Coherent Noise Suppression Research Articles

Hamiltonian Switching Control of Noisy Bipartite Qubit Systems

Abstract We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the Quantum Approximate Optimization Algorithm (QAOA), to mitigate environmental noise on qubits. We demonstrate the control for a central spin coupled to bath spins via isotropic Heisenberg interactions, and then make physical applications to the protection of quantum gates performed on superconducting transmon qubits coupling to environmental two-level-systems (TLSs) through dipole-dipole interactions, as well as on such qubits coupled to both TLSs and a Lindblad bath. The control field is classical and acts only on the system qubits. We use reinforcement learning with policy gradient (PG) to optimize the Hamiltonian switching control protocols, using a fidelity objective for specific target quantum gates. 
 We use this approach to demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) in the majority of our test cases and showing improvement beyond this to values of 0.999999999 (nine nines) upon a subsequent optimization by Gradient Ascent Pulse Engineering (GRAPE). We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite control of quantum gates.

Explainable artificial intelligence‐driven mask design for self‐supervised seismic denoising

AbstractThe presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self‐supervised denoising circumvents the common requirement of deep learning procedures of having noisy‐clean training pairs. However, self‐supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to ‘see inside the black box’ that is the denoising network and use the gained knowledge to replace the need for any prior knowledge of the noise itself. This is achieved in practice by leveraging bias‐free networks and the direct linear link between input and output provided by the associated Jacobian matrix; we show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels provides an indication of the most effective mask to suppress noise present in the data. The proposed method, therefore, becomes a fully automated denoising procedure requiring no clean training labels or prior knowledge. Realistic synthetic examples with noise signals of varying complexities, ranging from simple time‐correlated noise to complex pseudo‐rig noise propagating at the velocity of the ocean, are used to validate the proposed approach. Its automated nature is highlighted further by an application to two field data sets. Without any substantial pre‐processing or any knowledge of the acquisition environment, the automatically identified blind masks are shown to perform well in suppressing both trace‐wise noise in common shot gathers from the Volve marine data set and coloured noise in post‐stack seismic images from a land seismic survey.

Coherent Noise Suppression with Multimode Fiber Extended Light Source for Interferometer

Coherent Noise Suppression with Multimode Fiber Extended Light Source for Interferometer

Research of coherent noise suppression method based on rotational scatterer in dual-wavelength holographic microscopy

Laser light source is widely used in digital holography as a strongly coherent light source, its strong correlation will also introduce some unavoidable coherent noise to the system and affect the imaging quality. This paper proposes a method to suppress system noise by adding a rotational scatterer to the reflective dual-wavelength digital holographic microscope. In this paper, the magnification of the system is calibrated with the 1951 USAF resolution target. A one-dimensional micro- and nano-grid is used to determine the system resolution. By comparing the hologram reproduction results under the same experimental conditions both with and without a rotational scatterer, it is demonstrated that the addition of a rotational scatterer can effectively suppress the coherent noise of this system. The system is also verified to have certain stability through the experiments of repeatability measurement and multi-position measurement on the chip. Furthermore, this research offers a fresh measuring approach for microchannel detection in opaque chips.

Trace-wise coherent noise suppression via a self-supervised blind-trace deep-learning scheme

Seismic data denoising via supervised deep learning is effective and popular but requires noise-free labels, which are rarely available. Blind-spot networks circumvent this requirement by training directly on noisy data and have been demonstrated to be a powerful suppressor of random noise. In this work, we expand the methodology of blind-spot networks to create a blind-trace network that successfully removes trace-wise coherent noise. An extensive synthetic analysis illustrates the denoising procedure’s robustness to varying noise levels and varying numbers of noisy traces within shot gathers. It is demonstrated that the network can accurately learn to suppress the noise when up to 60% of the original traces are noisy. Furthermore, our procedure is implemented on the Stratton 3D field data set and demonstrates the restoration of the previously corrupted direct arrivals. In addition to trace-wise noise suppression, we adapt the blind-spot networks to the successful suppression of colored Gaussian noise, which exhibits varying coherent properties in time and spatial axes. Our adaptation of the blind-spot networks paves the way for its use in other applications, such as the suppression of coherent noise arising from wellsite activity, passing vessels, or nearby industrial activity.

