JAMSTEC

A new research program “Mathematical Seafloor Geomorphology” has been launched to establish a method to create a high resolution seafloor topography from the existing low resolution data by super-resolution using machine learning technology, and to merge the bottom topography pattern by feature extraction estimated for disaster prevention, environment, organisms, resources etc., aiming to perform quantitative evaluation and analysis, and to investigate the relationship between each field and bottom topography.  Contribution to The Nippon Foundation – GEBCO Seabed 2030 Project is expected and will be performed under the program.

Research Scheme

Scheme I

Creation of high resolution and high accuracy seafloor topography map by super resolution using machine learning method such as deep learning and Bayesian inference

Scheme II

Extraction of image feature quantity using machine learning

Scheme III

Collation of the image features with the data for each field to detect the underlying phenomenon that forms the seafloor topography

Special Scheme

Contribution to The Nippon Foundation-GEBCO Seabed 2030 Project

Brochure


Mathematical Seafloor Geomorphology(PDF:5.72MB)

Members

Leader:
Dr. Eiichi Kikawa (Research Institute for Value-Added-Information Generation (VAiG))
Research Staff:
Dr. Takefumi Kasaya (Physical Property and Exploration Geophysics Research and Development Group, Submarine Resources Research Center (SRRC), Research Institute for Marine Resources Utilization (MRU))
Dr. Tatsu Kuwatani (Solid Earth Geochemistry Research Group, Volcanoes and Earth’s Interior Research Center (VERC), Research Institute for Marine Geodynamics (IMG))
Mr. Taku Yutani (Solid Earth Geochemistry Research Group, Volcanoes and Earth’s Interior Research Center (VERC), Research Institute for Marine Geodynamics (IMG))
Dr. Daisuke Matsuoka (Information Engineering Program (IEP), Research Institute for Value-Added-Information Generation (VAiG))
Dr. Mitsuko Hidaka (Information Engineering Program (IEP), Research Institute for Value-Added-Information Generation (VAiG))
Mr. Koshiro Murakami (Information Engineering Program (IEP), Research Institute for Value-Added-Information Generation (VAiG))
Mr. Toshiaki Ueki, Dr. Oak Yono (Ocean High Technology Institute, Inc.)
Technical Staff:
Dr. Yukari Kido (Expedition Management Group, Operations Department, Institute for Marine-Earth Exploration and Engineering (MarE3))
Mr. Junji Kaneko ( Physical Property and Exploration Geophysics Research and Development Group, Submarine Resources Research Center(SRRC), Research Institute for Marine Resources Utilization (MRU))

Topics

2021/07/16
(Seabed2030) The South and West Pacific Regional Mapping Community Meeting has been held online on 21-23 July.
2021/03/26
(Seabed2030) 2021/03/30 ~ 04/01 CSBWG10 has been held online.
2021/03/26
2020/01/21 The progress of our project was reported at the 2021 NOAA-JAMSTEC Bilateral Joint Executive Meeting (online) held on 19-21 January, 2021.
2021/03/26
(Seabed2030) 2021/01/11 ~ 15 GEBCO Week has been held online.
2020/07/15
(Seabed2030) The DARWIN bathymetric survey wake map is now available on the NOAA/IHO/DCDB website.
2020/06/29
(Seabed2030) CSBWG9 has been held online.
2020/06/23
(Seabed2030) The South West Pacific Regional Mapping Conference was held online on 23-24 June, hosted by NIWA.

Research Results

2021
2020
  • Mitsuko Hidaka, Daisuke Matsuoka, Tatsu Kuwatani, Yukari Kido, Junji Kaneko, Takafumi Kasaya, Eiichi Kikawa, and Yoichi Ishikawa, Deep convolutional neural network approaches for the super-resolution of bathymetric maps, AGU Fall Meeting 2020: Virtual, December 1-17 2020
  • Mitsuko Hidaka, Daisuke Matsuoka, Tatsu Kuwatani, Yukari Kido, Junji Kaneko, Takafumi Kasaya, Eiichi Kikawa. Super-resolution for seafloor topography using deep convolutional neural networks. JpGU - AGU Joint Meeting 2020: Virtual. 12-16 July 2020. (Poster)
  • Tatsu Kuwatani, Geoinformatics Researches in the Research Institute for Marine Geodynamics of the JAMSTEC. Geoinformatics, 2020, 31, 2, p. 53-55, doi: 10.6010/geoinformatics.31.2_53
-2019
  • Tatsu Kuwatani, Earth materials science in a data-driven paradigm. Elements, 2019, 15, 4, p. 280-281, doi: 10.2138/gselements.15.4.280
  • 桑谷 立. 地球科学プロセス解明のためのデータ駆動型解析 ― 地質学分野における応用例―. 情報地質, 2018, 29, 2, p. 49-60, doi: 10.6010/geoinformatics.29.2_49