First-arrival picking and inversion of seismic waveforms based on U-shaped multilayer perceptron network

doi:10.11772/j.issn.1001-9081.2023060808

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (7): 2301-2309.DOI: 10.11772/j.issn.1001-9081.2023060808

• Frontier and comprehensive applications • Previous Articles Next Articles

First-arrival picking and inversion of seismic waveforms based on U-shaped multilayer perceptron network

Minghao SUN¹, Han YU¹(), Yuqing CHEN², Kai LU³

^1.School of Computer Science，Nanjing University of Posts and Telecommunications，Nanjing Jiangsu 210046，China
^2.School of Geophysics and Information Technology，China University of Geosciences，Beijing 100029，China
^3.Key Laboratory of Polar Science of Ministry of Natural Resources （Polar Research Institute of China），Shanghai 200136，China

Received:2023-06-26 Revised:2023-08-20 Accepted:2023-08-24 Online:2023-08-28 Published:2024-07-10
Contact: Han YU
About author:SUN Minghao， born in 1998， M. S. candidate. His research interests include signal and image processing.
CHEN Yuqing， born in 1989， Ph. D.， lecturer. His research interests include geophysical inversion， machine learning.
LU Kai， born in 1988， Ph. D.， assistant research fellow. His research interests include seismic exploration and inversion.
First author contact:YU Han， born in 1984， Ph. D.， associate professor. His research interests include geophysical inversion， scientific computing.
Supported by:
National Natural Science Foundation of China(12371440);China Postdoctoral Science Foundation(2021M692367);Natural Science Foundation of Nanjing University of Posts and Telecommunications(NY222140)

基于U形多层感知机网络的地震波初至拾取与反演

孙明皓¹, 余瀚¹(), 陈雨青², 陆恺³

^1.南京邮电大学计算机学院, 南京 210046
^2.中国地质大学(北京) 地球物理与信息技术学院, 北京 100029
^3.自然资源部极地科学重点实验室(中国极地研究中心), 上海 200136

通讯作者: 余瀚
作者简介:孙明皓（1998—），男，江苏南京人，硕士研究生，主要研究方向：信号与图像处理；
陈雨青（1989—），男，北京人，讲师，博士，主要研究方向：地球物理反演、机器学习；
陆恺（1988—），男，浙江宁波人，助理研究员，博士，主要研究方向：地震勘探与反演。
第一联系人：余瀚（1984—），男，江苏南京人，副教授，博士，CCF会员，主要研究方向：地球物理反演、科学计算；
基金资助:
国家自然科学基金资助项目(12371440);中国博士后科学基金资助项目(2021M692367);南京邮电大学校级自然科学基金资助项目(NY222140)

Abstract

Abstract:

A method for first-arrival picking and inversion of seismic waveforms based on U-shaped MultiLayer Perceptron （U-MLP） network was proposed to solve the safety problem in production because of the low quality of inverted velocity， caused by heavy workload， poor noise immunity and low accuracy in traditional first-arrival picking methods. Firstly， a Weighted cross-entropy Lovász-Softmax （WLS） loss function was designed to solve the problem of poor performance of U-shaped Network （U-Net） caused by the traditional cross-entropy loss function in processing unbalanced data categories. Then， residual connections were introduced in the feature fusion stage to reduce the discrepancies between low-level and high-level features， restoring more detailed information. Finally， a MultiLayer Perceptron （MLP） module was introduced for high-level features to learn local image features better， and reduce the number of parameters and computational complexity. Experimental results show that compared with U-Net， U-MLP network converges faster in training， and its maximum error of first-arrival picking decreases by at least 20% while its value of Intersection over Union （IoU） increases by about 2%. It can be seen that the proposed network model not only improves the accuracy of first-arrival picking significantly， but also produces first arrivals for inverting ideal velocity distributions in both the synthetic and the real datasets， hence demonstrating its better performance and stronger adaptability.

Key words: U-shaped Network (U-Net), MultiLayer Perceptron (MLP), first arrival picking, inversion, imaging

摘要：

针对传统勘探地震波初至拾取工作量大、抗噪性差和精度低所导致的低质量速度反演影响生产安全的问题，提出一种基于U形多层感知机（U-MLP）网络的地震波初至拾取与反演方法。首先，为解决传统U形网络（U-Net）中的交叉熵损失函数在数据类别不平衡时导致的性能变差问题，设计一种基于加权交叉熵Lovász归一化指数（WLS）的损失函数；然后，在特征融合阶段引入残差连接，缩小低级特征与高级特征间的差距，还原更多细节信息；最后，为使U-MLP网络更好学习图像局部特征，为高级语义引入标记化的多层感知机（MLP）模块，此模块降低了参数量和计算复杂度。实验结果表明，与U-Net相比，U-MLP网络在训练中收敛性更强，初至拾取最大误差降低了20%以上，交并比（IoU）值提升了约2%。可见，U-MLP网络在提取勘探地震波初至时不仅提高了拾取精度，而且拾取的初至在仿真数据和实际数据中的速度分布反演均达到了理想效果，具有更好的性能且适应性更强。

关键词: U形网络, 多层感知机, 初至拾取, 反演, 成像

CLC Number:

TP391.41

Minghao SUN, Han YU, Yuqing CHEN, Kai LU. First-arrival picking and inversion of seismic waveforms based on U-shaped multilayer perceptron network[J]. Journal of Computer Applications, 2024, 44(7): 2301-2309.

孙明皓, 余瀚, 陈雨青, 陆恺. 基于U形多层感知机网络的地震波初至拾取与反演[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2301-2309.

Figures/Tables 16

Fig. 1 Structure of U-Net

Fig. 2 Synthetic temporal-spatial seismogram with noise

Fig. 3 Improved residual connection embedded in U-Net

Fig. 4 Labeled MLP block

Fig. 5 Optimized U-MLP network structure

Fig. 6 First-arrival picking pictures with U-MLP network on training，validation and test datasets

Tab. 1 Error comparison of first arrivals picked by different network models

数据集	模型	Max_diff_fb	Min_diff_fb	Mean_diff_fb
训练集	U-Net	20.45	0.500 0	6.397
训练集	U-MLP网络	15.60	0.350 0	5.213
验证集	U-Net	20.23	0.416 7	9.458
验证集	U-MLP网络	15.14	0.364 7	8.438
测试集	U-Net	20.16	0.784 3	10.180
测试集	U-MLP网络	15.84	0.571 1	8.279

Tab. 2 IoU comparison between different network models

数据集	模型	IoU指标
训练集	U-Net	0.975 6
训练集	U-MLP网络	0.994 5
验证集	U-Net	0.973 2
验证集	U-MLP网络	0.994 1
测试集	U-Net	0.975 4
测试集	U-MLP网络	0.993 9

Fig. 7 IoU curves of U-Net and U-MLP network

Fig. 8 First arrivals picking pictures by U-Net and U-MLP network

Fig. 9 True 3-layered velocity distribution and generated first temporal-spatial shot gather #1

Fig. 10 Common shot gather #1 with noise and true picked first arrivals marked by crosses

Fig. 11 Inversion results of clean （noise free） data and noisy data

Fig. 12 Inverted results from picked first arrivals by U-Net and U-MLP network

Fig. 13 Real temporal-spatial shot gather #100 and first arrival picked results of its last 24 traces

Fig. 14 Inverted results based on true first arrivals and picked first arrivals by U-MLP model

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First-arrival picking and inversion of seismic waveforms based on U-shaped multilayer perceptron network

基于U形多层感知机网络的地震波初至拾取与反演

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