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基于深度残差傅里叶神经算子方法压制地震多次波

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  • 万方数据
  • 维普
多次波是一种较为严重影响地震成像的干扰波,如何有效压制多次波是需要关注的地震资料处理关键问题之一.本文基于傅里叶神经算子(FNO)和残差网络(ResNet),提出了基于深度残差傅里叶神经算子(DRFNO)网络的多次波压制方法.DRFNO是一种弱约束模型+数据驱动的人工智能算法,包含一次波和多次波的全波场炮集为输入,其中真实一次波炮集为标签训练网络,输出为压制多次波后的一次波炮集.DRFNO的网络结构中考虑了地震波场的数据特点,结合波动方程正演模拟的物理机理,约束网络训练过程.基于传统机器学习中的激活函数设置方法,该方法通过一个用于地震数据样本与标签预处理的激活函数(SDAF),克服地震炮集数据中因同相轴能量差异导致神经网络无法训练的问题.采用两套层状介质模型和Sigsbee2B复杂模型的模拟地震数据验证了DRFNO方法多次波压制处理的有效性,抗噪性和泛化能力.最后,通过一套实际地震数据实例表明本文提出的DRFNO方法应用于压制实际复杂地震波场中多次波的良好效果.
Suppressing seismic multiples based on deep residual Fourier neural operator
Multiples are interfering waves that seriously affect seismic imaging,which is one of the key problems for seismic data processing needed to be commonly concerned with how to suppress multiples effectively.Based on Fourier Neural Operator(FNO)and Residual network(ResNet),this paper proposes a seismic multiple suppressing method using Deep Residual Fourier Neural Operator(DRFNO).DRFNO is a weakly constrained model+data-driven Artificial Intelligence(AI)algorithm.In order to supervise the training of DRFNO,full wavefield data is used as input and primary data is utilized as labels.The objective is to obtain the optimal weights and biases in DRFNO,considering the characteristics of seismic wavefield data as well as the physical mechanism of wave equation forward modeling.The parameters are optimized through the minimizing of the error between the predicted and target label wavefield.Once trained,DRFNO can effectively suppress multiples from the full wavefield and reconstruct primaries.To address the issue of inconsistent seismic events energies within shot gathers for the proposed method,a Seismic Data Activation Function(SDAF)is used in data preprocessing.This function is derived from the traditional machine learning approach with setting activation functions and has been specifically defined to enable successful network training for seismic data.Next,two layered medium models synthetic and a complex synthetic—Sigsbee2B examples are applied to demonstrate the effectiveness,the noise immunity and the generalization ability of DRFNO based on the results of multiples suppressing.Finally,a real seismic data example shows that this proposed DRFNO method has some good results to suppress multiples within the real complex seismic wavefields.

Multiples suppressingFourier Neural Operator(FNO)Residual network(ResNet)Deep Residual Fourier Neural Operator(DRFNO)Seismic Data Activation Function(SDAF)

刘继伟、胡天跃、戴晓峰、郑晓东、黄建东、焦梦瑶、于珍珍、隋京坤

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北京大学地球与空间科学学院,北京 100871

中国石油勘探开发研究院,北京 100083

清华大学数学科学系,北京 100084

多次波压制 傅里叶神经算法(FNO) 残差网络(ResNet) 深度残差傅里叶神经算子(DRFNO)网络 地震数据激活函数(SDAF)

国家自然科学基金国家重点研发计划中国石油-北京大学战略合作基础研究项目联合

422741632018YFA0702503

2024

10.6038/cjg2023Q0337

地球物理学报
中国地球物理学会 中国科学院地质与地球物理研究所

地球物理学报

CSTPCD北大核心
影响因子：3.703
ISSN：0001-5733
年,卷(期)：2024.67(8)