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This study aims to detect the bioacoustics signal in the underwater soundscape, specifically those produced by snapping shrimp, using adaptive iterative transfer learning. The proposed network is initially trained with pre-classified snapping shrimp sounds and Gaussian noise, then applied to classify and remove snapping-free noise from field data. This separated ambient noise is subsequently used for transfer learning. This process was iterated to distinguish more effectively between ambient noise and snapping shrimp sounds characteristics, resulting in improved classification. Through iterative transfer learning, significant improvements in precision and recall were observed. The application to field data confirmed that the trained network could detect signals that were difficult to identify using existing threshold classification methods. Furthermore, it was found that the rate of false detection decreased, and detection probability improved with each stage. This research demonstrates that incorporating the noise characteristics of field data into the trained network via iterative transfer learning can generate more realistic training data. The proposed network can successfully detect signals that are challenging to identify using existing threshold classification methods. [ABSTRACT FROM AUTHOR]

Least‐squares reverse time migration, a linearized inversion problem, can provide high‐quality migration image by minimizing the misfit function, which is defined by predicted and observed data. According to the theory of data‐domain least‐squares reverse time migration, a forward source wavefield that is simulated with a fixed background velocity does not change during iterations. However, storing the forward source wavefield directly into computer memory involves substantial memory consumption. Although a source wavefield reconstruction technique can be applied during least‐squares reverse time migration iterations, this approach can increase the computational cost because of the need for additional wavefield simulations. To alleviate this computational issue in storing the forward source wavefield, we propose an efficient least‐squares reverse time migration scheme based on an excitation amplitude method. Unlike conventional excitation amplitude imaging conditions, the proposed least‐squares reverse time migration scheme enables one to reconstruct the forward source wavefield by convolving a source wavelet with the excitation amplitude of Green's function at the excitation time. With this excitation amplitude method, the forward source wavefield can be efficiently stored in the computer memory because the sizes of the excitation amplitude and excitation time maps are equal to the size of one snapshot. To validate the feasibility of our least‐squares reverse time migration scheme, we perform dot‐product tests and compare forward source wavefields, demigrated data and gradient vectors obtained by conventional least‐squares reverse time migration and our proposed least‐squares reverse time migration. Using numerical tests on synthetic data, we confirm that our least‐squares reverse time migration can produce high‐quality migration results with a significant improvement in computational efficiency with respect to performance time and memory consumption. [ABSTRACT FROM AUTHOR]

The reverse time migration (RTM) aims to image subsurface reflectors by incorporating with the two-way wave equation. RTM has advantages that it does not have dip limitations and can generate subsurface images clearly even in the case of complex geological structures in the near surface. We have proposed a reproduction wavefield reverse time migration (RWRTM) algorithm implemented with backward reproduced wavefield instead of the back-propagation of the observed data. Reproduced wavefield can be achieved by using the same modelling algorithm used in the source propagation. In general acquisition systems, e.g. streamer in marine or surface geophone in land, conventional RTM back-propagates data acquired on the top grid points in the numerical domain while RWRTM back-propagates reproduced data located on all grid points in the numerical domain, which lead to the generation of migration images with less migration noises and well-balanced migrated events. To validate RWRTM, numerical tests are conducted. For our examples, RWRTM images have better resolution, improved continuity of structure, reduced migration noises, and balanced amplitude compared to the conventional RTM. [ABSTRACT FROM AUTHOR]