Applied ichnology in sedimentary geology: Python scripts as a method to automatize ichnofabric analysis in marine core images

Casanova-Arenillas, Santiago; Rodrı́guez-Tovar, Francisco J.; Martı́nez-Ruiz, Francisca

doi:10.1016/j.cageo.2020.104407

Cited by 14 publications

(4 citation statements)

References 27 publications

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“…Python's extensive collection of adaptable modules and its clean code structure make it highly comprehensible and easily learnable [17]. These factors contribute to Python's popularity, as evidenced by its thriving and rapidly expanding community of users engaged in a wide range of applications, including traditional fields as well as cutting-edge domains such as artificial intelligence, data science, and machine learning [18].…”

Section: Sms-coastal Operationmentioning

confidence: 99%

SMS-Coastal, a New Python Tool to Manage MOHID-Based Coastal Operational Models

Mendonça,

Martins,

Janeiro

2023

JMSE

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This paper presents the Simulation Management System for Operational Coastal Hydrodynamic Models, or SMS-Coastal, and its novel methodology designed to automate forecast simulations of coastal models. Its working principle features a generic framework that can be easily configured for other applications, and it was implemented with the Python programming language. The system consists of three main components: the Forcing Processor, Simulation Manager, and Data Converter, which perform operations such as the management of forecast runs and the download and conversion of external forcing data. The SMS-Coastal was tested on two model realisations using the MOHID System: SOMA, a model of the Algarve coast in Portugal, and BASIC, a model of the Cartagena Bay in Colombia. The tool proved to be generic enough to handle the different aspects of the models, being able to manage both forecast cycles.

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Section: Sms-coastal Operationmentioning

confidence: 99%

SMS-Coastal, a New Python Tool to Manage MOHID-Based Coastal Operational Models

Mendonça,

Martins,

Janeiro

2023

JMSE

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“…Programming by Python and R is the most common approach used in data sciences (Casanova-Arenillas et al 2020;Gao et al 2021). However, the application of R programming (R Core Team, 2020) as applied scripting is possible for geomorphologic data visualization using special cartographic R packages: 'tmap' (Tennekes, 2012) and 'raster' (Hijmans and van Etten, 2012).…”

Section: Mapping By Rmentioning

confidence: 99%

Gobi Altai, Khangai and Khentii Mountains mapped by a mixed-method cartographic approach for comparative geophysical analysis

Lemenkova

2021

Mong. geosci.

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Geologic and geophysical mapping has been so far limited to the traditional single-method GIS-based mapping. A new approach combining integrated analysis of data on geology, gravity, topography and geomorphology is presented for regional characterization of the geophysical setting in Mongolia: the Gobi Altai Mountains, the Khangai Mountains and Khentii Mountains with surrounding areas. Nine new maps have been produced from the high-resolution datasets: GEBCO, gravity raster, USGS geological data and SRTM-90 DEM geomorphological grid. Methodology includes three tools for cartographic data visualization: i) Generic Mapping Tools (GMT), ii) R programming language (‘raster’ and ‘tmap’ libraries); iii) QGIS. The results demonstrated strong agreement between the estimated values in gravity and topography grids, distribution of geological units and provinces over the country and geomorphological landforms with respect to the mountain ranges: Altai, Khangai and Khentii Mountains. The highest values in the gravity anomalies correspond to the mountain ranges in the Altai Mountains and Khangai Mountains (<80 mGal); high values correspond to the Khentii Mountains (20–60 mGal). Contrariwise, the basins of the Uvs Nuur and Khyargas Nuur show negative values (<-80 mGal). The NE- to NNE-oriented faulting and rift basins are clearly visible in the geophysical grids and geologic maps. The geomorphometric analysis performed based on the SRTM-90 DEM using R scripting demonstrated (1) slope, (2) aspect, (3) hillshade and (4) elevation models of Mongolia supported by histograms of data distribution and frequency. The study contributed to the cartographic methods and regional geological studies of Mongolia.

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“…For this reason, ichnological features are reliable proxies when reconstructing bottomand pore-waters conditions and for palaeoceanographic interpretations (Löwemark et al, 2006;Löwemark, 2012;, 2018, 2019a, 2020aHodell et al, 2017;Sánchez Goñi et al, 2019). In the wake of a general ichnological study of sediment records from the Alboran Sea Basin, looking at the relationship between the abundance of bioturbation and climate changes during the Last Glacial Cycle (Casanova-Arenillas et al, 2020, 2021, the present study offers a detailed analysis of the westernmost ORLs distinguished over this time interval, so as to reconstruct palaeoceanographic dynamics. Our main aim is therefore to characterize the ORLs in terms of ichnology and sedimentology, beyond previous results, and typify ORLs based on such characterization.…”

Section: Introductionmentioning

confidence: 99%