Exploration Geophysics - Volume 51, Issue 5, 2020

The staggered grid finite difference (SGFD) method has been widely utilised in seismic forward modelling. The accuracy of forward modelling results has a great influence on the results of subsequent seismic imaging and seismic inversion. Numerical dispersion is one of the inherent problems in the SGFD method. To suppress dispersion, we here introduce the truncated window function method, which can get optimised finite difference operators after truncating spatial convolution series of the pseudo-spectral method. The empirical criteria of window functions are the main lobe width that should be as narrow as possible and the side lobe attenuation should be as large as possible. According to these criteria, we propose the combined window function method based on the Gaussian window and Hanning window. The result of the amplitude spectrum response shows that the new combined window has narrower main lobe and greater side lobe attenuation, which results in higher accuracy in numerical simulations with SGFD operators. Numerical tests also illustrate the effectiveness of this new method.

Many oil fields in the world are continental clastic facies lake basins, which develop many thin interbedded reservoirs. Conventional AVO inversion methods based on Zoeppritz equation are not suitable for the identification and characterisation of such reservoirs. In this study, we propose a new inversion method based on the vectorised reflectivity method with blockiness constraint in order to obtain the inversion results with high accuracy and resolution. The vectorised reflectivity method can provide the 1D analytical solution of elastic wave equation, and simulate full-wave field. Therefore, it overcomes the problems caused by internal multiples, converted waves, transmission loss, and reservoir thickness in the thin interbedded reservoirs. Applications of the synthetic data and field data demonstrate that, compared with the conventional method based on Zoeppritz equation, the proposed method shows advantages in estimating the elastic parameters of the thin interbedded reservoirs. In addition, the inversion of blocky model and logging data demonstrate that the differential Laplace constraint gives higher resolution and better numerical stability than Gaussian and Cauchy constraints. The computational efficiency and high accuracy that the proposed approach provides are expected to be exploited in characterising thin interbedded reservoirs in many fields.

Potential field data is commonly enhanced in various ways to extract detail about source locations and/or depths. Methods which use higher order derivatives are naturally more sensitive to noise than those which use lower order derivatives. Pseudo-derivatives combine the amplitude of one order of derivative with the phase of another, which can have various advantages. It is demonstrated here how pseudo-derivatives of different orders can be combined to produce an modified enhanced horizontal derivative filter which is less sensitive to noise while still retaining detail in the result. The method is demonstrated on synthetic data and on potential field data from Southern Africa.

Airborne geophysics provides one of the most relevant data for uranium exploration. However, the application of radiometric surveys is decreasing considerably as the depth of exploration is increasing. Notwithstanding, there is still a large potential for radiometric data, especially using recent data processing techniques such as machine learning methods. In this work, we propose a new method to detect uranium anomalies through regression using the Random Forest machine learning algorithm (RF). The RF regression allows combining airborne geophysical data to predict the expected uranium content, which represents the uranium content generated by environmental effects such as lithology and pedogenesis. Therefore, the deviation (Ud) between the measured uranium and the expected uranium represents the secondary effects such as weathering, soil alteration, hydrothermal alteration or mineralisation process. We evaluated the relevance of the geophysical parameters proposed by previous authors in the prediction of the expected uranium (thorium, thorium potassium ratio, uranium potassium ratio, and Total Gradient Amplitude). Randomly selecting only 10% of the database as training data, we estimate the expected uranium with an R² = 0.99 concerning the measured uranium. To assess the reliability of the Ud anomalies, we employed the proposed methodology in the Carajás Mineral Province (CMP), Brazil. In the CPM, the Ud anomalies showed a clear correlation with the several Iron Oxide Copper–Gold deposits (IOCG) and some IOCG-related and granite-related prospects. In situ measurements with a portable gamma-ray spectrometer in the Salobo mine supported the uranium anomalies. The Ud map also highlighted contrasts within granites that correlate with previously reported granitic facies. Therefore, the Ud map generated by RF regression is useful in setting exploration targets for conventional and unconventional uranium resources, as well as in high-detail granitic facies mapping.

