Subsurface stratigraphic modeling is crucial for a variety of environmental, societal, and economic challenges. However, the need for specific sedimentological skills in sediment core analysis may constitute a limitation. Methods based on Machine Learning and Deep Learning can play a central role in automatizing this time-consuming procedure. In this work, using a robust dataset of high-resolution digital images from continuous sediment cores of Holocene age that reflect a wide spectrum of continental to shallow-marine depositional environments, we outline a novel deep-learning-based approach to perform automatic semantic segmentation directly on core images, leveraging the power of convolutional neural networks. To optimize the interpretation process and maximize scientific value, we use six sedimentary facies associations as target classes in lieu of ineffective classification methods based uniquely on lithology. We propose an automated model that can rapidly characterize sediment cores, allowing immediate guidance for stratigraphic correlation and subsurface reconstructions.

Di Martino, A., Carlini, G., Castellani, G., Remondini, D., Amorosi, A. (2023). Sediment core analysis using artificial intelligence. SCIENTIFIC REPORTS, 13(1), 1-11 [10.1038/s41598-023-47546-2].

Sediment core analysis using artificial intelligence

Di Martino, Andrea
Co-primo
;
Carlini, Gianluca
Co-primo
;
Castellani, Gastone;Remondini, Daniel
Penultimo
;
Amorosi, Alessandro
Ultimo
2023

Abstract

Subsurface stratigraphic modeling is crucial for a variety of environmental, societal, and economic challenges. However, the need for specific sedimentological skills in sediment core analysis may constitute a limitation. Methods based on Machine Learning and Deep Learning can play a central role in automatizing this time-consuming procedure. In this work, using a robust dataset of high-resolution digital images from continuous sediment cores of Holocene age that reflect a wide spectrum of continental to shallow-marine depositional environments, we outline a novel deep-learning-based approach to perform automatic semantic segmentation directly on core images, leveraging the power of convolutional neural networks. To optimize the interpretation process and maximize scientific value, we use six sedimentary facies associations as target classes in lieu of ineffective classification methods based uniquely on lithology. We propose an automated model that can rapidly characterize sediment cores, allowing immediate guidance for stratigraphic correlation and subsurface reconstructions.
2023
Di Martino, A., Carlini, G., Castellani, G., Remondini, D., Amorosi, A. (2023). Sediment core analysis using artificial intelligence. SCIENTIFIC REPORTS, 13(1), 1-11 [10.1038/s41598-023-47546-2].
Di Martino, Andrea; Carlini, Gianluca; Castellani, Gastone; Remondini, Daniel; Amorosi, Alessandro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/962361
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