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Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications. INDEX TERMS Deep neural networks, OFDM systems, channel estimation, underwater acoustic communication.

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There are 9 major coal-accumulating periods during geological history in China, including the Early Carboniferous, Late Carboniferous-Early Permian, Middle Permian, Late Permian, Late Triassic, Early-Middle Jurassic, Early Cretaceous, Paleogene and Neogene. The coal formed in these periods were developed in different coal-accumulating areas (CAA) including the North China, South China, Northwest China, Northeast China, the Qinghai–Tibet area, and China offshore area. In this paper, we investigated depositional environments, sequence stratigraphy, lithofacies paleogeography and coal accumulation pattern of five major coal-accumulating periods including the Late Carboniferous to Middle Permian of the North China CAA, the Late Permian of the South China CAA, the Late Triassic of the South China CAA, the Early-Middle Jurassic of the North and Northwest China CAA, and the Early Cretaceous in the Northeast China CAA. According to distribution of the coal-bearing strata and the regional tectonic outlines, we have identified distribution range of the coal-forming basins, sedimentary facies types and coal-accumulating models. The sequence stratigraphic frameworks of the major coal-accumulating periods were established based on recognition of a variety of sequence boundaries. The distribution of thick coals and migration patterns of the coal-accumulating centers in the sequence stratigraphic framework were analyzed. The lithofacies paleogeography maps based on third-order sequences were reconstructed and the distribution of coal accumulation centers and coal-rich belts were predicted.

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