Scientists have discovered unidentified vast structures deep in the earth

Scientists have discovered a massive skeleton made of dense material occupying the boundary between the Earth’s liquid outer core and the bottom cover, an area on our feet 3,000 kilometers (1864 mi).

Researchers have used a machine learning algorithm originally developed to analyze distant galaxies to investigate the mysterious phenomenon occurring deep in our planet, according to research published on Thursday in the Journal of Science.

None of these massive anomalies, which lie deep under the Marquesas Islands, had been discovered before, while another structure had been found beneath Hawaii much larger than previous estimates.

Scientists led by Doon Kim, a seismologist and postdoctoral fellow at the University of Maryland, fed seismographs captured from hundreds of earthquakes that occurred between 1990 and 2018 in an algorithm called Sequencer. While seismic studies tend to focus on relatively small data sets for regional seismic activity, Sequencer has allowed Kim and his colleagues to analyze 7,000 earthquake measurements – each of at least 6.5 magnitude – that have rocked the underground world under the Pacific over the past three decades.

“This study is very special because, for the first time, we are taking a systematic look at the large data set that actually covers somewhat the entire Pacific basin,” Kim said in a phone call. He pointed out that although scientists have mapped the deep structures inside Earth, this study offers a rare opportunity “to bring everything together and try to explain it in a global context.”

Earthquakes create seismic waves that travel through the Earth’s interior as they become dispersed and distorted due to the deep structures inside our planet. These twisted patterns are captured in seismic patterns, and are records of waves’ activity within the Earth, enabling seismologists to capture rare glimpses of the inaccessible underworld on Earth.

The team focused on the seismic patterns produced by the shear waves (S) that travel along the boundary between the Earth’s core and the bottom of the mantle that bounds it. These are the slow secondary waves that track the primary tremors caused by earthquakes, called the primary waves (P), and generally produce clearer signals.

Kim said, “We usually like to use S waves because they are wider and data is more or less clean because P traffic is less.” In particular, the team looked for shear waves scattered along the mantle boundary. Kim said that “because of the scattering along this surface, it is a wonderful stage to search for these delicate structures located above the boundaries of the basic mantle.”

When shear waves hit these structures, they produce a type of echo-like signature known as a “poster indicator” (useful numbers for this process are found on Kim’s website). This echo indicates the presence of deep-ground anomalies called ULVZs, which are dense patches at the boundaries of the primary mantle.

No one knows exactly how ULVZs are formed or what they are made of, but it’s clear that their diagonals are about a hundred kilometers in diameter and are dense enough to slow the waves that pass through them.

By running thousands of seismic programs via Sequencer, Kim and his colleagues found that the strongest indications for the next indicator in their data set originated from beneath the Hawaiian and Marquesas Islands. This is bewildering evidence of the existence of two “large ULVZ” regions, spanning about 1,000 km or more.

An updated map of the interior of the Earth based on the new study. Image: D. Kim, V. Lekíc, B. Ménard, D. Baron and M. Taghizadeh-Popp

While the Hawaiian structure’s maps were partially mapped in previous studies, Kim’s team found their dimensions much larger than expected. Meanwhile, the mega-ULVZ detected under the Marquesas Islands is a “wave velocity anomaly not previously identified,” according to the study.

Mega-ULVZs are interesting structures not only because of their size, but because they may consist of foreign matter dating back to a time before Earth had a moon. These massive anomalies could be a partially dissolved substance preceding the moon formation event, which scientists believe was a massive collision between the early Earth and a Mars-sized body more than four billion years ago.

Kim said: “This is very interesting because this may indicate that ULVZ super vehicles are especially hosts and may host primitive geochemical signatures that have not been relatively mixed since Earth’s early history.”

The new study demonstrates algorithm applications such as Sequencer, which use a special kind of process called uncensored learning to process complex data sets such as those in astronomy, seismology, and countless other scientific fields. Unlike supervised learning algorithms, which are trained to sort information based on known labels, the unattended algorithms are designed to find insights into the unclassified data sets.

“What if we don’t really know what to look for in a data set?” Kim explained. “This is the typical question we would like to think about because the lower mantle, the goal of our study, still has many unknowns. It is not really surprising to find almost anything in the mantle because we can’t actually go in and look at it with our bare eyes.”

He continued: “When you use a sequencer, what it actually does is find additional information hidden behind this data set.” “So, what we did here is to find the perfect arrangement in the dataset itself. We don’t really change the dataset; we’re not doing anything but just rearranging and finding that perfect arrangement. That’s what Sequencer does.”

The team plans to further develop this new way of looking at the Earth by examining high-frequency waves that may give finer details about the mysterious structures on the boundary of the mantle. Researchers also hope to expand their data set to include earthquake patterns produced under the Atlantic Ocean.

Kim concluded his speech by saying: “We hope Sequencer can basically allow us to use all of these various datasets and bring them together to systematically search for these lower mantle structures.” “This is our vision to move forward, to answer more questions about the lower mantle in general.”

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