A direct inversion scheme for deep resistivity sounding data using artificial neural networks

Initialization of model parameters is crucial in the conventional 1D inversion of DC electrical data, since a poor guess may result in undesired parameter estimations. In the present work, we investigate the performance of neural networks in the direct inversion of DC sounding data, without the need ofa priori information. We introduce a two-step network approach where the first network identifies the curve type, followed by the model parameter estimation using the second network. This approach provides the flexibility to accommodate all the characteristic sounding curve types with a wide range of resistivity and thickness. Here we realize a three layer feed-forward neural network with fast back propagation learning algorithms performing well. The basic data sets for training and testing were simulated on the basis of available deep resistivity sounding (DRS) data from the crystalline terrains of south India. The optimum network parameters and performance were decided as a function of the testing error convergence with respect to the network training error. On adequate training, the final weights simulate faithfully to recover resistivity and thickness on new data. The small discrepancies noticed, however, are well within the resolvability of resistivity sounding curve interpretations.     

Absolute geomagnetic paleointensity as recorded by ∼1.09 Ga Lake Shore Traps (Keweenaw Peninsula, Michigan)

Absolute geomagnetic paleointensity measurements were made on 255 samples from 38 lava flows of the ～1.09 Ga Lake Shore Traps exposed on the Keweenaw Peninsula (Michigan, USA). Samples from the lava flows yield a well-defined characteristic remanent magnetization (ChRM) component within a ～375°C–590°C unblocking temperature range. Detailed rock magnetic analyses indicate that the ChRM is carried by nearly stoichiometric pseudo-single-domain magnetite and/or low-Ti titanomagnetite. Scanning electron microscopy reveals that the (titano)magnetite is present in the form of fine intergrowths with ilmenite, formed by oxyexsolution during initial cooling. Paleointensity values were determined using the Thellier double-heating method supplemented by low-temperature demagnetization in order to reduce the effect of magnetic remanence carried by large pseudosingle-domain and multidomain grains. One hundred and two samples from twenty independent cooling units meet our paleointensity reliability criteria and yield consistent paleofield values with a mean value of 26.3 ± 4.7μT, which corresponds to a virtual dipole moment of 5.9 ± 1.1×10²² Am². The mean and range of paleofield values are similar to those of the recent Earth’s magnetic field and incompatible with a “Proterozoic dipole low”. These results are consistent with a stable compositionally-driven geodynamo operating by the end of Mesoproterozoic.     

Modeling freshening time and hydrochemical evolution of groundwater in coastal aquifers of Shenzhen,China

This work investigated the freshening time and hydrochemical evolution of coastal groundwater in two brackish aquifers in Shenzhen, China. One was the brackish aquifer that resulted from heavy pumping, and the other was the aquifer reclaimed from the coastal sea. Freshening time and hydrochemical evolution of brackish aquifers were quantitatively evaluated using PHREEQC 2.0, a one-dimensional reactive-transport model. Freshening time was shown to mainly depend on pore water velocity, while the chemical composition of groundwater was determined by the cation exchange capacity of the aquifer. It was shown that after heavy pumping ceased, the freshening time for the original coastal aquifer ranged from 20 to over 80 years. While for the coastal reclaimed aquifer, the freshening time was from 85 to 140 years, which depended on the hydraulic conductivity of the fill materials in the reclaimed site. During aquifer freshening, groundwater evolved from Na–Cl type to Ca–Mg–HCO₃ or Na–HCO₃ type. A sensitivity analysis showed that the freshening time was most sensitive to the pore water velocity in the aquifer, while the groundwater chemical composition was most sensitive to the values of cation exchange capacity of the aquifer. As for the dispersivity, it had almost no effect on the freshening time and the chemical composition of groundwater.     

Macrobenthic Community in Tolo Harbour,Hong Kong and its Relations with Heavy Metals

The present study investigated the macrobenthic community in Tolo Harbour, Hong Kong, aiming at linking heavy metal concentrations to differences in macrobenthic community. The stations investigated in Tolo Harbour have widely contrasting features, with some areas located in the Plove Cove displaying both high species richness and abundance while other areas displaying quite impoverished or even void of macrobenthos. High diversity and abundance of macrobenthos in areas with low heavy metal concentrations were recorded. Strong negative correlation between macrobenthic diversity and heavy metal concentrations was found, and this implicated the pollution-induced degradation of macrobenthos in some locations in Tolo Harbour. These results support the Pearson–Rosenberg model for succession along a pollution gradient.     

Numerical modeling of a strong dust event over the south China region in March 2010

The mesoscale model WRF-Chem was used to simulate a severe dust storm event that occurred in March 2010. The storm affected a vast area of East Asia, including the south China region and Hong Kong. This southern region is rarely affected by dust weather. The performance of the WRF-Chem was evaluated by observational data such as the National Center for Atmospheric Research reanalysis data for atmospheric circulation, PM10 concentration from various ground stations, and satellite images of Moderate Resolution Imaging Spectroradiometer and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations. The dependence of the model’s performance on certain important parameterizations was examined in this study. For this particular dust storm event, the model results suggest that the simulation is not very sensitive to certain key physical parameterizations such as threshold wind speed of dust emission and the choice of land surface model. In general, the WRF-Chem is capable of capturing the key physical processes for this severe dust event. The analysis of the dust transport fluxes suggests that the dust transport to the south China region is mainly from the north, although there is a mountainous region in the northern part of the south China region.