Geophysical imaging of a kaolinite deposit at Sylvan,Manitoba, Canada

A geophysical survey was performed at Sylvan, Manitoba, Canada (51°5′N, 97°22′W) to investigate a Lower Cretaceous kaolinite deposit. The deposit consists of zones of kaolinite, silica sand, and lignitic clay located in a series of channels formed during karsting of the underlying Palaeozoic bedrock and is covered by 3 to 5 m of glacial drift. The aim of the study was to identify cost-efficient electrical and electromagnetic (EM) geophysical methods for locating, mapping, and assessing this target. Methods applied included terrain conductivity (EM31), VLF-EM, time-domain electromagnetics (TEM), DC-resistivity, and shallow seismic refraction. The survey showed that EM methods offer a viable alternative to more expensive seismic reflection surveys in the investigation of small industrial mineral deposits. Comparison of the geophysical survey results with those of a drilling program indicated that VLF-EM and TEM were the best methods for delineating the kaolinite deposit. VLF-EM was the most cost-efficient method for delineating the kaolinite deposit over a ca. 10 ha area and for exploring for further deposits within several kilometers of the main site. Joint interpretation of the in-phase and quadrature response is required for increased reliability in identifying the major kaolinite-filled channels. The TEM method provided more detailed resolution of the deposit than VLF-EM and was the optimal method for assessing its thickness. However, TEM data acquisition is too slow and inefficient for reconnaissance mapping of 10 ha sites. EM31 surveying is useful for defining the palaeokarst surface and overburden thickness in areas surrounding the deposit but cannot be used reliably for mapping the kaolinite deposit itself. The combined geophysical survey results show the kaolinite deposit at Sylvan to be located in a channel which is 100 m wide and about 25 m deep. The deposit has a bulk electrical conductivity between 13 mS m⁻¹ and 25 mS m⁻¹ consistent with low cation exchange capacity values measured for the kaolinite. The palaeokarst surface surrounding the deposit contains a series of sinuous, discontinuous, depressions with a depth variations of several meters.     

Joint AVO inversion, wavelet estimation and noise-level estimation using a spatially coupled hierarchical Bayesian model

The main objective of the AVO inversion is to obtain posterior distributions for P-wave velocity, S-wave velocity and density from specified prior distributions, seismic data and well-log data. The inversion problem also involves estimation of a seismic wavelet and the seismic-noise level. The noise model is represented by a zero mean Gaussian distribution specified by a covariance matrix. A method for joint AVO inversion, wavelet estimation and estimation of the noise level is developed in a Bayesian framework. The stochastic model includes uncertainty of both the elastic parameters, the wavelet, and the seismic and well-log data. The posterior distribution is explored by Markov-chain Monte-Carlo simulation using the Gibbs' sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The use of a coloured seismic-noise model resulted in about 10% lower uncertainties for the P-wave velocity, S-wave velocity and density compared with a white-noise model. The uncertainty of the estimated wavelet is low. In the Heidrun example, the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results.     

Soil partitioning and surface store controls on spring runoff from a boreal forest peatland basin in north‐central Manitoba,Canada

The mid‐ to high‐boreal forest in Canada occupies the discontinuous permafrost zone, and is often underlain by glaciolacustrine sediments mantled by a highly porous organic mat. The result is a poorly drained landscape dominated by wetlands. Frost‐table dynamics and surface storage conditions help to control runoff contributions from various landscape elements, hydrological linkages between these elements, and basin streamflow during spring snowmelt. Runoff components and pathways in a forested peatland basin were assessed during two spring snowmelts with contrasting input and basin conditions. Runoff from relatively intense melt (up to 16 mm day^?1) on slopes with limited soil thawing combined with large pre‐melt storage in surface depressions to produce high flows composed primarily of meltwater (78% of the 0·29 m³ s^?1 peak discharge) routed over wetland surfaces and through permeable upper peat layers. Melt intensity was less in the subsequent year (maximum of 10 mm day^?1) and active layer development was relatively greater (0·2 m deeper at the end of spring melt), resulting in less slope runoff. Coupling of reduced slope contributions with lower storage levels in basin wetlands led to relatively subdued streamflows dominated by older water (73% of the 0·09 m³ s^?1 peak discharge) routed through less‐permeable deeper peat layers and mineral soil. Interannual differences in runoff conditions provide important insight for the development of distributed hydrological models for boreal forest basins and into potential influences on biogeochemical cycling in this landscape under a warming climate. Copyright © 2001 John Wiley & Sons, Ltd.     

