地震资料保边的最大一致性倾角扫描去噪方法

在石油天然气勘探开发中,由于断层和裂缝等地质特征是发现和描述油藏的基础,所以分辨和分析地震数据中这些地质特征是非常重要的。本文提出的一种保边界的最大一致性倾角扫描去噪方法,可以在很好地保留地质边界特征的情况下,有效地压制噪音,准确地拾取地层倾角信息,提高油气藏识别的精度。     

A compound method for random noise attenuation

Random noise attenuation, preserving the events and weak features by improving signal‐to‐noise ratio and resolution of seismic data are the most important issues in geophysics. To achieve this objective, we proposed a novel seismic random noise attenuation method by building a compound algorithm. The proposed method combines sparsity prior regularization based on shearlet transform and anisotropic variational regularization. The anisotropic variational regularization which is based on the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation attenuates noises while preserving the events of seismic data and it effectively avoids the fine‐scale artefacts due to shearlets from the restored seismic data. The proposed method is formulated as a convex optimization problem and the split Bregman iteration is applied to solve the optimization problem. To verify the effectiveness of the proposed method, we test it on several synthetic seismic datasets and real datasets. Compared with three methods (the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation, shearlets and shearlet‐based weighted anisotropic total variation), the numerical experiments indicate that the proposed method attenuates random noises while alleviating artefact and preserving events and features of seismic data. The obtained result also confirms that the proposed method improves the signal‐to‐noise ratio.     

Adaptive hybrid diffusion model using variational mode decomposition for edge preserving noise attenuation

Preserving the structural and stratigraphic discontinuities or edges is essential in seismic data processing and interpretation. According to several numerical experiments, it is obvious that random noise has a constant spectral density, whereas the structural features vary significantly within different frequency bands, which means that the ratio between the densities of noise and structural features varies significantly in different frequency bands. Therefore, we propose a method called adaptive hybrid diffusion to attenuate random noise, which utilizes a novel adaptive frequency-based parameter. First, the adaptive hybrid diffusion method decomposes the seismic sections into several band-limited portions using variational mode decomposition. These portions are called intrinsic mode functions, in which noise and structural energy have distinct differences. Subsequently, utilizing the adaptive frequency-based parameter, each intrinsic mode function is divided into several monotonous portions that represent the noise or structural area. Afterwards, the total variation and L2 minimization algorithms are utilized separately to suppress the noise in different band-limited monotonous areas. The algorithms are chosen dynamically, as the portion changes with the change in the adaptive parameter. Finally, these denoised portions are combined to obtain the denoised seismic section. Experimental results on synthetic and field seismic data showed that seismic noise is effectively suppressed by the adaptive hybrid diffusion method, with the edge details of seismic events well preserved.     

Curvelet阈值迭代法地震随机噪声压制(英文)

本文将近些年发展起来的多尺度分析技术——Curvelet变换与求解优化反演问题的阈值迭代法相结合,研究了基于Curvelet变换的阈值迭代法在地震数据随机噪声衰减中的应用。充分利用了Curvelet变换对地震数据表示的稀疏性,提出将地震数据随机噪声压制问题转化为基于Curvelet稀疏变换的L1范数最优化问题,并采用前人提出的阈值迭代法求解。通过与常规的中值滤波、FX反褶积和小波阈值法去噪方法对比,理论合成数据和实际数据试算表明,Curvelet阈值迭代法去噪法具有优势,该法不仅能够获得较高的信噪比,而且对有效信号的损失较小。为充分利甩Curvelet的多尺度、多方向特性,提出了在Curvelet阈值迭代法去噪结果的基础上再进行方向控制,进一步提高了数据信噪比。     

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data’s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.     

地震资料去噪方法技术综合评述

地震资料去噪,无论是叠前还是叠后,都是处理中非常重要的内容.随着勘探技术的进步,地球物理界积累和开发的去噪软件已越来越多.对各种去噪方法进行分门别类,阐述其基本原理、物理意义、适用条件、发展前景,既有理论价值又有实际指导意义.本文从噪声的特征出发,首先对地震资料噪声进行了分类;然后综合评述了目前实际生产中常用的几种去噪方法,包括频率域滤波、频率波数域滤波、频率空间域滤波、Radon变换、聚束滤波、基于小波分解和重建的去噪方法等;最后还简述了去噪技术的应用及发展情况.     

