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Outline
Submarine mud volcanoes are uplifted landforms formed by the escape of submarine fluids. Studying their evolution process is of great significance for the exploration of marine oil and gas resources, the protection of marine biodiversity, and the prevention of marine geological disasters. Based on the existing research, this paper systematically reviews and summarizes the identification characteristics of submarine mud volcanoes under different acoustic detection methods using submarine acoustic instruments and platforms. It also summarizes the morphological features, causes, and evolution stages of submarine mud volcanoes, and analyzes and discusses the progress of quantitative research on the morphology of submarine mud volcanoes. The investigation results show that theacoustic data obtained by combining multiple acoustic methods can not only be used to obtain the morphological and profile characteristics of submarine mud volcanoes, but also serve as supporting data for quantitative research on the morphology of submarine mud volcanoes. Due to the low detection resolution and narrow detection range, the submarine mud volcanoes identified in China at present are mainly large and medium-scale and dome-shaped. Small-scale and complex-shaped submarine mud volcanoes are difficult to identify. The morphological parameters of submarine mud volcanoes are mainly water depth, diameter and slope. There is less research on other parameters such as the slope of the two wings and the depth of the depression. It is suggested to continuously improve the detection technology to enhance the detection resolution, and to use multiple carrier platforms to expand the detection range of the seabed to obtain higher-resolution and more abundant seabed topographic and geomorphic data. At the same time, on the basis of the existing detection technology and detection resolution, the quantitative research on the morphology of submarine mud volcanoes should be deepened to obtain rich morphological quantitative parameters and achieve precise quantitative research. Automatic identification methods such as deep learning should be introduced to realize the automatic identification of submarine mud volcanoes and improve the efficiency of identifying submarine mud volcanoes.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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