Seismic Wavelet Analysis Based on Finite Element Numerical Simulation

The practice of exploration and production has proved that explosives are excited in different surrounding rocks and the seismic wavelets collected have different characteristics. In this paper, by establishing a numerical model of the explosion in the well, using finite element analysis technology for numerical simulation, the simulation calculated the stress structure in the near-source area of the earthquake excitation, and extracted the seismic wavelet. The results show that the simulation seismic wavelet characteristics of different thin interbedded sand and mudstone structures have changed significantly. Through excitation simulation, the amplitude and spectrum information of seismic wavelets can be compared and analyzed, and the excitation parameters can be optimized. .

Improve Q estimates with spectrum correction based on seismic wavelet estimation

Characterization of seismic attenuation,quantified by Q,is desirable for seismic processing and interpretation.For seismic reflection data,the coupling between seismic wavelets and the reflectivity sequences hinders their usage for Q estimation.Removing the influence of the reflectivity sequences in reflection data is called spectrum correction. In this paper,we propose a spectrum correction method for Q estimation based on wavelet estimation and then design an inverse Q filter.The method uses higher-order statistics of reflection seismic data for wavelet estimation,the estimated wavelet is then used for spectral correction.Two Q estimation methods are used here,namely the spectral-ratio and centroid frequency shift methods.We test the characteristics of both Q estimation methods under different parameters through a synthetic data experiment.Synthetic and real data examples have shown that reliable Q estimates can be obtained after spectrum correction;moreover, high frequency components are effectively recovered after inverse Q filtering.

Phase spectrum estimation of the seismic wavelet based on a criterion function

Phase spectrum estimation of the seismic wavelet is an important issue in high-resolution seismic data processing and interpretation. On the basis of two patterns of constant-phase rotation and root transform for wavelet phase spectrum variation, we introduce six sparse criteria, including Lu’s improved kurtosis criterion, the parsimony criterion, exponential transform criterion, Sech criterion, Cauchy criterion, and the modified Cauchy criterion, to phase spectrum estimation of the seismic wavelet, obtaining an equivalent effect to the kurtosis criterion. Through numerical experiments, we find that when the reflectivity is not a sparse sequence, the estimated phase spectrum of the seismic wavelet based on the criterion function will deviate from the true value. In order to eliminate the influence of non-sparse reflectivity series in a single trace, we apply the method to the multi-trace seismogram, improving the accuracy of seismic wavelet phase spectrum estimation.

Seismic wavelet estimation via a system identification method

On the assumption that the wavelet is causal and nonminimum phase, an autoregressive moving average （ARMA） model is introduced to fit the seismic trace. Seismic wavelet extraction is converted to parameters estimation of the ARMA model. Singular value decomposition （SVD） of an appropriate matrix formed by autocorrelation is exploited to determine the autoregressive （AR） order, and the cumulant-based SVD-TLS （total least squares） approach is proposed to obtain the AR parameters. The author proposes a new moving average （MA） model order determination method via combining the information theoretic criteria method and higher-order cumulant method. The cumulant approach is used to achieve the MA parameters. Theoretical analysis and numerical simulations demonstrate the feasibility of the wavelet extraction approach.

Ground roll separation method via threshold fi ltering and constraint of seismic wavelet support in curvelet domain

Most traditional ground roll separation methods utilize only the difference in geometric characteristics between the ground roll and the refl ection wave to separate them.When the geometric characteristics of data are complex,these methods often lead to damage of the reflection wave or incompletely suppress the ground roll.To solve this problem,we proposed a novel ground roll separation method via threshold filtering and constraint of seismic wavelet support in the curvelet domain;this method is called the TFWS method.First,curvelet threshold fi ltering(CTF)is performed by using the diff erence of the curvelet coeffi cient of the refl ection wave and the ground roll in the location,scale,and slope of their events to eliminate most of the ground roll.Second,the degree of the local damaged signal or the local residual noise is estimated as the local weighting coeffi cient.Under the constraints of seismic wavelet and local weighting coeffi cient,the L1 norm of the refl ection coeffi cient is minimized in the curvelet domain to recover the damaged refl ection wave and attenuate the residual noise.The local weighting coeffi cient in this paper is obtained by calculating the local correlation coeffi cient between the high-pass fi ltering result and the CFT result.We applied the TFWS method to simulate and fi eld data and compared its performance with that of frequency and wavenumber filtering and the CFT method.Results show that the TFWS method can attenuate not only linear ground roll,aliased ground roll,and nonlinear noise but also strong noise with a slope close to the refl ection events.