地震资料去噪方法、技术综合评述

地震资料去噪，无论是叠前还是叠后，都是处理中非常重要的内容.随着勘探技术的进步，地球物理界积累和开发的去噪软件已越来越多.对各种去噪方法进行分门别类，阐述其基本原理、物理意义、适用条件、发展前景，既有理论价值又有实际指导意义.本文从噪声的特征出发，首先对地震资料噪声进行了分类；然后综合评述了目前实际生产中常用的几种去噪方法，包括频率域滤波、频率波数域滤波、频率空间域滤波、Radon变换、聚束滤波、基于小波分解和重建的去噪方法等；最后还简述了去噪技术的应用及发展情况.     

地震信号随机噪声压制的双树复小波域双变量方法

有效地压制地震信号中的噪声是地震信号解释和后续处理的重要环节之一.本文建立两种双树复小波域双变量模型对地震信号中的随机噪声进行压制.地震信号经双树复小波变换后,同一方向实部与虚部系数、实部（或虚部）系数与对应的模之间存在较强的相关性.鉴于此,对同一方向实部与虚部小波系数建立双变量模型,从含噪地震信号小波系数中估计原始信号的小波系数,再基于双树复小波逆变换重构得到降噪后的地震信号.进一步对同一方向实部（或虚部）系数与对应的模建立双变量模型,得到地震信号随机噪声压制的第二种双树复小波域双变量方法.最后对合成地震记录和实际地震资料中的随机噪声进行压制的实验结果证实本文两种方法都能够有效地压制地震信号中的随机噪声.     

The most homogeneous dip-scanning method using edgepreserving smoothing for seismic noise attenuation

Highlighting and analyzing the geological features of faults and fractures in seismic data is particularly important for hydrocarbon exploration and exploitation since they are often essential for finding and delineating reservoirs. We apply edge-preserving smoothing （EPS） to seismic processing and propose a most homogeneous dip-scanning method. The method preserves the geological features, eliminate random noise efficiently, obtain dip information, and improve the accuracy of identifying the oil and gas traps.     

基于多用户峰度准则的海洋强噪声衰减方法(英文)

海洋地震勘探过程中,由于采集设备的老化或电源的不稳定而造成的漏电,在地震记录表现为强噪音干扰,利用常规噪音衰减方法处理此类强噪音效果并不理想。鉴于强噪音在统计学上具有相同的特性,本文在基于峰度的盲分离(blind source separation,BSS)算法研究基础上,推导出一种基于多用户峰度(multiuser kurtosis,MUK)准则的噪音衰减算法来估计地震记录中具有相同统计特性的强噪音,并将其从地震记录中分离,从而达到衰减强噪音的目的。模型试验与实际资料的处理表明:该方法能够在好的衰减海洋地震勘探记录中的强噪音,保留了更多的有效信息,提高海洋地震数据的信噪比,具有可行性和应用前景。     

压制地震勘探随机噪声的分段时频峰值滤波方法

提高勘探资料信噪比是地震勘探的主要内容之一.本项研究利用短时能量将记录分成能量均衡的若干段,获得减少尺度变换误差的分段时频峰值滤波方法,并运用端点拓展和与地震信号特征匹配的多级时窗参数改进时频峰值滤波精度.理论模型和共炮点资料处理结果表明,分段时频峰值滤波很好地消除了尺度变化误差引起的信号波形阶梯状畸变,能够在压制强随机噪声的同时更好地保留信号特征.对低信噪比勘探资料处理具有较大的应用价值.     

一种黏声波方程逆时偏移成像中的衰减补偿方法

在存在强衰减介质的区域进行逆时偏移成像,需要既能补偿地震波传播过程中的振幅衰减,又能保持其相位不变的高效算法.为此,本文提出了一种利用有限差分模拟进行声波衰减补偿的方法来实现考虑衰减的逆时偏移.这种方法以线性黏弹性体模型为基础,保持其复模量的实部不变,并采用多项式多级优化方法修正复模量的虚部,从而实现在保持相位不变的同时补偿振幅,进而有效提高偏移成像的清晰程度,尤其是对于介质对地震波衰减极为强烈的情形.这一方法在声波方程中只增加了整数阶微分算子进行修正,能够保证利用有限差分法进行数值求解,有利于进行高效的大规模细粒度并行计算和GPU加速计算.数值实验结果验证了这一方法的可行性和有效性,能够很好地提高强衰减介质区域的偏移成像质量.

