Deep time spatio-temporal data analysis using pyGPlates with PlateTectonicTools and GPlately

IF 3.3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Geoscience Data Journal Pub Date : 2023-03-14 DOI:10.1002/gdj3.185

Ben R. Mather, R. Dietmar Müller, Sabin Zahirovic, John Cannon, Michael Chin, Lauren Ilano, Nicky M. Wright, Christopher Alfonso, Simon Williams, Michael Tetley, Andrew Merdith

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引用次数: 0

Abstract

PyGPlates is an open-source Python library to visualize and edit plate tectonic reconstructions created using GPlates. The Python API affords a greater level of flexibility than GPlates to interrogate plate reconstructions and integrate with other Python workflows. GPlately was created to accelerate spatio-temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. This object-oriented package enables the reconstruction of data through deep geologic time (points, lines, polygons and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction and seafloor spreading), the rapid comparison between multiple plate motion models, and the plotting of reconstructed output data on maps. All tools are designed to be parallel-safe to accelerate spatio-temporal analysis over multiple CPU processors.

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使用P - late - T构造工具和GP - late的py - GP - late深时时空数据分析

PyGPlates 是一个开源 Python 库，用于可视化和编辑使用 GPlates 创建的板块构造重建。Python API 提供了比 GPlates 更大的灵活性，可用于查询板块重建并与其他 Python 工作流集成。创建 GPlately 的目的是在简化的 Python 界面中利用 pyGPlates 和 PlateTectonicTools 加速时空数据分析。这个面向对象的软件包可以重建深地质年代的数据（点、线、多边形和栅格），查询板块运动信息（板块速度、俯冲和海底扩张速率），快速比较多个板块运动模型，并在地图上绘制重建的输出数据。所有工具的设计都是并行安全的，以便在多个 CPU 处理器上加速时空分析。

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来源期刊

Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

5.90

自引率

9.40%

发文量

审稿时长

4 weeks

期刊介绍： Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.