Automação sísmica híbrida: integração de inteligência artificial e métodos determinísticos na análise de velocidades.

Abstract

This research presents a hybrid seismic automation methodology that integrates artificial intelligence techniques with deterministic methods for the automatic analysis and estimation of seismic velocity fields. The main goal is to optimize hydrocarbon exploration by enhancing model accuracy and ensuring operational safety throughout geological interpretation. Traditionally, the construction of the velocity field relies on manual picking from semblance panels, a subjective and time-consuming procedure that demands expert interpretation, especially under noisy or geologically complex conditions. The proposed workflow combines statistical and machine learning approaches in a sequential and integrated manner. The process begins with a sample pre-clustering technique, responsible for the preliminary structuring of the data and for automatically determining the optimal number of clusters. Next, the joint application of the K-means++ algorithm and Principal Component Analysis (PCA) enables efficient dimensionality reduction, improving data coherence and representativeness. In the deterministic stage, the Dix equation is employed to convert RMS velocities into interval velocities, which serve as training data for a Multilayer Perceptron (MLP) neural network. This supervised model performs the final adjustment of the velocity field, ensuring physical consistency, smoothness, and monotonic behavior. The hybrid nature of the methodology arises from the synergistic integration between deterministic physical modeling and adaptive artificial intelligence prediction. The proposed approach was validated using both synthetic models and real seismic data from the Gulf of Mexico, demonstrating robustness, stability, and applicability across diverse geological scenarios. The results confirm that the hybrid seismic automation framework provides more realistic and continuous velocity models, substantially reducing human intervention and improving interpretive efficiency in complex exploration environments.