Application of machine-learning algorithms for tephrochronology: a case study of Plio-Quaternary volcanic fields in the South Aegean Active Volcanic Arc

Uslular, Göksu; Kıyıkçı, Fatih; Karaarslan, Enis; Kuşcu, Gonca Gençalioğlu

doi:10.1007/s12145-022-00797-5

Cited by 5 publications

(3 citation statements)

References 74 publications

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“…kNN displays more variable results in the literature. Good performances were observed in a study with Alaska tephras (Bolton et al, 2020) and kNN had the best performance in the Neapolitan region (Pignatelli & Piochi, 2021), yet it obtained relatively bad performances in the South Aegean Active Volcanic Arc (Uslular et al, 2022). Finally, LR displayed the worst performances considering both balanced and unbalanced accuracies, being lower than all other models outside of 1SD.…”

Section: Resultsmentioning

confidence: 96%

“…In particular, RF is among the best performing algorithms in previous work assessing the use of machine learning for identifying the volcanic source of volcanic products, (Bolton et al, 2020;Pignatelli & Piochi, 2021;Uslular et al, 2022). In contrast, GB was tested exclusively in the South Aegean Active Volcanic Arc, with similar performances to that of RF (Uslular et al, 2022). kNN displays more variable results in the literature.…”

Section: Resultsmentioning

confidence: 99%

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BOOM! Tephrochronological dataset and exploration tool of the Southern (33–46° S) and Austral (49–55° S) volcanic zones of the Andes

Martínez Fontaine,

Peña-Araya,

Marmo

et al. 2023

Quaternary Science Reviews

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

BOOM! Tephrochronological dataset and exploration tool of the Southern (33–46° S) and Austral (49–55° S) volcanic zones of the Andes

Martínez Fontaine,

Peña-Araya,

Marmo

et al. 2023

Quaternary Science Reviews

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“…Tephra correlation is another field where ML classification methods found large applications (e.g., Petrelli et al, 2017;Bolton et al, 2020;Uslular et al, 2022). For example, Bolton et al (2020), suggested the use of machine learning classifiers on tephra correlation.…”

Section: Classificationmentioning

confidence: 99%

Machine Learning in Petrology: State-of-the-Art and Future Perspectives

Petrelli

2024

Preprint

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The present manuscript reports on the state-of-the-art and future perspectives of Machine Learning (ML) in Petrology. To do that, it first introduces the basics of ML, including definitions, core concepts, and applications. Then, it starts reviewing the state-of-the-art of ML in petrology. Established applications mainly concern clustering, dimensionality reduction, classification, and regression. Among them, clustering and dimensionality reduction are particularly valuable for decoding the chemical record stored in igneous and metamorphic phases and to enhance data visualization, respectively. Classification and regression tasks find applications, for example, in petrotectonic discrimination and geothermobarometry, respectively. The main core of the manuscript consists of depicting the next future for ML in petrological applications. I propose a future scenario where ML methods will progressively integrate and support established petrological methods in boosting new findings, possibly providing a paradigm shift. In this framework, the use of multimodal data, data fusion, physics-informed neural networks, and ML-supported numerical simulations, will play a significant role. Also, the use of ML hypotheses formulation and symbolic regression could significantly boost new findings. In the proposed scenario, the main challenges are: a) progressively link machine learning algorithms with the physical and thermodynamic nature of the investigated petrologic processes, b) unblur deep learning algorithms that too often operate as black boxes, c) go ahead in exploring cutting edge tools that rise from researches in Artificial Intelligence, and overall, d) start a collaborative effort among researchers coming from different disciplines in research and teaching.

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Insight into the 2021 Semeru volcano eruption from rapid monitoring of its pyroclastic deposits using Google Earth Engine and multi-sensor data

Kurnianto,

Naim,

Susiati

et al. 2024

Remote Sensing Applications: Society and Environment

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Application of machine-learning algorithms for tephrochronology: a case study of Plio-Quaternary volcanic fields in the South Aegean Active Volcanic Arc

Cited by 5 publications

References 74 publications

BOOM! Tephrochronological dataset and exploration tool of the Southern (33–46° S) and Austral (49–55° S) volcanic zones of the Andes

BOOM! Tephrochronological dataset and exploration tool of the Southern (33–46° S) and Austral (49–55° S) volcanic zones of the Andes

Machine Learning in Petrology: State-of-the-Art and Future Perspectives

Insight into the 2021 Semeru volcano eruption from rapid monitoring of its pyroclastic deposits using Google Earth Engine and multi-sensor data

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