摘要
地震勘探广泛应用于地质构造分析、油气及其他矿产资源勘查等领域。受环境、仪器等因素影响,地震数据中不可避免地混杂随机噪声,无疑会对后续的资料处理和解释带来负面影响。文中提出一种用多层感知机(Multi-layer Perceptron,MLP)的去噪方法:首先用滑动窗口在已知地震数据中采样并将其转换为一维向量,作为训练集样本构建多层神经网络模型;再通过反向传播算法计算模型各层神经元的权重,使得模型训练均方误差最小;然后将合成或实测含噪地震数据输入到已训练模型,用该已训练得到的权重计算模型输出。将上述MLP方法处理结果与曲波方法去噪结果进行比较,发现MLP方法去噪结果的信噪比更高,且较好地保护了有效信号,尤其是对构造细节有显著的保护效果。
Seismic exploration has played an important role in tectonic analysis and prospecting of hydrocarbon and other mineral resources.Due to the influence of environment and instruments,seismic data contain random noises,which have a negative impact on processing and interpretation.We propose a multi-layer perceptron(MLP)method to reduce random noises.Seismic data are sampled using a moving window and then converted into a 1 D vector,which is utilized as training samples to establish a multi-layer neural network model.The weighting factor of neurons in each layer is calculated using the back propagation algorithm until the mean square training error reaches a minimum.Synthetic or measured noisy seismic data are imported into this established model,and the output is calculated using the weighting factors after training.We compare the denoising results derived from MLP and curvelet methods and conclude that MLP result exhibits higher signal to noise ratio and better signal preservation,especially for structural details.
作者
王琪琪
汤井田
张良
刘晓甲
徐志敏
WANG Qiqi;TANG Jingtian;ZHANG Liang;LIU Xiaojia;XU Zhimin(School of Geosciences and Info-Physics,Central South University,Changsha,Hunan 410083,China;Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University),Ministry of Education,Changsha,Hunan 410083,China;Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Detection,Changsha,Hunan 410083,China;Chengde Petroleum College,Chengde,Hebei 067000,China)
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2020年第2期272-281,I0002,共11页
Oil Geophysical Prospecting
基金
国家重点研发计划“深地资源勘查开采”重点专项(2018YFC0603202)
中南大学中央高校基本科研业务费专项资金项目“基于自学习型字典的大地电磁持续性强人文噪声分离”(2018zzts695)共同资助。
关键词
随机噪声
多层感知机
去噪
反向传播
曲波去噪
random noise
multi-layer perceptron
denoising
back propagation
curvelet-based denoising