Dimensionality reduction for efficient Bayesian estimation of groundwater flow in strongly heterogeneous aquifers

We focus on the Bayesian estimation of strongly heterogeneous transmissivity fields conditional on data sampled at a set of locations in an aquifer. Log-transmissivity, Y, is modeled as a stochastic Gaussian process, parameterized through a truncated Karhunen–Loève (KL) expansion. We consider Y fields characterized by a short correlation scale as compared to the size of the observed domain. These systems are associated with a KL decomposition which still requires a high number of parameters, thus hampering the efficiency of the Bayesian estimation of the underlying stochastic field. The distinctive aim of this work is to present an efficient approach for the stochastic inverse modeling of fully saturated groundwater flow in these types of strongly heterogeneous domains. The methodology is grounded on the construction of an optimal sparse KL decomposition which is achieved by retaining only a limited set of modes in the expansion. Mode selection is driven by model selection criteria and is conditional on available data of hydraulic heads and (optionally) Y. Bayesian inversion of the optimal sparse KLE is then inferred using Markov Chain Monte Carlo (MCMC) samplers. As a test bed, we illustrate our approach by way of a suite of computational examples where noisy head and Y values are sampled from a given randomly generated system. Our findings suggest that the proposed methodology yields a globally satisfactory inversion of the stochastic head and Y fields. Comparison of reference values against the corresponding MCMC predictive distributions suggests that observed values are well reproduced in a probabilistic sense. In a few cases, reference values at some unsampled locations (typically far from measurements) are not captured by the posterior probability distributions. In these cases, the quality of the estimation could be improved, e.g., by increasing the number of measurements and/or the threshold for the selection of KL modes.     

Bayesian estimation of the Modified Omori Law parameters for the Iranian Plateau

The forecasting of large aftershocks is a preliminary and critical step in seismic hazard analysis and seismic risk management. From a statistical point of view, it relies entirely on the estimation of the properties of aftershock sequences using a set of laws with well-defined parameters. Since the frequentist and Bayesian approaches are common tools to assess these parameter values, we compare the two approaches for the Modified Omori Law and a selection of mainshock–aftershock sequences in the Iranian Plateau. There is a general agreement between the two methods, but the Bayesian appears to be more efficient as the number of recorded aftershocks decreases. Taking into account temporal variations of the b-value, the slope of the frequency-size distribution, the probability for the occurrence of strong aftershock, or larger main shock has been calculated in a finite time window using the parameters of the Modified Omori Law observed in the Iranian Plateau.     

Detection of changes in precipitation and runoff over eastern Canada and U.S. using a Bayesian approach

Inference about the existence and characteristics of changes in mean level of hydrometeorological sequences that may be generated by climatic variability is an important step before developing management rules in water resources systems. This paper presents a Bayesian approach, based on a single shifting model, which can be used to study a change in the mean level of a set of independent normal random variables. Two different problems are considered: the first is the detection of a change, while the second is the estimation of the change-point and its amplitude under the assumption that a change has occurred. This method is applied to precipitation and runoff data series over eastern Canada and U.S. during the twentieth century. The main results show an increase in the late sixties in the Eastern North American precipitation. This supports conclusions drawn from a number of studies which identified the late sixties to early seventies as a period of possible change.     

Model updating and seismic loss assessment for a portfolio of bridges

The majority of bridge infrastructures in Italy were built in the 1960s and ‘70s without any specific seismic provision being made. As a consequence, it is expected that these bridges would be highly vulnerable if subjected to a significant seismic event. Given this background, it is natural that the rapid and accurate assessment of economic losses incurred to the bridge infrastructure as a result of such an event could play a crucial role in emergency management in the immediate aftermath of an earthquake. Focusing on the infrastructure system of highway bridges in the Campania region in Italy, this paper demonstrates how both state-of-the-art methodologies in portfolio loss assessment and the available data can be used to assess the probability distribution of the repair costs incurred due to the 1980 Irpinia earthquake. Formulating a probabilistic loss assessment for a portfolio of bridges as a standard Monte Carlo simulation problem helps to resolve the spatial risk integral efficiently. One of the specific features of this case study is the use of statistical methods for updating models of: (a) ground motion predictions, (b) vulnerability/fragility and (c) exposure/costs, based on the available data. It has been observed that alternative hypotheses concerning the ground motion correlation structure can significantly affect the distribution of direct economic losses. Furthermore, updating the ground motion prediction based on available recordings may significantly reduce the dispersion in the estimate of the direct economic losses.     

