Research and application of intelligent algorithm for 3D geological modeling integrating multi-source seismic attributes

Aiming at the problems that traditional 3D geological modeling methods face when dealing with multi-source seismic attribute data, such as attribute redundancy and conflict, difficulty in capturing nonlinear relationship and low computational efficiency, this paper proposes an intelligent 3D geological modeling algorithm integrating multi-source seismic attributes. The algorithm constructs a feature fusion module based on attention mechanism and Generative Adversarial Networks (GAN), which dynamically adjusts the weight of seismic attributes and ensures the physical consistency of data. Convolutional neural network (CNN) and implicit function modeling technology are combined to realize the continuous expression of 3D geological bodies, and uncertainty quantification is introduced to evaluate the prediction confidence. In the stage of multi-source seismic attribute analysis and preprocessing, the quantile standardization method is used to normalize the data, and the data quality is improved through multi-scale registration and outlier detection mechanism based on self-encoder. The experimental results show that the modeling accuracy, efficiency and uncertainty quantification ability of this algorithm are better than traditional methods and deep learning benchmarks, which provides a new technical means for resource exploration and geological disaster prevention, and has important scientific significance and application value.