Influence of inaccurate wavelet phase estimation on seismic inversion

On the assumption that the seismic wavelet amplitude spectrum is estimated accurately, a group of wavelets with different phase spectra, regarded as estimated wavelets, are used to implement linear least-squares inversion. During inversion, except for the wavelet phase, all other factors affecting inversion results are not taken into account. The inversion results of a sparse reflectivity model （or blocky impedance model） show that： （1） although the synthetic data using inversion results matches well with the original seismic data, the inverted reflectivity and acoustic impedance are different from that of the real model. （2） the inversion result reliability is dependent on the estimated wavelet Z transform root distribution. When the estimated wavelet Z transform roots only differ from that of the real wavelet near the unit circle, the inverted reflectivity and impedance are usually consistent with the real model; （3） although the synthetic data matches well with the original data and the Cauchy norm （or modified Cauchy norm） with a constant damping parameter has been optimized, the inverted results are still greatly different from the real model. Finally, we suggest using the L1 norm, Kurtosis, variation, Cauchy norm with adaptive damping parameter or/and modified Cauchy norm with adaptive damping parameter as evaluation criteria to reduce the bad influence of inaccurate wavelet phase estimation and obtain good results in theory.

Identification method of seismic phase in three-component seismograms on the basis of wavelet transform

This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.

Wavelet-Based Response Computation for Base-Isolated Structure under Seismic Excitation

This paper presents a wavelet-based approach for estimating the response of the base-isolated structure under seismic ground motions. The seismic ground motion record is expressed as the multi-scale wavelet coefficients which presents the time frequency characteristics of the seismic excitation. The wavelet domain governing differential equation between the wavelet coefficients of the excitation and response is derived. Numerical study on a one-storey base isolated structure is performed. The result shows that the wavelet based response computation method is of high precision.

鲁东—黄海地块西部中强地震活动特征及周边地震对该地区的影响

鲁东—黄海地块西部为华北地块东侧的次级地块,其地震活动具有显著的周期特征,地块内部的中强地震活动特征和周边大震对该地区的影响对研究该地区地震活动状态有重要意义。采用小波分析方法定量计算了鲁东—黄海地块西部历史地震的活动周期,并结合中强地震活动的定性分析、时空演化、能量释放及周边大震等资料,探讨了该地区的地震活动特征。结果显示:(1)鲁东—黄海地块西部M5.0以上地震活动存在着60.6—78.6 a的长周期,该周期反映了地震活跃—平静的韵律特征,据此M≥5.0地震可划分为活跃时段Ⅰ(1844—1879年)、活跃时段Ⅱ(1905—1949年)和活跃时段Ⅲ(1974年至今)。1997年以来,该地区处于活跃时段末期的弱释放阶段;(2)在三个活跃时段,地震活动的主体地区不同,活跃时段Ⅰ,Ⅱ以海域地震活动为主,并且地震活动水平海域强、陆域弱。活跃时段Ⅲ陆域、海域均有M≥6.0地震活动;(3) 1815年以来,鲁东—黄海地块西部M≥5.0地震与华北M≥6.0地震活动大致同步,当鲁东—黄海地块西部中强地震恰好处于较强活跃时段时,华北地块M6.0地震显著平静后,M≥6.0地震先从鲁东—黄海地块西部开始活动,然后向华北地块中西部地区活动;(4)当太平洋板块西侧的俯冲带上M_(W)≥5.0中深源地震活跃,并发生M_(W)≥7.0地震时,鲁东—黄海地块西部易于发生M_(S)≥6.0地震,这可能是太平洋板块西侧深部俯冲构造动力作用增强导致的结果。

