基于深度学习的重力异常与重力梯度异常联合反演

Joint gravity and gravity gradient inversion based on deep learning

高效高精度的反演算法在重力大数据时代背景下显得尤为重要,受深度学习卓越的非线性映射能力的启发,本文提出了一种基于深度学习的重力异常及重力梯度异常的联合反演方法.文中首先提出了一种基于网格点几何格架的重力异常及重力梯度异常的空间域快速正演算法,这为本文深度学习反演算法的实现奠定了基础;其次对大量的不同密度模型进行正演计算获得样本数据集;然后设计了一种端到端的深度学习网络结构(GraInvNet),再利用样本数据对该网络结构进行训练;最后进行反演预测.组合模型试验表明,多维度数据联合反演相比单一分量反演其结果更“聚焦”,且与模型边界高度吻合,并且对于复杂模型的姿态与物性预测具有极为显著的优势,以及对于含噪声数据的反演,其质量也不会降低;Vinton岩丘实测重力数据也验证了文中方法的有效性;从而证明了深度学习在重力数据的高效高精度反演方面具有的巨大潜力.

In the era of big data,high-efficient and high-precise inversion algorithms of gravity data become particularly important.Inspired by the excellent nonlinear mapping capability of deep learning,we propose a joint inversion method of gravity anomaly and gravity gradient anomaly based on deep learning.The main contents of the paper are as follow:Firstly,a fast forward algorithm in spatial domain of gravity anomaly and gravity gradient anomaly based on grid point geometric grid is put forward,which establishes a foundation to realize a new deep-learning inversion algorithm;Secondly,sample data sets are constructed by forward calculation of a great quantity models of different densities;Thirdly,an end-to-end deep learning network GraInvNet is creatively designed,which is then trained using the sample data sets;Finally,the inversion forecast is carried out.As shown by the model tests above,the results of joint inversion of multi-dimensional data are more focused than that of its counterpart using single component,and also more consistent with the model boundary.Moreover,the former possesses a significant advantage in predicting the buried depth,shape and physical properties of complex models,particularly in the inversion of noisy data.The validity of the method is further verified by the results of field data inversion using the gravity data of Vinton Salt-Rock,which,thus,effectively proves to us the great potential of deep learning in high-efficient and high-precise inversion of gravity data.