Seismic random noise attenuation using artificial neural network and wavelet packet analysis

In this paper, we present a method for attenuating background random noise and enhancing resolution of seismic data, which takes advantage of semi-automatic training of feed forward back propagation (FFBP) artificial neural network (ANN) in a multiscale domain obtained from wavelet packet analysis (WPA). The images of approximations and details of the input seismic sections are calculated and utilized to train neural network to model coherent events by an automatic algorithm. After the modeling of coherent events, the remainder data are assumed to be related to background random noise. The proposed method is applied on both synthetic and real seismic data. The results are compared with that of the adaptive Wiener filter (AWF) in synthetic shot gather and real common midpoint gather and also with that of band-pass filtering on real common offset gather. The comparison indicates substantially higher efficiency of the proposed method in attenuating random noise and enhancing seismic signals.     

Fractal analysis revisited by the continuous wavelet transform of AVO seismic data

The main goal of this paper is to establish reservoirs media heterogeneities by the wavelet transform modulus maxima lines. First, we gathered amplitude versus offsets (AVO) amplitudes at the top of the reservoirs, then we calculated the 2D wavelet transform after we calculated its maxima, and we estimated the Hölder exponent at each maxima. Variation of the Hölder exponent can give more information about lithology and fluid nature at any point. We applied the proposed idea at a 2D synthetic AVO intercept model, obtained results showed that the wavelet transform modulus maxima lines can be used as a seismic image processing tool. We suggest application of the proposed idea on real AVO seismic data and its attributes. It can give more ideas about reservoirs model.     

随采地震噪声衰减研究

鉴于煤矿井下环境的封闭性及对安全生产的高要求特点,炸药震源在煤矿井下地震勘探施工中存在众多局限性,而以采煤机作为震源可以规避炸药震源的许多固有缺陷,具有可以在不影响回采工作面正常作业的情况下对工作面内部构造及应力开展实时探测的优势。但由于采煤机产生的能量远小于炸药震源,因此采集到信号的质量通常受到环境噪声影响,通过分析大量实际数据发现噪声中能量占比较多的为单频噪声和随机噪声,严重影响探测精度。为提高随采地震数据信噪比,提出了同时衰减单频噪声和随机噪声的方法,首先基于互相关技术在构建常规炮集记录的同时衰减单频噪声,然后通过小波变换衰减随机和残余单频噪声,通过模拟数据和实际数据证实,该方法可以在很大程度上提高信号的信噪比。     

f-x域共偏移距道集的EEMD随机噪声压制方法

f-x域随机噪声压制方法面临着2个问题:叠前共炮点道集或CMP道集反射波同相轴为双曲线型,去噪同时会损害有效波;地震信号为复杂的非平稳信号,要求去噪方法具有自适应性。基于f-x EEMD的共偏移距道集随机噪声压制方法利用了共偏移距道集反射波同相轴为水平满足f-x域去噪假设条件和EEMD算法对非平稳信号的良好适应性,对f-x域每一个等频率切片做EEMD分解,并去除以高频随机噪声为主的第一个IMF分量,最后将f-x域数据反变换回t-x域,实现噪声分离。正演模拟和实际地震数据试算结果表明:该方法在压制随机噪声的同时,能够保持有效信号。      

基于小波变换的阈值降噪方法在地震资料处理中的应用

这里主要讨论了小波变换阈值降噪方法的基本原理,并且将广泛使用的通用阈值方法与Birge-Massart阈值方法进行了比较,展现了Birge-Massart阈值选取方法的优越性。通过对模拟数据和实际地震资料的处理结果表明,基于小波变换的Birge-Massart阈值降噪方法对地震信号降噪具有很好的效果。     

应用改进的KL变换提高叠后地震记录信噪比

常规KL变换叠后去噪仅能加强水平方向同相轴,对倾斜或弯曲同相轴处理效果较差,对能量较强的相干噪声去除效果不佳,且在处理大量数据时计算成本很高,在实际生产中难以广泛应用。本文针对常规KL变换的缺陷分别进行了三点改进:使用倾角扫描叠加KL变换,可在有效压制地震剖面噪声的同时,较好保持倾斜同相轴和弯曲同相轴;使用本文提出的时空变-倾角KL变换能去除能量较强的相干噪声;使用数据分块技术可减少运算量。模型测试和实际资料处理效果表明:改进后的KL变换适应性强,去噪效果大大改善。     

