Abstract
Graphic logs are the most common way geologists characterize and communicate the composition and variability of clastic and carbonate 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 (e.g., thickness, grain size) 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 an analog state in a field notebook. Despite this, geologists have long been attempting to quantify graphic log data to better distinguish stacking patterns, depositional processes, and depositional environments to aid in prediction of stratigraphic architecture and earth-resource distribution. We present litholog, an open-source software package in python that stores, plots, and analyzes graphic-log data. We also include software in R and MATLAB that digitize hand-drawn graphic logs into a tabular format readable by litholog. We discuss the diversity of graphic log data, the implementation of graphic log data in a digital, structured, tabular format; finally, we recommend methods and provide a template for standardizing collection of this important type of stratigraphic data. 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.
Highlights
Graphic logs are the most common way geologists characterize and communicate the composition and variability of clastic sedimentary successions in both outcrops and subsurface cores (Fig. 1)
There are equal-width bins for each sand grain size class on the template (Fig. 7), and micron values separate the classes. This equal-width binning has three main advantages over arbitrary-width bins: (1) the bins are intuitive as they match most grain size cards and most closely resemble the Wentworth log2 grain size scale, (2) grain size can be drawn to the nearest 1⁄2 Ψ increment, as described above; and (3) it allows for simple and objective digitalization of sand grain size from a pencil-drawn log using a simple logarithmic transform of the x-axis
We advocate for the careful collection and digitalization of graphic logs; the adoption of these methods across the entire sedimentary geology community would generate a large and ever-growing source of quantitative, classified, machine-readable data that can be harnessed for statistical analysis and machine-learning purposes
Summary
Graphic logs generally consist of a series of hand-drawn lithologic intervals that can be drawn at many different scales (Fig. 1), from the detailed characterization of mm-thick depositional units in a single thin-section (e.g., Boulesteix et al, 2019) to every bed in a 10-m-thick outcrop or core succession (e.g., Pierce et al, 2018) to a simplistic description of lithology over 1000s of meters (e.g., Thompson et al, 2015). Research has focused on the finer scale, where detailed, bed-scale graphic-log data has been digitized and used to distinguish submarine depositional environments and subenvironments (e.g., Hansen et al, 2017, Malkowski et al, 2018, Tokés and Patacci, 2018, Fryer and Jobe, 2019) as well as document grain size variations in riverine and shallowmarine settings (Reynolds, 2019) While these aggregate statistics retrieved from graphic logs are incredibly valuable, they are often difficult to produce for a large number of stratigraphic sections covering either thick intervals or large spatial areas due to (1) the painstaking analog nature of data collection, and (2) a lack of software to digitize the logs. We hope that these software packages and recommendations about consistent data collection, combined with advances in machine-learning algorithms, will lead to new discoveries in sedimentary geology
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