Discrimination of Internal Solitary Waves from Coarse Time Resolution Field Observational Data by Using Wavelet Analysis

Internal solitary waves (ISWs) are large-amplitude internal waves which would destroy underwater engineering. Finding an easy way to discriminate ISWs from field observational data is crucial. Two time--series datasets, one contained ISWs and another only containing internal tides, were obtained from filed observations. Based on single-layer velocity data, wavelet spectrum shows significant high value in short time-scale domain when ISWs pass, whilst having no signal in that domain when only internal tides exist, indicating the capability of wavelet analysis on ISWs detection. Wavelet variances of the dataset with ISWs has a bimodal distribution versus periods with two peaks around 40 min and 110 min, which can also be reproduced by a numerical model, indicating that the energy within period band of 10–120 min is caused by ISWs. By using the conceived signal processing techne, data reconstruction can precisely obtain the arrival time of ISWs and retain about 91.4% of the original signal. It is found that, based on a field observational dataset with even a coarse sampling interval for up to 20 min, the existence of ISWs can be easily discriminated by using wavelet analysis, which provides us an economic method for the early warning of ISWs in ocean engineering.