3:00 PM - 3:15 PM
[SCG55-06] Deep-learning based exploration of seafloor hydrothermal activities at Higashi-Aogashima Knoll Calder
Keywords:Seafloor mineral resources, Seafloor massive sulfide, Deep learning, Object detection
Seafloor massive sulfide deposits are expected to be a prominent resource for various metals such as Pb, Zn, Pb, Au, Ag [1, 2]. Previous studies have shown that the signatures of hydrothermal activities can be detected as acoustic scattering anomalies in the water column images obtained by multi-beam echo sounder (MBES images) [3–5]. However, the conventional method required skilled observers carefully observing numerous MBES images to detect hydrothermal emission signals, which have required enormous time and efforts.
Recently, we have shown that object detection, a type of deep learning (DL) technique, is applicable to finding the signals from MBES images, which is fast enough for real-time observations [6]. We have also proposed a method to reduce false positives (misdetections) by time-series analysis [7].
Here, we report the practical application of the DL-based observations. MBES images were captured during research cruises KM22-11C, KM23-01 and KM23-02 at the Higashi-Aogashima Knoll Caldera hydrothermal site, Izu-Bonin Arc. First, we have confirmed the applicability of the DL system by surveying on known sites. Then, a wide-area survey within the caldera was conducted. The DL model successfully identified prominent sites of hydrothermal activities based on a vast number of images. Notably, during the succeeding cruises KM23-08_09C and KM24-09, an existence of the hydrothermal activity was newly confirmed from one of the prominent sites by ROV survey.
References:
[1] Hannington et al. (2011) Geology, 39, 1155–1158.; [2] Iizasa et al. (2019) Mineralium Deposita, 54, 117–132.; [3] Nakamura et al. (2015) Geochem. J., 49, 579–596.; [4] Kasaya et al. (2015) Geochem. J., 49, 597–602.; [5] Kaneko and Kasaya (2022) Geoinformatics, 33, 87–94. [6] Mimura et al. (2023) IEEE-JSTARS, 16, 2703–2710.; [7] Genda et al. (2025) TechRxiv. https://doi.org/10.36227/techrxiv.173895078.87591845/v1.
Recently, we have shown that object detection, a type of deep learning (DL) technique, is applicable to finding the signals from MBES images, which is fast enough for real-time observations [6]. We have also proposed a method to reduce false positives (misdetections) by time-series analysis [7].
Here, we report the practical application of the DL-based observations. MBES images were captured during research cruises KM22-11C, KM23-01 and KM23-02 at the Higashi-Aogashima Knoll Caldera hydrothermal site, Izu-Bonin Arc. First, we have confirmed the applicability of the DL system by surveying on known sites. Then, a wide-area survey within the caldera was conducted. The DL model successfully identified prominent sites of hydrothermal activities based on a vast number of images. Notably, during the succeeding cruises KM23-08_09C and KM24-09, an existence of the hydrothermal activity was newly confirmed from one of the prominent sites by ROV survey.
References:
[1] Hannington et al. (2011) Geology, 39, 1155–1158.; [2] Iizasa et al. (2019) Mineralium Deposita, 54, 117–132.; [3] Nakamura et al. (2015) Geochem. J., 49, 579–596.; [4] Kasaya et al. (2015) Geochem. J., 49, 597–602.; [5] Kaneko and Kasaya (2022) Geoinformatics, 33, 87–94. [6] Mimura et al. (2023) IEEE-JSTARS, 16, 2703–2710.; [7] Genda et al. (2025) TechRxiv. https://doi.org/10.36227/techrxiv.173895078.87591845/v1.