共查询到19条相似文献，搜索用时 218 毫秒
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多源地震混合采集采用随机线性编码方式同时激发多个震源,检波器连续接收地震信号,获得波场混叠的炮记录.该采集技术能够显著提升采集效率和成像质量,其实现关键在于炮分离,即将相互混叠的多源波场数据彼此分离,获得传统采集的单炮记录.最小平方算法只能得到伪分离记录,不能去除混叠噪声.在高混合度的混叠数据中,混叠噪声的能量往往数倍于有效信号,炮分离难度倍增.但在伪分离记录中,该噪声在除共炮点道集以外的其它时域均为随机分布.本文研究提出了一种多时域组合迭代去噪的炮分离技术:通过运用多级中值滤波与Curvelet阈值迭代去噪算法,在不同时域根据混叠噪声特性采用相应的去噪手段,并设计迭代算法优化炮分离结果.实际资料处理结果证明:将本方法应用于高混合度的混叠数据,无论是分离质量还是计算效率,都有明显提升. 相似文献
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地震数据本质上是时变的,不仅有效同相轴表现出确定性信号的时变特征,而且复杂地表和构造条件以及深部探测环境总是引入时变的非平稳随机噪声.标准的频率-空间域预测滤波只适合压制平面波信号假设下的平稳随机噪声,而处理非平稳地震随机噪声时,需要将数据体分割为小窗口进行分析,但效果不够理想,而传统非预测类随机噪声压制方法往往适应性不高,因此开发能够保护地震信号时变特征的随机噪声压制方法具有重要的工业价值.压缩感知是近年出现的一个新的采样理论,通过开发信号的稀疏特性,已经在地震数据处理中的数据插值以及噪声压制中得到了应用.本文系统地分析了压缩感知理论框架下的地震随机噪声压制问题,建立了阈值消噪的数学反演目标函数;针对时变有效信息具有的可压缩性,利用有限差分算法求解炮检距连续方程,构建有限差分炮检距连续预测算子(FDOC),在seislet变换框架下,提出一种新的快速稀疏变换域———FDOC-seislet变换,实现地震数据的高度稀疏表征;结合非平稳随机噪声不可压缩的特征,提出了一种整形迭代消噪方法,该方法是一种广义的迭代收缩阈值(IST)算法,在无法计算稀疏变换伴随算子的条件下,仍然能够对强噪声环境中的时变有效信息进行有效恢复.通过对模型数据和实际数据的处理,验证了FDOC-seislet稀疏变换域随机噪声迭代压制方法能够在保护复杂构造地震波信息的前提下,有效地衰减原始数据中的强振幅随机噪声干扰. 相似文献
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多震源地震采集技术允许一次性激发不同位置处的震源,得到来自多个震源的混合地震数据,该技术采集效率高,能有效降低采集成本.多震源地震数据成像效率高,但在偏移剖面中会引入串扰噪声,影响成像精度.最小二乘偏移常被用于压制多震源地震数据成像中的串扰噪声,但常规的最小二乘偏移并不能很好的消除串扰噪声对成像结果的影响,难以满足成像精度的要求.因此,为了保证反演的稳定性并改善反演结果,根据反射系数在Seislet域的稀疏性,本文引入了Seislet变换作为变换域稀疏约束的变换算子,实现了基于Seislet变换的稀疏约束多震源最小二乘逆时偏移,数值实验表明该方法能有效压制串扰噪声. 相似文献
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混合震源采集技术相对于传统地震数据采集具有改善成像质量、提高采集效率的优势.减小混合炮中单炮之间的随机延时范围能够有效的提高采集效率,但这也给之后的混采数据分离带来了影响.混采数据经伪分离后非共炮域数据中的混叠噪声明显更加集中,不利于对混叠噪声进行压制.本文提出基于脉冲检测方法对混采数据进行分离,并且与迭代的多级中值滤波方法作对比,时间延时范围较大时,两种方法都能得到很好的分离结果;时间延时范围较小时,本文方法能更有效的去除混叠噪声,同时也能更好的保留细节信息.实际数据计算结果表明,本文方法一定程度上还能够有效压制其他随机噪声. 相似文献
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常规全波形反演利用全部炮集参与计算,反演的计算量巨大。针对这一问题,本文分析了不同频率反演对炮数的需求,进而提出一种基于频率多尺度反演方法的加速策略。该方法利用反演所需炮数与频率正相关的特性,在反演低频数据时,每次迭代只抽取一部分炮集参与反演,频率升高时,相应地引入更多的炮集参与运算,两次迭代之间通过组内随机炮采样的方法实现炮集的轮换,避免炮集信息的丢失。该方法通过降低反演炮数从而减少计算量,由于不涉及炮集的串扰,因此不会引入额外的噪声,也不受限于观测系统。模型测试结果表明,该方法在炮集数量较多时可以明显减少计算时间,同时,该方法具有一定的抗噪能力,对含噪声的地震记录也能得到较好的反演结果。 相似文献
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在地震资料中,噪声干扰严重影响了有效信号的提取,为此必须进行信噪分离处理.本文提出一种基于Curvelet变换和KL变换相结合的软硬阈值折衷处理方法.首先对地震数据进行Curvelet变换,然后对各尺度系数选取适当阈值压制噪声干扰,再利用KL变换提取数据中的相干有效信号,最后重构得到去噪后的记录.经合成记录和实际地震资料处理实验证明,该方法与小波变换法相比较,更能有效进行信噪分离,提高地震剖面信噪比和分辨率. 相似文献
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高密度采集可以提高地震资料品质,改善成像精度,但也会增加地震采集成本.为了提高采集效率降低生产成本,混采技术得到了推广应用.但是该采集方式会产生严重的混叠噪声,降低地震数据的信噪比.针对此问题,本文结合中值滤波、动校正(NMO)和复曲波变换阈值去噪的优势,设计了一种优化的复曲波变换压制混源噪声方法.该方法首先采用大步长中值滤波对经过NMO处理的数据进行滤波,再利用基于复曲波域的阈值去噪方法提取剩余信号,计算滤波结果的伪分离记录和原始混叠数据的差值,再将该差值返回到第一步进行迭代,每次迭代中值滤波步长逐步减小,直到达到初始设定的期望信噪比为止.与基于F-K域和curvelet域的迭代阈值方法相比,本文方法可以在压制混叠噪声的同时,更好的保护有效信号,由于本文方法仅需较少的迭代次数,计算效率也可以大大提高. 相似文献
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地震勘探方法在深部固体矿产资源勘探中发展潜力巨大,同时也面临挑战.由于固体矿产资源地下分布呈现陡峭构造、尺度小,物性差异小的特点,常规偏移方法对小尺度矿体成像的分辨率提高有限.本文研究了一种基于稀疏促进约束的最小二乘逆时偏移方法.首先,将非均匀分布的矿体等效为随机介质,建立小尺度扰动的矿体模型;其次,改进现有最小二乘偏移方法,以稀疏模型为先验信息约束成像结果,并通过Curvelet变换压缩成像空间,经过多次迭代计算,可以提高小尺度散射体的成像分辨率;再次,对炮域记录进行随机震源编码,减少成像所需的炮集个数,通过稀疏促进约束条件,降低串扰噪声引起的成像误差.通过庐枞金属矿模型数值计算,验证本文方法可以较好的成像包含小尺度散射体的金属矿地质模型. 相似文献