Features of the Application of Coherent Noise Suppression Methods in the Digital Holography of Particles

The paper studies the influence of coherent noises on the quality of images of particles reconstructed from digital holograms. Standard indicators (for example, signal-to-noise ratio) and such indicators as the boundary contrast and boundary intensity jump previously proposed by the authors are used to quantify the image quality. With the use of these parameters, for examples of some known methods of suppressing coherent noises in a holographic image (eliminating the mutual influence of virtual and real images in in-line holography, and time averaging), the features and ranges of applicability of such correction were determined. It was shown that the use of the complex field amplitude reconstruction method based on the Gerchberg–Saxton algorithm and the spatial-frequency method improves the quality of determining the particle image boundary (by boundary intensity jump) starting from the distance between a hologram and a particle, which is about twice the Rayleigh distance. In physical experiments with model particles, averaging methods were studied to suppress non-stationary coherent noises (speckles). It was also shown that averaging over three digital holograms or over three holographic images is sufficient to provide a quality of particle image boundary suitable for particle recognition. In the case of multiple scattering, when it is necessary to impose a limit on the working volume length (depth of scene) of the holographic camera, the paper provides estimates that allow selecting the optimal working volume length. The estimates were made using the example of a submersible digital holographic camera for plankton studies.

Designing diffractive optical elements for shaping partially coherent beams by proximity correction.

We propose a new method to design diffractive optical elements (DOE) for shaping partially coherent beams. The diffraction patterns of a DOE under a certain partially coherent beam can be modeled by the convolution of the coherent diffraction pattern and the inherent degree of coherent function. Two basic types of diffraction anomalies induced by partially coherent beams are discussed, including line-end shortening and corner rounding. A proximity correction (PC) method similar to the optical proximity correction (OPC) technique in lithography is used to compensate for these anomalies. The designed DOE exhibits good performance in partially coherent beam shaping and noise suppression.

Pre-stack Fidelity Denoising Technique in Loess Plateau Area

Based on the data of P work area in Ordos Basin, this paper makes a comparative analysis and summarizes its difficulties: there are many kinds of interference sources in the work area, linear noise is developed, external interference, abnormal noise interference and other background noise are serious, and the signal-to-noise ratio of the original data is low due to the influence of the thickness of the low deceleration zone, the thickness of loess cover and the excitation and reception conditions. Therefore, aiming at this difficulty, a set of pre stack fidelity denoising technology is adopted according to different noise types. For surface wave, refraction wave, linear interference, abnormal energy interference and other noise, classification, zoning, step-by-step, domain, frequency and time-sharing fidelity processing are used to improve the data imaging effect. Frequency division abnormal amplitude suppression technology, prestack coherent noise suppression and ground roll wave simulated removal technology are used to solve the problem of noise interference. The results show that the signal-to-noise ratio and resolution of seismic data in this area have been significantly improved by using the above technologies.

Simulation and error analysis of illumination system with coherent noise suppression

提出通过离轴照明系统抑制相干噪声的方案,设计了一种具有抑制相干噪声能力的照明系统。通过Zemax的非序列模式对照明系统进行仿真，得到了厚度为90 μm的离轴环形光斑，验证了此照明系统对相干噪声的抑制能力。将其作为19.05 mm（3/4英寸）Fizeau干涉仪的光源，设置对比实验，光源波长为632 nm，设置圆形噪点尺寸为120 μm~500 μm和尺寸为100 μm×350 μm的划痕。对设计的照明系统进行误差分析，优化照明系统成像质量，调整干涉系统参数，最终在照明光环宽度为42 μm、干涉腔长为15 mm情况下，干涉条纹的对比度提升至97.60%。

Phase-shifting digital holographic microscopy for microstructure measurement by sweeping the repetition rate of femtosecond laser

This paper proposes a phase-shifting digital holographic microscopy (PSDHM) for microstructure measurement by sweeping the repetition rate of femtosecond laser, and a multiple reflection arrangement between two quasi-parallel mirrors is constructed for optical multiplication. High precision phase-shifting can be achieved by sweeping the repetition rate of the femtosecond laser referenced to a Rb atomic clock without any mechanical sweeping. Optical multiplication can shorten the spatial distance of the optical delay line used for pulses alignment, make the PSDHM structure compact and stable, and avoid certain environmental disturbances. In the experiments, a ten-step phase-shifting test was first carried out for evaluating the phase-shifting accuracy, and the phase-shifting error was calculated to be in the range of −1° to 0.25°. Then, a USAF 1951 resolution target and a microstructure standard target were measured using a four-step PSDHM, and the measurement results were compared with those from a stylus profiler and a white light interferometer, respectively. The lateral resolution of PSDHM was tested to be about 2.1 μm, and the maximum error of the longitudinal measurement was within 6 nm. Experiments verify that the PSDHM system has good performance in terms of phase-shifting accuracy, surface topography measurement and coherent noise suppression.

