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| | Abstract | | Recently, we are trying to discover buried hydrothermal deposit by mapping the marine electric field. It is so-called the self-potential (SP) survey, which can detect the anomalies due to redox potential frequently observed at land mineral deposits. Noise is normally included in the observed electric field, and often interrupts quantitative estimation of current sources of SP below the seafloor. Removal of noise in the time series data of electric field is essential. However, we cannot eliminate the noise by using the conventional simple stacking method because the SP signal has some correlations with the noise. Here, we propose noise removal with the Independent Component Analysis (ICA), recently used for breaking down multicomponent-mixed signals into some independent signals. We tested effectiveness of ICA through the application to observed data. As a result, we succeeded to extract and remove the noise by using ICA, whose efficiency is better than those analyzed by using simple stacking.. I also added an artificial signal to observed electric field similar to the anomaly due to a sub-seafllor mineral deposit. , We confirmed that the ICA could pick out the signal and eliminate the noise more clearly than the simple stacking. We conclude that ICA can be used as a new tool for the noise reduction in marine observations. |
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