Relationship between transmissivity and specific capacity in the volcanic aquifers of Jeju Island, Korea

Transmissivity is often estimated using specific capacity data when standard pumping test data are not available or the drawdown is stabilized early, as in this study. Previous researchers studied the relationship between transmissivity and specific capacity in the leaky aquifer system of volcanic rocks on Jeju Island, Korea, using the Cooper–Jacob equation. The current study utilizes the Moench leaky aquifer model. The linear relationship between transmissivity and specific capacity on a log–log scale for volcanic aquifers on Jeju Island is remarkably strong, with a correlation coefficient of 0.94. The width of the 90% prediction interval is about 0.89 log cycles, indicating a ±0.44 order of magnitude uncertainty when transmissivity is estimated using specific capacity.     

Bayesian estimation of strong earthquakes in the Inner Messiniakos fault zone, southern Greece, based on seismological and geological data

Statistical estimates of earthquake magnitudes are unreliable when based onvery few historical data. Additional sources of information, such asgeological data, are then necessary to update estimates of seismicityparameters. The Bayesian probability theory is a tool to combine priorinformation of seismicity obtained from geological data with historicalobservations. This theory is tested in the case of the Inner Messiniakosfault zone, southern Greece, for the estimation of the probability ofoccurrence of strong earthquakes. Prior estimates of seismicity aredeveloped from slip rate measurements, obtained from offsets of geologicalformations, on the basis of both onshore and offshore neotectonic data.The analysis emphasizes the importance of the input seismicity parameters,particularly the significance of the upper bound magnitude in the estimationof the seismic potential of active faults.     

Optimality in the estimation of a MA system from a long AR model for simulation studies

The determination of moving average (MA) models from a prior autoregressive (AR) approximation of a specified (target) spectral matrix is addressed; this is done in context with the need to simulate ground shaking and other natural phenomena as multivariate random processes. First, an existing technique based on a direct modeling of the target expression is revisited. In this regard, the influence of the order of the prior AR approximation, and the number of its harmonics used in the determination of the MA model, is described. Further, a simple selection technique of these parameters is presented that leads to an optimum MA approximation. Next, the relationship between a method based on the Cholesky factorization of the coveriance matrix and the present technique is investigated to derive additional insight into its convergence properties. Finally, an alternative modeling technique based on an AR representation of the inverse of the target spectral matrix is presented.     

Some analytical solutions for sensitivity of well tests to variations in storativity and transmissivity

The theory of a pumping test or a slug test to measure aquifer transmissivity or storativity assumes that the aquifer properties are uniform around the well. The response of the drawdown to small spatial variations in aquifer properties in the volume of influence is determined by spatial weighting functions or Fréchet kernels, which in general are functions of space and time. The Fréchet kernels determine the effective “volume of influence” of the measurements at any time. Under the assumption that the well is a line sink we derive explicit analytical expressions for the Fréchet kernels for storativity and for transmissivity for both pumping and slug tests. We also derive the total sensitivity functions for uniform variations in storativity and transmissivity and show that they are the spatial integrals of the Fréchet kernels. We consider both the case of separate pumping and observation wells and also the radially symmetric case of observations made at the pumped or slugged well. The “volume of influence” is symmetric with respect to the pumping or slugged well and the observation well, and far from the well the contours of equal spatial sensitivity approach the shapes of ellipses with a well at each focus, rather than circles centered on the pumping well. We use the analytical solutions to investigate the nature of the singularities in the spatial sensitivity functions around the wells, which govern the importance of inhomogeneities close to the well or observation point.     

