DetritalPy: A Python‐based toolset for visualizing and analysing detrital geo‐thermochronologic data. (4th July 2018)

• Record Type: Journal Article
• Authors: Sharman, Glenn R.; Sharman, Jonathan P.; Sylvester, Zoltan
• Is Part Of: Depositional record. Volume 4:Number 2(2018)
• Page Start: 202
• Page End: 215
• Publication Date: 2018-07-04
• Subjects: Detrital zircon -- geochronology -- Python -- thermochronology; Sediments (Geology) -- Periodicals; Sedimentology -- Periodicals; Sedimentation and deposition -- Periodicals; 552.5
• DOI: 10.1002/dep2.45
• Languages: English
• ISSNs: 2055-4877

Abstract

Abstract: Detrital geochronology and thermochronology have emerged as primary methods of reconstructing the tectonic and surficial evolution of the Earth over geological time. Technological improvements in the acquisition of detrital geo‐thermochronologic data have resulted in a rapid increase in the quantity of published data over the past two decades, particularly for the mineral zircon. However, existing tools for visualizing and analysing detrital geo‐thermochronologic data generally lack flexibility for working with large datasets, hampering efforts to utilize the large quantity of available data. Abstract : This paper presents detritalPy, a Python‐based toolset that is designed for flexibility in visualizing and analysing large detrital geo‐thermochronologic datasets. Any number of samples, or groups of samples, can be selected for plotting and/or analysis. Functionality includes: (a) plotting detrital age distributions using the most commonly employed visualization types, (b) plotting sample locations within an interactive mapping interface, (c) calculating and plotting maximum depositional age, (d) creating multi‐dimensional scaling plots and (e) calculating inter‐sample similarity and dissimilarity matrices, among other functions. detritalPy is implemented using a Jupyter Notebook, requires no significant coding expertise, and can be modified as needed to meet users' specific requirements. It is anticipated that detritalPy will provide a platform for analysing detrital geo‐thermochronologic data within a 'Big Data' framework, providing a much needed toolset for efficient utilization of ever‐increasing quantities of data.