ALBERT

Description: Seismic reflections from the oceanic water column contain information about ocean temperature and salinity. Even though seismic waveform inversion is effective for studying oceanic structure, its application is limited in the absence of sufficient direct temperature/velocity measurements. Here, two methods are developed to invert pre-stack seismic waveform data for temperature and salinity when independent temperature/velocity data are sparse or unavailable, allowing estimation of water-column temperature/salinity from any marine seismic reflection data set. The first method combines a genetic algorithm (GA) with non-linear least squares inversion, and the second method is a parallel implementation of a GA. Both methods produce results to an accuracy between 0 and 0.1 °C in estimating temperature when applied to a field data set from the South China Sea. Although the second approach is superior, it is computationally demanding and requires large parallel computers. The first approach runs extremely fast on parallel computers and can even be run on much smaller machines to provide results in a reasonable runtime. While both methods are viable choices for estimating temperature and salinity, the choice of one over the other will largely depend upon the available computational resources and the time frame within which the inversion needs to be completed.

Description: Consideration of azimuthal anisotropy, at least to an orthorhombic symmetry is important in exploring the naturally fractured and unconventional hydrocarbon reservoirs. Full waveform inversion of multicomponent seismic data can, in principle, provide more robust estimates of subsurface elastic parameters and density than the inversion of single component ( P wave) seismic data. In addition, azimuthally dependent anisotropy can only be resolved by carefully studying the multicomponent seismic displacement data acquired and processed along different azimuths. Such an analysis needs an inversion algorithm capable of simultaneously optimizing multiple objectives, one for each data component along each azimuth. These multicomponent and multi-azimuthal seismic inversions are non-linear with non-unique solutions; it is therefore appropriate to treat the objectives as a vector and simultaneously optimize each of its components such that the optimal set of solutions could be obtained. The fast non-dominated sorting genetic algorithm (NSGA II) is a robust stochastic global search method capable of handling multiple objectives, but its computational expense increases with increasing number of objectives and the number of model parameters to be inverted for. In addition, an accurate extraction of subsurface azimuthal anisotropy requires multicomponent seismic data acquired at a fine spatial resolution along many source-to-receiver azimuths. Because routine acquisition of such data is prohibitively expensive, they are typically available along two or at most three azimuthal orientations at a spatial resolution where such an inversion could be applied. This paper proposes a novel multi-objective methodology using a parallelized version of NSGA II for waveform inversion of multicomponent seismic displacement data along two azimuths. By scaling the objectives prior to ranking, redefining the crowding distance as functions of the scaled objective and the model spaces, and varying the crossover and mutation parameters over generations, the proposed methodology is also an improvement of the original NSGA II in overall computational efficiency, preservation of population diversity, and rapid sampling of the model space. By first inverting the near-offset pre-stack data for the background isotropic properties and obtaining constraints on the vertical velocities, followed by an inversion of the long-offset data, it is demonstrated that the proposed method can reliably estimate density and azimuthally anisotropic subsurface properties up to the complexity of an orthorhombic symmetry on noisy synthetic data computed from a model based on a real well log under an assumption of 1-D subsurface layers where the ambiguities between lateral heterogeneity and anisotropy could be ignored. In addition, a practical way to approximately compute the uncertainty values in the derived parameters using the method is also demonstrated.

Description: Prostate cancer is the most common non-skin cancer in males, with a ~1.5–2-fold higher incidence in African American men when compared with whites. Epidemiologic evidence supports a large heritable contribution to prostate cancer, with over 100 susceptibility loci identified to date that can explain ~33% of the familial risk. To explore the contribution of both rare and common variation in coding regions to prostate cancer risk, we sequenced the exomes of 2165 prostate cancer cases and 2034 controls of African ancestry at a mean coverage of 10.1 x . We identified 395 220 coding variants down to 0.05% frequency [57% non-synonymous (NS), 42% synonymous and 1% gain or loss of stop codon or splice site variant] in 16 751 genes with the strongest associations observed in SPARCL1 on 4q22.1 (rs13051, Ala49Asp , OR = 0.78, P = 1.8 x 10 –6 ) and PTPRR on 12q15 (rs73341069, Val239Ile , OR = 1.62, P = 2.5 x 10 –5 ). In gene-level testing, the two most significant genes were C1orf100 ( P = 2.2 x 10 –4 ) and GORAB ( P = 2.3 x 10 –4 ). We did not observe exome-wide significant associations (after correcting for multiple hypothesis testing) in single variant or gene-level testing in the overall case–control or case–case analyses of disease aggressiveness. In this first whole-exome sequencing study of prostate cancer, our findings do not provide strong support for the hypothesis that NS coding variants down to 0.5–1.0% frequency have large effects on prostate cancer risk in men of African ancestry. Higher-coverage sequencing efforts in larger samples will be needed to study rarer variants with smaller effect sizes associated with prostate cancer risk.

Description: 〈span〉〈div〉Summary〈/div〉We developed a multi-objective optimization method for inverting marine controlled source electromagnetic data using a fast-non-dominated sorting genetic algorithm. Deterministic methods for inverting electromagnetic data rely on selecting weighting parameters to balance the data misfit with the model roughness and result in a single solution which do not provide means to assess the non-uniqueness associated with the inversion. Here, we propose a robust stochastic global search method that considers the objective as a two-component vector and simultaneously minimizes both components: data misfit and model roughness. By providing an estimate of the entire set of the Pareto-optimal solutions, the method allows a better assessment of non-uniqueness than deterministic methods. Since the computational expense of the method increases as the number of objectives and model parameters increase, we parallelized our algorithm to speed up the forward modeling calculations. Applying our inversion to noisy synthetic data sets generated from horizontally stratified earth models for both isotropic and anisotropic assumptions and for different measurement configurations, we demonstrate the accuracy of our method. By comparing the results of our inversion with the regularized genetic algorithm, we also demonstrate the necessity of casting this problem as a multi-objective optimization for a better assessment of uncertainty as compared to a scalar objective optimization method.〈/span〉