Panaji: For decades, if scientists wanted to know how the Earth’s climate behaved millions of years ago, they would have to embark on a painstaking and destructive process: drilling into ancient, mud-caked ocean floor cores and carefully picking out microscopic, fossilised shells.These tiny creatures, known as benthic foraminifera, act as “time capsules”. By analysing their chemistry, researchers can determine what the ocean and atmosphere were like in the distant past. But the process is slow, expensive, and literally destroys the very samples scientists work so hard to recover.Now, a breakthrough study by the National Centre for Polar and Ocean Research suggests a faster, non-destructive way to read these records, thanks to the power of artificial intelligence.
CJP Jantar Mantar Protest Updates
“Reconstructing past climate is essential since the past is the best guide we have for predicting our own future. Yet, the conventional way of building these records is slow, expensive, and require intense expert labour,” said group director Manish Tiwari.Traditionally, paleoclimatologists have been stuck between two choices. They can use the gold standard of isotopic analysis, which provides highly accurate data on ancient ocean health and carbon cycles, but requires slow, lab-intensive, destructive sampling.Alternatively, they can use modern, high-speed X-ray scanners. These scanners provide a wealth of detailed elemental data from sediment cores without damaging them. The catch? The data is 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 quite read. In a new study, researchers have built a translator using supervised machine learning. By training an AI system on both the slow-but-clear isotopic data and the fast-but-cryptic X-ray data, the team taught the computer to recognize the subtle patterns connecting the two.“The idea was to teach the software to translate fast X-ray scan data into the high-quality isotopic measurements we normally get only through destructive sampling. This is part of a wider programme we’re developing different machine-learning and deep-learning tools that translate climate proxies into climate variables, and computer-vision systems that identify archive materials without needing scarce, labour-intensive expertise”- said scientist Vikash Kumar.Think of it like training a model to translate a complex, ancient language into a modern one. Once the AI understood how to interpret the X-ray language it could predict the high-quality isotopic information without researchers having to perform a single destructive test.“This framework demonstrates that supervised machine learning can be effective at balancing the trade-off between the paleoceanographic interpretability of destructive sampling and high-resolution data acquisition,” the authors noted in a research paper.“This isn’t just about saving time in the lab. By reducing destructive sampling, it preserves valuable deep-sea cores for future research while enabling high-resolution climate records to be generated much faster. It also reflects NCPOR’s commitment to adopt emerging technologies, including artificial intelligence and machine learning, to make better use of existing data and archives in a resource-efficient manner,” said director Thamban Meloth.By turning archives of raw, unread scan data into clear climate histories, scientists are unlocking a treasure trove of information that was effectively sitting in the dark, waiting to be read. This means the ability to reconstruct the Earth’s past is entering a new era, one where scientists can see the fine-grained details of ancient climate change faster, more sustainably, and with greater precision than ever before.
