Automated Detection of Hydrothermal Emission Signatures from Multi-Beam
Echo Sounder Images Using Deep Learning
Abstract
Seafloor massive sulfide deposits have attracted attention as a mineral
resource, as they contain a wide variety of base, precious, and other
valuable critical metals. Previous studies have shown that signatures of
hydrothermal activity can be detected by a multi-beam echo sounder
(MBES), which would be beneficial for exploring sulfide deposits.
Although detecting such signatures from acoustic images is currently
performed by skilled humans, automating this process could lead to
improved efficiency and cost effectiveness of exploration for seafloor
deposits. Herein, we attempted to establish a method for automated
detection of MBES water column anomalies using deep learning models.
First, we compared the “Mask R-CNN” and “YOLO-v5” detection model
architectures, wherein YOLO-v5 yielded higher F1 scores. We then
compared the number of training classes and found that models trained
with two classes (signal and noise) exhibited superior performance
compared with models trained with only one class (signal). Finally, we
examined the number of trainable parameters and obtained the best model
performance when the YOLO-v5l model with a large number of trainable
parameters was used in the two-class training process. The best model
had a precision of 0.928, a recall of 0.881, and an F1 score of 0.904.
Using this method, the detection speed was 20−25 ms per frame, which is
faster than the pace at which MBES images can generally be generated.
Therefore, our best model can be applied in the field for automatic and
real-time exploration of seafloor hydrothermal deposits.
This work has been submitted to the IEEE for possible publication.
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