深度学习重力异常反演方法发展综述

打开文本图片集

中图分类号：P631 文献标识码：A DOI：10.13810/j.cnki.issn.1000-7210.20240517

Abstract：The gravity anomaly inversion，which infers the density distribution subsurface anomalies from sur face gravity data，is an essential toolin geophysical explorationand is widelyappliedin fields suchasoilfields， mineral deposits，geological structures，and underground works detection. Traditional gravity inversion methods face challenges complex computation，low resolution，and dependence on prior information for inversion results. However， deep learning-based gravity anomaly inversion techniques show significant advantages，par ticularly in terms improving inversion accuracy and reducing computation time，without the reliance on initial models or prior information. This paper reviews the development and limitations traditional gravity anomaly forward and inversion methods and summarizes the current research on deep learning-based gravity inversion methods.Meanwhile，it introduces the improvements and innovations diffrent gravity inversion problems in four respects，including data preparation， network models，network optimization，and network validation.Additionally，，the application effect various gravity inversion methods on the measured data from Vinton Dome in Louisiana，the USA，and the San Nicolas ore deposit in Mexico. The multi-task framework CDUNet yields the most accurate inversion depth values on data Vinton Dome，while the 3D U-Net+ + network obtains clearer and more accurate inversion results on the data the San Nicolas ore deposit than the U-Net network. Keywords： gravity anomaly inversion，deep learning，data-driven，network model，network optimization

黄兴业，胡青青，邝文俊，等，深度学习重力异常反演方法发展综述[J].石油地球物理勘探，2025，60（4）：1046-1058. HUANG Xingye，HU Qingqing， KUANG Wenjun，et al. Review deep learning-based gravity anomaly inversion methods[J]. Oil Geophysical Prospecting，2025，60（4） ：1046-1058.

0 引言

下密度异常体的空间位置、几何结构、密度等参数，广泛应用于石油勘探、矿产勘查、地质结构分析和地下工程探测等领域。（剩余22854字）