Time lapse seismic is a vital component of the monitoring and verification of virtually any geosequestration project. Stage 2C of the CO2CRC Otway project is designed to test the limits of land 3D time-lapse (4D) seismic in the monitoring of small-scale CO2 injection. The 4D seismic monitoring program of Stage 2C of the Otway Project includes a baseline survey (BL); three monitor surveys (M1–M3) obtained during the injection of CO2-rich gas into the saline aquifer at a depth of 1.5 km at the injection intervals of 5, 10, and 15 kt; and two post-injection surveys (M4 and M5), acquired one and two years after the end of injection. Reliable time-lapse changes among the 4D surveys were obtained using a buried receiver array. Analysis of the seismic cubes and attributes demonstrates that during and after injection the plume spreads laterally only in southeastern direction. The results demonstrate strong time-lapse signal corresponding to the injection level and the absence of any observed leakage into the overburden. The plume evolution is reliably observed and traced from M1 to M5 surveys with most of the changes occurring between the BL and M4 surveys.

Fine-scale thermohaline structure within ocean column can be mapped seismically in the Kuroshio Current, off the Muroto Peninsula of Shikoku Island, Japan. In this paper, we present the application of automatic sound speed picking analysis to the multi-channel seismic reflection data acquired in a different period to estimate time-lapse sound speed distribution across the Kuroshio Current. This method is based on an optimal velocity trajectory solving by the eikonal equation with a finite-difference algorithm. In contrast to the seismic inversion technique, this automatic analysis enables us to obtain contrast sound speed profiles without heavy dependency on sound speed or temperature data directly measured at discrete locations. As a result, this method can visualise sound speed profiles of fine-scale thermohaline structure developed at interleaving or diapycnal mixing processes of different water masses in the Kuroshio Current. The images of all profiles mapped from automatic sound speed analysis distinguish water masses and their fine-scale internal structure such as cold and warm water eddies, thermohaline staircases and internal waves revealing acoustic contrasts at interfaces across where sound speed and temperature change. Applying our approach for individual seismic line acquired in different time-steps for 3D seismic data can provide time–space variant images of fine-scale thermohaline structure for studies of oceanographic processes as well as large-scale ocean current and climate systems.

In seismic exploration, the dominant applications are still based on the acoustic wave assumption because of the simplicity and computational efficiency. Compared to the acoustic wave equation, the elastic wave simulation is more accurate, especially for land data, to characterise the elastic property of the real earth, but it is also far more expensive in terms of the computational cost. The traditional way to solve the first order velocity-stress elastic wave equation is based on a second order staggered grid time domain finite difference stencil, and this stencil is expensive because the time step is usually very small due to the limitation the stability condition and numerical dispersion relations. When the fourth order Lax-Wendroff stencil is applied, the time step could be larger but the computational cost triples that of the second order stencil for each time step. To improve the efficiency of the Lax-Wendroff stencil, we rewrite the first order velocity-stress equations into a combination of second order and zeroth order temporal derivative equations. When the second order stencil is applied to the new equation, there is no benefit compared to the original equation; when the fourth order Lax-Wendroff stencil is applied, the computational cost only doubles for each time step, which is 33% faster than the Lax-Wendroff stencil applied on the original equation. Both memory consumption and computational cost are compared among the four different numerical stencils, and a staggered grid Pseudo Spectral method is applied to prevent the numerical dispersion for computing the spatial derivatives. Numerical tests are performed on a 2D homogeneous media and a 2D line of the SEAM II Arid model. The results suggest that although the memory cost is increased, the Lax-Wendroff stencil applied to the second order temporal derivative equations is the optimal option in terms of the both accuracy and computational cost.

We have developed a novel technique for airborne time-domain electromagnetic data levelling using inequality-constrained polynomial fitting. In the approach, channel data of a certain survey area are levelled under the constraint of off-time attenuation rule. Based on the correlations between flight lines, Huang has proposed that level errors could be fitted by the differences between the flight line data and its reference line data. Thus, the level errors represented as polynomial functions are determined by the least-squares algorithm subjecting to the constraint of the adjacent channel. In addition, only the survey area data in the absence of anomalous features are used in the levelling process to avoid fake level problem. We validate the levelling method by applying it to synthetic data and field data, comparing with line-to-line correlation levelling.