Ground-penetrating radar (GPR) surveys were acquired of rocks on the highly fractured summit of Turtle Mountain in Canada. In 1903 a disastrous rock slide occurred at Turtle Mountain and it still poses a geologic hazard. Dips, shapes, and penetration depths of fractures are important parameters in slope-stability analysis. Determination of fracture orientation at Turtle Mountain has been based mostly on areal geologic mapping and, most recently, on data collected from boreholes. The purpose of GPR surveys was to test, confirm, and extend information about fractures and bedding planes. Data acquisition was complicated by the rough terrain; because slopes are steep and uneven. This also complicated analysis of the data. Measurement of in situ velocity — an important value for migration — was impossible. Instead, data were migrated with different velocities and data results were chosen that were considered to be reasonable. Analysis and interpretation of the data, resulted in confirmation and extension of the a priori information on orientations of fractures and bedding planes at Turtle Mountain. Despite the rough terrain and highly fractured rock mass, GPR surveys provide reliable information about the shapes and density of fractures — information important for slope-stability evaluation. The most reliable migration results obtained for velocities were considerably less than the standard velocities recorded for limestone, the dominant lithofacies at Turtle Mountain. We interpret this observation as an indicator of water within the rock. However, thorough investigation of this conclusion remains a project for future work.

Resolving thinner layers and focusing layer boundaries better in inverted seismic sections are important challenges in exploration and production seismology to better identify a potential drilling target. Many seismic inversion methods are based on a least-squares optimization approach that can intrinsically lead to unfocused transitions between adjacent layers. A Bayesian seismic amplitude variation with angle (AVA) inversion algorithm forms sharper boundaries between layers when enforcing sparseness in the vertical gradients of the inversion results. The underlying principle is similar to high-resolution processing algorithms and has been adapted from digital-image-sharpening algorithms. We have investigated the Cauchy and Laplace statistical distributions for their potential to improve contrasts betweenlayers. An inversion algorithm is derived statistically from Bayes’ theorem and results in a nonlinear problem that requires an iterative solution approach. Bayesian inversions require knowledge of certain statistical properties of the model we want to invert for. The blocky inversion method requires an additional parameter besides the usual properties for a multivariate covariance matrix, which we can estimate from borehole data. Tests on synthetic and field data show that the blocky inversion algorithm can detect and enhance layer boundaries in seismic inversions by effectively suppressing side lobes. The analysis of the synthetic data suggests that the Laplace constraint performs more reliably, whereas the Cauchy constraint may not find the optimum solution by converging to a local minimum of the cost function and thereby introducing some numerical artifacts.

No abstract

No abstract