CANCELLING MULTIPLE REFLECTIONS BY FEEDBACK *

For horizontal layering and plane horizontal impulsive wavefronts it is theoretically possible to get rid of multiple reflections by a feedback procedure which can easily be derived using raypath philosophy. To reduce the increase of noise inherent in the method the precise theoretical formulae are altered in such a manner that a practical application becomes possible. For this purpose the autocorrelation will be used. Application of the new process to a CRP section where the shot geophone distances in the field had not been long enough to attenuate multiple reflections effectively gave favourable results.     

X-ray luminous IRAS galaxies

A sample of 14708 extragalactic IRAS sources selected from the Point Source Catalog via statistical classification has been cross-correlated with the ROSAT All Sky Survey (Bolleret al., 1992). 244 galaxies emerge as being detected both by ROSAT and IRAS.The most interesting point is the discovery of a dozen of normal spirals whose X-ray luminosities reach nearly 10⁴³ erg s^–1 (0.1–2.4 keV), higher than previous detection limits of a few 10⁴¹ erg s^–1 (Stockeet al. 1991; Green, Anderson and Ward, 1992). We obtained optical spectra for nine of them, showing Seyfert spectra for three of the objects (thus previous mis-classifications), spectra close to LINERs for two further objects and normal IRAS galaxy spectra for the last four.In the case of normal galaxies, the source of energy is not clear yet and could be related to the high rate of star formation likely to occur in the central regions or also to a low level active nucleus. The study of these objects is of interest to the nature of LINERs and, more generally, to possible relations between AGN and starbursts (Sanderset al., 1988).     

Rheology of the continental lithosphere: Progress and new perspectives

While the surface of Tibet is undergoing pervasive pure shear, stable terranes, straddling subsurface sutures, remain in the sub-continental lithospheric mantle (SCLM), attesting to its strength. Furthermore, sub-horizontal, cohesive remnant of Indian SCLM is traced northward from the Himalayan deformation front for about 600 km, exemplifying the longevity of buoyant, strong SCLM of Archean shields. Bimodal distribution of earthquake depths, with peaks concentrating in the upper/middle crust and near the Moho, has been a longstanding evidence for strong SCLM. Recent results from the Himalayas—Tibet and along the East African rift system not only corroborate the bimodal distribution but also firmly established that large earthquakes occur below the Moho. Intriguingly, non-volcanic tremors—newly discovered mode of elastic strain release—also occur near the Moho but well below the seismogenic zone in the upper/middle crust. Considering recent field observations and laboratory experiments of viscosity contrast across the Moho, the SCLM must be strong enough to accumulate elastic strain, a prerequisite for earthquakes, over geological time. Moreover, under laboratory conditions, recent advances that link the termination of frictional instability, an analogue for earthquakes, and the onset of crystal plasticity, provided a physical basis for limiting temperatures of crustal (~ 300-400 °C) and mantle (~ 600-700 °C) earthquakes. While any single rheological model cannot possibly account for all tectonic settings (which also evolve with time), lithological contrast across the Moho is important in shaping the bimodal distribution of strength in the continental lithosphere.     

Determination of Surface Precipitation Type Based on the Data Fusion Approach

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.