Members
Leader:Data Science Research Group, Center for Earth Information Science and Technology (CEIST),
Research Institute for Value-Added-Information Generation (VAiG)

Dr. Eiichi Kikawa

Research Staff :Dr. Takefumi Kasaya

Submarine Resources Research Center (SRRC),
Research Institute for Marine Resources Utilization (MRU)

Dr. Tatsu Kuwatani

Solid Earth Data Science Research Group, Volcanoes and Earth’s Interior Research Center (VERC),
Research Institute for Marine Geodynamics (IMG)

Dr. Daisuke Matsuoka

Data Science Research Group, Center for Earth Information Science and Technology (CEIST),
Research Institute for Value-Added-Information Generation (VAiG)

Dr. Mitsuko Hidaka

Data Science Research Group, Center for Earth Information Science and Technology (CEIST),
Research Institute for Value-Added-Information Generation (VAiG)

Mr. Koshiro Murakami

Data Science Research Group, Center for Earth Information Science and Technology (CEIST),
Research Institute for Value-Added-Information Generation (VAiG)

Mr. Toshiaki Ueki, Mr. Satoshi Okawara, Dr. Oak Yono［Until February 2024］ Ocean High Technology Institute, Inc.

Technical Staff :Dr. Yukari Kido

Ocean STEAM Project Section, Marine Science and Technology Strategy Department（MaSTS）

Mr. Junji Kaneko

Physical Property and Exploration Geophysics Research and Development Group, Submarine Resources Research Center (SRRC),
Research Institute for Marine Resources Utilization (MRU)

Research Results：
2023

・ Yono O, Kuwatani T, Ueki T, Kikawa E., Removal of MBES Point Cloud Data Using Sparse Coding, Proceedings
WR-17 & AIG-14, Sendai, Japan, Aug. 18-22

2022

・ T. Kuwatani, H Hino, K Nagata, T Kawashima, M Toriumi, M Okada, Hyperparameter estimation using resolution
matrix for Bayesian sensing Inverse Problems, 38, 124004

2022

・ T Yutani, O Yono, T. Kuwatani, D Matsuoka, J Kaneko, M Hidaka, T Kasaya, Y Kido, Y Ishikawa, T Ueki, E Kikawa,
Super-Resolution and Feature Extraction for Ocean Bathymetric Maps Using Sparse Coding Sensors, 22, 3198

2021

・ Mitsuko Hidaka, Daisuke Matsuoka, Tatsu Kuwatani, Yukari Kido, Junji Kaneko, Takafumi Kasaya,YoichiIshikawa, and Eiichi Kikawa Super-resolution for Ocean Bathymetric Maps Using Deep Learning Approaches: AComparison and Validation, Geoinformatics, 2021, 32, 1, pp. 3-13, doi: 10.6010/geoinformatics.32.1_3

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 bathymetricmaps, 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

60, doi: 10.6010/geoinformatics.29.2_49

Related Sites

国立研究開発法人

海洋研究開発機構
2-15, Natsushima-cho, Yokosuka-city, Kanagawa, 237-0061, Japan
E-mail：seabed2030 ＠ jamstec.go.jp― 地質学分野における応用例―. 情報地質, 2018, 29, 2, p. 49-

Mathematical
SeaﬂoorGeomorphology
Advancing Our Understandings
for the Earth :Extraction of image feature quantity using machine learningIntroduction

Sparse modeling is a mathematical and information-scientificmethodology which assumes that observed high-dimensional

The Mathematical Seafloor Geomorphology Research Project, which started in 2019, hasbeen working on increasing the resolution of bathymetric image analysis using deep learningand advancing the sophistication of seafloor topography image analysis by sparse modeling.
In the future, we will further develop these analysis methods and integrate the bathymetricstructure patterns obtained through image feature extraction with knowledge from various
natural domains such as disaster prevention, environment, living organisms, and resources, fromwhich we expect to understand the origin of seafloor topography and its effects on the naturaldomains. The image analysis method established in this research project makes it possible tocreate detailed bathymetric data for the same area from existing rough bathymetric data. This
will make a significant contribution to the Nippon Foundation-GEBCO Seabed2030 project byapplying it to ocean areas where insufficient data is collected.

data can be represented by a small number of essential variables.

tribute to several important scientific issues, such as disaster prevention, resource exploration, and environmental problems.

