To acquire the best path-entangled photon Fock states for robust quantum-optical metrology with parity detection, we calculate phase information from a lossy interferometer by using twin entangled Fock states. We show that (a) when loss is less than 50% twin entangled Fock states with large photon number difference give higher visibility, while when loss is higher than 50%, the ones with less photon number difference give higher visibility; and (b) twin entangled Fock states with large photon number difference give sub-shot-noise limit sensitivity for phase detection in a lossy environment. This result provides a reference on what particular path-entangled Fock states are useful for real world metrology applications.
One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision. Traditionally, this is accomplished by visually picking the salt/sediment boundaries, which requires a great amount of manual work and may introduce systematic bias. With recent progress of deep learning algorithm and growing computational power, a great deal of efforts have been made to replace human effort with machine power in salt body interpretation. Currently, the method of Convolutional neural networks (CNN) is revolutionizing the computer vision field and has been a hot topic in the image analysis. In this paper, the benefits of CNNbased classification are demonstrated by using a state-of-art network structure U-Net, along with the residual learning framework ResNet, to delineate salt body with high precision. Network adjustments, including the Exponential Linear Units (ELU) activation function, the Lovász-Softmax loss function, and stratified K-fold cross-validation, have been deployed to further improve the prediction accuracy. The preliminary result using SEG Advanced Modeling (SEAM) data shows good agreement between the predicted salt body and manually interpreted salt body, especially in areas with weak reflections. This indicates the great potential of applying CNN for salt-related interpretations. arXiv:1812.01101v1 [physics.geo-ph] 24 Nov 2018Deep learning, which is capable of extracting extremely detailed features from given data, has had a huge impact on the development of image analysis, especially, semantic segmentation. Recently, deep learning found its application in oil and gas industry, such as well log correlation, fault interpretation (Maniar et al., 2015) and rock facies classification (Chen and Zeng, 2018). Convolutional Neural Network (CNN), being one of the most powerful 'weapons' in the deep learning arsenal, utilizes numerous convolving/pooling/activation layers to obtain a collection of underlying features from the original image. The effectiveness of CNN in salt-body identification has been shown in a recent study (Di et al., 2018), where a proof-of-principle study focusing on factors contributing to the superiority of CNN has been provided.In this paper, we aim to extend on the work by utilizing the state-of-art CNN with U-Net architecture to fully exploit its potential in regards to salt-body identification. We will first describe the deployed convolutional network structure, and then discuss the adjustments we have made to improve the network training. Finally, we will show the preliminary salt interpretation result and will have some discussions on its possible applications and how to further improve.
There has been much recent interest in quantum metrology for applications to sub-Raleigh ranging and remote sensing such as in quantum radar. For quantum radar, atmospheric absorption and diffraction rapidly degrades any actively transmitted quantum states of light, such as N00N states, so that for this high-loss regime the optimal strategy is to transmit coherent states of light, which suffer no worse loss than the linear Beer's law for classical radar attenuation, and which provide sensitivity at the shot-noise limit in the returned power. We show that coherent radar radiation sources, coupled with a quantum homodyne detection scheme, provide both longitudinal and angular super-resolution much below the Rayleigh diffraction limit, with sensitivity at shot-noise in terms of the detected photon power. Our approach provides a template for the development of a complete super-resolving quantum radar system with currently available technology. V C 2013 AIP Publishing LLC. [http://dx.
There has been much recent interest in quantum metrology for applications to sub-Raleigh ranging and remote sensing such as in quantum radar. For quantum radar, atmospheric absorption and diffraction rapidly degrades any actively transmitted quantum states of light, such as N00N states, so that for this high-loss regime the optimal strategy is to transmit coherent states of light, which suffer no worse loss than the linear Beer's law for classical radar attenuation, and which provide sensitivity at the shot-noise limit in the returned power. We show that coherent radar radiation sources, coupled with a quantum homodyne detection scheme, provide both longitudinal and angular super-resolution much below the Rayleigh diffraction limit, with sensitivity at shot-noise in terms of the detected photon power. Our approach provides a template for the development of a complete super-resolving quantum radar system with currently available technology.
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