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Noise suppression for microseismic data by non‐subsampled shearlet transform based on singular value decomposition 
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下载免费PDF全文 The existence of strong random noise in surface microseismic data may decrease the utility of these data. Non‐subsampled shearlet transform can effectively suppress noise by properly setting a threshold to the non‐subsampled shearlet transform coefficients. However, when the signal‐to‐noise ratio of data is low, the coefficients related to the noise are very close to the coefficients associated with signals in the non‐subsampled shearlet transform domain that the coefficients related to the noise will be retained and be treated as signals. Therefore, we need to minimise the overlapping coefficients before thresholding. In this paper, a singular value decomposition algorithm is introduced to the non‐subsampled shearlet transform coefficients, and low‐rank approximation reconstructs each non‐subsampled shearlet transform coefficient matrix in the singular value decomposition domain. The non‐subsampled shearlet transform coefficients of signals have bigger singular values than those of the random noise, which implies that the non‐subsampled shearlet transform coefficients can be well estimated by taking only a few largest singular values. Therefore, those properties of singular value decomposition may significantly help minimise overlapping of noise and signals coefficients in the non‐subsampled shearlet transform domain. Finally, the denoised microseismic data are obtained easily by giving a simple threshold to the reconstructed coefficient matrix. The performance of the proposed method is evaluated on both synthetic and field microseismic data. The experimental results illustrate that the proposed method can eliminate random noise and preserve signals of interest more effectively. 相似文献
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地震勘探资料噪声压制及信噪比提高是整个地震勘探信号处理过程中的重要任务,随着地震勘探深度的增加及其复杂性,人们对地震数据质量的要求越来越高.勘探环境的复杂化使得采集到的地震资料中有效信号被大量噪声淹没,无法清晰辨识,严重影响后续的数据处理与解释.小波去噪是地震勘探中常用且发展较成熟的一种方法,但是其涉及到的阈值函数选取问题一直令人困扰,虽然已有多种阈值函数被提出,但仍存在各自的缺陷.本文利用小波分解在时域及频域良好的信号细节体现特性,引入模式识别中的非负矩阵分解(NMF)谱分离思想,针对小波系数阈值优化问题,提出了一种小波域图非负矩阵分解(GNMF)消噪算法.该方法首先在小波分解基础上,利用GNMF算法实现小波分解系数谱中信号分量与噪声分量的谱分离,然后通过反变换重构各分离子谱对应的子信号,最后利用K均值聚类算法将得到的多个子信号划分为信号类及噪声类,最终得到重构信号及分离噪声.合成记录和实际地震资料的消噪结果验证了新方法在提高信号与噪声分离准确性和精度方面的有效性,同时新方法避免了阈值选取造成的噪声压制不理想或有效成分损失问题.与小波消噪结果的对比及数值分析也说明了新方法在噪声压制及有效成分保持方面的优势. 相似文献
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To suppress the strong noise in seismic data with wide range of amplitudes, commonly used methods often yield unsatisfactory denoising results owing to inappropriate thresholds and require parametric testing as well as iterations to achieve the anticipated results. To overcome these problems, a data-driven strong amplitude suppression method based on the decibel criterion in the wavelet domain (ISANA) is proposed. The method determines the denoising threshold based on the decibel criterion and statistically analyzes the amplitude index rather than the abnormally high amplitudes. The method distinguishes the frequency band distributions of the valid signals in the time–frequency domain based on the wavelet transformation and then calculates thresholds in selected time windows, eventually achieving frequency-divided noise attenuation for better denoising. Simulations based on theoretical and real-world data verify the adaptability and low dependence of the method on the size of the time window. The method suppresses noise without energy loss in the signals. 相似文献