Coherent noise suppression in digital holographic microscopy based on label-free deep learning

Deep learning techniques can be introduced into the digital holography to suppress the coherent noise. It is often necessary to first make a dataset of noisy and noise-free phase images to train the network. However, noise-free images are often difficult to obtain in practical holographic applications. Here we propose a label-free training algorithms based on self-supervised learning. A dilated blind spot network is built to learn from the real noisy phase images and a noise level function network to estimate a noise level function. Then they are trained together via maximizing the constrained negative log-likelihood and Bayes’ rule to generate a denoising phase image. The experimental results demonstrate that our method outperforms standard smoothing algorithms in accurately reconstructing the true phase image in digital holographic microscopy.

Coherent Noise Suppression of Single-Shot Digital Holographic Phase Via an Untrained Self-Supervised Network

In digital holography, the coherent noise affects the measurement accuracy and reliability greatly due to the high spatial and temporal coherence of the laser. Especially, compared with the speckle noise of intensity in digital holography, the coherent noise of phase contains more medium- and low-frequency characteristics, which hinders the effectiveness of noise suppression algorithms. Here, we propose a single-shot untrained self-supervised network (SUSNet) for the coherent noise suppression of phase, requiring only one noisy phase map to complete the optimization and learning. The SUSNet can smoothen and suppress the background fluctuations, parasitic fringes, and diffraction loops in a noisy phase and shows good generalization performance for samples with different shapes, sizes, and phase ranges. Compared with the traditional algorithms and the ground truth-supervised neural network (DnCNN), the SUSNet has the best noise suppression performance and background smoothing effect. As a result, the SUSNet can suppress the fluctuation range to ∼20% of the original range.

Adaptive ground-roll attenuation using local nonlinear filtering

Source-generated coherent noise suppression is a long-standing issue in land seismic data processing. Traditional methods—such as f–k filtering, τ-p transform, and median filtering—often fail to remove coherent noise in irregular data, especially in the case that the noise highly overlaps with the signal. To address this issue, we developed a method based on local nonlinear filtering (LNF) to attenuate coherent noise in seismic data. In the proposed method, a novel similarity coefficient algorithm is presented to determine the optimal velocity of coherent noise. An initial model of coherent noise is constructed using a mixed median–mean filter and is then refined by the proposed weighting correction algorithm. After modeling the noise, coherent noise in seismic data can be then suppressed by adaptively subtracting the estimated noise from the raw seismic data. To verify the effectiveness of the proposed method, we applied it to regular and irregular synthetic data and a 3D field dataset containing strong ground roll. The results demonstrate that the new method successfully attenuates the ground roll and obtains better results in ground-roll attenuation and the preservation of useful signals compared with the three commonly used methods. Synthetic and real data examples indicate that the method is applicable to the attenuation of coherent noise in irregular data.

Advances in differential photoacoustic spectroscopy for trace gas detection

AbstractPhotoacoustic spectroscopy (PAS), as a nondestructive method, permits high sensitivity and selectivity in trace gas detection fields. Advances in differential PAS configurations have dramatically suppressed the background noise and triggered the detectivity improvement. A detailed review on the development of differential photoacoustic gas sensors is presented: using two identical cavity differential cells and optical path types provides good performance for coherent noise suppression, and differential mode excitation technique, which evaluates the signal ratios with two frequency excitations, suppresses the common mode noise and signal drifts generated by the light power or the responsivity of the PA system. An overview of the latest developments of the differential PAS sensors is reported. Distinguished results in terms of sensitivity, selectivity, and miniaturization are presented. The advantages and limitations of differential sensors with different photoacoustic sensing technologies are discussed.

