In this work, we propose an artificial neural network (ANN) with seven input parameters for the prediction of disturbance storm time (Dst) index 1 to 12 hr ahead. The ANN uses past near‐Earth solar wind parameter values to forecast the Dst. The input parameters are the solar wind interplanetary magnetic field, north‐south component of interplanetary magnetic field, temperature, density, speed, pressure, and electric field. The ANN was trained on the data period from 1 January 2007 to 31 December 2015, which contains 78,888 hourly data samples. While the period from 1 January 2016 to 31 May 2017 was used to test the prediction capabilities of the ANN. Several ANN structures were tested and the best results were determined using the correlation coefficient (R) during the training and prediction phases. The results indicate an adequate accuracy of R = 0.876 for prediction 2 hr in advance and R = 0.857 for prediction 12 hr in advance. The power of the proposed ANN was illustrated using the strongest six storms recorded during the prediction period. Generally, the duration and number of the input parameters significantly affect the training and prediction performance of the applied ANN. The results are outstanding in term of accuracy when considering a medium‐term prediction of 12 hr in advance and in terms of timing of the Dst minimum occurrence. In addition, the results show a strong dependence on the solar wind electric current.

Rock magnetic properties of the Nile mud are reported. They indicate that the carrier of magnetization in the Nile mud is predominantly magnetite. Fourty air-dried ceramic samples made of Nile mud were manufactured to ceramics by stepwise heating to 700• C at various field intensities between 0.03 mT and 0.09 mT and with various angles θ between the laboratory field (F L ) direction and the ceramics. The partial (pTRM) and the total thermoremanent magnetization (TRM) increase linearly as the magnetic field (F L ) increases. The rate of increase of the pTRM with both F L and temperature T depends on θ , so that it decreases by 25% as θ increases from 0• to 90• . In extreme cases, the effect of the magnetic anisotropy results in overestimating the determined palaeointensity by 33% and underestimating it by 25% from the correct value. The direction of TRM is the same as that of the ambient magnetic field independent of the anisotropy. Applying the laboratory field in the direction of the stable natural remanent magnetization during a Thellier-type experiment results in accurate determination of the palaeointensity.

A B S T R A C TThe main outbuildings of the Amenemhat II pyramid complex in Dahsour were yet to be discovered due to a very long subjection of the area to the military authorities and also the demolition of the pyramid itself. We describe the discovery of some of these outbuildings using near-surface magnetic investigations. A gradiometer survey was conducted in the area east of the pyramid to measure the vertical magnetic gradient with a high resolution instrument at 0.5 m sampling interval. The data showed some undesirable field effects such as grid discontinuities, grid slope, traverse stripe effects, spikes and high frequencies originating from recent ferrous contamination. These undesirable effects were addressed to produce an enhanced display. We have successfully detected four main structures in the area east of the pyramid; the causeway that connected the mortuary temple with the valley temple during the Middle Kingdom of the 12 th Dynasty, the mortuary temple and its associated rooms, ruins of an ancient working area and an Egyptian-style tomb structure called a Mastaba. An improved recognition for these structures was accomplished by using the analytic signal and Euler deconvolution techniques. Excavation of a small part within the study area has proven the reliability of magnetic discoveries and the shallowness and composition of the detected features.

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