Brígida R.P. Rocha

Fractal Geometry, porosity and complex resistivity: from hand specimen to field data

Abstract In the induced polarization (IP) method of geophysical prospecting, the frequency dependence of the constitutive parameters (conductivity and permittivity) of rocks is exploited to distinguish between different types of rocks with different types of mineralization. Unavoidably, the field data measured using either a four electrode array or an inductive type setup employing ungrounded loops will not directly provide the induced polarization properties of the geological medium to the exclusion of the ‘electromagnetic coupling’. In fact one can not measure one effect without the other. Inference of the medium constitutive parameters from field data is often masked and confused by the purely electromagnetic effects that would be present even when the medium is non-dispersive. In this paper we present a framework by which one can separate the electrochemical effects from the electromagnetic effects which are usually intertwined in a complicated manner in the measured field data. We formulate the problem in a fairly general way without introducing the customary quasi-static approximation. The model we use is that of a layered earth with one of the layers representing a polarizable medium and the rest of the layers are non-polarizable. The effective complex resistivity of the polarizable medium, which is described in a companion paper, is given by a generalized Cole-Cole type resistive network.

Water Quality Monitoring in Large Reservoirs Using Remote Sensing and Neural Networks

Water quality monitoring in lakes and reservoirs using water samples and laboratorial analysis is expensive and time consuming. The use of artificial neural networks to predict water quality using satellite images shows great potential to make this process faster and at lower costs. This article discusses an indirect method to estimate the concentration of pigments (chlorophyll-a), an optically active parameter in water quality. A model based on artificial neural networks, using radial base functions architecture, was developed to predict Tucuruis Reservoir chlorophyll-a concentrations. As input to the neural networks spectral information from Landsat imagery was used, while pigment concentration were used as output information. To train and validate the model we used data from the years 1987, 1988, 1995, 1999, 2000 and 2004. The tested model showed a correlation coefficient of 0.92 for the estimation of pigment (chlorophyll-a) concentrations, indicating its applicability to predict this water quality parameter.

Fractal Geometry, porosity and complex resistivity: from rough pore interfaces to hand specimens

Abstract We propose a new model to interpret the electrical behavior of rocks containing metallic or clay particles. This new model encompasses some of the other commonly used models as special cases. This model is a generalization of two models, one developed by Dias (Journal of Geophysical Research 77, 4945–4956, 1972) and another by Pelton et al. (Geophysics Geophysics, 589–609, 1978). Its circuit analog includes an impedance K(iω)−η which simulates the effects of the fractal rough pore interfaces between the conductive grains (metallic or clay minerals which are blocking the pore paths) and the electrolyte. This generalized Warburg impedance is in series with the resistance of the blocking grains and both are shunted by the double layer capacitance. This combination is in series with the resistance of the electrolyte in the blocked pore passages. The unblocked pore paths are represented by a resistance which corresponds to the normal DC resistivity of the rock. The parallel combination of this resistance with the ‘bulk’ sample capacitance is finally connected in parallel to the rest of the above-mentioned circuit. The model was tested over a wide range of frequencies against experimental data obtained for amplitude and phase of resistivity or conductivity as well as for the complex dielectric constant. The samples studied are those of sedimentary, metamorphic and igneous rocks.

Estimation of Aboveground Forest Biomass in Amazonia with Neural Networks and Remote Sensing

This paper proposes an integrated methodology for estimating aboveground forest biomass in Amazon region. It is based on remote sensing, artificial neural networks and geographical information systems technologies for achieving confident results with a lesser cost than traditional methods of forest inventory. This methodology was tested and validated in Tucurui Reservoir, Brazil.

A Low-Cost System for Measuring Lightning Electric Field Waveforms, its Calibration and Application to Remote Measurements of Currents

A low-cost system for measuring lightning electric field waveforms (Lightning Detection and Waveform Storage System or LDWSS) is presented. The system has been developed for research and operational use in the Amazon region of Brazil. In order to quantify the magnitudes of recorded fields, the LDWSS was calibrated relative to the electric field measuring system operating at the Lightning Observatory in Gainesville (LOG), Florida. The various factors that can influence electric field measurements are discussed. As an example of application of the system, peak current of a return stroke in lightning triggered using the rocket-and-wire technique at Camp Blanding, Florida, was inferred using the corresponding electric field waveform recorded by LDWSS and compared with the directly measured current. Additionally, peak currents inferred from LDWSS field measurements were compared with those reported by the U.S. National Lightning Detection Network.

