Sparsity‐enhanced wavelet deconvolution

We propose a three‐step bandwidth enhancing wavelet deconvolution process, combining linear inverse filtering and non‐linear reflectivity construction based on a sparseness assumption. The first step is conventional Wiener deconvolution. The second step consists of further spectral whitening outside the spectral bandwidth of the residual wavelet after Wiener deconvolution, i.e., the wavelet resulting from application of the Wiener deconvolution filter to the original wavelet, which usually is not a perfect spike due to band limitations of the original wavelet. We specifically propose a zero‐phase filtered sparse‐spike deconvolution as the second step to recover the reflectivity dominantly outside of the bandwidth of the residual wavelet after Wiener deconvolution. The filter applied to the sparse‐spike deconvolution result is proportional to the deviation of the amplitude spectrum of the residual wavelet from unity, i.e., it is of higher amplitude; the closer the amplitude spectrum of the residual wavelet is to zero, but of very low amplitude, the closer it is to unity. The third step consists of summation of the data from the two first steps, basically adding gradually the contribution from the sparse‐spike deconvolution result at those frequencies at which the residual wavelet after Wiener deconvolution has small amplitudes. We propose to call this technique “sparsity‐enhanced wavelet deconvolution”. We demonstrate the technique on real data with the deconvolution of the (normal‐incidence) source side sea‐surface ghost of marine towed streamer data. We also present the extension of the proposed technique to time‐varying wavelet deconvolution.     

Elimination of the water-layer response from multi-component source and receiver marine electromagnetic data

This paper presents the theory to eliminate from the recorded multi‐component source, multi‐component receiver marine electromagnetic measurements the effect of the physical source radiation pattern and the scattering response of the water‐layer. The multi‐component sources are assumed to be orthogonally aligned above the receivers at the seabottom. Other than the position of the sources, no source characteristics are required. The integral equation method, which for short is denoted by Lorentz water‐layer elimination, follows from Lorentz' reciprocity theorem. It requires information only of the electromagnetic parameters at the receiver level to decompose the electromagnetic measurements into upgoing and downgoing constituents. Lorentz water‐layer elimination replaces the water layer with a homogeneous half‐space with properties equal to those of the sea‐bed. The source is redatumed to the receiver depth. When the subsurface is arbitrary anisotropic but horizontally layered, the Lorentz water‐layer elimination scheme greatly simplifies and can be implemented as deterministic multi‐component source, multi‐component receiver multidimensional deconvolution of common source gathers. The Lorentz deconvolved data can be further decomposed into scattering responses that would be recorded from idealized transverse electric and transverse magnetic mode sources and receivers. This combined electromagnetic field decomposition on the source and receiver side gives data equivalent to data from a hypothetical survey with the water‐layer absent, with idealized single component transverse electric and transverse magnetic mode sources and idealized single component transverse electric and transverse magnetic mode receivers. When the subsurface is isotropic or transverse isotropic and horizontally layered, the Lorentz deconvolution decouples into pure transverse electric and transverse magnetic mode data processing problems, where a scalar field formulation of the multidimensional Lorentz deconvolution is sufficient. In this case single‐component source data are sufficient to eliminate the water‐layer effect. We demonstrate the Lorentz deconvolution by using numerically modeled data over a simple isotropic layered model illustrating controlled‐source electromagnetic hydrocarbon exploration. In shallow water there is a decrease in controlled‐source electromagnetic sensitivity to thin resistors at depth. The Lorentz deconvolution scheme is designed to overcome this effect by eliminating the water‐layer scattering, including the field's interaction with air.     

Seismic and electromagnetic controlled-source interferometry in dissipative media

Seismic interferometry deals with the generation of new seismic responses by crosscorrelating existing ones. One of the main assumptions underlying most interferometry methods is that the medium is lossless. We develop an ‘interferometry‐by‐deconvolution’ approach which circumvents this assumption. The proposed method applies not only to seismic waves, but to any type of diffusion and/or wave field in a dissipative medium. This opens the way to applying interferometry to controlled‐source electromagnetic (CSEM) data. Interferometry‐by‐deconvolution replaces the overburden by a homogeneous half space, thereby solving the shallow sea problem for CSEM applications. We demonstrate this at the hand of numerically modeled CSEM data.     

