SEDIMENTARY GRAPHIC LOGS: A TOOLKIT FOR DIGITALIZATION AND A TEMPLATE FOR STANDARDIZED DESCRIPTION

Graphic logs are the most common way geologists characterize and communicate the composition and variability of clastic sedimentary successions; through a simple drawing, a graphic log imparts complex geological concepts (e.g., the Bouma turbidite sequence or a shoreface parasequence). The term ‘graphic log’ originates from a geologist graphically drawing (i.e., ‘logging’) an outcrop or core; other synonymous terms include measured section and stratigraphic column. Graphic logs generally have thickness/depth on the y axis, while the x axis can represent grain size, texture, or a weathering profile; however, there is no standardized format or template. Additionally, graphic logs can be drawn at vastly different scales, from the characterization of every bed in sections 10s of meters thick to a rough description of lithology over 1000s of meters, making comprehensive, quantitative comparison difficult.

Many geologists carefully hand-draw graphic logs at fine-scale in a field notebook, and then digitally retrace them in drawing software. However, this detailed data that may have taken days or weeks to collect is often never captured in a machine-readable, tabular format. So, while tens of thousands of meters of graphic logs exist to quantify lithologic heterogeneity and stacking patterns within and between depositional environments, this data is rarely digital and available – it often remains in a field notebook. In spite of this, geologists have long been attempting to quantify graphic log data to better distinguish depositional processes, environments, and stacking patterns at the bed and lithofacies scale in order to aid in prediction of stratigraphic architecture and earth-resource distribution.

We present three open-source stratigraphic software packages in python, R, and Matlab that (1) digitize hand-drawn graphic logs into a numerical, tabular format and (2) summarize this data format into key statistics. We also discuss important considerations regarding the collection of graphic log data and recommend methods for standardizing data collection for educational and research purposes. It is our hope that these software packages, combined with advances in ‘big data’ analytics and machine-learning algorithms, will lead to new discoveries in sedimentary geology and help educate the next generation of sedimentary geologists.