Machine olfaction is an intelligent system that combines a cross-sensitivity chemical sensor array and an effective pattern recognition algorithm for the detection, identification, or quantification of various odors. Data collected by the sensor array are the multivariate time series signals with a complex structure, and these signals become more difficult to analyze due to sensor drift. In this work, we focus on improving the classification performance under sensor drift by using the deep learning method, which is popular nowadays. Compared with other methods, our method can effectively tackle sensor drift by automatically extracting features, thus not only removing the complexity of designing the hand-made features but also making it pervasive for a variety of application in machine olfaction. Our experimental results show that the deep learning method can learn the features that are more robust to drift than the original input and achieves high classification accuracy. C 2015 Wiley Periodicals, Inc.

Abstract. Crustal thickness is an important factor affecting lithospheric structure and deep geodynamics. In this paper, a deep learning neural network based on a stacked sparse auto-encoder is proposed for the inversion of crustal thickness in eastern Tibet and the western Yangtze craton. First, with the phase velocity of the Rayleigh surface wave as input and the theoretical crustal thickness as output, 12 deep-sSAE neural networks are constructed, which are trained by 380 000 and tested by 120 000 theoretical models. We then invert the observed phase velocities through these 12 neural networks. According to the test error and misfit of other crustal thickness models, the optimal crustal thickness model is selected as the crustal thickness of the study area. Compared with other ways to detect crustal thickness such as seismic wave reflection and receiver function, we adopt a new way for inversion of earth model parameters, and realize that a deep learning neural network based on data driven with the highly non-linear mapping ability can be widely used by geophysicists, and our result has good agreement with high-resolution crustal thickness models. Compared with other methods, our experimental results based on a deep learning neural network and a new Rayleigh wave phase velocity model reveal some details: there is a northward-dipping Moho gradient zone in the Qiangtang block and a relatively shallow north-west–south-east oriented crust at the Songpan–Ganzi block. Crustal thickness around Xi'an and the Ordos basin is shallow, about 35 km. The change in crustal thickness in the Sichuan–Yunnan block is sharp, where crustal thickness is 60 km north-west and 35 km south-east. We conclude that the deep learning neural network is a promising, efficient, and believable geophysical inversion tool.

Abstract. Crustal thickness is an important factor affecting lithosphere structure and deep geodynamics. In this paper, we propose to apply deep learning neural networks called stacked sparse auto-encoder to obtain crustal thickness for eastern Tibet and western Yangtze craton. Firstly taking phase and group velocities of Rayleigh surface wave simultaneously as input and theoretical crustal thickness as output, we construct twelve deep neural networks trained by 70,000 and tested by 30,000 theoretical models. We then invert observed phase and group velocities by these twelve neural networks. Based on test errors and misfits with other crustal thickness models, we select the optimized one as crustal thickness for study areas. Compared with other ways detected crustal thickness such as seismic wave reflection and receiver function, we adopt a new way for inversion of earth model parameters, and realize that deep learning neural network based on data driven with the highly nonlinear mapping ability can be widely used by geophysical inversion method, and our result has good agreement with high-resolution crustal thickness models. We conclude that deep learning neural network is a promising, efficient and believable tool for geophysical inversion.

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Abstract.Crustal thickness is an important factor affecting lithosphere structure and therefore deep geodynamics. In this paper, we propose to apply deep learning neural networks called stacked sparse 10 auto-encoder to obtain crustal thickness for eastern Tibet and western Yangtze craton. Firstly taking phase and group velocities simultaneously as input and theoretical crustal thickness as output, we construct twelve deep neural networks trained by 70,000 and tested by 30,000 theoretical models. We then invert observed phase and group velocities by these twelve neural networks. Based on test errors and misfits with other crustal thickness models, we select the optimized one as crustal thickness for 15 study areas. Compared with other ways detected crustal thickness such as seismic wave reflection and receiver function, we conclude that deep learning neural network is a promising, believable and inexpensive tool for geophysical inversion.