Meteoroids striking Mars produce seismic signals that can reach deeper into the planet than previously known. That’s the finding of a pair of new papers comparing marsquake data collected by NASA’s InSight lander with impact craters spotted by the agency’s Mars Reconnaissance Orbiter (MRO).
AI and seismic science
The papers, published on Monday, Feb. 3, in Geophysical Research Letters (GRL), highlight how scientists continue to learn from InSight, which NASA retired in 2022 after a successful extended mission. InSight set the first seismometer on Mars, detecting more than 1,300 marsquakes, which are produced by shaking deep inside the planet (caused by rocks cracking under heat and pressure) and by space rocks striking the surface.
By observing how seismic waves from those quakes change as they travel through the planet’s crust, mantle, and core, scientists get a glimpse into Mars’ interior, as well as a better understanding of how all rocky worlds form, including Earth and its Moon.
Marsquakes and impact craters: a seismic connection
Researchers have in the past taken images of new impact craters and found seismic data that matches the date and location of the craters’ formation. But the two new studies represent the first time a fresh impact has been correlated with shaking detected in Cerberus Fossae, an especially quake-prone region of Mars that is 1,019 miles (1,640 kilometers) from InSight.
The impact crater is 71 feet (21.5 meters) in diameter and much farther from InSight than scientists expected, based on the quake’s seismic energy. The Martian crust has unique properties thought to dampen seismic waves produced by impacts, and researchers’ analysis of the Cerberus Fossae impact led them to conclude that the waves it produced took a more direct route through the planet’s mantle.
A seismic highway through Mars’ mantle
InSight’s team will now have to reassess their models of the composition and structure of Mars’ interior to explain how impact-generated seismic signals can go that deep.
“We used to think the energy detected from the vast majority of seismic events was stuck traveling within the Martian crust,” said InSight team member Constantinos Charalambous of Imperial College London. “This finding shows a deeper, faster path — call it a seismic highway — through the mantle, allowing quakes to reach more distant regions of the planet.”
How machine learning revolutionizes planetary science
A machine learning algorithm developed at NASA’s Jet Propulsion Laboratory in Southern California to detect meteoroid impacts on Mars played a key role in discovering the Cerberus Fossae crater. In a matter of hours, the artificial intelligence tool can sift through tens of thousands of black-and-white images captured by MRO’s Context Camera, detecting the blast zones around craters. The tool selects candidate images for examination by scientists practiced at telling which subtle colorations on Mars deserve more detailed imaging by MRO’s High-Resolution Imaging Science Experiment (HiRISE) camera.
“Done manually, this would be years of work,” said InSight team member Valentin Bickel of the University of Bern in Switzerland. “Using this tool, we went from tens of thousands of images to just a handful in a matter of days. It’s not quite as good as a human, but it’s super fast.”
Bickel and his colleagues searched for craters within roughly 1,864 miles (3,000 kilometers) of InSight’s location, hoping to find some that formed while the lander’s seismometer was recording. By comparing before-and-after images from the Context Camera over a range of time, they found 123 fresh craters to cross-reference with InSight’s data; 49 of those were potential matches with quakes detected by the lander’s seismometer. Charalambous and other seismologists filtered that pool further to identify the 71-foot Cerberus Fossae impact crater.
Deciphering more, faster
The more scientists study InSight’s data, the better they become at distinguishing signals originating inside the planet from those caused by meteoroid strikes. The impact found in Cerberus Fossae will help them further refine how they tell these signals apart. “We thought Cerberus Fossae produced lots of high-frequency seismic signals associated with internally generated quakes, but this suggests some of the activity does not originate there and could actually be from impacts instead,” Charalambous said.
The findings also highlight how researchers are harnessing AI to improve planetary science by making better use of all the data gathered by NASA and ESA (European Space Agency) missions. In addition to studying Martian craters, Bickel has used AI to search for landslides, dust devils, and seasonal dark features that appear on steep slopes, called slope streaks or recurring slope lineae. AI tools have been used to find craters and landslides on Earth’s Moon as well.
The future of AI in space exploration
“Now we have so many images from the Moon and Mars that the struggle is to process and analyze the data,” Bickel said. “We’ve finally arrived in the big data era of planetary science.” AI is becoming an indispensable tool in planetary exploration, accelerating discoveries and helping scientists process vast amounts of data from space missions. As future missions aim to explore even more distant worlds, machine learning will continue to play a crucial role in uncovering the secrets of our solar system.
Source: NASA Jet Propulsion Laboratory.
Image credit: NASA/JPL-Caltech/University of Arizona.
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