     

一种改进的重力梯度全张量数据的边界识别方法

边界识别是重力资料解释中的一项重要任务.随着重力梯度测量技术的迅速发展,重力梯度张量数据在边界识别中的应用越来越广泛.本文重点研究了随着深度的增加,边界识别能力下降,正负异常中出现假边缘的问题.另外,有些边缘检测方法对走向不同的地质体识别能力有所差异.本文对基于重力梯度张量的水平方向Theta法进行改进,通过选择合适的...     

Seismic attenuation qualitative characterizing method based on adaptive optimal-kernel time–frequency representation

The main issue of seismic attenuation characterizing method based on time–frequency analyzing method is the time–frequency resolution. The adaptive optimal-kernel time–frequency representation, which has high time–frequency resolution comparing with commonly used time–frequency analyzing method, is investigated. The seismic attenuation qualitative characterizing method based on adaptive optimal-kernel time–frequency representation is proposed. The synthetic data example and 3D field-data example reveal that the proposed method can qualitatively characterize seismic attenuation, and the attenuated anomaly of this field-data example coincides with gas reservoir well.     

An amplitude suppression method based on the decibel criterion

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.     

An adaptive response surface method for continuous Bayesian model calibration

Non-linear numerical models of the injection phase of a carbon sequestration (CS) project are computationally demanding. Thus, the computational cost of the calibration of these models using sampling-based solutions can be formidable. The Bayesian adaptive response surface method (BARSM)—an adaptive response surface method (RSM)—is developed to mitigate the cost of sampling-based, continuous calibration of CS models. It is demonstrated that the adaptive scheme has a negligible effect on accuracy, while providing a significant increase in efficiency. In the BARSM, a meta-model replaces the computationally costly full model during the majority of the calibration cycles. In the remaining cycles, the full model is used and samples of these cycles are utilized for adaptively updating the meta-model. The idea behind the BARSM is to take advantage of the fact that sampling-based calibration algorithms typically tend to sample more frequently from areas with a larger posterior density than from areas with a smaller posterior density. This behavior of the sampling-based calibration algorithms is used to adaptively update the meta-model and to make it more accurate where it is most likely to be evaluated. The BARSM is integrated with Unscented Importance Sampling (UIS) (Sarkarfarshi and Gracie, Stoch Env Res Risk Assess 29: 975–993, 2015), which is an efficient Bayesian calibration algorithm. A synthesized case of supercritical CO₂ injection in a heterogeneous saline aquifer is used to assess the performance of the BARSM and to compare it with a classical non-adaptive RSM approach and Bayesian calibration method UIS without using RSM. The BARSM is shown to reduce the computational cost compared to non-adaptive Bayesian calibration by 87 %, with negligible effect on accuracy. It is demonstrated that the error of the meta-model fitted using the BARSM, when samples are drawn from the posterior parameter distribution, is negligible and smaller than the monitoring error.     

Improved amplitude preservation for prestack depth migration by inverse scattering theory

A prestack reverse time-migration image is not properly scaled with increasing depth. The main reason for the image being unscaled is the geometric spreading of the wavefield arising during the back-propagation of the measured data and the generation of the forward-modelled wavefields. This unscaled image can be enhanced by multiplying the inverse of the approximate Hessian appearing in the Gauss–Newton optimization technique. However, since the approximate Hessian is usually too expensive to compute for the general geological model, it can be used only for the simple background velocity model.We show that the pseudo-Hessian matrix can be used as a substitute for the approximate Hessian to enhance the faint images appearing at a later time in the 2D prestack reverse time-migration sections. We can construct the pseudo-Hessian matrix using the forward-modelled wavefields (which are used as virtual sources in the reverse time migration), by exploiting the uncorrelated structure of the forward-modelled wavefields and the impulse response function for the estimated diagonal of the approximate Hessian. Although it is also impossible to calculate directly the inverse of the pseudo-Hessian, when using the reciprocal of the pseudo-Hessian we can easily obtain the inverse of the pseudo-Hessian. As examples supporting our assertion, we present the results obtained by applying our method to 2D synthetic and real data collected on the Korean continental shelf.     

