Surrogate-based parameter inference in debris flow model

This work tackles the problem of calibrating the unknown parameters of a debris flow model with the drawback that the information regarding the experimental data treatment and processing is not available. In particular, we focus on the evolution over time of the flow thickness of the debris with dam-break initial conditions. The proposed methodology consists of establishing an approximation of the numerical model using a polynomial chaos expansion that is used in place of the original model, saving computational burden. The values of the parameters are then inferred through a Bayesian approach with a particular focus on inference discrepancies that some of the important features predicted by the model exhibit. We build the model approximation using a preconditioned non-intrusive method and show that a suitable prior parameter distribution is critical to the construction of an accurate surrogate model. The results of the Bayesian inference suggest that utilizing directly the available experimental data could lead to incorrect conclusions, including the over-determination of parameters. To avoid such drawbacks, we propose to base the inference on few significant features extracted from the original data. Our experiments confirm the validity of this approach, and show that it does not lead to significant loss of information. It is further computationally more efficient than the direct approach, and can avoid the construction of an elaborate error model.     

Extreme water levels along the central Red Sea coast of Saudi Arabia: processes and frequency analysis

Natural Hazards - Knowledge about extreme water levels is essential for efficient planning and design of coastal infrastructure. This study uses a high-resolution (~?60 m) coupled...     

Prediction of safety factors for slope stability: comparison of machine learning techniques

Natural Hazards - Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast...     

Origin and variability of a terminal Proterozoic primary silica precipitate,Athel Silicilyte,South Oman Salt Basin,Sultanate of Oman

The Precambrian–Cambrian Athel Silicilyte is a 400 m thick, salt‐encased siliceous succession in the South Oman Salt Basin. It is a self‐sourcing hydrocarbon reservoir and comprises up to 95% microcrystalline quartz and exhibits wavy discontinuous lamination, comprising thin, alternating organic‐rich and silica‐rich layers. Textures and geochemical fingerprinting indicate that it is a primary precipitate formed by microbially mediated precipitation of silica from sea water, within the water column at the sulphidic/oxic interface. The unique occurrence of the Athel Silicilyte in the terminal Proterozoic implies that optimal conditions for this style of silica precipitation occurred only briefly. Basin anoxia, coupled with the growth of microbial mats, low pH and high silica pore water saturations, created optimal chemical conditions for silica precipitation. Volumes of microcrystalline quartz are highest within the transgressive and early highstand systems tract and towards the centre of the Athel Basin. At the basin margins, and within the late highstand systems tract, volumes of microcrystalline quartz decreased as the volume of detrital sediment increased. Mass‐balance calculations indicate that silica‐enriched sea water would have been supplied to the basin by infrequent marine incursions that replenished ambient sea water in the upper part of the water column. In conclusion, precipitation of the Athel Silicilyte was driven by the coincidence of basin restriction, limited clastic input, availability of organic matter and water column anoxia. The observation that there are few documented examples of chert deposits younger than ca 700 Ma, prior to the Cambrian explosion, suggests that although silica budgets within marine basins probably remained high prior to the evolution of silica‐secreting organisms, direct precipitation from sea water was restricted. This is tentatively related to the gradual increase in alkalinity of sea water through the Palaeo‐Proterozoic and Meso‐Proterozoic, such that silica precipitation could only occur through the coincidence of basin anoxia and low siliciclastic input.     

Quantifying uncertainties in fault slip distribution during the Tōhoku tsunami using polynomial chaos

An efficient method for inferring Manning’s n coefficients using water surface elevation data was presented in Sraj et al. (Ocean Modell 83:82–97 2014a) focusing on a test case based on data collected during the Tōhoku earthquake and tsunami. Polynomial chaos (PC) expansions were used to build an inexpensive surrogate for the numerical model GeoClaw, which were then used to perform a sensitivity analysis in addition to the inversion. In this paper, a new analysis is performed with the goal of inferring the fault slip distribution of the Tōhoku earthquake using a similar problem setup. The same approach to constructing the PC surrogate did not lead to a converging expansion; however, an alternative approach based on basis pursuit denoising was found to be suitable. Our result shows that the fault slip distribution can be inferred using water surface elevation data whereas the inferred values minimize the error between observations and the numerical model. The numerical approach and the resulting inversion are presented in this work.     