Klauder wavelet removal before vibroseis deconvolution

The spiking deconvolution of a field seismic trace requires that the seismic wavelet on the trace be minimum phase. On a dynamite trace, the component wavelets due to the effects of recording instruments, coupling, attenuation, ghosts, reverberations and other types of multiple reflection are minimum phase. The seismic wavelet is the convolution of the component wavelets. As a result, the seismic wavelet on a dynamite trace is minimum phase and thus can be removed by spiking deconvolution. However, on a correlated vibroseis trace, the seismic wavelet is the convolution of the zero-phase Klauder wavelet with the component minimum-phase wavelets. Thus the seismic wavelet occurring on a correlated vibroseis trace does not meet the minimum-phase requirement necessary for spiking deconvolution, and the final result of deconvolution is less than optimal. Over the years, this problem has been investigated and various methods of correction have been introduced. In essence, the existing methods of vibroseis deconvolution make use of a correction that converts (on the correlated trace) the Klauder wavelet into its minimum-phase counterpart. The seismic wavelet, which is the convolution of the minimum-phase counterpart with the component minimum-phase wavelets, is then removed by spiking deconvolution. This means that spiking deconvolution removes both the constructed minimum-phase Klauder counterpart and the component minimum-phase wavelets. Here, a new method is proposed: instead of being converted to minimum phase, the Klauder wavelet is removed directly. The spiking deconvolution can then proceed unimpeded as in the case of a dynamite record. These results also hold for gap predictive deconvolution because gap deconvolution is a special case of spiking deconvolution in which the deconvolved trace is smoothed by the front part of the minimum-phase wavelet that was removed.     

Drill‐bit seismic interferometry with and without pilot signals*

We use different interferometry approaches to process the seismic signals generated by a drill‐bit source in one well and recorded by seismic receivers located both in a second borehole and at the surface near the source well. We compare the standard interferometry results, obtained by using the raw drill‐bit data without a pilot signal, with the new interferometry results obtained by using the drill‐bit seismograms correlated with a reference pilot signal. The analysis of the stationary phase shows that the final results have different S/N levels and are affected by the coherent noise in the form of rig arrivals. The interferometry methods are compared by using different deconvolution approaches. The analysis shows that the results agree with the conventional drill‐bit seismograms and that using the reference pilot signal improves the quality of the drill‐bit wavefields redatumed by the interferometry method.     

Wideband dipole logging based on segment linear frequency modulation excitation

A wideband dipole signal is required for dipole dispersion correction and near-borehole imaging. To obtain the broadband flexural wave dispersion, we use a nonlinear frequency modulation (NLFM) signal and propose a segment linear frequency modulation (SLFM) signal as the dipole excitation signal to compensate for the excitation intensity. The signal-to-noise ratio (SNR) of the signal over the entire frequency band is increased. The finite-difference method is used to simulate the responses from a Ricker wavelet, a linear frequency modulation (LFM) signal, an NLFM signal, and an SLFM signal in two borehole models of a homogeneously hard formation and a radially stratified formation. The dispersion and radial tomography results at low SNR of the sound source signals are compared. Numerical modeling suggests that the energy of the flexural waves excited by the Ricker wavelet source is concentrated near the Airy phase. In this case, the dispersion is incomplete and information regarding the formation near or far from the borehole cannot be obtained. The LFM signal yields dispersion information near the Airy phase and the high-frequency range but not in the low-frequency range. Moreover, the information regarding the formation far from the borehole is not accurate. The NLFM signal extends the frequency range of the flexural waves by compensating for the excitation intensity and yields information regarding the formation information, but it is not easy to obtain. The SLFM signal yields the same results as the NLFM signal and is easier to implement. Consequently, the dipole detection range expands and the S-wave velocity calculation accuracy improves.     

