Scientific Webinars

Join us as we explore the diverse world of Sedimentology. Drawing from a tranche of past BSRG talks and more, examples will be presented of sedimentary structures in ice and snow, the desert, the sky as well as more conventional settings. Prepare to be baffled by some real head scratchers and wowed by some of Earth’s largest ever structures, and finally head into space to speculate on the sedimentology of exoplanets.

Kathleen is a science team member for Mars 2020, This talk will give an overview of halite and gypsum on Mars and describe their potential to host microorganisms and organic compounds as solid inclusions and within fluid inclusions – as salt minerals on Earth do. The talk will place the search for life in salt minerals on Mars in context of the sample return plan for the Perseverance rover.

In the present day, Mars’ mid-to-high latitudes host abundant water ice within diverse and stunning glacial landscapes. In this talk, I will explore the recent history of glaciation and glacial meltwater on Mars, and discuss some of the morphological mysteries that remain.

During the webinar we shall report our recent studies to survey and sample the ultra-deep water hadal trenches that allow an unravelling the earthquake history of subduction zones and provide new insight into sediment mass and carbon transfer into the hadal trench – one of the least-explored sedimentary environments on our planet.

Strata may contain a signal that records the history of the tectonic and climatic forcing that controls how they form, and many conceptual models tie themselves in logical knots by assuming that these signals are always present in the strata. More interesting than assuming that a signal is present is testing what the signal might look like if it was present, and exploring how it can be extracted from the noise and autogenic patterns that may also be present. This presentation will show some results from numerical experiments using Lobyte3D, a simple stratigraphic forward model of a deep-water fan system, to investigate how an external signal is recorded, and how it can be distinguished, or not, from the autogenic patterns also present in the strata.

Geology has traditionally been a descriptive science with a significant portion of the data coming from observations of features at a range of scales. Modern practices in the oil industry still rely in a large part on this legacy of observational data, for instance when rock facies are used to derive regional stratigraphic trends from core data, or as a building block for petrophysical classifications. However, a recent study has shown that even experienced carbonate sedimentologists will often classify the same facies using different textural names. This problem is compounded in industry by large teams often collaborating on a project, resulting in a heterogeneous attribution of facies to similar rocks despite the use of a common classification scheme. This problem reduces the reliability of descriptive data. In this presentation, I will talk about our research applying machine learning to automatic identification of carbonate facies using the Dunham classification scheme. We used high-resolution core images from the Integrated Ocean Discovery Program (IODP) Leg 194. Core images are used to train a model written in the Python programming language using the TensorFlow machine learning library. Specifically, we used Google’s Inception V3 network as a pre-trained Convolutional Neural Network (CNNs), and applied a method called ‘transfer learning’ to train Inception V3 to recognize carbonate core images. Results show that our CNN can achieve up to 90% accuracy for identification of Mudstone to Rudstone and Crystalline Dolomite. The main misclassifications were between matrix and grain supported facies, and fine and coarse-grained facies, textures also commonly misclassified by control tests with geologists. Interestingly, the bias observed in core description by the algorithm is very similar to human biases: a tendency to give a greater weight to grains as they stand out from the matrix, called ‘saliency’. But the CNNs were able to identify facies 60 times faster than humans, and with a much greater consistency. The results of our study demonstrate the potential of artificial neural networks to reliably interpret and quantify descriptive data for the oil and gas industry, in a fast, automated, high-resolution manner. Current and future work will focus on acquiring a larger dataset of core and thin section images, improving the training of the neural network, and coupling image recognition with logging and petrophysical data estimation.

The threat posed by plastic pollution to ecosystems and human health is under increasing scrutiny and the amount of mismanaged plastic waste entering the environment is growing at a staggering rate. In particular microplastics (plastic particles <1 mm in size) have been discovered in every sedimentary system on the planet and thus became a new type of sediment particle. As such, sedimentology represents an important and powerful tool to understand and predict the transport, dispersal, and ultimate fate of microplastics in different environments. However, due to the complex shapes and low densities the transport and sedimentation behavior of this new sediment particle may differ significantly from those of natural sediments. The presence of microplastics in the environments poses new challenges for the field of sedimentology, but may also provide opportunities to better understand the dynamics of sedimentary systems. In this talk I will provide an overview on global plastic-pollution, microplastic as a new and unique sediment particle, and on microplastics in seafloor sediments.

The Halimeda algal bioherms of the Great Barrier Reef, Australia represent the largest living, actively accumulating Halimeda deposits worldwide. Following the Holocene post-glacial marine transgression, these bioherms kicked off the outer-shelf carbonate factory some 2000 years earlier than the nearby coral reefs. Recent multi-disciplinary work has revealed new insights into their surface geomorphology, subsurface architecture and depositional environment that may be of interest to those working on their fossil counterparts.

Based on a recent review of the literature a data base of absolute values of short term (<3my) Cretaceous sea level rises and falls has been created. This shows an overall amplitude range of 5 to >65m, organised in four broad trends. The potential of aquifer eustasy has been investigated using climate modelling which showed a maximum impact of 5 to 10 meters. This leaves Glacio-eustasy as the key driver for short term high magnitude sea level changes in the Cretaceous.