Search

The incorporation of hard data in geostatistical modeling is crucial for enhancing the accuracy of interpolating or stochastically estimating subsurface spatial features. The hard data at specified points in the model domain serve as a guide in optimizing the unknown parameters to follow the patterns of the hard data. Recently, a novel approach to solving hydrogeologic/reservoir modeling problems has emerged by using deep generative models, specifically generative adversarial networks (GANs), to generate realistic and diverse images of channelized aquifers. This subsequently can be coupled with inverse models to solve parameter estimation problems. This study focused on using an improved GAN, called a progressive growing generative adversarial network (PGGAN), conditioned with hard data to perform parameter estimation of complex facies models by coupling an ensemble smoother with multiple data assimilation (ES-MDA). First, the PGGAN was trained to an image with 128 × 128 resolution. The trained PGGAN was used to generate hydraulic conductivity fields when fed an ensemble of latent variables and hard data. The ES-MDA then was used to update the latent variable with the help of hydraulic head data obtained from the groundwater model. The approach was tested on synthetic hydraulic conductivity data. Results show that this approach was able to perform efficient estimation of an unknown facies model domain. Additionally, the proposed method was applied to a different test case of a facies model exhibiting different statistical characteristics. The results were satisfactory, demonstrating that the method is not constrained to the particular hydraulic conductivity fields introduced in the generator's training. • Coupling deep learning with data assimilation for groundwater modeling. • Both hard data and soft data are conditioned. • Synthetic examples are used to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]

This paper examines the effect of the inclusion of non-interest income on efficiency and economies of scale of Ghanaian banks in a two-stage analysis. The data envelopment analysis technique is employed to estimate efficiency scores with and without non-interest income of 26 Ghanaian banks from 2003 to 2011 in the first-stage analysis. In the second stage, a truncated bootstrapped regression is estimated to examine the effect of contextual variables on bootstrapped efficiency scores. The findings indicate that the exclusion of non-interest income as output variable leads to the under-estimation of efficiency scores. From the second-stage regression analysis, we find a curve-linear relationship between bank size and efficiency to suggest that bank efficiency increases as size also increase due to economies of scale but only up to an optimal point after which inefficiency sets. Market concentration, leverage, and loan loss provisions are also identified as the other significant determinants of efficiency. Policy implications for improving bank efficiency are discussed. [ABSTRACT FROM PUBLISHER]

The global economic volatility has given rise to diverse firm operational strategies including internationalization through subsidiary establishment. This has aroused the interest of scholars to the study of multinational firm performance. The study seeks to ascertain the factors that contribute to the performance of multinational subsidiary banks in Ghana. Using an unbalanced random effects panel regression estimation following the Hausman specification test, the study found that increasing bank size does not necessarily lead to performance. As it stands, the Multinational Subsidiary (MNS) banks under perform as they increase in size. It was further noted that the banks are inefficient and so shift their costs and risks to customers in the form of high interest charges on credit. Notwithstanding, it appears that older MNSs perform better than the relatively younger ones. The study found that leadership origin does not have any significant impact on bank performance. Evidence was, however, found about the significant influence of the host country economic conditions on MNS performance. [ABSTRACT FROM AUTHOR]