The role of meteorological forcing and snow model complexity in winter glacier mass balance estimation,Columbia River basin,Canada

Glaciers are commonly located in mountainous terrain subject to highly variable meteorological conditions. High resolution meteorological (HRM) data simulated by atmospheric models can complement meteorological station observations in order to assess changes in glacier energy fluxes and mass balance. We examine the performance of two snow models, SnowModel and Alpine3D, forced by different meteorological data for winter mass balance simulations at four glaciers in the Canadian portion of the Columbia Basin. The Weather Research and Forecasting model (WRF) with resolution of 1 km and the North American Land Data Assimilation System with ~12 km resolution, provide HRM data for the two snow models. Evaluation is based on the ability of the snow models to simulate snow depth at both point locations (automated snow weather stations) and over the entire glacier surface (airborne LiDAR [Light Detection and Ranging] surveys) during the 2015/2016 winter accumulation. When forced with HRM data, both models can reproduce snow depth to within ±15% of observed values. Both models underestimate winter mass balance when forced by HRM data. When driven with WRF data, SnowModel underestimates winter mass balance integrated over the glacier area by 1 and 10%, whilst Alpine3D underestimates winter mass balance by 12 and 22% compared with LiDAR and stake measurements, respectively. The overall results show that SnowModel forced by WRF simulated winter mass balance the best.     

Hybrid testing with model updating on steel panel damper substructures using a multi-axial testing system

This paper describes a series of hybrid tests performed on a steel panel damper (SPD) specimen by using a multi-axial testing system. The building under investigation adopted a three-dimensional six-story moment resisting frame with four SPDs installed at each story as the main seismic resisting system. The structural model was subjected to bi-directional ground excitations of three hazard levels. The finite element analysis program “Platform of Inelastic Structural Analysis for 3D Systems” (PISA3D) was used as the analysis kernel for hybrid tests. Relevant programming extensions in PISA3D were created to support geographically distributed hybrid testing in a general-purpose manner. An external displacement control (EDC) method was developed such that the actual boundary deformation of the specimen fixtures could be continuously measured and immediately compensated during tests. An online model updating (OMU) technique was developed and employed such that the material properties of the specimen could be identified directly from the specimen response during the test. The identified material properties were then immediately used to update those of the other relevant numerical elements to enhance the overall simulation fidelity. Superior flexibility of the underlying software architecture was well demonstrated in this series of hybrid tests since no hardcoding was used to support all the complex test settings. Test results confirmed the effectiveness of the proposed EDC method, as well as the capacity of the proposed OMU technique to satisfactorily and efficiently capture the hysteretic properties of the specimen.     

Combination of FEM and CMA‐ES algorithm for transmissivity identification in aquifer systems

In this paper, we propose a coupling of a finite element model with a metaheuristic optimization algorithm for solving the inverse problem in groundwater flow (Darcy's equations). This coupling performed in 2 phases is based on the combination of 2 codes: This is the HySubF‐FEM code (hydrodynamic of subsurface flow by finite element method) used for the first phase allowing the calculation of the flow and the CMA‐ES code (covariance matrix adaptation evolution strategy) adopted in the second phase for the optimization process. The combination of these 2 codes was implemented to identify the transmissivity field of groundwater by knowing the hydraulic head in some point of the studied domain. The integrated optimization algorithm HySubF‐FEM/CMA‐ES has been validated successfully on a schematic case offering an analytical solution. As realistic application, the integrated optimization algorithm HySubF‐FEM/CMA‐ES was applied to a complex groundwater in the north of France to identify the transmissivity field. This application does not use zonation techniques but solves an optimization problem at each internal node of the mesh. The obtained results are considered excellent with high accuracy and fully consistent with the hydrogeological characteristics of the studied aquifer.However, the various numerical simulations performed in this paper have shown that the CMA‐ES algorithm is time‐consuming. Finally, the paper concludes that the proposed algorithm can be considered as an efficient tool for solving inverse problems in groundwater flow.     

Seismic anisotropy in granite at the Underground Research Laboratory, Manitoba

The Shear-Wave Experiment at Atomic Energy of Canada Limited's Underground Research Laboratory was probably the first controlled-source shear-wave survey in a mine environment. Taking place in conjunction with the excavation of the Mine-by test tunnel at 420 m depth, the shear-wave experiment was designed to measure the in situ anisotropy of the rockmass and to use shear waves to observe excavation effects using the greatest variety of raypath directions of any in situ shear-wave survey to date. Inversion of the shear-wave polarizations shows that the anisotropy of the in situ rockmass is consistent with hexagonal symmetry with an approximate fabric orientation of strike 023° and dip 35°. The in situ anisotropy is probably due to microcracks with orientations governed by the in situ stress field and to mineral alignment within the weak gneissic layering. However, there is no unique interpretation as to the cause of the in situ anisotropy as the fabric orientation agrees approximately with both the orientation expected from extensive-dilatancy anisotropy and that of the gneissic layering. Eight raypaths with shear waves propagating wholly or almost wholly through granodiorite, rather than granite, do not show the expected shear-wave splitting and indicate a lower in situ anisotropy, which may be due to the finer grain size and/or the absence of gneissic layering within the granodiorite. These results suggest that shear waves may be used to determine crack and mineral orientations and for remote monitoring of a rockmass. This has potential applications in mining and waste monitoring.     