一种改进STGCN的深地时空域地震子波提取方法

地震子波的准确提取可有效提高全波形反演和偏移成像等方法的准确性,对储层预测和油气分析具有重要意义。由于深层能量衰减和复杂地质构造,地震子波不仅具有时变特性,同时也具有不可忽略的空变特性。而传统时变子波提取方法仅通过单道地震记录提取时变子波,忽略了多道地震记录之间子波的空间变化。同时,传统时空域子波提取方法,如经验模态分解(EMD)方法,对测井资料等先验信息依赖程度较高,实际应用范围受限。深度学习为时空域子波提取提供了新的思路,针对以上问题,提出了一种改进时空图卷积神经网络(STGCN)的时空域子波提取方法。首先,根据目标区地震数据分布特征与非平稳性质,建立以非平稳地震剖面为输入,时空域子波为标签的合成训练数据,再利用传统EMD时变子波提取方法逐道提取目标区子波,有针对性地构建以目标区地震剖面为输入,目标区时空域子波为标签的实际训练数据。最后,利用两种训练数据对改进后的STGCN进行训练,使其能够融合提取的子波时空特征,从而实现目标区时空域子波的有效提取。合成数据和实际地震数据的处理结果表明,该方法对于深地时空域子波的提取有效且准确,相较于传统方法更具优越性,具有较好的实际应用价值。

Singularity detection of the thin bed seismic signals with wavelet transform

The location of singularities may be detected by local maxima of the wavelet transform modulus. The digital modeling and focusing process to wavelet transform of the reflecting seismic signals have been done. It has been found that the locations of singularities after wavelet transform are only affected by two factors, their original locations and the seismic wavelet length, which says it does not matter with what shape the wavelet will be. The wavelet length can be determined according to the wavelet transform results and be eliminated thereafter so that we are able to detect thin bed seismic signal with resolution of l/32 wavelength. The singularities have been recovered with improved resolution of the seismic section by real data processing.

Gauss linear frequency modulation wavelet transforms and its application to seismic phases identification

Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base function to study the change characteristics of Gauss linear frequency modulation wavelet transform with difference wavelet and signal parameters, analyzes the error origin of seismic phases identification on the basis of Gauss linear frequency modulation wavelet transform, puts forward a kind of new method identifying gradual change style seismic phases with background noise which is called fixed scale wavelet transform ratio, and presents application examples about simulation digital signal and actual seismic phases recording onsets identification.

融合时频空间特征的土石坝地震易损性分析改进MLP模型研究

针对现有地震易损性分析中采用的峰值加速度、峰值速度等地震动指标未能充分反映地震动复杂的时频空间特征,且现有基于对数空间线性函数关系假设的地震需求模型难以揭示地震动指标与地震响应间复杂非线性关系的问题,提出一种融合时频空间特征的土石坝地震易损性改进多层感知机(Multi Layer Perceptron,MLP)模型。该模型利用胶囊网络(Capsule Network,CapsNet)能够充分捕捉和表征目标特征空间位置分布的优势,从地震动小波时频图中提取反映地震动时频空间分布的深层特征,并以特征拼接的方式与既有特征进行融合,构建地震动融合指标;进一步地,采用树形Parzen优化算法(Tree structured Parzen Estimator,TPE)对MLP的神经元数量、学习率等超参数进行优化,提出基于TPE-MLP的土石坝地震需求模型,以反映地震动融合指标与地震响应间的复杂非线性关系,进而实现土石坝地震易损性的可靠分析。案例分析表明,相比于既有地震动指标,基于地震动时频空间特征融合指标的土石坝地震需求模型的MAE降低了40.5%,表明了所提模型的可靠性和优越性。

