地震勘探在野外采集到的地震数据受随机噪声和相干噪声的干扰而导致信噪比被降低,从而影响地震资料的后续处理,例如地震偏移和成像.因此,开发一种高效且自适应的方法来衰减地震数据中的随机与相干噪声是必要的.常规的监督学习算法需要人工生成大量标签来训练网络,这对于数据体量较小的地震勘探领域是十分困难的.此外,基于监督学习的噪声衰减方法在计算和人力成本上十分昂贵.为了解决该问题,本文构建了一种基于无监督学习策略的自适应深度学习框架来衰减多维地震数据中的随机和不规则(异常值)噪声.该方法采用编码和解码相对应的结构来压缩和重构数据特征.为了提高网络对重要波形特征的关注,本文采用软注意力机制以加权的方式给重要的波形特征分配更大的权重.本文采用小尺度地震数据分割技术将多维含噪数据分割为大量一维信号输入到网络进行迭代,从而自适应的衰减地震数据中的随机和异常值噪声.这种小尺度信号去噪方法可以有效地提升网络的噪声衰减表现,并有助于避免产生伪影.本文采用更具鲁棒性的Huber损失函数来衰减随机和异常值噪声,该损失函数结合了带有l2 范数的均方根误差和l1 范数的平均绝对误差损失.此外,在构建的网络中加入总变分(Total Variation,TV)正则化项来捕捉地震资料的局部光滑结构.通过实验调整Huber损失函数与TV正则化项的权重,使得网络获取最佳的去噪表现.本文构建的方法可直接用于多维地震数据的随机和异常值噪声衰减,并保证重构后地震信号的横向连续性.我们将提出的框架与经典的地震数据去噪方法和基于无监督学习的噪声衰减方法进行去噪对比来分析各方法的优劣.二维和三维合成数据与实际地震数据的测试结果表明本文提出的方法具有更好的噪声衰减和有效信号保护能力.

Random noise and coherent noise interfere with seismic data collected in the field,resulting in the reduction of the signal-to-noise ratio,which affects the subsequent processing of seismic data,such as seismic migration and imaging.There-fore,it is necessary to develop an efficient and adaptive method to attenuate random and coherent noise in real seismic data.Conventional supervised learning algorithms need to manually generate a large number of labels to train the network,which is very difficult in the field of seismic exploration where the data volume is small.In addition,supervised learning-based noise attenuation methods are expensive in terms of computation and labor costs.To solve this problem,this paper constructs an adaptive deep learning framework based on unsupervised learning strategies to attenuate random and irregular(erratic)noise in multi-dimensional seismic data.This method uses the corresponding structure of encoding and decoding to compress and reconstruct data features.In order to improve the network's attention to important waveform features,this paper uses a soft attention mechanism to assign more weight to important waveform features in a weighted way.In this paper,the multi-di-mensional noisy data is segmented into a large number of one-dimensional noisy signals,which are input into the network for iteration,so as to adaptively attenuate random and erratic noise in seismic data.This small-scale signal denoising method can effectively improve the noise attenuation performance of the network and help to avoid artifacts.In this paper,we use a more robust Huber loss function to attenuate random and erratic noise,which combines the root-mean-square error with l2 norm and the average absolute error loss with l1 norm.In addition,a Total Variation(TV)regularization term is added to the constructed network to capture the local smooth structure of the seismic data.By adjusting the weight of Huber loss function and TV regularization term,the network can obtain the best denoising performance.The method constructed in this paper can be directly used for attenuation of random and erratic noise of multi-dimensional seismic data,and ensure transverse continuity of seismic signals after reconstruction.We compare the proposed framework with classical seismic data denoising methods and noise attenuation methods based on unsupervised learning to analyze the advantages and disadvantages of each method.The test results of 2D and 3D synthetic data and actual seismic data show that the proposed method has better noise attenuation and useful signal protection capabilities.