A finite element model for seismic response analysis of deformable rocking frames

A new finite element model to analyze the seismic response of deformable rocking bodies and rocking structures is presented. The model comprises a set of beam elements to represent the rocking body and zero‐length fiber cross‐section elements at the ends of the rocking body to represent the rocking surfaces. The energy dissipation during rocking motion is modeled using a Hilber–Hughes–Taylor numerically dissipative time step integration scheme. The model is verified through correct prediction of the horizontal and vertical displacements of a rigid rocking block and validated against the analytical Housner model solution for the rocking response of rigid bodies subjected to ground motion excitation. The proposed model is augmented by a dissipative model of the ground under the rocking surface to facilitate modeling of the rocking response of deformable bodies and structures. The augmented model is used to compute the overturning and uplift rocking response spectra for a deformable rocking frame structure to symmetric and anti‐symmetric Ricker pulse ground motion excitation. It is found that the deformability of the columns of a rocking frame does not jeopardize its stability under Ricker pulse ground motion excitation. In fact, there are cases where a deformable rocking frame is more stable than its rigid counterpart. Copyright © 2016 John Wiley & Sons, Ltd.     

PRINCIPLES AND APPLICATION OF MAXIMUM KURTOSIS PHASE ESTIMATION1

Methods of minimum entropy deconvolution (MED) try to take advantage of the non-Gaussian distribution of primary reflectivities in the design of deconvolution operators. Of these, Wiggins’(1978) original method performs as well as any in practice. However, we present examples to show that it does not provide a reliable means of deconvolving seismic data: its operators are not stable and, instead of whitening the data, they often band-pass filter it severely. The method could be more appropriately called maximum kurtosis deconvolution since the varimax norm it employs is really an estimate of kurtosis. Its poor performance is explained in terms of the relation between the kurtosis of a noisy band-limited seismic trace and the kurtosis of the underlying reflectivity sequence, and between the estimation errors in a maximum kurtosis operator and the data and design parameters. The scheme put forward by Fourmann in 1984, whereby the data are corrected by the phase rotation that maximizes their kurtosis, is a more practical method. This preserves the main attraction of MED, its potential for phase control, and leaves trace whitening and noise control to proven conventional methods. The correction can be determined without actually applying a whole series of phase shifts to the data. The application of the method is illustrated by means of practical and synthetic examples, and summarized by rules derived from theory. In particular, the signal-dominated bandwidth must exceed a threshold for the method to work at all and estimation of the phase correction requires a considerable amount of data. Kurtosis can estimate phase better than other norms that are misleadingly declared to be more efficient by theory based on full-band, noise-free data.     

Vibroseis deconvolution: A comparison of pre and post correlation vibroseis deconvolution data in real noisy data

Vibroseis is a source used commonly for inland seismic exploration. This non-destructive source is often used in urban areas with strong environmental noise. The main goal of seismic data processing is to increase the signal/noise ratio where a determinant step is deconvolution. Vibroseis seismic data do not meet the basic minimum-phase assumption for the application of spiking and predictive deconvolution, therefore various techniques, such as phase shift, are applied to the data, to be able to successfully perform deconvolution of vibroseis data.This work analyzes the application of deconvolution techniques before and after cross-correlation on a real data set acquired for high resolution prospection of deep aquifers. In particular, we compare pre-correlation spiking and predictive deconvolution with Wiener filtering and with post-correlation time variant spectral whitening deconvolution. The main result is that at small offsets, post cross-correlation spectral whitening deconvolution and pre-correlation spiking deconvolution yield comparable results, while for large offsets the best result is obtained by applying a pre-cross-correlation predictive deconvolution.     

