Panaji: For decades, if scientists wanted to understand how Earth’s climate changed millions of years ago, they had to embark on an arduous and destructive process. The idea was to drill into an ancient, mud-caked ocean floor core and carefully extract microscopic fossilized shells.These tiny creatures, known as benthic foraminifera, act as “time capsules.” By analyzing its chemistry, researchers can learn what the ocean and atmosphere were like in the distant past. But this process is slow, expensive, and literally destroys the very samples scientists are working so hard to recover.Now, groundbreaking research by the National Center for Polar and Marine Research suggests a faster, non-destructive way to read these records, thanks to the power of artificial intelligence.
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“Reconstructing past climate is essential because the past is the best guide to predicting our own future. However, traditional methods of building these records are time-consuming, expensive, and require an intensive workforce of experts,” said Group Director Manish Tiwari.Traditionally, paleoclimatologists have been torn between two options. They can use the gold standard of isotope analysis. This provides highly accurate data on the health and carbon cycle of ancient oceans, but is time-consuming, requires laboratory-intensive and destructive sampling.Alternatively, you can use modern high-speed X-ray scanners. These scanners provide rich and detailed elemental data from sediment cores without damaging them. What about the prey? The data are semi-quantitative and often difficult to interpret directly as climate history. It’s like having a library full of books written in a language you can’t read at all. In the new study, researchers used supervised machine learning to build the translator. By training an AI system on both slow but clear isotopic data and fast but mysterious X-ray data, the researchers taught the computer to recognize subtle patterns that connect the two.“The idea was to teach software to convert high-speed X-ray scan data into high-quality isotope measurements that are typically obtained only through destructive sampling. This is part of a broader program that is developing a range of machine learning and deep learning tools to convert climate proxies into climate variables, and computer vision systems to identify archival material without the need for rare and labor-intensive expertise,” said scientist Vikash Kumar.Think of it like training a model to translate a complex ancient language into a modern language. Once AI understands how to interpret X-ray language, researchers can predict high-quality isotope information without having to perform a single destructive test.“This framework demonstrates that supervised machine learning is effective in balancing the trade-off between paleoceanographic interpretability of destructive sampling and high-resolution data acquisition,” the authors write in their research paper.“This not only saves time in the lab; by reducing destructive sampling, we will be able to generate high-resolution climate records more quickly while preserving valuable deep ocean cores for future research. It also reflects NCPOR’s commitment to deploying emerging technologies such as artificial intelligence and machine learning and making better use of existing data and archives in a resource-efficient manner,” said Director Tambang Meros.By turning an archive of raw, unread scan data into a clear climate history, scientists are unlocking a treasure trove of information that has virtually been sitting in the dark, waiting to be read. This means we are entering a new era in our ability to reconstruct Earth’s past, allowing scientists to see the details of ancient climate change faster, more persistently, and with greater precision than ever before.