基于改进混沌果蝇优化小波阈值法地震信号随机噪声压制

由于野外采集地震资料往往带有较多的随机噪声,给资料解释造成困难。针对小波阈值去噪的阈值选取通常需要对信号进行先验估计,带有较强猜测性,阈值选取难以获得最优结果。本文提出基于改进混沌果蝇优化的小波阈值法,将基于广义交叉验证(GCV)函数设定为阈值选取目标函数,在混沌果蝇优化算法中引入调节系数实现对该目标函数的迭代寻优,在无先验信息前提下,获取最优小波阈值。通过将本文算法用于合成地震记录和实际地震记录进行去噪处理,并对比常用小波阈值去噪算法,证明了本文算法的有效性。     

Multiscale analysis of noisy 3D GPR data using the directional continuous wavelet transform

Here, we present a new technique of noise effect attenuation in the 3D ground-penetrating radar (GPR) data analysis using the 2D directional continuous wavelet transform (DCWT). The proposed technique is based on the application of a Gaussian low pass filter to the modulus of the 2D DCWT for low scales. After application of the low pass filter, maxima of the CWT are mapped for all range of scales. Application to a noisy GPR data shows that the proposed idea can improve GPR data analysis by the continuous wavelet transform.     

Aeromagnetic data analysis using the 2D directional continuous wavelet transform (DCWT)

In this paper, we have analyzed the geomagnetic anomaly field reduced to the pole by the 2D directional continuous wavelet transform (DCWT). The objective is to identify geological contacts. The proposed idea has been applied firstly at a synthetic model, the obtained results showed the robustness of the proposed technique, after that the synthetic data has been noised; analysis shows that contacts identified by DCWT are affected by noise. To resolve this problem, we have proposed an algorithm to reduce the noise effect in the contacts model. Application on the real geomagnetic data of In Ouzzal area located in the West of Hoggar (Algeria) shows clearly the robustness of the proposed method. Comparison with the analytic signal solutions exhibits a difference between the two models of contacts. Our method gives results that are accurate with the structural geological map.     

小波分析在地震资料去噪中的应用

小波变换方法已广泛应用于信号处理领域。应用多尺度小波分析方法来消除地震观测信号中的噪声是一种行之有效的方法。这里从小波变换的基本原理出发,详细介绍了地震信号的阈值去噪原理,并根据模拟信号和实测地震信号的频谱分析,讨论了如何选择小波基及去噪过程中的阈值取值问题。从小波分解理论知道,利用多尺度分解方式对地震资料进行分析处理,相当于对实测地震资料进行不同尺度的细化分析,由于对不同地区、不同资料的精度要求不同,我们只要使用不同的尺度进行小波变换处理,就可以得到去除原信号的细部巨变(噪声干扰)特征的信号。同时,我们对小波变换处理后重构的地震信号与原信号进行了对比分析,误差结果分析表明该方法切实可行。我们还利用MATLAB语言及其小波工具箱,实现了对地震资料的去噪处理。     

井间地震时频域联合速度和衰减系数同步层析反演

井间地震资料中蕴含丰富的地下储层岩性和物性信息,利用井间地震直达波初至信息,通过层析反演可以得到两井之间的速度剖面。通过对井间地震直达波振幅衰减信息的处理,可以实现井间的吸收衰减层析成像。基于井间地震直达波射线路径的一致性,提出了井间地震时域直达波走时层析反演和频域质心频率衰减层析同步反演方法,增加了约束条件,增强了抗干扰能力。对模型和实际资料的处理结果表明,速度和吸收衰减同步反演方法,提高了速度层析反演精度,得到了对油气更为敏感的衰减属性,增强了井间地震在储层描述和油气预测中的应用能力。     

Monitoring volcanoes using seismic noise correlations

In this article, we summarize some recent results of measurements of temporal changes of active volcanoes using seismic noise cross-correlations. We first present a novel approach to estimate volcano interior temporal seismic velocity changes. The proposed method allows to measure very small velocity changes (≈ 0.1%) with a time resolution as small as one day. The application of that method to Piton de la Fournaise Volcano (La Réunion Island) shows velocity decreases preceding eruptions. Moreover, velocity changes from noise cross-correlations over 10 years allow to detect transient velocity changes that could be due to long-lasting intrusions of magma without eruptive activity or to pressure buildup associated to the replenishing of the magma reservoir. We also present preliminary results of noise cross-correlation waveform perturbation associated with the occurrence of dike injection and volcanic eruption. We show that such an analysis leads us to locate the areas of dike injection and eruptive fissures at Piton de la Fournaise Volcano. These recent results suggest that monitoring volcanoes using seismic noise correlations should improve our ability to forecast eruptions, their intensity and thus potential environmental impact.     