Recently, we have been developing a point cloud data analysis

Because the sparse modeling enables maximum information ex-method that applies sparse modeling to multibeam bathymetricproblems including high-speed imaging of medical MRI and direct

maps. So far, it has been found that by training appropriate super-traction even from a small amount of data, it is applied to various

observation of black holes. In this research program, we think thatsparse modeling would be effective for the analysis of seafloorgeomorphology. In other words, the problem is the same as theimaging of the MRI and the observation of black holes, and by

making effective use of limited available data as much as possible,
the original seafloor geomorphology can be accurately obtained.
By improving the previous methodologies so that it candata, which is the primary data for creating seabed topographic

vised data, we can automatically remove noise that occurs for various reasons. Currently, by combining these analysis methods, wecontinue our research and development with the goal of constructing a framework that can create high-precision, high-resolution

seabed topographic maps, even in marine areas where it is difficult
to obtain good quality bathymetric data.

be applied to image data in natural science and applying it

to seafloor topographic maps, we clarified that it is possible

not only to improve the resolution but also to automatically
capture spatial patterns such as microtopography that arecharacteristic of the target area. At present, we are trying tolink the obtained characteristic geomorphologic patterns
with knowledge of geology and biology, in order to con-Super-resolution for ocean bathymetric maps using machine learning approaches
Recently, deep learning, a machine learning method that uses

In this research program, we implemented five different DCNN

Contribution to the Nippon Foundation - GEBCO Seabed2030 Project
GEBCO aims to provide the most authoritative, publicly available

learning approaches (Kikawa et al., AGU2023 Fall Meeting) and AUV

multi-layered neural networks, has been attracting attention

architectures (SRCNN, FSRCNN, ESPCN, SRGAN, and ESRGAN) forbathymetry of the world’s ocean. It operates under the joint auspices（Autonomous Underwater Vehicles）development to obtain higher

convolutional neural networks (DCNNs), which specialize in image

maps of the middle Okinawa Trough acquired by JAMSTEC. The

Intergovernmental Oceanographic Commission (IOC) of UNESCO.

to greatly contribute to Seabed 2030.

in various research and industrial fields. In particular, deep
recognition, have been remarkable at extracting features froma large amount of data. They have achieved highly significant

performance, which is far exceeding that of conventional methods
in image classification, object detection, anomaly detection,and image super-resolution. Image super-resolution, which isthe processing of converting low-resolution images into high-resolution ones, is performed by learning the correspondingtransformation process.

image super-resolution and tuned them for the topographic

figure below shows an example of super-resolution of a 100-m
mesh bathymetry map to a 50-m mesh, for a 3.2-km-square area.
The super-resolution results of all architectures showed better

accuracy than that of bicubic interpolation, especially in the case ofrugged terrain and missing values. We are currently improving thegeneralization performance and magnification factor of DCNNs,
to enable their application to a wider range of areas, as well as
developing those which incorporate bathymetric knowledge.

of the International Hydrographic Organization (IHO) and theThe GEBCO Chart series originated from its initiation by Prince Albert I

quality data from wider areas have been made, which are expected
The Seabed 2030 Project aims to cover 100% of the world’s oceanof Monaco in 1903. The development of GEBCO has covered 24.9% offloor by the year 2030. To achieve this goal, an integrated data

leaving three-quarters of the survey blank yet. To map the gaps,sectors, especially fisheries, through a newly organized Seabed 2030

the world’s oceans to date (based on the press release in May 2023),
oceanographic experts from hydrographic associations, and relatedorganizations in Europe, the United States, Asia, and Oceania haveheld meetings regularly and updated the bathymetric chart. One ofcollection methodology has been initiated, which involves the private

crowd-sourced bathymetry working group, and the currently formed
automatic data transfer systems.

the meetings, namely the Regional Mapping Community Meeting,
was attended by the regional and global centers of Seabed 2030.
The meeting agenda included championing mapping activities,

assembling and compiling bathymetric information, in collaboration
with existing mapping initiatives within the sea area in charge.

GEBCO WEEK is a series of meetings held once a year, over theduration of a week, that calls for pre-registration and widespreadparticipation from marine -related researchers, governments,
private sectors, and media, by providing meeting and symposium

information. During GEBCO WEEK, reports and discussions on thestatus of bathymetric data collection, introduction to new analysis

software along with a brief training, and outreach activities to makeSeabed 2030 widely known have been carried out in plenary sessions
and subcommittees.

The JAMSTEC post-processed bathymetry data are publicly

available and was provided to the National Institute of Water andAtmospheric Research (NIWA), which is the Seabed 2030 regional
center responsible for compiling the bathymetric map of theSouth and West Pacific Ocean. Attempts to convert low-resolution
bathymetr y images to higher-resolution ones by machine-

Track chart of bathymetric surveys around Japan conducted by 7 JAMSTEC
fleets (KAIREI, NATSUSHIMA, YOKOSUKA, KAIYO, MIRAI, KAIMEI and SHINSEIMARU).The color of the track lines is as follows: Blue=KAIREI, Red=NATSUSHIMA,
Green=YOKOSUKA, Yellow=KAIYO, Orange=MIRAI, Purple=KAIMEI, Skyblue=SHINSEIMARU. Data provided by Research Data Publish Technology Group, Center for Earth
Information Science and technology (CEIST), JAMSTEC.