University of Alabama Fairbanks scientists hope a new forecasting method can predict major earthquakes months before they strike.

The aftermath of the 2018 earthquake near the city of Wasla. (Image courtesy of the USGS)

In 2018, a 7.1 magnitude earthquake struck south-central Alaska, causing injuries and millions of dollars in damage. While major earthquakes seem to come out of nowhere, researchers at the University of Alaska Fairbanks are hoping a new research method can help predict them in the state months before they happen.

Tarsillo Girona of the University of Alaska Fairbanks' Institute of Geophysics says he and other researchers trained an algorithm to analyze 30 years of earthquake data in Alaska and California.

He listens:

This interview has been edited for length and clarity.

Tarsillo Girona: Basically, we asked the algorithm to look at… during this time period, there was an earthquake or during this time period, there was no earthquake. And we teach the algorithm to recognize any kind of patterns that emerge over different time periods. And then, as I said before, we use these algorithms trained on previous earthquakes to see if we can detect similar anomalous behavior in other earthquakes that were not used in the training process. In this case, for the Anchorage earthquake and also for the Ridgecrest series that occurred in California.

Wesley Earley: It seems like studying two earthquakes requires a small data set. How confident are you that your method is accurate in predicting large earthquakes?

TJ: Well, we can never forget that science is a construction process. So the most important thing here is that we are discovering some anomalous behavior in low-magnitude earthquakes that has not been reported until now, before large earthquakes. So this discovery is of great significance. And then the point is, does this happen before other large-magnitude earthquakes and in other parts of the world? So these are questions that we still have to address in the future. There are some important things to consider here. We decided to use these two earthquakes primarily because we have a great record of data. So in California, we have a very good earthquake catalog, thanks to a lot of research that has been done over the last 34 years. That means we would have needed 30 to 40 years of data to be able to train an algorithm like this. I highlight this because in other parts of the world, it may not be possible to apply this methodology now, because we don’t have as much data, and that is the challenge that we face. And that is another reason why we can’t work with more earthquakes at this point. But as I said at the beginning, the key point here is that with these two studies, we were able to discover some anomalous behavior in low-magnitude earthquakes that had not been reported until now, and this is something we think is important to keep in mind, because this opens new doors and new horizons for using these statistical anomalies to better predict future earthquakes.

We: So it seems that you need to study areas with well-documented seismic data in order to apply this method.

TG: That's right, because with these machine learning algorithms, we need to train the algorithms using historical data from that specific region. Of course, one could train the algorithms using data from California, for example, or from Alaska, but applying the same algorithm to a different region, that's not an easy step. So I always recommend training the algorithms using earthquakes in the specific region that we want to explore and analyze. But again, we don't have very long datasets for any earthquake region in the world, so there are some caveats here, and there's certainly a lot of work to be done in the future to continue to help us detect the emergence of these statistical anomalies.

We: One thing I’ve noticed is that earthquake forecasting is a bit of a double-edged sword. False alarms can create unnecessary panic and economic problems, while incorrect predictions can be quite deadly. How do you hope to balance these two things?

T.J.: Well, that could get into a very long discussion. Certainly here in this research, in this project, we’ve focused on the science part, and understanding the preparatory phase of a large magnitude earthquake is important for many reasons. One is that we can use that to make predictions. But the other reason is that we can also use that to better understand how faults behave, and better understand how the Earth works. And that was the main focus of this project, to try to better understand the preparatory phase of a large magnitude earthquake, and to explore how low-magnitude earthquakes can tell us in some way about the period of unrest in a given area. But then, of course, as we move from science to the application of science, we need to deal with these societal challenges. And that’s certainly something that we’re going to need to figure out. There’s no easy answer to that right now, but as I also said, at some point, science is a building block. We need to move forward to better understand how the Earth works, but also to make sure that we’re able to move forward in terms of creating algorithms and generating predictions that can help society.

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