A study on forecasting sound velocity of sea-floor sediments based on GA-BP method

Chen Wenjing; Guo Changsheng; Wang Jingqiang; Hou Zhengyu

doi:10.3969/j.issn.0253-4193.2016.01.011

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Article Navigation > Haiyang Xuebao > 2016 > 38(1): 116-123

Chen Wenjing, Guo Changsheng, Wang Jingqiang, Hou Zhengyu. A study on forecasting sound velocity of sea-floor sediments based on GA-BP method[J]. Haiyang Xuebao, 2016, 38(1): 116-123. doi: 10.3969/j.issn.0253-4193.2016.01.011

Citation:

Chen Wenjing, Guo Changsheng, Wang Jingqiang, Hou Zhengyu. A study on forecasting sound velocity of sea-floor sediments based on GA-BP method[J]. Haiyang Xuebao, 2016, 38(1): 116-123. doi: 10.3969/j.issn.0253-4193.2016.01.011

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A study on forecasting sound velocity of sea-floor sediments based on GA-BP method

doi: 10.3969/j.issn.0253-4193.2016.01.011

1.
Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;Key Laboratory of Marine Geology and Environment, Chinese Academy of Sciences, Qingdao 266071, China;University of Chinese Academy of Sciences, Beijing 100049, China
2.
Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;Key Laboratory of Marine Geology and Environment, Chinese Academy of Sciences, Qingdao 266071, China
3.
Key Laboratory of Marine Sedimentology and Environment Geology, the First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China

Received Date: 2015-01-19
Rev Recd Date: 2015-06-23

Abstract

Abstract

In the sea-floor sediments velocity prediction,there exist many problems according to the empirical equations,such as poor accuracy,the narrow scope of application,lack of exact physical meaning. Based on the existing BP neural network,genetic algorithm (GA) is used to optimize the initial weights and threshold. A sea-floor sediment sound velocity forecasting model is established with the relationship of water content,porosity and velocity. Measurement data of study samples from the southern South China Sea are applied. These data are divided into two parts,120 groups including continental shelf,slope,trough samples selected as the training data,the other 6 groups as test data. Experiments show that BP neural network based on GA is superior to the traditional single-parameter,double-parameter sound velocity forecasting empirical equation,which is recommended for the forecasting sound velocity of sea-floor sediments. This GA-BP method has certain scientific basis and broad application prospects in the future,can provide reference for the establishment of the accurate,uniform model.
- GA,
- BP neural network,
- sea-floor sediments,
- sound velocity forecast

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References(34)

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