重力异常AlexNet深度神经网络反演

doi:10.19657/j.geoscience.1000-8527.2022.079

摘要/Abstract

摘要：

针对传统反演方法存在的初始模型依赖、计算时间较长等问题,提出了一种新的基于AlexNet深度神经网络的重力异常反演方法。该方法首先借鉴经典的深度神经网络AlexNet设计了一种用于重力异常反演的Alex反演网络(AlexInvNet),接着设计大量密度异常体模型并通过正演计算得到带标签的数据集,然后用该数据集训练AlexInvNet网络,最后将重力异常数据输入训练好的AlexInvNet网络直接得到反演结果。理论模型反演结果表明,该方法相较于全连接网络深度学习反演方法,能够更好地反演出异常体的位置和密度,具有较好的泛化能力和抗噪声能力。实测数据反演结果表明,该方法能有效解决重力异常反演问题。

关键词: 重力异常, 反演, 深度神经网络, Alex反演网络

Abstract:

In order to solve the problems of traditional inversion methods, such as dependence of initial model and long time for calculation, this paper proposes a noval gravity anomaly inversion method based on AlexNet deep neural network. This method designs an Alex inversion network (AlexInvNet) for gravity anomaly inversion inspired by classical deep neural network AlexNet firstly; constructs labeled datasets by forward modeling using a large number of synthetic density models secondly; uses the dataset train the AlexInvNet thirdly; and finally inputs the gravity anomaly data to the trained AlexInvNet to obtain the inversion result directly. The inversion experimental results of synthetic models show that this method can invert the position and density of anomaly body accurately, with good generalization and anti-noise ability, better than the full connected network deep learning inversion method. The field data inversion result demonstrates that this method can solve gravity anomaly inversion problem effectively.

Key words: gravity anomaly, inversion, deep neural network, AlexInvNet

中图分类号:

P631

刘彩云, 李梦迪, 熊杰, 王蓉. 重力异常AlexNet深度神经网络反演[J]. 现代地质, 2023, 37(01): 164-172.

LIU Caiyun, LI Mengdi, XIONG Jie, WANG Rong. Inversion of Gravity Anomaly Based on AlexNet Deep Neural Network[J]. Geoscience, 2023, 37(01): 164-172.

图/表 11

图1 地下网格剖分及棱柱体重力异常计算示意图

Fig.1 Schematic diagram of underground grid division and gravity anomaly calculation of prism body

图2 AlexInvNet重力异常反演流程图

Fig.2 Flow chart of gravity anomaly inversion using AlexInvNet

图3 AlexInvNet网络结构图

Fig.3 AlexInvNet network structure diagram

表1 AlexInvNet网络结构参数

Table 1 Parameters of AlexInvNet network structure

	类型	卷积/池化核大小	通道数	步长	输入		输出
第①层	卷积	1×11	96	4	1×101		45×96
第①层	池化	1×3	None	2	1×101		45×96
第②层	卷积	1×5	256	1		45×96		20×256
第②层	池化	1×3	None	2		45×96		20×256
第③层	卷积	1×3	384	1	20×256		18×384
第④层	卷积	1×3	384	1	18×384		16×384
第⑤层	卷积	1×3	256	1		16×384		6×256
第⑤层	池化	1×3	None	2		16×384		6×256
第⑥层	全连接	None	1024	None	6×256		1×1024
第⑦层	全连接	None	800	None	1×1024		1×800

表2 AlexInvNet网络相关参数

Table 2 Related parameters for AlexInvNet network

分类	参数	值
数据集	训练集	4432
数据集	测试集	1109
网络参数	学习率	1×10^-3
	激活函数	ReLU
	优化器	Adam
	$λ$	1
	$μ$	0.01
训练过程	Epochs	40000
	Batch size	1000

表2 AlexInvNet网络相关参数

Table 2 Related parameters for AlexInvNet network

分类	参数	值
数据集	训练集	4432
数据集	测试集	1109
网络参数	学习率	1×10^-3
	激活函数	ReLU
	优化器	Adam
	$λ$	1
	$μ$	0.01
训练过程	Epochs	40000
	Batch size	1000

图4 部分验证集反演结果

Fig.4 Inversion results of typical validate dataset

图5 复杂模型泛化能力测试结果

Fig.5 Inversion results of generation tests of complex models

图6 不同密度参数得到的重力异常反演结果

Fig.6 Inversion results of gravity anomaly with different density parameters

图7 叠加5%和10%高斯白噪声重力异常反演结果

Fig.7 Inversion results of gravity anomaly added 5% and 10% Gaussian white noises

图8 圣尼古拉斯硫化物铜锌矿区剩余重力数据[1]

Fig.8 Gravity anomaly of San Nicolas massive sulfide copper-zinc deposit[1]

图9 圣尼古拉斯硫化物铜锌矿区重力数据(N=600 m)处的反演结果

Fig.9 Inversion results of the gravity anomaly of San Nicolas massive sulfide copper-zinc deposit(N=600 m)

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