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Further improvement of temporal resolution of seismic data by autoregressive (AR) spectral extrapolation 总被引：1，自引：0，他引：1
Hakan Karsl&#x; 《Journal of Applied Geophysics》2006,59(4):324-336
Seismic data have still no enough temporal resolution because of band-limited nature of available data even if it is deconvolved. However, lower and higher frequency information belonging to seismic data is missing and it is not directly recovered from seismic data. In this paper, a method originally applied by Honarvar et al. [Honarvar, F., Sheikhzadeh, H., Moles, M., Sinclair, A.N., 2004. Improving the time-resolution and signal–noise ratio of ultrasonic NDE signals. Ultrasonics 41, 755–763.] which is the combination of the most widely used Wiener deconvolution and AR spectral extrapolation in frequency domain is briefly reviewed and is applied to seismic data to improve temporal resolution further. The missing frequency information is optimally recovered by forward and backward extrapolation based on the selection of a high signal–noise ratio (SNR) of signal spectrum deconvolved in signal processing technique. The combination of the two methods is firstly tested on a variety of synthetic examples and then applied to a stacked real trace. The selection of necessary parameters in Wiener filtering and in extrapolation are discussed in detail. It is used an optimum frequency windows between 3 and 10 dB drops by comparing results from these drops, while frequency windows are used as standard between 2.8 and 3.2 dB drops in study of Honarvar et al. [Honarvar, F., Sheikhzadeh, H., Moles, M., Sinclair, A.N., 2004. Improving the time-resolution and signal–noise ratio of ultrasonic NDE signals. Ultrasonics 41, 755–763.]. The results obtained show that the application of the purposed signal processing technique considerably improves temporal resolution of seismic data when compared with the original seismic data. Furthermore, AR based spectral extrapolated data can be almost considered as reflectivity sequence of layered medium. Consequently, the combination of Wiener deconvolution and AR spectral extrapolation can reveal some details of seismic data that cannot be observed in raw signal or which lost during the previous processing. 相似文献
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A data variance technique for automated despiking of magnetotelluric data with a remote reference 总被引：5，自引：0，他引：5
Karl N. Kappler 《Geophysical Prospecting》2012,60(1):179-191
