A target-oriented algorithm is developed for the prediction of multiples recorded on ocean-bottom cables by utilizing apparent slowness relations in common-source and common-receiver gathers. It is based on combining offsets and times of direct waves and primary reflections to predict multiples by matching apparent slownesses at all source and receiver locations; all higher-order multiples can be predicted by matching apparent slownesses alternately in common-source and common-receiver gathers. No knowledge of the subsurface velocity is required. Traveltimes of the direct waves and primary reflections need to be picked from common-source gathers. The subtraction of multiples involves flattening the predicted times of the multiple events, subtracting a local spatial average trace from each trace, within a fixed time window containing the wavelet of the multiple, and then shifting the data back to its original times. Tests of synthetic and field data indicate that the proposed method predicts multiples very well and removes them from seismic data efficiently with negligible affect on the primary reflections, as long as the primary and multiple reflections do not overlap in time and slowness over substantial windows in the domain in which the removal is done.
The high-resolution microwave (MW) spectroscopy is employed to measure the rotational structures of ultracold RbCs molecules prepared in the XΣ (v = 0) ground state. These ground-state molecules are created using short-range photoassociation (PA) followed by the spontaneous emission. Using a combination of continuous-wave (CW) depletion spectroscopy and photoionization (PI) technique, we obtain the MW spectroscopy by coupling the neighboring rotational levels of ground-state molecules. Based on the frequency spacing obtained from the MW spectroscopy, the rotational constant of XΣ (v = 0) can be accurately determined with the rigid rotor model. The precision of the measurement by MW spectroscopy is found to be 3 orders of magnitude higher than the CW depletion spectroscopy. Our scheme provides a simple and highly accurate method for the measurement of molecular structure.
The transition dipole moments (TDMs) of ultracold 85Rb133Cs molecules between the lowest vibrational ground level, X1Σ+ (v = 0, J = 1), and the two excited rovibrational levels, 23Π0+ (v′ = 10, J′ = 2) and 21Π1 (v′ = 22, J′ = 2), are measured using depletion spectroscopy. The ground-state 85Rb133Cs molecules are formed from cold mixed component atoms via the 23Π0− (v = 11, J = 0) short-range level, then detected by time-of-flight mass spectrum. A home-made external-cavity diode laser is used as the depletion laser to couple the ground level and the two excited levels. Based on the depletion spectroscopy, the corresponding TDMs are then derived to be 3.5(2)×10−3ea0 and 1.6(1)×10−2ea0, respectively, where ea0 represents the atomic unit of electric dipole moment. The enhance of TDM with nearly a factor of 5 for the 21Π1 (v′ = 22, J′ = 2) excited level means that it has stronger coupling with the ground level. It is meaningful to find more levels with much more strong coupling strength by the represented depletion spectroscopy to realize direct stimulated Raman adiabatic passage transfer from scattering atomic states to deeply molecular states.
This paper presents a novel method for sensors data fusion based on Neural Adaptive kalman filter. The method is applied in fusing data from MIMU, GPS and Micro magnetism compass integrated navigation system for Micro autonomous Uninhibited Aerial Vehicles (UAV).The noise covariance of kalman filter is modified "online" by the Neural Adaptive Controller in order to modulate kalman filter to be optimal and to improve the positioning, velocity and attitude angle accuracy of the integrated navigation system. To demonstrate the effectiveness and accuracy of this method, an example is outlined. By simulation in the MIMU/GPS/MMC integrated navigation system, it is proved that the Neural Adaptive Kalman Filter has better accuracy than the regular Extended Kalman Filter.
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