Crustal structure of northern Egypt from joint inversion of receiver functions and surface wave dispersion velocities

In this study, we used a combined inversion of body wave receiver functions and surface wave dispersion measurements to provide constraints on the crustal structure of northern Egypt. The two techniques are complementary to each other: receiver functions (RFs) are sensitive to shear-wave velocity contrasts, while surface wave dispersion (SWD) measurements are sensitive to finite variations of shear-wave velocity with depth. A database of 122 teleseismic events digitally recorded by the Egyptian National Seismological Network (ENSN) stations has been used as well. To enhance the resulting RFs at each ENSN station, the H-k stacking method was applied. A joint inversion process between the resulting receiver functions and the surface wave dispersion curves was applied as well. We have produced three averaged velocity structure models for distinct geographic and tectonic provinces namely Sinai, eastern desert, and western desert from east to the west respectively. These models will deeply help in estimation the epicenter distance of earthquake, focal mechanism solutions, and earthquake hazard analysis in northern Egypt. An obvious image of the subsurface structure has been determined which shows that generally the crustal structure of northern Egypt consists of three layers covered with a sequence of sediments that differs in thickness from across the region except in the Sharm area where the sedimentary cover is absent. The obtained results indicate that crustal thickness differs from east to west and reaches its maximum value of about 36 km at Siwa station (SWA) in the western desert and its minimum value of about 28 km at Sharm station (SHR) of the southern tip of the Sinai Peninsula. The Vp/Vs ratio varies between 1.71 and 2.07 in northern Egypt. Generally, the high values (1.93) of (Vp/Vs) at SWA station may reflect the well-known rich aquifer with fully saturated sediments of the Swia Oasis in the Western Desert. Moreover, the highest value (2.07) of (Vp/Vs) at BNS station may be attributed to the widespread recently discovered hydrocarbon fields at the Beni-Suef Basin along the Eastern Desert. Finally, an integrated geophysical and hydrological study of the dimensions and physical properties of the aquifer and hydrocarbon fields at SWA and BNS stations to confirm if they are sufficient to produce the elevated Vp/Vs ratios or not become essential and highly recommended.     

Bayesian inference of spatially varying Manning’s n coefficients in an idealized coastal ocean model using a generalized Karhunen-Loève expansion and polynomial chaos

Ocean Dynamics - Bayesian inference with coordinate transformations and polynomial chaos for a Gaussian process with a parametrized prior covariance model was introduced in Sraj et al. (Comput...     

Start‐Up Study on Biohydrogen from Palm Oil Mill Effluent in a Pilot‐Scale Reactor

A start‐up study for biohydrogen production from palm oil mill effluent (POME) is carried out in a pilot‐scale up‐flow anaerobic sludge blanket fixed‐film reactor (UASFF). A substrate with a chemical oxygen demand (COD) of 30 g L^?1 is used, starting with molasses solution for 30 days and followed by a 10% v/v increment of POME/molasses ratio. At 100% POME, a hydrogen content of 80%, hydrogen production rate of 36 L H₂ per day, and maximum COD removal of 48.7% are achieved. Bio‐kinetic coefficients of Monod, first‐order, Grau second‐order, and Stover‐Kincannon kinetic models are calculated to describe the performance of the system. The steady‐state data with 100% POME shows that Monod and Stover‐Kincannon models with bio‐kinetic coefficients of half‐velocity constant (K_s) of 6000 mg COD L^?1, microbial decay rate (K_d) of 0.0015 per day, growth yield constant (Y) of 0.786 mg volatile suspended solids (VSS)/mg COD, specific biomass growth rate (μ_max) of 0.568 per day, and substrate consumption rate of (U_max) 3.98 g/L day could be considered as superior models with correlation coefficients (R²) of 0.918 and 0.989, respectively, compared to first‐order and Grau's second‐order models with coefficients of K₁ 1.08 per day, R² 0.739, and K_2s 1.69 per day, a = 7.0 per day, b = 0.847.     

Data-Worth Assessment for a Three-Dimensional Optimal Design in Nonlinear Groundwater Systems

Groundwater model predictions are often uncertain due to inherent uncertainties in model input data. Monitored field data are commonly used to assess the performance of a model and reduce its prediction uncertainty. Given the high cost of data collection, it is imperative to identify the minimum number of required observation wells and to define the optimal locations of sampling points in space and depth. This study proposes a design methodology to optimize the number and location of additional observation wells that will effectively measure multiple hydrogeological parameters at different depths. For this purpose, we incorporated Bayesian model averaging and genetic algorithms into a linear data-worth analysis in order to conduct a three-dimensional location search for new sampling locations. We evaluated the methodology by applying it along a heterogeneous coastal aquifer with limited hydrogeological data that is experiencing salt water intrusion (SWI). The aim of the model was to identify the best locations for sampling head and salinity data, while reducing uncertainty when predicting multiple variables of SWI. The resulting optimal locations for new observation wells varied with the defined design constraints. The optimal design (OD) depended on the ratio of the start-up cost of the monitoring program and the installation cost of the first observation well. The proposed methodology can contribute toward reducing the uncertainties associated with predicting multiple variables in a groundwater system.