Seismic velocity inversion based on CNN-LSTM fusion deep neural network

Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.

Effect of UV-B irradiation on interspecific competition between Ulva pertusa and Grateloupia filicina

We report the effect of UVoB irradiation （9.6 kJ m-2 day^-） on interspecific competition between two species of macroalgae, Ulva pertusa （U） and Grateloupiafilicina （G）, in co-culture. Growth of U. pertusa and G. filicina was inhibited by UV-B irradiation in mono-culture and specific growth rate （μ） declined as a result. Interspecific competition between U. pertusa and G filicina was closely related to the initial weights when co-cultured. When initial ratios of U. pertusa （U） to G filicina （G） were U：G=I.2：I and 1：1, U. pertusa was the dominant algae. When the initial U：G ratio was 1：1.2, G. filicina was competitively dominant in the earlier stage, but U. pertusa grew faster, superseding G. filicina in the later stage. At initial ration U：G = 1：1.4, G. filicina was predominant. Under UV-B irradiation, the competitive ability of G filicina was weakened and the interspecific competitive balance favored U. pertusa, which suggests that G. filicina was more sensitive to UV-B irradiation. We also probed the potential allelopathic effects between the two species, which led to mutual growth inhibition.

Down image recognition based on deep convolutional neural network

Since of the scale and the various shapes of down in the image,it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy,even for the Traditional Convolutional Neural Network(TCNN).To deal with the above problems,a Deep Convolutional Neural Network(DCNN)for down image classification is constructed,and a new weight initialization method is proposed.Firstly,the salient regions of a down image were cut from the image using the visual saliency model.Then,these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters,which accord with the statistical characteristics of dataset.At last,a DCNN with Inception module and its variants was constructed.To improve the recognition accuracy,the depth of the network is deepened.The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN,when recognizing the down in the images.The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN.