Matt Fox joins the LGC

Matthew Fox joins the LGC as a NERC Independent Research Fellow. Matthew's work focuses on using thermochronometric data to study a range of earth surface processes from large scale geodynamics to the incision of canyons. Matthew integrates diverse datasets and fieldwork with both inverse and forward numerical models. More information about Matthew's work can be found here here.

The provenance of Taklamakan desert sand

In an article in EPSL, following the conclusion of a three-year, multidisciplinary research project, we analyse a 'Big Data' multi-proxy data set derived from samples collected in the Tarim Basin (Xinjiang, China), to understand the likely sediment sources and pathways in this area. The Taklamakan is a significant producer of atmospheric dust. Our larger goal was to compare the Tarim Basin sediments to the extensive aeolian sequences found on the Chinese Loess Plateau (CLP), and to establish whether the Taklamakan could be a source of this material. From chemical, mineralogical and petrological datasets derived from 39 sites, we determined that the bulk of Taklamakan desert sand comes from the Kunlun Mountains in the south, and is transported by seasonal fluvial processes against the dominant northerly wind direction. The Junggar Basin north of the Tian Shan plays no major role as a sediment source for the Tarim Basin. Compositional similarity between Taklamakan sands and the CLP likely reflects a common source, rather than direct aeolian transport from the former to the latter.

Rittner, M., Vermeesch, P., Carter, A., Bird, A., Stevens, T., Garzanti, E., Andò, S., Vezzoli, G., Dutt, R., Xu, Z., Lu, H., 2016. The provenance of Taklamakan desert sand. Earth Planet. Sci. Lett. 437, 127–137. doi:10.1016/j.epsl.2015.12.036

New paper in Chemical Geology



There is a lot to do on the Internet about the concept of 'Big Data', in which huge online databases are 'mined' to reveal previously hidden trends and relationships in society. One could argue that sedimentary geology has entered a similar era of 'Big Data', as modern provenance studies routinely use multiple proxies to dozens of samples, resulting in large multivariate datasets comprising thousands of data points. Just like the Internet, sedimentary geology now requires specialised statistical tools to visualise and interpret such large datasets. Pieter Vermeesch (LGC) and Eduardo Garzanti (University of Milan - Bicocca) introduce 3-way multidimensional scaling and Procrustes analysis as simple yet powerful tools to achieve this goal.

Reference: Vermeesch, P. and Garzanti, E., 2015, Making geological sense 'Big Data' in sedimentary provenance analysis. Chemical Geology, doi:10.1016/j.chemgeo.2015.05.004, v.409, 20-27

Leverhulme grant to study the history of the Milky Way


A £174,468 grant entitled Assessing the potential of lunar geology as a window into galactic history, was awarded by the Leverhulme Trust to Professor Crawford (Birkbeck College) and colleagues Dr Pieter Vermeesch (UCL) and Dr Katherine Joy (University of Manchester). Dr Louise Alexander (Birkbeck) will be employed as a postdoctoral researcher on the grant, and will carry out cosmogenic 3He, 21Ne and 38Ar at the LGC, in order  to find out what the Moon may tell us about the history of the Milky Way Galaxy.

QEMSCAN @ LGC

Facilitated by the UCL Corporate Partnerships team, the LGC has teamed up with Rocktype Ltd. to host a FEI’s QEMSCAN® WellSite™. Rocktype's co-founder and Chief Geologist Dr. Jenny Omma will join the LGC as an honorary member of staff.

The QEMSCAN WellSite technology is a high-resolution scanning electron microscope (SEM) that enables the detection, classification and quantitative analysis of mineralogy and lithology utilising energy-dispersive X-ray spectrometers. The combination of QEMSCAN with the LGC’s existing LA-ICP-MS facilities creates a new instrument suite capable of fingerprinting sediments with high resolution and speed. In addition, the ruggedised nature of the QEMSCAN WellSite provides a robust, field-reliable platform that Rocktype will use to rapidly analyse chemical and rock properties to calibrate well logs and more accurately predict matrix density.

This new capability we enable exciting new applications in petrophysical modelling, basin modelling, reservoir quality assessment and sedimentary provenance studies.

Further information about the collaboration can be found in this press release