An Approximation to Sparse-Spike Reflectivity Using the Gold Deconvolution Method

Wiener deconvolution is generally used to improve resolution of the seismic sections, although it has several important assumptions. I propose a new method named Gold deconvolution to obtain Earth’s sparse-spike reflectivity series. The method uses a recursive approach and requires the source waveform to be known, which is termed as Deterministic Gold deconvolution. In the case of the unknown wavelet, it is estimated from seismic data and the process is then termed as Statistical Gold deconvolution. In addition to the minimum phase, Gold deconvolution method also works for zero and mixed phase wavelets even on the noisy seismic data. The proposed method makes no assumption on the phase of the input wavelet, however, it needs the following assumptions to produce satisfactory results: (1) source waveform is known, if not, it should be estimated from seismic data, (2) source wavelet is stationary at least within a specified time gate, (3) input seismic data is zero offset and does not contain multiples, and (4) Earth consists of sparse spike reflectivity series. When applied in small time and space windows, the Gold deconvolution algorithm overcomes nonstationarity of the input wavelet. The algorithm uses several thousands of iterations, and generally a higher number of iterations produces better results. Since the wavelet is extracted from the seismogram itself for the Statistical Gold deconvolution case, the Gold deconvolution algorithm should be applied via constant-length windows both in time and space directions to overcome the nonstationarity of the wavelet in the input seismograms. The method can be extended into a two-dimensional case to obtain time-and-space dependent reflectivity, although I use one-dimensional Gold deconvolution in a trace-by-trace basis. The method is effective in areas where small-scale bright spots exist and it can also be used to locate thin reservoirs. Since the method produces better results for the Deterministic Gold deconvolution case, it can be used for the deterministic deconvolution of the data sets with known source waveforms such as land Vibroseis records and marine CHIRP systems.     

Shallow virtual source redatuming by multi‐dimensional deconvolution

Recently, new on‐shore acquisition designs have been presented with multi‐component sensors deployed in the shallow sub‐surface (20 m–60 m). Virtual source redatuming has been proposed for these data to compensate for surface statics and to enhance survey repeatability. In this paper, we investigate the feasibility of replacing the correlation‐based formalism that undergirds virtual source redatuming with multi‐dimensional deconvolution, offering various advantages such as the elimination of free‐surface multiples and the potential to improve virtual source repeatability. To allow for data‐driven calibration of the sensors and to improve robustness in cases with poor sensor spacing in the shallow sub‐surface (resulting in a relatively high wavenumber content), we propose a new workflow for this configuration. We assume a dense source sampling and target signals that arrive at near‐vertical propagation angles. First, the data are preconditioned by applying synthetic‐aperture‐source filters in the common receiver domain. Virtual source redatuming is carried out for the multi‐component recordings individually, followed by an intermediate deconvolution step. After this specific pre‐processing, we show that the downgoing and upgoing constituents of the wavefields can be separated without knowledge of the medium parameters, the source wavelet, or sensor characteristics. As a final step, free‐surface multiples can be eliminated by multi‐dimensional deconvolution of the upgoing fields with the downgoing fields.     

Non-minimum phase wavelet estimation by non-linear optimization of all-pass operators

Convolution of a minimum‐phase wavelet with an all‐pass wavelet provides a means of varying the phase of the minimum‐phase wavelet without affecting its amplitude spectrum. This observation leads to a parametrization of a mixed‐phase wavelet being obtained in terms of a minimum‐phase wavelet and an all‐pass operator. The Wiener–Levinson algorithm allows the minimum‐phase wavelet to be estimated from the data. It is known that the fourth‐order cumulant preserves the phase information of the wavelet, provided that the underlying reflectivity sequence is a non‐Gaussian, independent and identically distributed process. This property is used to estimate the all‐pass operator from the data that have been whitened by the deconvolution of the estimated minimum‐phase wavelet. Wavelet estimation based on a cumulant‐matching technique is dependent on the bandwidth‐to‐central‐frequency ratio of the data. For the cumulants to be sensitive to the phase signatures, it is imperative that the ratio of bandwidth to central frequency is at least greater than one, and preferably close to two. Pre‐whitening of the data with the estimated minimum‐phase wavelet helps to increase the bandwidth, resulting in a more favourable bandwidth‐to‐central‐frequency ratio. The proposed technique makes use of this property to estimate the all‐pass wavelet from the prewhitened data. The paper also compares the results obtained from both prewhitened and non‐whitened data. The results show that the use of prewhitened data leads to a significant improvement in the estimation of the mixed‐phase wavelet when the data are severely band‐limited. The proposed algorithm was further tested on real data, followed by a test involving the introduction of a 90°‐phase‐rotated wavelet and then recovery of the wavelet. The test was successful.     

