Exploration Geophysics - Volume 52, Issue 5, 2021

A novel method for Q (quality factor Q) estimation is proposed based on crosscorrelation function and S-transform (CRST). We use the S-transform to analyse the time–frequency spectra of the crosscorrelation coefficients and extract the amplitude spectra corresponding to the maximum energy time in time–frequency spectra. The Q can be estimated using the spectra ratio based on the linear relationship between spectral ratio and frequency. Meanwhile, two time window factors are added to the Gaussian window function in S-transform to make the S-transform applicable for Q estimation. Firstly, through numerical tests and standard sample experiments, the feasibility and noise immunity of the CRST method are studied. Secondly, the applicability and stability of this method are studied using artificial samples with different Q. Finally, the stability and accuracy of the CRST method are analysed by comparing with the conventional spectrum ratio method (SR) through rock samples. The experimental results show that the Q of samples can be obtained by using the time–frequency spectrum information of the crosscorrelation coefficient. The proposed time window factors can effectively eliminate the errors caused by the conventional Gaussian window function, which the relative errors can reach about 40%. The CRST can reduce the effect of the frequency bandwidth for regression analysis. The new method can ensure that the maximum error of different Q factors (Q > 15) is about 5%. Compared with the conventional spectrum ratio method, the CRST method not only has better noise immunity, but also has higher stability and accuracy.

Frequency-dependent anisotropy can be used for detection and evaluation of fractured reservoirs. In this study, three numerical models of fractured reservoirs are designed to study the frequency dependence of amplitude variation with azimuth (AVAZ). Here, two factors, which can make the AVAZ responses frequency dependent, are taken into consideration, the fluid flow and the tuning effect. Model I is used for the analysis of frequency-dependent AVAZ induced only by the fluid flow. Model II is used to study frequency-dependent AVAZ induced only by the tuning effect. For Model III, these two factors are in the presence at the same time. In this study, the fluid flow is simulated by Chapman’s multi-scale rock physics model, and the azimuthal seismic gathers are generated by propagating matrix method. Spectral decomposition is implemented based on the smoothed pseudo Wigner-Ville distribution, so that the frequency dependence for the AVAZ gathers can be discussed. It is demonstrated that both the fluid flow and the tuning effect can cause significant frequency-dependent AVAZ, and the frequency-dependent AVAZ is helpful for fluid identification and anisotropic thin bed detection. Besides, for both factors, the incident angle can influence the frequency-dependent AVAZ responses, accordingly azimuthal seismic gathers with large incident angles are necessary for field data application.

Pre-stack Q migration can eliminate the absorption effect and accurately image underground structures, which is conducive to subsequent reservoir interpretation and hydrocarbon prediction. However, the instability of Q migration amplifies high-frequency noise, which seriously reduces the imaging quality. To solve the instability problem, this paper studies the stability conditions for Q migration in the frequency domain. The generalised standard linear solid (GSLS) model can well describe the attenuation characteristics of underground media by combining different basic rheological models. Based on the Von Neumann stability analysis for the finite difference scheme combined with parameter settings in the GSLS model, this paper focuses on the stability of frequency domain Q migration and theoretically deduces the stability conditions suitable for the GSLS model. The given stability conditions can be directly implemented in the frequency domain Q migration process and constrain only the maximum reference angle frequency rather than the wave field frequencies, which avoids the Gibbs effect like the high-frequency cut method. In addition, the stability conditions can be adjusted adaptively with the computed frequencies, without the problem of over- or insufficient compensation. The model and practical application indicate that based on the GSLS model and its stability conditions, the attenuation effect can be compensated stably, lost energy and frequencies can be recovered, and high-quality imaging results are obtained.

Geomagnetic measurements detect the superposition of both regional and residual anomalies. Regional anomalies are caused by deep geological structures, while the residual ones are due to shallow sources. The presence of regional anomalies complicates the interpretation of magnetic data; to this end, a variety of techniques have been proposed for regional-residual anomaly separation. Most of them are based on Fourier transforms. Although valid results can be obtained from these methods, they have some limitations, such as requiring basic knowledge on approximate cut-off frequencies. This study focuses on spatial filtering employing factorial kriging (FK) for geomagnetic anomaly separation. FK employs geostatistical filtering based on spatial dependencies of the data. In contrast to the upward continuation, which is a trial and error method and needs to be checked with different distances of continuations, the utilisation of FK on synthetic data revealed that FK could automatically perform the separation and also helps to find the optimum distance for the upward continuation method. Following experiments with a synthetic model, the method was applied to real magnetic data of Bashmaq in northwestern Iran. Applying FK on a complicated real case study showed that it could separate the regional and residual components satisfactorily. Furthermore, results from the proposed approach have been compared against the Gaussian Separation Method (GSM). The results are compared to borehole information. The lithological columns are presented as part of the geology investigation. As demonstrated in the case study, the FK method has proven to be an accurate tool to better determine the mineralisation zones compared to GSM, which, in turn, facilitates the interpretation.