基于第二代Curvelet变换的地震资料随机噪声衰减

噪声衰减是地震资料处理中的关键问题之一。根据Curvelet变换对含有光滑边界的二维二阶连续可微函数所具有的稀疏表示性能,给出了Curvelet变换域地震资料随机噪声衰减的阈值方法;并给出了基于地震资料中随机噪声是独立同分布的高斯白噪假设条件下的阈值估计方法。通过合成数据和叠后实际数据算例,对该方法的有效性进行验证。结果表明,Curvelet变换不仅可以很好地衰减随机噪声,并且能较好地保持有效信号。     

一种降低地震数据随机噪声的方法

从砂岩型铀矿勘探需求出发,提出了一种改善地震勘探分辨率的方法——随机噪声衰减法,并开发了相应的处理软件。实践证明,采用该处理方法,可以提高地震剖面的质量。     

Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network

The main goal of this paper is to show that the fractal analysis based on the continuous wavelet transform is not able to improve lithofacies classification using the self-organizing map (SOM) neural network model from well-logs data. The proposed idea consists to inject many inputs in SOM neural network machines and to choose the best map. These inputs are: data set 1: the five raw well-logs data which are: the gamma ray, density, neutron porosity, photoelectric absorption coefficient and sonic well-log; data set 2: the estimated Hölder exponents using the continuous wavelet transform of the data set 1; data set 3: data set 1 and the three radioactive elements concentrations; data set 4: the estimated Hölder exponents of the data set 1 and the Hölder exponents of the radioactive elements concentrations; data set 5: the estimated Hölder exponents of the data set 1 and the three radioactive elements concentrations logs. Application of the proposed idea at two boreholes located in the Algerian Sahara shows that the Hölder exponents estimated with the continuous wavelet transform as an input of the SOM neural network are not able to give geological details. However, the raw well-logs as an input give more details and precision especially when they are enhanced with the natural gamma ray spectrometry data.     

海上地震勘探斜缆采集中鬼波产生机理及压制效果分析

在海洋油气勘探中,为了准确获取勘探目的 层高精度、高分辨率的反射信息,实现更加细腻的成像,海洋地震宽频采集及处理技术近年来得到了极大的创新和发展.本文研究了海洋地震勘探中鬼波的产生机理、鬼波的分类和特性及其在实际地震资料中的表现形式.采用频率域高精度斜缆Radon变换作为主要处理方法,对某直斜缆宽频数据进行了鬼波的衰减...     

Multiscale analysis of 3D GPR data using the 2D directional continuous wavelet transform (DCWT)

The main goal of this paper is to extract land topographical information by analyzing the 3D ground-penetrating radar (GPR) data by the 2D directional continuous wavelet transform method. The first objective is to identify direction of obstacles, and the second is to characterize each one with a special roughness coefficient. Application on real data shows that the proposed tool can enhance the GPR data interpretation.     

Buried pipe detection by ground penetrating radar using the discrete wavelet transform

Ground penetrating radar (GPR) is widely used for non-invasive examination of man-made structures, especially to determine the depth of pipes buried underground. Unfortunately, shallower objects may obscure GPR raw data that is reflected from deeper ones. This study introduces a signal processing technique, called the discrete wavelet transform (DWT), to filter and enhance the GPR raw data in order to obtain higher quality profile images. Laboratory experiments were conducted and the locations of buried pipes under different conditions were analyzed. The buried pipes were made of plastic and metal, and both single and two parallel horizontal pipes are discussed. The experimental results indicate that the DWT profiles can provide more information than the traditional GPR profile. The images of the diameter and position of pipes, even two pipes of different materials and in horizontal alignment, can be enhanced by using the DWT profile.     

Structural boundaries delimitation from geomagnetic data using the continuous wavelet transform. Application to Hoggar (Algeria)

The main goal of the proposed work is to delineate structural boundaries in a very complex geology environment using the spatial and statistical properties of the potential field data. The analysis is performed using magnetic anomaly of the total field data over In Ouzzal, an Archaean north–south elongated block belonging to the Hoggar (Algeria). This region is geologically and geophysically very poorly known except some localized areas. The intrinsic properties of high-frequency signals and the related causative sources are explored, thanks to two-dimensional continuous wavelet transform. The obtained results, represented by spatial distribution of the maxima of the modulus of the wavelet transform at each scale, clearly show that the major magnetic singularities of the field may be related to geological features. Comparison with the Euler’s deconvolution solutions exhibits a very good correlation. Even though where geological structures are known, our method shows better resolution and accuracy. The proposed multiscale method proves to be more powerful, easy to use, and versatile where classical methods of potential field interpretation fail or are very constraining. However, work is still ongoing to try to better and fully characterize the causative sources of the potential fields.