Study of acceleration measurement in gravitational wave detection

Position-meter and speed-meter interferometers have been analysed for detecting gravitational waves. Speed-meter is proposed to reduce the radiation pressure noise, which is dominant at low frequency. We introduce the concept of acceleration measurement in comparison with position and speed measurement. In this paper, we describe a general acceleration measurement and derive its standard quantum limit. We provide an example of an acceleration-meter interferometer configuration. We show that shot noise dominates at low frequency following a frequency dependence of $1/\Omega^2$, while radiation pressure noise is constant. The acceleration-meter has even a stronger radiation pressure noise suppression than speed-meter.

Meander-DGS Effect on Electromagnetic Bandgap Structure for Power/Ground Noise Suppression in High-Speed Integrated Circuit Packages and PCBs

In this paper, we present the impact of a meander-shaped defected ground structure (MDGS) on the slow-wave characteristics of a lowest-order passband and a low cutoff frequency of the first stopband of an electromagnetic bandgap (EBG) structure for power/ground noise suppression in high-speed integrated circuit packages and printed circuit boards (PCBs). A semi-analytical method is presented to rigorously analyze the MDGS effect. In the analytical method, a closed-form expression for a low cutoff frequency of the MDGS-EBG structure is extracted with an effective characteristic impedance and a slow-wave factor. The proposed analytical method enables the fast analysis of the MDGS-EBG structure so that it can be easily optimized. The analysis of the MDGS effect revealed that the low cutoff frequency increases up to approximately 19% while comparing weakly and strongly coupled MDGSs. It showed that the miniaturization of the MDGS-EBG structure can be achieved. It was experimentally verified that the low cutoff frequency is reduced from 2.54 GHz to 2.00 GHz by decreasing the MDGS coupling coefficient, which is associated with the miniaturization of the MDGS-EBG structure in high-speed packages and PCBs.

Random noise suppression and super-resolution reconstruction algorithm of seismic profile based on GAN

In this paper, we propose a random noise suppression and super-resolution reconstruction algorithm for seismic profiles based on Generative Adversarial Networks, in anticipation of reducing the influence of random noise and low resolution on seismic profiles. Firstly, the algorithm used the residual learning strategy to construct a de-noising subnet to accurate separate the interference noise on the basis of protecting the effective signal. Furthermore, it iterated the back-projection unit to complete the reconstruction of the high-resolution seismic sections image, while responsed sampling error to enhance the super-resolution performance of the algorithm. For seismic data characteristics, designed the discriminator to be a fully convolutional neural network, used a larger convolution kernels to extract data features and continuously strengthened the supervision of the generator performance optimization during the training process. The results on the synthetic data and the actual data indicated that the algorithm could improve the quality of seismic cross-section, make ideal signal-to-noise ratio and further improve the resolution of the reconstructed cross-sectional image. Besides, the observations of geological structures such as fractures were also clearer.

A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation

Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the signal-to-noise ratio (SNR) of seismic data has always been a key step in seismic data processing. Deep learning approaches have been successfully applied to suppress seismic random noise. The training examples are essential in deep learning methods, especially for the geophysical problems, where the complete training data are not easy to be acquired due to high cost of acquisition. In this work, we propose a natural images pre-trained deep learning method to suppress seismic random noise through insight of the transfer learning. Our network contains pre-trained and post-trained networks: the former is trained by natural images to obtain the preliminary denoising results, while the latter is trained by a small amount of seismic images to fine-tune the denoising effects by semi-supervised learning to enhance the continuity of geological structures. The results of four types of synthetic seismic data and six field data demonstrate that our network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.