To improve the computational efficiency of reverse-time migration (RTM) for vertically transverse isotropic (VTI) media, various acoustic approximations of the elastic wave equations have been presented. Among these, the pseudo-acoustic wave equation, which combines differential and scalar operators, has the advantage that it does not produce shear wave artefacts. In this study, we investigate the feasibility of pseudo-acoustic anisotropic RTM (PA-RTM) for the analysis of synthetic and observed ocean-bottom cable (OBC) datasets of the Volve oil field in the North Sea. To analyse the influence of anisotropic parameters on RTM images and the sensitivity of data components to seismic anisotropy, we perform PA-RTM using synthetic data by incorporating various background models, as well as pressure wavefields and vertical particle acceleration. The synthetic experiments demonstrate that the anisotropic parameter ε plays an important role, whereas δ is negligible in PA-RTM for VTI media, and that vertical particle acceleration is less affected by seismic anisotropy than the pressure wavefields. Our experiments using observed data show that reflectors are better imaged by PA-RTM than by isotropic-RTM, particularly near the reservoir below the strongly anisotropic region, which is supported by angle-domain common image gathers. These results indicate that PA-RTM using only the vertical component is a suitable option for VTI media when insufficient seismic anisotropy data are available.

The industry has widely accepted that AEM data are more frequently affected by induced polarisation (IP) effects than previously acknowledged. However, we still lack a clear understanding of how much, where, and when IP is present. Full modelling of airborne IP (AIP) is time and computationally intensive. As an alternative, we derive a novel tool, the “AIP scanner”, based on a combination of extensive data – space and limited model – space analysis. The basic assumption is that failing to model IP, when present, increases AEM inversion misfits. Several data space metrics, on negatives and on decay rates, are correlated to misfit from inversion ignoring IP over a small portion of the dataset. The correlation is used to predict the presence of AIP over the entire dataset. The last step is a recursive comparison between the map of predicted AIP and the results of full AIP modelling over a few selected lines. The resulting “AIP scanner” map indicates areas of definite AIP effects, areas possibly affected, and areas probably unaffected by AIP. Such maps are extremely useful tools for the exploration industry wishing to leverage AEM data information content. A case study from South Australia illustrates the scanner results relative to mapped geology and demonstrates the relationships between chargeability and the often unpredictable consequences for the resistivity inversions.

In this paper, we introduce a fast iterative adaptive approach for the reconstruction of 3D seismic data with randomly missing traces. Our method starts by transforming 3D seismic volume to 2D harmonic signal for each frequency slice, and then the power spectrum of the frequency slice is iteratively estimated by a weighted least square fitting criterion. The missing data can be recovered with the obtained spectral estimate using a linear minimum mean-squared error estimator. However, estimation of the power spectrum depends on matrix-vector multiplications for each iteration that leads to high computation complexity when the data are large scale and high dimension. To solve the associated spectrum estimation problem above, a fast iterative adaptive scheme is adopted by utilising 2D fast discrete Fourier transform, which makes use of the block-Vandermonde structure and the 2D Fourier property of the steering matrix. Simulation experiments on synthetic and field data verify the effectiveness of the proposed algorithm. Compared with other reconstruction methods based on frequency slice, such as minimum-weighted norm interpolation approach, multi-channel singular spectrum analysis, and Curvelet transform method, the proposed method achieves better reconstruction performance and low computational complexity.

Local shear wave (S-wave) velocity structure and sedimentary cover thickness are essential parameters that control local amplification of ground motion and associated seismic hazard during earthquakes. Recently, analysis of environmental background noise from individual stations has been used to estimate horizontal-to-vertical spectral ratio (H/V) curves. In the present study, we inverted H/V curves using environmental noise immediately prior to earthquake P-wave arrival to retrieve S-wave velocity (or shear velocity, VS) profiles at four temporary seismic monitoring stations (YDF, YDS, YDU, and YDD) near the Yedang Reservoir Dam. In the first step, we used a random search algorithm to constrain the subvolume of the parameter space (S-wave velocity structure) where the minimum of the misfit was located. In the second step, we independently applied two non-linear processes (a Monte Carlo sampling algorithm and a simulated annealing algorithm) to force the inversion towards an optimal solution, using the minimum misfit model determined in the first step as an initial estimate; we then compared the results. The feasibility and effectiveness of this two-step approach were verified by inversion of H/V curves for seismic noise recorded at four seismic monitoring stations near the Yedang Reservoir Dam. Borehole and topographical data from the four stations provided a well-constrained estimation of the local shear wave profile. Comparisons of synthetic and observed H/V curves showed that combining the two inversion algorithms efficiently overcame the extreme non-linearity of the inversion problem and provided a good resolution of S-wave structures at the Yedang Reservoir Dam. The S-wave velocity profile at the YDF station, which is situated on fresh, uniform bedrock, ranged from ∼2300 to 2700 m/s, which was consistent with the borehole data. Both the YDU station (which exhibited fundamental and first-order resonance frequency harmonics) and the YDD station (which exhibited fundamental, first-, and second-order resonance harmonics) showed significant stepwise velocity profiles for the entire depth. Thus, the entire depth can be regarded as three layers, including bedrock. At the YDS station, located near the dam spillway, the uppermost layer showed the lowest VS values among all four stations (∼726–728 m/s), suggesting that the concrete dam body is well weathered or partially infiltrated by water. Because of these low VS values of the concrete dam body, the Yedang Dam may require a full safety diagnosis in the near future. Analysis of the velocity profiles of all four stations near the Yedang Dam indicated that the bedrock, composed of fresh rock (∼VS > 2700 m/s), varies greatly in depth among stations.