Segmentation of Homogeneous Regions of Gravity Field Properties by Machine Learning Method in Central Area of Vietnam

• Autorzy: Thi Hong Phan , Minh Phuong Do , Van Huu Tran

Identyfikatory

DOI

10.29227/IM-2023-02-17

Warianty tytułu

PL

Segmentacja jednorodnych regionów właściwości pola grawitacyjnego metodą uczenia maszynowego w centralnym obszarze Wietnamu

• Konferencja: POL-VIET 2023 — the 7th International Conference POL-VIET
• Języki publikacji: EN

Abstrakty

EN

This paper presents the results of applying the unsupervised learning method (K-means clustering) on the gravity anomaly field in the central region of Vietnam to separate the research area into different clusters, which are homologous in physical properties. In order to achieve the optimal results, the input parameter plays an important role. In this paper, we chose 04 input attributes including the gravity anomalous field attribute, the horizontal gradient attribute, the variance attribute, and the tracing coefficient of the gravity anomalous axis. The obtained results have shown that the research area could be divided into 7 clusters, 9 clusters, 11 clusters, and 13 clusters with close characteristics of the physical properties of the gravity field. The research results show that the Southwest, the Center, and the South of the study area have complex changing physical properties, this result reflects the complicated tectonic activities in these areas with the presence of crumpled and fractured rock layers in different directions and these locations are the potential places to form endogenous mineral deposits of magma origin. The Northwest, the North, and the East parts of the research area witness negligible changes in the field's physical properties, reflecting the stability of the soil and rock layers in this area, with the direction of extending structure from the Northwest to the Southeast. The clustering results according to the K-means unsupervised learning algorithm in central Vietnam initially increase the reliability of the decisions of geologists and geophysicists in interpreting the geological structure and evaluating the origin of deep-hidden mineral deposits in the area.

Słowa kluczowe

EN

K-means; unsupervised learning method; gravity field; central area of Vietnam; COSCAD 3D

PL

Wietnam; właściwości fizyczne; grawitacja

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2023
• Tom: no. 2, vol. 1
• Strony: 97--102
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Thi Hong Phan

• HUMG Hanoi University of Mining and Geology, Faculty of Petroleum and Energy, Hanoi, Vietnam

autor

Minh Phuong Do

• Geophysical Division, General Department of Geology and Minerals, Hanoi, Vietnam

autor

Van Huu Tran

• HUMG Hanoi University of Mining and Geology, Faculty of Petroleum and Energy, Hanoi, Vietnam

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)