Algorithm raises new questions about Cascadia earthquake record | Jackson School of Geosciences

August 27, 2024

Research professors Zoltan Sylvester (left) and Jacob Kovault at the Core Display Facility at the Bureau of Economic Geology at The University of Texas at Austin. The algorithm they developed to correlate turbidite sediments in geological cores raises questions about the Cascadia earthquake record. Examples of turbidite sediments from Cascadia are shown on the screen behind them. Copyright: The University of Texas at Austin Jackson School of Geosciences.

The Cascadia subduction zone in the Pacific Northwest has a history of powerful and destructive earthquakes that have submerged forests and triggered tsunamis that reached the shores of Japan.

The last major earthquake was in 1700. But it is unlikely to be the last. The areas most likely to be affected now are large, crowded cities with millions of people.

The Cascadia Subduction Zone is located off the Pacific coast of northwestern North America and has a history of generating powerful earthquakes. Copyright: National Oceanic and Atmospheric Administration

The Cascadia Subduction Zone is located off the coast of the Pacific Northwest of North America and has a history of generating powerful earthquakes. Image credit: National Oceanic and Atmospheric Administration.

Knowing how often earthquakes occur—and when the next “big one” will happen—is an active scientific question that involves looking for signs of past earthquakes in the geological record in the form of shaking rocks, sediments, and landscapes.

However, a study by scientists at the University of Texas at Austin and colleagues calls into question the reliability of a record of earthquakes spanning thousands of years—a type of geological deposit called turbidite, which is found in layers of the seafloor.

The researchers analyzed a selection of turbidite layers from the Cascadia subduction zone dating back about 12,000 years using an algorithm to assess how closely the turbidite layers correlate with each other.

The researchers found that the correlation between turbidite sediment samples was often no better than random. Because turbidite sediments can be caused by a range of phenomena, not just earthquakes, the results suggest that the correlation of the turbidite record with past earthquakes is more ambiguous than previously thought.

“We want anyone who cites earthquake periods in Cascadia to understand that this study questions those time periods,” said Joan Gomberg, a USGS research geologist and co-author of the study. “More research is needed to refine those time periods. What we do know is that Cascadia has been seismically active in the past and will continue to be so in the future, so ultimately, people need to be prepared.”

The researchers said the findings do not necessarily change estimates of the frequency of earthquakes in Cascadia, which is about every 500 years. The current frequency estimate is based on a range of data and interpretations, not just the turbidite sediments analyzed in this study. However, the findings highlight the need for more research into turbidite sediment layers, specifically, and how they relate to each other and to large earthquakes.

The algorithm offers a quantitative tool that provides a repeatable way to interpret ancient earthquake records, which are typically based on more qualitative descriptions of the geology and its possible connections, said co-author Jacob Kovault, a research professor at the University of Texas Jackson School of Geosciences.

“This tool provides a reproducible result, so everyone can see the same thing,” said Covolt, co-principal investigator in the Quantitative Limestone Laboratory at the Bureau of Economic Geology at Jackson College. “You can argue with this result, but at least you have a baseline, a reproducible approach.”

The results were published in the journal GSA Bulletin. Researchers from the U.S. Geological Survey, Stanford University and the Alaska Division of Geological and Geophysical Survey participated in the study.

Turbidites are the remains of underwater landslides. They consist of sediment that has settled back to the seafloor after being thrown into the water by the turbulent movement of sediment flowing across the ocean floor. The sediments in these layers have a distinct gradation, with coarser grains at the bottom and finer grains at the top.

(Right) Image and CT scan of a layer of turbidite in a core sample collected during a scientific expedition to study the geology near the Cascadia subduction zone. Subduction zones can cause large, destructive earthquakes. Researchers are interested in clarifying how the turbidite layers relate to the past earthquake record. Copyright: Zoltan Sylvester using data from Goldfinger et al.

But there’s more than one way to create turbidity. Earthquakes can cause landslides when they shake the seafloor. But storms, floods and a host of other natural phenomena can also cause them, albeit on a smaller geographic scale.

Currently, linking turbidites to past earthquakes typically involves finding them in geological samples taken from the seafloor. If turbidites appear in roughly the same place in multiple samples across a relatively large area, they are considered to be remnants of a past earthquake, the researchers say.

Although carbon dating samples can help narrow down the timing, there is still a lot of uncertainty in interpreting whether samples that appear at roughly the same time and place are related to the same event.

This method inspired researchers to apply a more quantitative method—an algorithm called “dynamic temporal distortion”—to turbidity data. This algorithm dates back to the 1970s and has a wide range of applications, from voice recognition to smoothing graphics in dynamic virtual reality environments.

This is the first time it has been applied to the analysis of turbid sediments, said co-author Zoltan Sylvester, a research professor at the Jackson School and co-principal investigator in the Quantum Clays Laboratory, who led the adaptation of the algorithm to analyze turbid sediments.

“This algorithm has been a staple in many of the projects I've worked on, but it remains largely underutilized in the Earth sciences,” Sylvester said.

The algorithm detects similarity between two samples that may differ over time, and determines how closely the data matches between them.

For speech recognition software, this means recognizing keywords even if they are spoken at different speeds or pitches. For turbidites, this involves recognizing common magnetic properties among different turbidite samples that may sound different from place to place even though they originated from the same event.

“Connecting turbidite deposits is not an easy task,” said co-author Nora Nieminski, coastal hazards program manager for the Alaska Geological and Geophysical Survey. “Turbidite deposits typically exhibit significant lateral variability that reflects their changing flow dynamics. Therefore, turbidite deposits are not expected to maintain the same depositional character over large distances, or even small distances in many cases, especially along active margins such as Cascadia or across different depositional environments.”

The researchers also subjected the correlations produced by the algorithm to another level of scrutiny. They compared the results to correlation data calculated using synthetic data made by comparing 10,000 pairs of random turbidity layers. This synthetic comparison served as a control against coincident matches in the actual samples.

Graph comparing results of previous research on turbidity correlation with results calculated by an algorithm developed at the University of Texas at Austin. Black dashed lines indicate similar research results. Red dashed lines indicate different results. Copyright: Zoltan Sylvester.

The researchers applied their technique to magnetic susceptibility records of turbidite layers in nine geological cores collected during a scientific expedition in 1999. They found that in most cases, the correlation between previously linked turbidite layers was no better than random. The only exception to this trend was turbidite layers that were relatively close together—no more than about 15 miles apart.

The researchers stress that the algorithm is only one way to analyze turbidity, and that including other data could alter the degree of correlation between the cores in some way. But according to these results, the presence of turbidity at the same time and in the same general area in the geological record is not enough to conclusively link it to each other.

Although algorithms and machine learning methods can help with this task, it is up to geoscientists to interpret the results and see where the research leads.

“We’re here to answer questions, not just implement the tool,” Sylvester said. “But at the same time, if you’re doing this kind of work, it forces you to think very carefully.”

For more information, please contact: Anton Caputo, Jackson School of Geosciences, 512-232-9623; Monica Korcha, Jackson School of Geosciences, 512-471-2241; Konstantino Panagopoulos, University of Texas Institute of Geophysics.

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