Deep Neural Networks with Extreme Learning Machine for Seismic Data Compression

Advances on seismic survey techniques require a large number of geophones. This leads to an exponential growth in the size of data and prohibitive demands on storage and network communication resources. Therefore, it is desirable to compress the seismic data to the minimum possible, without losing important information. In this paper, a stacked auto-encoder extreme learning machine (AE-ELM) for seismic data compression is proposed. First, a deep asymmetric auto-encoder is constructed, in which nonlinear activation functions are used in the encoder hidden layers and linear activation functions are utilized in the decoder layers. Second, the encoder hidden layers are connected in a cascade way, so that outputs of a hidden layer are considered as the inputs to the succeeding hidden layer. Third, the optimal weights of connections between the layers of the decoder are solved analytically. Lastly, the AE-ELMs are stacked to create the complete encoder/decoder. The extreme learning machine (ELM) is selected due to its analytical calculation of weights efficient training that is suitable for practical implementation. In this neural network, data compression is achieved by transforming the original data through the encoder layers where the size of outputs from the last encoder hidden layer is smaller than the original data size. The proposed method exhibits a comparable reconstruction quality on a real dataset but with a much shorter training duration than other deep neural networks methods. This neural network with more than 8000 hidden units achieved 1.28 × 10 ^{- 3} of normalized mean-squared error for 10:1 of compression ratio with only 8.23 s of training time.