Acta Geophysica

The results of palaeomagnetic, rock magnetic, and microscopic study of Early Paleozoic metabasites and granulites from the OrlicaŚnieżnik Dome (OSD, Sudetes) have been combined with geochronological data. In the eastern part of the OSD (Śnieżnik Massif, SM) ferrimagnetic pyrrhotite is prevalent, accompanied by various amounts of Fe-oxides. In the western part of the OSD (Orlica-Bystrzyca Massif, OBM) Fe-oxides dominate. All magnetic minerals originated during hydrothermal and weathering processes. The palaeomagnetic study revealed the presence of three secondary components of natural remanence: Late Carboniferous, Late Permian, and Mesozoic. Two Paleozoic components are related to volcanic activity in the Sudetes. They are carried by pyrrhotite and Fe-oxides and were isolated only in SM rocks. The Mesozoic component was determined in both parts of the OSD and is carried by Fe-oxides. It covers a time span, from ∼160 to ∼40 Ma, corresponding to a long period of alteration.

We develop a new method of using feed-forward back-propagation (FFBP) neural networks to simultaneously estimate shape factor and depth of gravity anomalies. The advantages compared to neural network methods are the following: no pre-assumptions are made on source shape, the FFBP neural network estimates both depth and shape factor of source bodies and, once trained, works well for any new data in the training space, without repeating the initial calculations. The optimum number of neurons in the hidden layer was achieved with a novel multi-start algorithm. The FFBP model after training with suitable data sets and testing with different levels of noisy data is more robust than non-linear least squares minimization methods, especially for data with higher noise levels. The FFBP was tested for two sets of gravity field data over a major container terminal at Freeport, Grand Bahama, and a cavity anomaly at the Medford site, Florida, USA. The estimated parameters of the cavities agree well with the actual values.

We have calculated integral fluences of solar protons and helium nuclei at 19 energy thresholds between 1.6 and 90 MeV/n from the SOHO/ERNE measurements during the years 1996–2005. We have also calculated fluences of oxygen and iron in the energy range from 10 up to a few hundred MeV/n for nineteen solar energetic particle (SEP) events. These are the first results of the work aiming at a full employment of the ERNE data in investigating the fluence distributions of SEP events over the entire solar activity cycle 23 and in deriving the total dose received on-board SOHO during its mission. Some instrumental problems are identified and future developments are presented.

The paper presents numerical simulations of water infiltration in unsaturated porous media containing coarse-textured inclusions embedded in fine-textured background material. The calculations are performed using the two-phase model for water and air flow and a simplified model known as the Richards equation. It is shown that the Richards equation cannot correctly describe flow in the presence of heterogeneities. However, its performance can be improved by introducing appropriately defined effective capillary and permeability functions, representing largescale behaviour of the heterogeneous medium.

In least squares migration (LSM), multiples are usually a type of noise. Although they contain information about underground structures, they also cause artifacts in imaging. Therefore, multiple attenuation is an important way to reduce these artifacts in LSM images. Reweighted least squares reverse time migration (RWLSRTM) can use the weighting matrix and the predicted multiples to eliminate artifacts. Because the LSM provides a high resolution model, we can predict the internal multiples by using high-order Born modeling. The method is based on the inverse scattering series (ISS), and the difference is that it forwards the modeling of the internal multiples in the time domain; the model is constructed by the RWLSRTM. Because this method does not require performing as many Fourier transforms as the ISS method, it requires less calculation. We have applied the predicted multiples in the RWLSRTM to remove the artifacts caused by the multiples. The RWLSRTM image can also serve as a parameter of multiple predictions and can make the results of multiple predictions more accurate. The results of numerical tests using synthetic data show that this method can remove artifacts of internal multiples well. A comparison with the ISS method shows that our method can reduce the calculation.

A better understanding of intra/inter-annual streamflow variability and trends enables more effective water resources planning and management for current and future needs. This paper investigates the variability and trends of streamflow data from five stations (i.e. Ashti, Chindnar, Pathgudem, Polavaram, and Tekra) in Godavari river basin, India. The streamflow data were obtained from the Indian Central Water Commission and cover more than 30 years of mean daily records (i.e. 1972–2011). The streamflow data were statistically assessed using Gamma, Generalised Extreme Value and Normal distributions to understand the probability distribution features of data at inter-annual time-scale. Quantifiable changes in observed streamflow data were identified by Sen’s slope method. Two other nonparametric, Mann–Kendall and Innovative Trend Analysis methods were also applied to validate findings from Sen’s slope trend analysis. The mean flow discharge for each month (i.e. January to December), seasonal variation (i.e. Spring, Summer, Autumn, and Winter) as well as an annual mean, annual maximum and minimum flows were analysed for each station. The results show that three stations (i.e. Ashti, Tekra, and Polavaram) demonstrate an increasing trend, notably during Winter and Spring. In contrast, two other stations (i.e. Pathgudem, Chindnar) revealed a decreasing trend almost at all seasons. A significant decreasing trend was observed at all station over Summer and Autumn seasons. Notably, all stations showed a decreasing trend in maximum flows; remarkably, Tekra station revealed the highest decreasing magnitude. Significant decrease in minimum flows was observed in two stations only, Chindnar and Pathgudem. Findings resulted from this study might be useful for water managers and decision-makers to propose more sustainable water management recommendations and practices.