Density interface prediction using neural networks: Incorporating fluctuation features from observed gravity data

ABSTRACT Predicting density interfaces in the subsurface using neural networks is a promising research area in deep learning-based gravity exploration. Constructing a comprehensive training data set that captures the fluctuation characteristics of the density interface is vital for predictions. However, large data sets often introduce extraneous features and significantly increase the computational time. To address these challenges, we have developed a method that enhances the accuracy of the prediction by integrating fluctuation features of observed gravity data, the z-component gravity data, into the training data set. This is achieved by perturbing and deforming the approximate spectrum of the density interface derived from a physics-driven algorithm, thereby generating training samples that closely match the prediction target. Such an approach enables the neural network to concentrate on learning relevant fluctuation features associated with the target. Experimental results underscore the efficacy of our method in accurately discerning density interface features and its robustness against noise interference. Furthermore, our method maintains high prediction accuracy with smaller data sets, outperforming the conventional approaches. This advantage enhances the efficiency and practicality of neural networks in addressing density interface problems by reducing the computational costs for predictions with high accuracy.