Sedimentary Facies Analysis and Segmentation

Di Martino, Andrea ; Amorosi, Alessandro (2023) Sedimentary Facies Analysis and Segmentation. University of Bologna. DOI 10.6092/unibo/amsacta/7308. [Dataset]
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Abstract

The study of subsoil, by nature inaccessible to direct observation, relies on sediment cores analysis, representing a fundamental information source. Sedimentary facies, in particular, i.e. sediment bodies or packages of strata formed in specific depositional environments, is essential for a wide range of scientific applications such as climate change studies, engineering geology, land subsidence calculation, and reservoir characterization. High-resolution facies analysis requires specific skills and training that can be a limitation to a proper understanding of the subsoil. This dataset consists of a robust database of digital images of sedimentary cores acquired in the Po Plain and the Adriatic coastal plain of Marche, Abruzzo, and Apulia regions (Italy). This database has been used to perform semantic segmentation of sedimentary facies using conventional neural networks, identifying six target classes that reflect a wide spectrum of continental to shallow-marine depositional environments.

Abstract
Document type
Dataset
Creators
CreatorsAffiliationORCID
Di Martino, AndreaDepartment of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna0000-0002-9860-4253
Amorosi, AlessandroDepartment of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna0000-0002-2263-4691
Keywords
Artificial Intelligence, Neural Networks, Stratigraphy, Sedimentary facies, Semantic segmentation,Convolutional Neural Networks
Subjects
DOI
Contributors
Name
Affiliation
ORCID
Type
Di Martino, Andrea
Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna
Contact person
Deposit date
20 Jun 2023 10:56
Last modified
20 Sep 2023 21:00
Project name
PASS - The Po-Adriatic Source-to-Sink system (PASS): from modern sedimentary processes to millennial-scale stratigraphic architecture
Funding program
Ministero dell'Università e della Ricerca - Progetti di Rilevante Interesse Nazionale - ANNO 2017 (PRIN 2017)
URI

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