A new method for aquifer system identification and parameter estimation

The standard practice for assessing aquifer parameters is to match groundwater drawdown data obtained during pumping tests against theoretical well function curves specific to the aquifer system being tested. The shape of the curve derived from the logarithmic time derivative of the drawdown data is also very frequently used as a diagnostic tool to identify the aquifer system in which the pumping test is being conducted. The present study investigates the incremental area method (IAM) to serve as an alternative diagnostic tool for the aquifer system identification as well as a supplement to the aquifer parameter estimation procedure. The IAM based diagnostic curves for ideal confined, leaky, bounded and unconfined aquifers have been derived as part of this study, and individual features of the plots have been identified. These features were noted to be unique to each aquifer setting, which could be used for rapid evaluation of the aquifer system. The effectiveness of the IAM methodology was investigated by analyzing field data for various aquifer settings including leaky, unconfined, bounded and heterogeneous conditions. The results showed that the proposed approach is a viable method for use as a diagnostic tool to identify the aquifer system characteristics as well as to support the estimation of the hydraulic parameters obtained from standard curve matching procedures. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatial variability in organic material sinking export in the Hudson Bay system,Canada, during fall

Spatial variations in the sinking export of organic material were assessed within the Hudson Bay system (i.e., Hudson Bay, Hudson Strait and Foxe Basin) during the second oceanographic expedition of ArcticNet, on board the CCGS Amundsen in early fall 2005. Sinking fluxes of particulate organic material were measured using short-term free-drifting particle interceptor traps deployed at 50, 100 and 150 m for 8–20 h at eight stations. Measurements of chlorophyll a (chl a), pheopigments (pheo), particulate organic carbon (POC), biogenic silica (BioSi), protists, fecal pellets and bacteria were performed on the collected material. In parallel, sea surface salinity and temperature were determined at 121 stations in the Hudson Bay system. Three hydrographic regions presenting different sedimentation patterns were identified based on average surface salinity and temperature. Hudson Strait was characterized by a marine signature, with high salinity (average=32.3) and low temperature (average=2.1 °C). Eastern Hudson Bay was strongly influenced by river runoff and showed the lowest average salinity (26.6) and highest average temperature (7.6 °C) of the three regions. Western Hudson Bay showed intermediate salinity (average=29.4) and temperature (average=4.4 °C). Sinking fluxes of total pigments (chl a+pheo: 3.37 mg m⁻² d⁻¹), diatom-associated carbon (19.8 mg m⁻² d⁻¹) and BioSi (50.2 mg m⁻² d⁻¹) at 50 m were highest in Hudson Strait. Eastern Hudson Bay showed higher sinking fluxes of total pigments (0.52 mg m⁻² d⁻¹), diatom-associated carbon (3.29 mg m⁻² d⁻¹) and BioSi (36.6 mg m⁻² d⁻¹) compared to western Hudson Bay (0.19, 0.05 and 7.76 mg m⁻² d⁻¹, respectively). POC sinking fluxes at 50 m were low and relatively uniform throughout the Hudson Bay system (50.0–76.8 mg C m⁻² d⁻¹), but spatial variations in the composition of the sinking organic material were observed. A large part (37–78%) of the total sinking POC was unidentifiable by microscopic observation and was qualified as amorphous detritus. Considering only the identifiable material, the major contributors to the POC sinking flux were intact protist cells in Hudson Strait (28%), fecal pellets in eastern Hudson Bay (52%) and bacteria in western Hudson Bay (17%). A significant depth-related attenuation of the POC sinking fluxes (average loss between 50 and 150 m=32%) and a significant increase in the BioSi:POC ratio (average increase between 50 and 150 m=76%) were observed in Hudson Strait and eastern Hudson Bay. For all other sinking fluxes and composition ratios, we found no statistically significant difference with depth. These results show that during fall, the sinking export of total POC from the euphotic zone remained fairly constant throughout the Hudson Bay system, whereas other components of the organic sinking material (e.g., chl a, BioSi, fecal pellets, protist cells) showed strong spatial variations.