Putting the Lessons to Practical Use

When Anemone headed back to Wyoming in July 2012, he didn’t set out on his usual routes or follow his usual routines. Instead, he used his new data, gathering coordinates for several areas where the neural network predicted fossils. He also had a new companion for the fieldwork.

Emerson, who has spent much of his career analyzing images from an office, decided to go into the badlands, too. Characterizing himself as a “newbie” in fieldwork, he wanted to see with his own eyes the sites that he helped find with the neural network. If on-site examinations could show that fossils were more prevalent in the areas identified by the satellite, then he would consider the project a success.

Photograph of the rich fossil locality discovered by Anemone in 2012.

With the aid of their satellite- and neural network-derived maps, Anemone’s team discovered a new fossil location in the summer of 2012. Relatively flat and far from roads, he would not have found the site searching from the ground. (Photograph ©2012 Robert Anemone.)

“Jay picked some areas from the predictive model that showed up with a very high positive probability,” said Anemone after returning from the field. “We programmed those into our hand-held GPSs and went out to see what we could find. A couple of interesting things happened.”

The first surprise came when the satellite analysis exposed a problem with old geologic maps. Paleontologists working in Wyoming frequently encounter two geological formations: the Green River and the Wasatch. The formations are roughly the same age and contain fossils from the Eocene Epoch, an unusually warm period in Earth’s history about 56 to 34 million years ago. But the two formations were created in different environments.

Anemone, Emerson, and their team first visited Luman Rim, which geological maps consider part of the Wasatch Formation. “Wasatch was deposited along rivers and streams,” Anemone explained. He gives the example of a point bar—a crescent-shaped feature that occurs along the inside bend of a meandering river. “Things fall out of sediment as the river slows down. Sandbars get formed and fossils get deposited.” Including mammal fossils, which Anemone and his team wanted to collect.

But their prospective bed was not what they expected. “We found a bunch of fossils, but not very many mammals,” Anemone said. Instead the team found crocodiles, turtles, and fish. They found the kinds of fossils a paleontologist would expect to find in an old lakebed like the Green River Formation.

“As we spent some time in this area and looked at the geology, we realized it had been mapped incorrectly,” said Anemone. “It is not Wasatch Formation; it’s Green River. So our model brought us to a place where there were sandstones, but they were in the wrong formation.”

The second interesting development was proof of the neural network’s skill in uncovering brand new fossil sites. After Luman Rim, Anemone and Emerson visited an area they had never seen before, but that the satellite image analysis had flagged as promising.

“We parked about a mile and a half away and walked through sagebrush-covered, flat-lying, recent sediments—not the kind of place where we would expect to find fossils,” Anemone recalled. “But our GPS eventually brought us to a beautiful, big sort of sunken sandstone exposure. It was in the middle of nowhere. We never would have found this site the old way. It didn’t look promising on the maps, and it was pretty far from the road.” This new site wasn’t as productive as the tasty one they discovered in 2009, but it did yield mammal fossils from the Eocene Epoch, just as he had hoped. “In terms of the way the neural network performed, we were extremely encouraged.”

When they initially planned the field season, Anemone and Emerson thought they would spend about half of their time visiting sites that were predicted to have few, if any fossils. They planned to scour the surface of these low-probability sites, making sure that the neural network not only identified fossil-rich sites, but also fossil-poor sites. But once in the field, they found the rich sites too tempting to leave. Anemone didn’t regret the decision as he learned, right before departing for the field in July 2012, that he would be funded for future expeditions.

“The preliminary success shows that our model is picking up on something that is working for us,” Anemone notes. He hopes that the analysis technique may help not only with his usual North American sites, but also with fossil hominid sites in both East and South Africa, where he has also done research.

As for convincing fellow paleontologists to adopt the satellite approach, Anemone is less sure of success. “The majority of paleontologists still find their fossils the old-fashioned way,” he says. That approach is mostly: “You’re either lucky or you’re not.” But the challenges for paleontologists to make the most of their limited time and money for fieldwork may spur more of them to adopt more efficient techniques.

“This approach saves the time and hassle of staging a 10- to 15-person crew in the field, where you have to carry your food and shelter and where you are making good time if you’re driving 10 miles per hour,” Anemone says. “This new approach won’t guarantee results, but it sure helps you prioritize your time in the field, instead of just relying on a hunch.”

“Even if it doesn’t guarantee results,” Emerson adds, “it can at least stack the deck in your favor.”

    References

  1. Anemone, R., Emerson, C., Conroy, G. (2011) Finding fossils in new ways: An artificial neural network approach to predicting the location of productive fossil localities. Evolutionary Anthropology. 20, 169-180.
  2. Callaway, E. (2011, November 8) Artificial intelligence finds fossil sites. Nature. Accessed March 19, 2012.
  3. Lang, H.R., Paylor, E.D. (1994) Spectral stratigraphy: Remote sensing lithostratigraphic procedures for basin analysis, central Wyoming examples. Natural Resources Research. 3(1), 25-45.
  4. NASA. (2012, March 15) About Landsat. Accessed March 19, 2012.
  5. Vincent, Robert K. (1997) Fundamentals of Geological and Environmental Remote Sensing. Prentice Hall, Upper Saddle River, New Jersey.
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