Rough seas and statistical deconvolution

The rough‐sea reflection‐response varies (1) along the streamer (2) from shot to shot and (3) with time along the seismic trace. The resulting error in seismic data can be important for time‐lapse imaging. One potential way of reducing the rough‐sea receiver error is to use conventional statistical deconvolution, but special care is needed in the choice of the design and application windows. The well‐known deconvolution problem associated with the non‐whiteness of the reflection series is exacerbated by the requirement of an unusually short design window – a requirement that is imposed by the non‐stationary nature of the rough‐sea receiver wavelet. For a synthetic rough‐sea data set, with a white 1D reflection series, the design window needs to be about 1000 ms long, with an application window about 400 ms long, centred within the design window. Although such a short design window allows the deconvolution operator to follow the time‐variation of the rough‐sea wavelet, it is likely to be too short to prevent the non‐whiteness of the geology from corrupting the operator when it is used on real data. If finely spatial‐sampled traces are available from the streamer, the design window can be extended to neighbouring traces, making use of the spatial correlations of the rough‐sea wavelet. For this ‘wave‐following’ approach to be fruitful, the wind (and hence the dominant wave direction) needs to be roughly along the line of the streamer.     

Nonparametric estimation of wave dispersion in high‐rise buildings by seismic interferometry

Interferometric identification and health monitoring of high‐rise buildings has been gaining increasing interest in recent years. The wave dispersion in the structure has been largely ignored in these efforts but needs to be considered to further develop these methods. In this paper, (i) the goodness of estimation of vertical wave velocity in buildings, as function of frequency, by two nonparametric interferometric techniques is examined, using realistic fixed‐base Timoshenko beam benchmark models. Such models are convenient because the variation of phase and group velocities with frequency can be derived theoretically. The models are those of the NS and EW responses of Millikan Library. One of the techniques, deconvolution interferometry, estimates the phase velocity on a frequency band from phase difference between motions at two locations in the structure, while the other one estimates it approximately at the resonant frequencies based on standing wave patterns. The paper also (ii) examines the modeling error in wave velocity profiles identified by fitting layered shear beam in broader band impulse response functions of buildings with significant bending flexibility. This error may affect inferences on the spatial distribution of damage from detected changes in such velocity profiles. Copyright © 2014 John Wiley & Sons, Ltd.     

Multiridge Euler deconvolution

Potential field interpretation can be carried out using multiscale methods. This class of methods analyses a multiscale data set, which is built by upward continuation of the original data to a number of altitudes conveniently chosen. Euler deconvolution can be cast into this multiscale environment by analysing data along ridges of potential fields, e.g., at those points along lines across scales where the field or its horizontal or vertical derivative respectively is zero. Previous work has shown that Euler equations are notably simplified along any of these ridges. Since a given anomaly may generate one or more ridges we describe in this paper how Euler deconvolution may be used to jointly invert data along all of them, so performing a multiridge Euler deconvolution. The method enjoys the stable and high‐resolution properties of multiscale methods, due to the composite upward continuation/vertical differentiation filter used. Such a physically‐based field transformation can have a positive effect on reducing both high‐wavenumber noise and interference or regional field effects. Multiridge Euler deconvolution can also be applied to the modulus of an analytic signal, gravity/magnetic gradient tensor components or Hilbert transform components. The advantages of using multiridge Euler deconvolution compared to single ridge Euler deconvolution include improved solution clustering, increased number of solutions, improvement of accuracy of the results obtainable from some types of ridges and greater ease in the selection of ridges to invert. The multiscale approach is particularly well suited to deal with non‐ideal sources. In these cases, our strategy is to find the optimal combination of upward continuation altitude range and data differentiation order, such that the field could be sensed as approximately homogeneous and then characterized by a structural index close to an integer value. This allows us to estimate depths related to the top or the centre of the structure.     

