ABSTRACT
Reflection of high-frequency acoustic signals from an air-sea interface with waves is considered in terms of determining travel times for acoustic tomography. Wave-induced, multi-path rays are investigated to determine how they influence the assumption that the time of the largest matched filter magnitude between the source and receiver signals is the best estimate of the arrival time of the flat-surface specular ray path. A simple reflection model is developed to consider the impact of in-plane, multi-path arrivals on the signal detected by a receiver. It is found that the number of multi-path rays between a source and receiver increases significantly with the number of times the ray paths strike the ocean surface. In test cases, there was always one of the multi-path rays that closely followed the flat-surface specular ray path. But all the multi-path rays arrive at the receiver almost simultaneously, resulting in interference with the signal from the flat-surface specular ray path. As a result, multi-path arrivals due to open ocean surface waves often distort the received signal such that maxima of matched filtering magnitudes will not always be a reliable indicator of the arrival time of flat-surface specular ray paths.
ABSTRACT
A tomographic scheme is presented that ingests ocean acoustic measurements into an ocean model using data from bottom-mounted hydrophones. The short distances between source-receiver pairs (1-10 km) means arrival times at frequencies of 8-11 kHz are readily detectable and often distinguishable. The influence of ocean surface motion causes considerable variability in acoustic travel times. Techniques are presented for measuring travel times and removing the variability due to surface waves. An assimilation technique is investigated that uses differences in measured and modeled acoustic travel times to impose corrections on the oceanographic model. Equations relating travel time differences to oceanographic variables are derived, and techniques are presented for estimating the acoustic and ocean model error covariance matrices. One test case using a single source-receiver pair shows that the tomographic information can have an impact on constraining the solution of the ocean circulation model but can also introduce biases in the predictions. A second test case utilizes knowledge of a bias in a model-predicted variable to limit grid cells that are impacted by the tomographic data. In this case, using the tomographic data results in significant improvements in the model predictions without introducing any biases.
