Spatiotemporal Synergistic Ensemble Deep Learning Method and Its Application to S-Wave Velocity Prediction

S-wave velocity (Vs) data are crucial in prestack seismic inversion, lithology interpretation, and fluid identification. However, most drilling wells currently lack Vs data. The use of traditional data-driven Vs prediction methods has limitations as these methods fail to adaptively extract effective features from logging signals and cannot fully account for the trends with depth. In this letter, we propose a novel spatiotemporal synergistic ensemble deep learning method, that is, the ensemble convolutional bidirectional memory network (ECBMN), to predict Vs. The ECBMN is built by integrating a convolutional neural network and a bidirectional long short-term memory network to leverage their complementary strengths and achieve a spatiotemporal synergistic learning. Using this method, Vs can be estimated from a series of input log data by accounting for the correlation of different log series and Vs, the variation trend, and context information with depth. The results of these applications to field data show that the proposed method can provide more reliable and accurate predictions compared with the existing methods, thereby demonstrating its potential for accurately estimating Vs from log data.