From source to sink: Investigating Pleistocene Himalaya-sourced megaturbidites from IODP Expedition 354 in the distal Bengal Fan using OSL/IRSL dating

Abstract

The Bengal Fan, the world’s largest depositional system, is fed by Himalayan materials through the Brahmaputra and Ganges rivers. Active drilling campaigns have been underway since the 20th century to investigate the evolution of the fan and what controlled its development. The modern active channel, located about 400 km away from the shelf margin, is predominantly mud-rich. However, thick sandy turbidites of Pleistocene age were discovered by IODP Expedition 354 in 2015, which drilled seven core sites across the lower Bengal Fan at 8°N, approximately 1400 km south of the Bengal shelf margin. Moreover, a 10-cm Conifer log, originating from the high-elevation (>2km) Himalayas and dated to 50 ka, was discovered atop the thickest turbidite of U1454B. Critical questions emerge regarding: 1) the mechanisms that drove the transportation of these coarse sediments; and 2) the journey of this Himalayan Conifer Log from a source-to-sink perspective, traveling thousands of kilometers (~3000 km) from the highest mountain to the largest fan but still remaining intact, without breaking into small pieces or decayed. This study endeavors to place these megaturbidites (grain size >= very fine sand; thickness > 1.5m) within a temporal framework aligned with a sea level curve and ice volume, using Optically- and Infrared-Stimulated Luminescence (OSL and IRSL) (OSL/IRSL) to provide numerical age estimates that should be able to constrain the timing of individual megaturbidite deposition to specific glacial vs. interglacial MIS for further comprehensive analysis. Ultimately, the research aims to provide significant insights about how signals of climate-driven sea-level change were propagated through the world’s largest sediment-dispersal system during the Pleistocene.

Biography

I was born in a small town crisscrossed by rivers in Jiangsu Province, southeastern China. My childhood was intertwined with these waters, instilling in me a sense of tranquility and a burgeoning curiosity about aquatic environments. I obtained B.E. in Petroleum Geological Engineering in the China University of Petroleum-Beijing, which enhanced my inherent interest in Earth’s surface processes and the pivotal role of water in shaping our planet. All these steered me towards sedimentology.

My academic pursuits have also been complemented by a keen interest in coding. I find satisfaction in crafting scripts that streamline complex tasks, thus making sedimentological research more quantitative. This dual interest in geology and computer science has culminated in a collaborative computer vision project. Together with a computer scientist and a geophysicist, I am developing an automated pipeline for the identification of thin sections. Recognizing different minerals under microscopes was one of my favorite things when I was an undergraduate. However, counting the percentage was the least. Our current plan is to use clastic thin sections from IODP 354, training a machine learning model for mineral recognition and automating quantification.