一种基于改进门控循环单元的叠前时变子波提取方法

子波的精确提取是地震勘探后续反演与成像的前提,针对传统时变子波提取方法受到的各类假设限制,且需分别提取子波振幅谱与相位谱的问题,本文提出了一种基于改进门控循环单元(GRU)网络的叠前时变地震子波提取方法.根据实际叠前地震数据分布特征与非平稳性质,本方法首先建立非平稳地震记录与添加随机噪声的时变子波训练数据集;为对提取出的时序特征进行拓展,提升传统GRU网络对长时序列的处理能力,本方法搭建起含多层GRU模块与全连接神经网络的改进门控循环单元网络模型;利用建立的训练数据集对网络模型进行训练使网络具备提取时变子波的能力;为提高训练效率与提取精度,本方法在训练的反向传播过程中应用自定义WaveLoss损失函数衡量误差,最终实现叠前时变子波的估计.经合成数据仿真实验与不同方法对比验证,本文提出的叠前时变子波提取方法具有更高的准确度;经对中国西部不同地区实际叠前地震资料处理与反褶积验证分析,该方法可有效提高目标区叠前地震剖面分辨率.

Impedance inversion of pre-stack seismic data in the depth domain

The extensive application of pre-stack depth migration has produced huge volumes of seismic data,which allows for the possibility of developing seismic inversions of reservoir properties from seismic data in the depth domain.It is difficult to estimate seismic wavelets directly from seismic data due to the nonstationarity of the data in the depth domain.We conduct a velocity transformation of seismic data to make the seismic data stationary and then apply the ridge regression method to estimate a constant seismic wavelet.The estimated constant seismic wavelet is constructed as a set of space-variant seismic wavelets dominated by velocities at different spatial locations.Incorporating the weighted superposition principle,a synthetic seismogram is generated by directly employing the space-variant seismic wavelets in the depth domain.An inversion workflow based on the model-driven method is developed in the depth domain by incorporating the nonlinear conjugate gradient algorithm,which avoids additional data conversions between the time and depth domains.The impedance inversions of the synthetic and field seismic data in the depth domain show good results,which demonstrates that seismic inversion in the depth domain is feasible.The approach provides an alternative for forward numerical analyses and elastic property inversions of depth-domain seismic data.It is advantageous for further studies concerning the stability,accuracy,and efficiency of seismic inversions in the depth domain.

基于粗-细网络模型分步训练的地震数据重建方法

由于地形等复杂条件的限制,叠前地震数据在空间上存在不完整或不规则分布的情况,导致数据出现缺失或混淆等现象。近年来,基于卷积神经网络的方法已经广泛应用于缺失地震数据重建工作。然而一步训练过程的网络模型不足以重建具有宽振幅范围的缺失地震数据,低振幅缺失部分的重建结果仍需改进。因此本文提出一种具有分步训练过程的粗-细网络模型。该模型由粗网络和细网络组成,分步恢复宽振幅范围内的缺失地震数据。在细网络中引入离散小波变换代替池化操作,其可逆性在上采样阶段有利于保留细节特征。模型采用混合损失函数重建缺失信号的真实细节。粗网络的初步恢复结果经过掩码操作处理后输入到细网络,细网络进一步精确恢复缺失部分的低振幅信号。实验结果表明,与残差网络(ResNet)、U型网络(U-Net)和多级小波卷积神经网络(MWCNN)的重建方法相比,本文方法在合成数据和真实数据上展现出更卓越的重建性能:在缺失75%的合成数据上,信噪比为18.818 5 dB;在缺失50%的真实数据上,信噪比为12.2551 dB。在消融研究中,本文模型重建的均方误差为1.689 3×10^(-4),信噪比为19.284 6 dB,峰值信噪比为43.743 5 dB,结构相似性为0.984 1,均优于其他三组对照实验。

