Logging Data Completion Based on an MC-GAN-BiLSTM Model

Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have occurred during the geophysical logging data collection process. Since it is difficult to establish a geophysical model based on logging data and geological information, the data complementation effect of conventional methods is not very satisfactory. In this paper, we propose an MC-GAN-BiLSTM model based on spatiotemporal sequence prediction. In the model, we adopt a generative adversarial network (GAN) as a network framework, and a long short-term memory (LSTM) neural network and a bi-directional long short-term memory (Bi-LSTM) as the basic modules. We use the LSTM instead of a fully-connected layer in the GAN to extract the potential information in the logging data depth domain. We complete the logging data missing values through an encoding-decoding structure that includes the Bi-LSTM. In addition, the generator module also uses multiscale convolution to fully extract the logging data features. We use logging data random missing values and consecutive missing values to simulate a field data acquisition environment and threshold control to simulate a laboratory processing environment for experiments. The experimental results show that the coefficient of determination (R²) of the GAN-LSTM model reaches 0.906 when 30% of random logging data are missing and 0.851 when 30% of consecutive logging data are missing. The effect of the model proposed in this paper is significantly higher than the commonly used random forest (RF), sequence to sequence (seq2seq) and generative adversarial interpolation network (GAIN) models.