Inversion of gravity anomalies based on a deep neural network
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摘要: 为解决传统线性反演方法容易陷入局部极小,计算效率低等问题,本文提出了一种基于深度学习的重力异常反演方法。该方法首先构造不同形状的二维密度模型,正演得到重力异常,组成数据集;然后用该数据集训练深度神经网络;最后将重力异常数据输入到训练好的深度神经网络,直接得到反演结果。实验结果表明,该方法能快速、准确地反演出地下异常体的位置和形态,且具有较好的泛化能力和抗噪声能力,可用于重力异常反演。Abstract: Traditional linear inversion of gravity anomalies is liable to encounter local minima and suffer low computational efficiency. Given this, this paper proposed a deep learning-based inversion of gravity anomalies. Specifically, two-dimensional density models of various shapes were firstly established, and gravity anomalies were obtained through forward simulation using these models to form a dataset. Then, a deep neural network was trained using the dataset. Finally, gravity anomaly data were input into the deep neural network to directly yield inversion results. Experimental results show that the inversion method proposed in this study can determine the locations and shapes of underground anomalies quickly and accurately, with high generalization ability and anti-noise ability. Therefore, this method can be used for the inversion of gravity anomalies.
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Key words:
- deep neural network /
- gravity anomaly /
- inversion
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[1] Le C Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[2] 吴渤. 基于深度神经网络的语音去混响方法研究[D]. 西安: 西安电子科技大学, 2018.
[3] Wu B. Research on speech de-reverberation method based on deep neural network[D]. Xi'an: Xidian University, 2018.
[4] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. NIPS, 2012, 25(2):1-9.
[5] 王逸宸, 柳林涛, 许厚泽. 基于卷积神经网络识别重力异常体[J]. 物探与化探, 2020, 44(2):394-400.
[6] Wang Y C, Liu L T, Xu H Z. Identification of gravity anomalies based on convolutional neural network[J]. Geophysical and Geochemical Exploration, 2020, 44(2): 394-400.
[7] Hu Z L, Liu S, Hu X Y, et al. Inversion of magnetic data using deep neural networks[J]. Physics of the Earth and Planetary Interiors, 2021, 311:106653.
[8] Roy L, Sen M K, Richter T, et al. Inversion and uncertainty estimation of gravity data using simulated annealing: An application over Lake Vostok, East Antarctica[J]. Society of Exploration Geophysicists, 2005, 70(1):J1.
[9] Boschetti F, Dentith M, List R. Inversion of potential field data by genetic algorithms[J]. Geophysical Prospecting, 1997, 45(3):461-478.
[10] Xu H L, Wu X P. 2-D Resistivity inversion using the neural network method[J]. Chinese Journal of Geophysics, 2006, 49(2):507-514.
[11] 熊杰, 刘彩云, 邹长春. 基于粒子群优化算法的感应测井反演[J]. 物探与化探, 2013, 37(6):1141-1145.
[12] Xiong J, Liu C Y, Zou C C. Induction logging inversion based on particle swarm optimization algorithm[J]. Geophysical and Geochemical Exploration, 2013, 37(6): 1141-1145.
[13] Liu S, Hu X Y, Liu T Y, et al. Ant colony optimisation inversion of surface and borehole magnetic data under lithological constraints[J]. Journal of Applied Geophysics, 2015, 112:115-128.
[14] Zhang Z, Alkhalifah T. Regularized elastic full-waveform inversion using deep learning[J]. Geophysics, 2019, 84(5): 741-751.
[15] 马国庆, 吴琪, 熊盛青, 等. 基于重磁数据梯度比值的深度学习技术实现场源位置反演方法[J]. 地球科学, 2021, 46(9):3365-3375.
[16] Ma G Q, Wu Q, Xiong S Q, et al. Deep learning technology based on the gradient ratio of gravity and magnetic data to realize the field source position inversion method[J]. Earth Science, 2021, 46(9):3365-3375.
[17] 梁立锋, 刘秀娟, 张宏兵, 等. 超参数对GRU-CNN混合深度学习弹性阻抗反演影响研究[J]. 物探与化探, 2021, 45(1):133-139.
[18] Liang L F, Liu X J, Zhang H B, et al. Study on the influence of hyperparameters on GRU-CNN hybrid deep learning elastic impedance inversion[J]. Geophysical and Geochemical Exploration, 2021, 45(1): 133-139.
[19] Wu B, Meng D, Wang L, et al. Seismic impedance inversion using fully convolutional residual network and transfer learning[J]. IEEE Geosci. Remote Sens. Lett., 2020, 17(12): 1-5.
[20] 付超, 林年添, 张栋, 等. 多波地震深度学习的油气储层分布预测案例[J]. 地球物理学报, 2018, 61(1):293-303.
[21] Fu C, Lin N T, Zhang D, et al. A case of oil and gas reservoir distribution prediction based on multi-wave seismic deep learning[J]. Chinese Journal of Geophysics, 2018, 61(1): 293-303.
[22] 黄旭日, 代月, 徐云贵, 等. 基于深度学习算法不同数据集的地震反演实验[J]. 西南石油大学学报, 2020, 42(6):16-25.
[23] Huang X R, Dai Y, Xu Y G, et al. Seismic inversion experiments based on different data sets of deep learning algorithm[J]. Journal of southwest petroleum university, 2020, 42(6):16-25.
[24] Zhao M, Chen S, Yuen D. Waveform classification and seismic recognition by convolution neural network[J]. China University of Geoscience, 2019, 62(1): 374-382.
[25] Wang H, Yan J, Fu G. Current status and application prospect of deep learning in geophysics[J]. Prog. Geophys., 2018, 35(2): 642-655.
[26] Viens L, Van H C. Denoising ambient seismic field correlation functions with convolutional autoencoders[J]. Geophysical Journal International, 2020, 220(3):1521-1535.
[27] 杨磊. 基于BP神经网络的重力异常分离[J]. 工程地球物理学报, 2021, 18(1):90-97.
[28] Yang L. Gravity anomaly separation based on BP neural network[J]. Journal of Engineering Geophysics, 2021, 18(1): 90-97.
[29] 张志厚, 廖晓龙, 曹云勇, 等. 基于深度学习的重力异常与重力梯度异常联合反演[J]. 地球物理学报, 2021, 64(4):1435-1452.
[30] Zhang Z H, Liao X L, Cao Y Y, et al. Joint inversion of gravity anomaly and gravity gradient anomaly based on deep learning[J]. Chinese Journal of Geophysics, 2021, 64(4): 1435-1452.
[31] Yang Q G, Hu X Y, Liu S, et al. 3D gravity inversion based on deep convolution neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5.
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