Journal of Marine Sciences ›› 2021, Vol. 39 ›› Issue (3): 12-20.DOI: 10.3969/j.issn.1001-909X.2021.03.002

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Automatic recognition of volcanic cones at mid-ocean ridges based on the seabed DEM data

DANG Niu1,2,3, YU Xing*2,3, HAN Xiqiu2,3, CHEN Anqing1   

  1. 1.Institute of Sedimentary Geology, Chengdu University of Technology, Chengdu 610059, China;
    2.Key Laboratory of Submarine Geosciences, Ministry of Natural Resources, Hangzhou 310012, China;
    3.Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  • Online:2021-09-15 Published:2021-09-15

Abstract: The mid-ocean ridge is where the plates spread and the new oceanic crust forms. There are often well-developed central eruptive volcanic cones in addition to fissure eruptive lavas parallel to the ridge axis. These volcanic cones are of great significance for understanding the local and regional magmatism and tectonic activities. These volcanic cones can be identified using manual or machine interpretation methods based on seabed multi-beam bathymetric data. In this study the automatic extraction method of volcanic cones near the mid-ocean ridge was tried by means of unsupervised classification using the DEM data from Carlsberg Ridge obtained from the Chinese DY24 Cruise. The slope, surface roughness, positive and negative topography and other derivative parameters were calculated based on the original DEM data. The morphological characteristics and spatial features of volcanic cones were enhanced by feature transformation. The ISO clustering unsupervised classification method was selected to cluster and analyze the images after feature transformation. Then the method of landscape shape index in landscape ecology was introduced to complete the automatic extraction of volcanic cones. The accuracy of automatic recognition can reach around 0.8 with overlapping rate of ~0.7 comparing with manual extraction. Thus, the automatic extraction of submarine volcanic cones by means of unsupervised classification is robust and efficient, which can be of great help to large-scale data process and interpretation.

Key words: volcanic cone, mid-ocean ridge, unsupervised classification, feature transformation, DEM, automatic recognition

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