Exploring seismic inversion methodologies for non‐stationary geological environments: a benchmark study between deterministic and geostatistical seismic inversion

This paper presents a comparison between subsurface impedance models derived from different deterministic and geostatistical seismic inversion methodologies applied to a challenging synthetic dataset. Geostatistical seismic inversion methodologies nowadays are common place in both industry and academia, contrasting with traditional deterministic seismic inversion methodologies that are becoming less used as part of the geo‐modelling workflow. While the first set of techniques allows the simultaneous inference of the best‐fit inverse model along with the spatial uncertainty of the subsurface elastic property of interest, the second family of inverse methodology has proven results in correctly predicting the subsurface elastic properties of interest with comparatively less computational cost. We present herein the results of a benchmark study performed over a realistic three‐dimensional non‐stationary synthetic dataset in order to assess the performance and convergence of different deterministic and geostatistical seismic inverse methodologies. We also compare and discuss the impact of the inversion parameterisation over the exploration of the model parameter space. The results show that the chosen seismic inversion methodology should always be dependent on the type and quantity of the available data, both seismic and well‐log, and the complexity of the geological environment versus the assumptions behind each inversion technique. The assessment of the model parameter space shows that the initial guess of traditional deterministic seismic inversion methodologies is of high importance since it will determine the location of the best‐fit inverse solution.     

Modal analysis of a fast patrol boat made of composite material

The structural dynamic behaviour of a fast patrol boat is studied with two- and three-dimensional idealizations for dry-hull analysis. A preliminary two-dimensional beam analysis is conducted by means of the Prohl-Myklestad method to derive a first estimate of the first four symmetric mode shapes. A more complex three-dimensional finite element model is developed in order to evaluate the modal characteristics for both symmetric and coupled anti-symmetric distortions, with the emphasis placed on the former.     

Modelling of multipeaked directional wave spectra

An approach for modelling of multipeaked directional wave spectra is proposed. For model identification, a numerical optimization technique that uses the random linear search algorithm is applied. This technique allows the fitting of spectral models to measured or hindcast data. The HIPOCAS hindcast data for North Atlantic are used for an application study.     

Sensitivity of wave model predictions to wind fields in the Western Mediterranean sea

The paper compares the wave hindcast in the Western Mediterranean sea using the reanalysis wind fields from HIPOCAS and ERA-40 from ECMWF for November 2001. The study has concentrated on the Mediterranean coast of Spain where there are known difficulties with the wind and wave modelling. Two winter storms have been compared. The main differences between the significant wave heights using the ERA-40 reanalysis (ECMWF) and HIPOCAS reanalysis winds were observed to increase when moving southwards in the geographical domain at the offshore locations. Systematic negative biases of Hs were obtained with the ERA-40 data at all the coastal locations analyzed, whereas positive biases are typical for the HIPOCAS reanalysis. For offshore and coastal locations when using the ERA-40 data the Hs biases increased moving to South, while this pattern was not so clear for the HIPOCAS data. The inconsistencies in the comparisons of modelled waves against measurements seem to be associated with the quality of the wind fields.     

Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics

Empirical models designed to simulate and predict urban land‐use change in real situations are generally based on the utilization of statistical techniques to compute the land‐use change probabilities. In contrast to these methods, artificial neural networks arise as an alternative to assess such probabilities by means of non‐parametric approaches. This work introduces a simulation experiment on intra‐urban land‐use change in which a supervised back‐propagation neural network has been employed in the parameterization of several biophysical and infrastructure variables considered in the simulation model. The spatial land‐use transition probabilities estimated thereof feed a cellular automaton (CA) simulation model, based on stochastic transition rules. The model has been tested in a medium‐sized town in the Midwest of São Paulo State, Piracicaba. A series of simulation outputs for the case study town in the period 1985–1999 were generated, and statistical validation tests were then conducted for the best results, based on fuzzy similarity measures.     

Hydrogeochemical and Geophysical Evaluation of the Recharge Zone of Guarani Aquifer in Municipally of Candelária,Rio Grande do Sul,Brazil

Geotechnical and Geological Engineering - A large part of the Guarani Aquifer is located in the state of Rio Grande do Sul (Brazil) and additionally possesses a recharge area located in its central...     

The definition of potential infiltration areas in Guaratinguetá watershed, Paraíba do Sul Basin, Southeastern Brazil: an integrated approach using physical and land-use elements

This work describes the methodological approach used for mapping the potential infiltration areas of the Guaratinguetá watershed (160?km²), situated in Southeastern Brazil. The method is considered a qualitative approach, which takes into account thematic maps (geology, pedology, geomorphology, and land use/land cover) and the precipitation spatial distribution. A group of experts in Earth Sciences and Geotechnical Engineering has applied an infiltration potential scale factor that ranges from 5 (highest influence) to 1 (lowest influence). The final infiltration map was produced using several ArcGIS^? tools. The results showed that the most suitable (very high) infiltration areas represent only around 7% of the watershed area, which are associated with smooth and gentle hills, fluvial Tertiary sediments and yellow oxisols. However, growing impacts caused by land farming and urban developments require urgent planning for this region. Areas with high to moderate capacity of infiltration represent around 56% of the watershed and are found in the domain of igneous-metamorphic rocks associated with steep hill-slopes and relatively well-preserved forest fragments. This region requires a land-use strategy, such as reforestation programs, in order to increase the infiltration capacity of the watershed.