Deep-Learning Gravity Inversion Method with Depth-Weighting Constraints and Its Application in Geothermal Exploration

Abstract

As a key component of remote-sensing technology, satellite gravity observation offers extensive coverage and high accuracy, effectively compensating for the shortcomings of terrestrial gravity measurements. Three-dimensional gravity data inversion can predict the physical property and spatial distribution of geological formations beneath the surface by analyzing the gravity data. In this paper, the heat source position within the Gonghe Basin’s geothermal system is identified through the analysis of satellite gravity data, and a constrained deep-learning inversion method is proposed. This method adds the fitting data constraints and depth-weighting function into the network model establishment of deep learning, and trains the network through a large number of datasets, so that the network is constrained by physical information in the training process to obtain the results with a better data-fitting accuracy and higher depth resolution. The proposed method is employed to verify the synthetic model data, and the inversion results indicated that, compared to other methods, the deep-learning gravity inversion method with the addition of physical information constraints has a higher inversion accuracy and depth resolution. Finally, the inversion results based on satellite gravity data revealed the presence of numerous low-density bodies in the underground range of 10–35 km in the research area. It is speculated that this part could be the heat source of the geothermal system in the Gonghe Basin. The findings from this study are expected to contribute to a deeper comprehension of the formation of the geothermal system in the region.

Keywords

• physical constraint
• depth-weighting function
• deep learning
• gravity data
• physical property inversion