强震区抗滑桩加固碎石土边坡动力响应特征与桩身损伤识别:以川青铁路九寨沟双线大桥附近工点为例

为掌握西南强震区服役抗滑桩的地震响应特征与损伤状态,基于川青铁路位于强震区典型工点,构建了地震作用下不同损伤状态抗滑桩加固碎石土边坡三维有限差分模型,研究了坡体的加速度响应、抗滑桩结构内力变化以及桩身初始损伤对坡体动力响应的影响;引入小波包能量谱法,基于桩身加速度响应数据,构建能量损伤判别指标对桩身进行损伤状态、损伤位置以及损伤程度的识别。结果表明,抗滑桩能有效降低桩身周围土体的加速度响应,坡体加速度总体表现为高程放大效应,滑面附近是桩身受力集中区域且桩身损伤会导致其抗弯、抗剪承载力下降,侧向水平位移增大,进而加剧坡体加速度响应。损伤判别指标D PERV沿桩身的分布特征能够有效识别抗滑桩的损伤位置和损伤程度,建立的桩顶残余位移、桩顶损伤指标D PERV与损伤程度三者之间的定量关系能够评估桩身相对损伤程度,为边坡工程中抗滑桩服役性能评价提供新的思路和方法。

基于多阶振幅谱面积差的Q值估计方法

常规Q值估计方法易受频段、子波叠加及噪声等因素的影响。为此,在振幅谱指数项1~4阶泰勒级数展开基础上推导出不同阶次振幅谱面积差的Q值估计方法(ASAD法),并将对数谱面积差法(LSAD法)及1~4阶ASAD法应用于实际叠前CMP道集中。结果表明:相对于LSAD法而言,不同阶次ASAD算法受频段选择及子波宽度的影响更小,抗噪性更强;新方法还可以处理复杂叠后数据,并能够获得良好的Q值估计结果;2~4阶ASAD法的Q值估计结果一致性较强,且ASAD法Q估计值的反Q滤波结果同向轴连续性更强、纵向成像分辨率更高。

基于粒子群优化算法的地震数据品质因子及混合相位子波同时估计

非稳态地震数据高分辨率处理及反演是通过求解包含地震子波滤波效应及地层吸收衰减效应的反演方程,获得地下反射界面的地震响应。为实现高分辨率处理及反演中初始地震子波及数据品质因子Q模型的实施条件,结合地震子波Z变换的根移动编码及等效Q模型的二进制—十进制转换策略,引入粒子群全局优化算法,建立非稳态地震数据混合相位初始地震子波及数据品质因子同时估计的方法。利用井旁道与数据的互相关作为准则函数,判定粒子群是否收敛至最大,输出最佳初始地震子波及Q。应用理论合成数据和实际数据测试进行验证。结果表明:相较于传统常相位地震子波估计及反Q滤波,该方法在复杂干扰条件下能更精确捕获真实的子波波形及衰减参数。利用输出的地震子波及Q进行时变反褶积处理得到高分辨率剖面,说明方法具有准确性、计算效率高且输出结果优,估计的地震子波和Q更符合实际地震波传播特征,为后续高分辨率地震数据处理提供基本参数。

应用遗传算法估计非稳态地震数据的混合相位子波及Q值

地震高分辨率处理或反演的目的是获得精确的反射系数或弹性参数模型。然而地层滤波效应模糊了地震记录中的地层反射信息,因此有必要消除这种滤波效应。现有的大部分提高分辨率方法并不能完全摒弃关于地震子波和Q模型的一些假设。为了获得更为切合实际的地震子波,同时自适应获取Q模型,文中将地震子波相位估计与Q模型估计相结合,提出了基于遗传算法的非稳态地震数据混合相位子波及Q值估计方法。首先通过井旁道记录拟合得到初始地震子波的振幅信息,然后依据子波Z变换的根关于单位圆移动与否构建用于遗传算法的编码链条。另一方面,十进制Q模型对应的二进制表示形式同样能够利用编码链条表征,因此利用全局优化算法能够同时估计地震混合相位子波以及Q模型。结合根变换和遗传算法不断改变子波相位的同时自适应生成Q模型,利用子波和Q模型构造的时变子波矩阵与测井反射系数得到合成地震记录,并与井旁道记录进行匹配,最终得到合理的混合相位子波和地层Q模型,进而构造时变子波矩阵进行时变反褶积。通过井旁道记录与测井数据拟合得到的混合相位子波,与实际地震子波相位更为接近,理论数据和实际数据处理结果证实了该方法的有效性。