor in the Earth and space sciences department. “Once our AI algorithm enhances the data, we can actually use the wiggles to do science.”

The fiber optic cable network caught researchers’ attention in the last decade, when they realized its potential for recording solid Earth data. The cables transmit bits of information across great distances in the form of photons, or particles of light. A sensor — called an interrogator — sends a pulse of light down the cable, but imperfections in the core sometimes cause light to deflect back toward the signal’s origin.

Disturbances near the cable can knock the deflected particles off course, and when they arrive back at the origin, researchers plot their path to locate the disturbance.

“When the earthquake is small or faraway, the energy on the cable is relatively low compared to the ocean, and the signal gets buried in background noise,” said co-author, a former 91̽��postdoctoral researcher in the Earth and space sciences department who is now a seismologist at Rice University.

, 91̽��researchers developed an algorithm that isolates the signal and amplifies it over the background noise by as much as 2.5 times. All they have to do is let the algorithm explore the data and it will learn how to recognize the signal — in this case, an earthquake. The researchers used data from 285 earthquakes that occurred in Alaska’s Cook Inlet in 2023 as the training dataset.

“A well-trained model will identify earthquakes that the human eye cannot see,” Shi said. “This marks the first step toward a general-purpose foundational model for earthquakes”

To confirm that it would also filter data collected elsewhere, the researchers validated their model at the test site in Oregon, using a live cable. Previous experiments, including the test-run in Alaska, have sourced data from inactive cables, or dark fibers.

In Oregon, the researchers demonstrated that they could collect high-quality data while the cables were transmitting data. They plugged into the Regional Cabled Array, which contains fiber optic cables, and tuned the algorithm to the frequency of seismic waves coming from small- to medium-sized earthquakes far away. The researchers then traced the signal back to specific regions of the subduction zone and pinpointed the precise location of an earthquake.

“It’s the closest we can get to where the action is,” Denolle said. “So for addressing scientific questions, for monitoring, and for early tsunami and earthquake warnings, it’s our best shot.”

The system is also portable, requiring just a modest amount of computing power to operate.

The recent experiment in Oregon lasted just 3 days and produced large volumes of high-quality data, arguably more than the team knows what to do with, Denolle added. Their challenge now is figuring out how to manage the data. Both datasets were published free to access, and the one from Alaska is the largest single-site data of its kind. The team is now in the process of negotiating permanent placements for their monitoring system and exploring collaborations.

“This is the future,” Denolle said. “We’re going to understand plate tectonics by studying small earthquakes and this system gives us unprecedented access to that data.”

Additional co-authors on this paper are , a postdoctoral researcher in the Earth and space sciences department; , an assistant professor in the Earth and space sciences department; , a professor in the oceanography department; a professor in the oceanography department and director of the Ocean Observatory Initiatives Regional Cabled Array and , a research coordinator in Earth and space sciences.

This research was funded by the National Science Foundation, U.S. Geological Survey, David and Lucile Packard Foundation, 91̽��Geohazard Initiative and Jerome M. Paros Endowed Chair in Sensor Networks.

For more information, contact Shi at qibins@uw.edu and Denolle at mdenolle@uw.edu.