Avoidable Euler Errors – the use and abuse of Euler deconvolution applied to potential fields

Window‐based Euler deconvolution is commonly applied to magnetic and sometimes to gravity interpretation problems. For the deconvolution to be geologically meaningful, care must be taken to choose parameters properly. The following proposed process design rules are based partly on mathematical analysis and partly on experience.

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Retrieving virtual reflection responses at drill‐bit positions using seismic interferometry with drill‐bit noise

In the field of seismic interferometry, researchers have retrieved surface waves and body waves by cross‐correlating recordings of uncorrelated noise sources to extract useful subsurface information. The retrieved wavefields in most applications are between receivers. When the positions of the noise sources are known, inter‐source interferometry can be applied to retrieve the wavefields between sources, thus turning sources into virtual receivers. Previous applications of this form of interferometry assume impulsive point sources or transient sources with similar signatures. We investigate the requirements of applying inter‐source seismic interferometry using non‐transient noise sources with known positions to retrieve reflection responses at those positions and show the results using synthetic drilling noise as source. We show that, if pilot signals (estimates of the drill‐bit signals) are not available, it is required that the drill‐bit signals are the same and that the phases of the virtual reflections at drill‐bit positions can be retrieved by deconvolution interferometry or by cross‐coherence interferometry. Further, for this case, classic interferometry by cross‐correlation can be used if the source power spectrum can be estimated. If pilot signals are available, virtual reflection responses can be obtained by first using standard seismic‐while‐drilling processing techniques such as pilot cross‐correlation and pilot deconvolution to remove the drill‐bit signatures in the data and then applying cross‐correlation interferometry. Therefore, provided that pilot signals are reliable, drill‐bit data can be redatumed from surface to borehole depths using this inter‐source interferometry approach without any velocity information of the medium, and we show that a well‐positioned image below the borehole can be obtained using interferometrically redatumed reflection responses with just a simple velocity model. We discuss some of the practical hurdles that restrict the application of the proposed method offshore.     

基于广义高斯分布的地震盲反褶积方法研究

Bussgang算法是针对褶积盲源分离问题提出的,本文将其用于地震盲反褶积处理.由于广义高斯概率密度函数具有逼近任意概率密度函数的能力,从反射系数序列的统计特征出发,引入广义高斯分布来体现反射系数序列超高斯分布特征.依据反射系数序列的统计特征和Bussgang算法原理,建立以Kullback-Leibler距离为非高斯性度量的目标函数,并导出算法中涉及到的无记忆非线性函数,最终实现了地震盲反褶积.模型试算和实际资料处理结果表明,该方法能较好地适应非最小相位系统,能够同时实现地震子波和反射系数估计,有效地提高地震资料分辨率.     

地震子波处理的二步法反褶积方法研究

针对玛湖斜坡区三块三维地震资料和赛汉塔拉凹陷二块三维地震资料连片处理中的特点,结合地质任务和处理目标要求,提出了地震数据连片处理中的地震子波处理的方法.该方法主要体现了两次反褶积,一次是采用地表一致性反褶积,将不同震源的频带拓宽到一个标准上;再一次采用相位校正反褶积,将不同震源的数据校正到相同相位上.为了保证提取的相位校正反褶积算子稳定,采用叠后地震道提取（主要考虑到叠后地震道信噪比高,算子稳定性强）,然后将该算子应用到叠前地震道,进行相位校正.