An attenuation model for distant earthquakes

Large magnitude earthquakes generated at source–site distances exceeding 100km are typified by low‐frequency (long‐period) seismic waves. Such induced ground shaking can be disproportionately destructive due to its high displacement, and possibly high velocity, shaking characteristics. Distant earthquakes represent a potentially significant safety hazard in certain low and moderate seismic regions where seismic activity is governed by major distant sources as opposed to nearby (regional) background sources. Examples are parts of the Indian sub‐continent, Eastern China and Indo‐China. The majority of ground motion attenuation relationships currently available for applications in active seismic regions may not be suitable for handling long‐distance attenuation, since the significance of distant earthquakes is mainly confined to certain low to moderate seismicity regions. Thus, the effects of distant earthquakes are often not accurately represented by conventional empirical models which were typically developed from curve‐fitting earthquake strong‐motion data from active seismic regions. Numerous well‐known existing attenuation relationships are evaluated in this paper, to highlight their limitations in long‐distance applications. In contrast, basic seismological parameters such as the Quality factor (Q‐factor) could provide a far more accurate representation for the distant attenuation behaviour of a region, but such information is seldom used by engineers in any direct manner. The aim of this paper is to develop a set of relationships that provide a convenient link between the seismological Q‐factor (amongst other factors) and response spectrum attenuation. The use of Q as an input parameter to the proposed model enables valuable local seismological information to be incorporated directly into response spectrum predictions. The application of this new modelling approach is demonstrated by examples based on the Chi‐Chi earthquake (Taiwan and South China), Gujarat earthquake (Northwest India), Nisqually earthquake (region surrounding Seattle) and Sumatran‐fault earthquake (recorded in Singapore). Field recordings have been obtained from these events for comparison with the proposed model. The accuracy of the stochastic simulations and the regression analysis have been confirmed by comparisons between the model calculations and the actual field observations. It is emphasized that obtaining representative estimates for Q for input into the model is equally important.Thus, this paper forms part of the long‐term objective of the authors to develop more effective communications across the engineering and seismological disciplines. Copyright © 2003 John Wiley & Sons, Ltd.     

沙漠地区地震勘探随机噪声建模及其在噪声压制中的应用

随机噪声是影响地震勘探有效信号的主要因素,其存在大大降低了地震记录的信噪比.在噪声压制方法不断被改进的同时,对随机噪声特性进行研究,了解噪声的产生机制是对其进行压制的先决条件,目前对噪声的研究主要是特性研究以寻找规律性,对其进行定性定量的分析还比较少.本文根据塔里木沙漠地区实际采集环境,考虑到噪声的连续性给计算带来的不便,假设各类噪声源以点源的形式分布在检波器周围,依据相应理论确定各类噪声源的源函数,其激发的噪声经由波动方程传播,将随机噪声作为各类噪声源共同作用的综合波场,建立随机噪声的理论模型.通过分析不同种噪声对地震记录的影响,选取合适的滤波方法对其进行压制,实验结果表明,通过建立沙漠地区随机噪声的理论模型,为选择有效的滤波方法,提高地震记录信噪比起到理论指导作用.     

基于正交多项式变换的CMP动校正道集随机噪声压制

本文提出了一种新的基于正交多项式变换来压制动校正后CMP道集上随机噪声的方法,将地震资料经过正交多项式变换,建立正交多项式系数谱,该谱描述了地震资料在正交多项式域的能量分布,可以较好地分离有效波和随机噪声,相对于固定阶次的基于正交多项式变换压制噪声的方法,所提出的方法根据能量随阶次变化的规律,自适应确定表达有效信号的正交多项式阶次,既提高了信号和噪声的分离效果,又有效地保护了地震信号中的AVO信息,对人工合成数据和实际资料的处理结果表明了所提出方法的有效性.