Gravity Modeling in Sedimentary Basins With a Mixed Machine Learning–Inversion Method

We describe a mixed machine learning (ML) inversion algorithm for sedimentary basin modeling of gravity data. We consider two typical targets of basin modeling, i.e., the basement depth and the diapirs, both being of fundamental importance for energy exploration or tectonic studies. First, ML is implemented to interpret gravity anomalies along a set of profiles crossing the whole anomaly. Then, the 2-D interpreted models are merged into a reference model to constrain the final 3-D inversion of the whole set of data. The learning of our convolutional neural network (CNN) is based on the simplest gravity sources, i.e., faults. Such a choice transfers a strong a priori information to the interpretative problem, since gravimetric anomalies due to diapirs, or to the morphology of the basement, can be seen as composed of the constructive interference of anomalies generated by the edges of the sources. We considered up to 221.100 different fault models with variable parameters (depth, density, thickness, and depth-to-top), corresponding to low/high dip-angle faults and mixed faults. Our workflow was shown to be suitable for reconstructing different structures in either synthetic or real-data cases: a diapir-shaped source and the basement of a huge sedimentary basin, the Caltanissetta basin (Italy), respectively. In both cases, it is shown that our mixed approach produces significant results thanks to the self-constraints extracted during the ML step.