The magnetotelluric method employs co‐located surface measurements of electric and magnetic fields to infer the local electrical structure of the earth. The frequency dependent ‘apparent resistivity’ curves can be inaccurate at long periods if input data are contaminated – even when robust remote reference techniques are employed. Data despiking prior to processing can result in significantly more reliable estimates of long period apparent resistivities. This paper outlines a two‐step method of automatic identification and replacement for spike‐like contamination of magnetotelluric data; based on the simultaneity of natural electric and magnetic field variations at distant sites. This simultaneity is exploited both to identify windows in time when the array data are compromised as well as to generate synthetic data that replace observed transient noise spikes. In the first step windows in data time series that contain spikes are identified according to an intersite comparison of channel ‘activity’– such as the variance of differenced data within each window. In the second step, plausible data for replacement of flagged windows are calculated by Wiener filtering coincident data in clean channels. The Wiener filters – which express the time‐domain relationship between various array channels – are computed using an uncontaminated segment of array training data. Examples are shown where the algorithm is applied to artificially contaminated data and to real field data. In both cases all spikes are successfully identified. In the case of implanted artificial noise, the synthetic replacement time series are very similar to the original recording. In all cases, apparent resistivity and phase curves obtained by processing the despiked data are much improved over curves obtained from raw data. 相似文献
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Conventional full-waveform inversion is computationally intensive because it considers all shots in each iteration. To tackle this, we establish the number of shots needed and propose multiscale inversion in the frequency domain while using only the shots that are positively correlated with frequency. When using low-frequency data, the method considers only a small number of shots and raw data. More shots are used with increasing frequency. The random-in-group subsampling method is used to rotate the shots between iterations and avoid the loss of shot information. By reducing the number of shots in the inversion, we decrease the computational cost. There is no crosstalk between shots, no noise addition, and no observational limits. Numerical modeling suggests that the proposed method reduces the computing time, is more robust to noise, and produces better velocity models when using data with noise. 相似文献
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卷积神经网络(Convolutional Neural Network,CNN)是一种基于数据驱动的学习算法,简化了传统从特征提取到分类的两阶段式处理任务,被广泛应用于计算机科学的各个领域.在标注数据不足的地震数据去噪领域,CNN的推广应用受到限制.针对这一问题,本文提出了一种基于数据生成和增广的地震数据CNN去噪框架.对于合成数据,本文对无噪地震数据添加不同方差的高斯噪声,增广后构成训练集,实现基于小样本的CNN训练.对于实际地震数据,由于无法获得真实的干净数据和噪声来生成训练样本集,本文提出一种直接从无标签实际有噪数据生成标签数据集的方法.在所提出的方法中,我们利用目前已有的去噪方法从实际地震数据中分别获得估计干净数据和估计噪声,前者与未知的干净数据具有相似纹理,后者与实际噪声具有相似的概率分布.人工合成数据和实际数据实验结果表明,相较于F-X反褶积,BM3D和自适应频域滤波算法,本文方法能更好地压制随机噪声和保护有效信号.最后,本文采用神经网络可视化方法对去噪CNN的机理进行了探索,一定程度上解释了网络每一层的学习内容. 相似文献
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GOCE卫星重力测量中有色噪声滤波器设计 总被引：1，自引：0，他引：1
本文根据卫星重力梯度测量的有色噪声特性,设计了Wiener、AR、FIR三种滤波器,并利用模拟的有色噪声数据对其滤波效果进行了测试,结果表明:对于文中采用的有色噪声数据,AR的滤波效果最好,其次为Wiener滤波器,FIR的滤波效果最差;三种滤波器均可用于GOCE卫星重力测量中有色噪声数据滤波,但其实用性尚需利用实测数据进行检验;可以利用不同的滤波器对含有色噪声的卫星重力梯度数据进行多次滤波,以进一步减弱有色噪声对卫星重力梯度测量精度的影响. 相似文献