Evaluation of STARNET lightning detection performance in the Amazon region

The lightning detection performance of the very low frequency (VLF) long-range Sferics Tracking and Ranging Network (STARNET) was evaluated by comparison with a simultaneous data collection made by the Sistema de Proteção da Amazônia Lightning Detection Network (SIPAM-LDN). The study period was 110 days between August 2008 and March 2009, corresponding to a stable period of the STARNET network when it was operating with five sensors to cover the eastern Amazon region. The selected area of study corresponded to a circle of 130 km diameter within a homogeneous zone of detection efficiency (DE) of the SIPAM network. A method of coinciding flash identification (time window of 1 ms and spatial range of 50 km) was used for these two networks. The coinciding cloud-to-ground (CG) flashes were discriminated by polarity and peak current values from the SIPAM-LDN measurements. The total number of coincident CG flashes represented about 9.7% of the CG SIPAM data set. Moreover, 94 % of the coinciding CG flashes had a time error <300 µs. The spatial error of the coincident CG flashes yielded a mean of around 16 km. The final result shows that the relative detection efficiency (RDE) of the coincident CG flashes decreased with the value of their peak currents. RDE values were below 10% for peak currents lower than 20 kA, between 10% and 30% for peak currents between 20 and 40 kA, and above 30% for peak currents greater than 40 kA.

Recurrent Self-Organizing Map for Severe Weather Patterns Recognition

Weather patterns recognition is very important to improve forecasting skills regarding severe storm conditions over a given area of the Earth. Severe weather can damage electric and telecommunication systems, besides generating material losses and even losses of life (Cooray et al., 2007; Lo Piparo, 2010; Santos et al., 2011). In especial for the electrical sector, is strategic to recognize weather patterns that may help predict weather caused damages. Severe weather forecast is crucial to reduce permanent damage to the systems equipments and outages in transmission or distribution lines.

Cloud-to-ground lightning observations over the Eastern Amazon region: Subsidies for the protection of electric systems

Cloud-to-ground (CG) lightning variables, space and time distributions near the equator, over Eastern Amazon, were analyzed in this work. Over seven million lightning flash events of this type, detected by a VAISALA sensor network operated by the Brazilian Amazon Protection System (SIPAM) between 2006 and 2008 were processed. The results show the sub-areas of higher frequency of occurrences, as well as the monthly and hourly distributions of CG lightning polarity type, current peak intervals for this region. The regional values of the CG lightning parameters should be taken into consideration on electric and telecommunication systems protection projects, against atmospheric electric discharges in the Amazon Region.

Inversão dos dados de medidas das resistividades do solo

No estudo de dados de polarizacao induzida, a aplicacao do metodo de Gauss-Newton para o processo de inversao aliado com a aplicacao do metodo dos elementos finitos para a modelagem direta mostrou-se eficiente, porem com um alto custo computacional. Aplicando os mesmos metodos para o estudo da resistividade eletrica do solo, os resultados obtidos tao bons quanto em dados de polarizacao induzida e com menor custo computacional.

Resposta de Polarização Induzida em Poço usando o Modelo Fractal para Resistividade Complexa

A resposta de polarizacao induzida sobre um meio com camadas cilindricas foi obtida. O modelo fractal para resistividade complexa foi empregado como propriedade intrinseca das camadas polarizaveis. A influencia do parâmetro fractal do modelo na resposta foi investigado. Os resultados mostraram que o parâmetro fractal domina a resposta de fase da resistividade aparente; medidas de dados de polarizacao induzida permitem a determinacao das propriedades fractais do meio sem que os efeitos de acoplamento eletromagnetico sejam perceptiveis para frequencias inferiores a 104 Hz.