Method of Eliminating Helicopter Vibration Interference Magnetic Field with a Pair of Magnetometers

The low-frequency electromagnetic fields and magnetic anomalies generated by ships and other underwater platforms are widely recognized as important features for ocean target detection. Low-frequency magnetic fields and anomalies are typically measured by optically pumped magnetometers installed on aircraft. However, the interference that is generated by the aircraft platform may significantly affect the detection performance. The traditional aeromagnetic compensation model has a good effect on eliminating the interference magnetic field that is caused by the carrier attitude variation. Usually, the magnetometer is fixed at the top of a long probe on the aircraft to avoid the influence from the main body in the aircraft. However, the probe is sensitive to external vibrations, and vibration-induced magnetic interference can occur in the measurements. The magnetometer is especially easily affected by the interference magnetic field, including the vibration frequency and harmonic frequency of the probe, in a moving platform, such as a helicopter. These interference fields usually have independent frequency characteristics that can be eliminated by compensation methods. In this paper, we propose a method based on the improved coherent noise suppression method with a pair of magnetometers to eliminate the effects from these magnetic field disturbances and improve the detection performance of the measurement system. The results of the flight experiment show that the method can effectively eliminate low-frequency vibration interference and improve the detection ability of weak signals from targets.

Navigation-grade resonant fiber-optic gyroscope using ultra-simple white-light multibeam interferometry

The miniaturization of the gyroscope is critical for spacecrafts, drones, wellbore surveys, etc. The resonant fiber-optic gyroscope (RFOG) is a competitive candidate due to its potential in both miniaturization and high resolution, while its actual performance is well below expectation because of laser-induced noise and complexity. Here we report the first navigation grade RFOG with a bias instability of 0.009°/h and an angle random walk of 0.0093°/ h . The results are realized using a fiber resonator with finesse of 63 containing 100-m long fiber. Compared with the traditional RFOGs using narrow-linewidth lasers, the key feature of the proposed RFOG is that it is driven with a broadband light source. A white-light multibeam interference method is proposed to detect the Sagnac effect, representing the simplest scheme of RFOG to date. The complexity caused by multiple feedback loops and coherent noise suppression in traditional RFOG scheme is avoided. The minimal scheme and simple modulation algorithm will also promote the on-chip waveguide gyroscope.

Highly sensitive broadband differential infrared photoacoustic spectroscopy with wavelet denoising algorithm for trace gas detection

Enhancement of trace gas detectability using photoacoustic spectroscopy requires the effective suppression of strong background noise for practical applications. An upgraded infrared broadband trace gas detection configuration was investigated based on a Fourier transform infrared (FTIR) spectrometer equipped with specially designed T-resonators and simultaneous differential optical and photoacoustic measurement capabilities. By using acetylene and local air as appropriate samples, the detectivity of the differential photoacoustic mode was demonstrated to be far better than the pure optical approach both theoretically and experimentally, due to the effectiveness of light-correlated coherent noise suppression of non-intrinsic optical baseline signals. The wavelet domain denoising algorithm with the optimized parameters was introduced in detail to greatly improve the signal-to-noise ratio by denoising the incoherent ambient interference with respect to the differential photoacoustic measurement. The results showed enhancement of sensitivity to acetylene from 5 ppmv (original differential mode) to 806 ppbv, a fivefold improvement. With the suppression of background noise accomplished by the optimized wavelet domain denoising algorithm, the broadband differential photoacoustic trace gas detection was shown to be an effective approach for trace gas detection.

GROUND ROLL NOISE ATTENUATION IN 3D LAND SEISMIC DATA IN PARTS OF NIGER DELTA, NIGERIA

Three-dimensional (3D) land seismic datasets were acquired from Central Depobelt in the Niger Delta region, Nigeria, with with the aim of attenuating ground roll noise from the dataset. The Omega (Schlumberger) software 2018 version was used along with frequency offset coherent noise suppression (FXCNS) and Anomalous Amplitude Attenuation (AAA) algorithms for ground roll attenuation. From the results obtained, Frequency Offset Coherent Noise Suppression (FXCNS) attenuates ground roll while AAA algorithm attenuates the residual high amplitude noise from the seismic data. Average frequency of the ground roll in the seismic data is 10.50Hz which falls within the actual range of ground roll frequency which is within the range of 3.00 – 18.00Hz. The average velocity of the ground roll in the seismic data is 477.36ms-1 while the velocity of ground roll ranges between 347.44 and 677.37ms-1. The wavelength of ground roll in the seismic data is 50.28m. The amplitude of the ground roll of -6.24dB is maximum at 4.2Hz. Frequency of signal ranges between 10.21 and 25.12Hz with an average of 17.67Hz. Signal amplitude of -8.32dB is maximum at 6.30Hz, while its wavelength is 57.12m. The results of this work can be used in the seismic source-receiver design for application in the area of study. Moreover, with ground roll noise attenuated, a better image of the subsurface geology is obtained hence reducing the risk of obtaining a wild cat drilling.

Random and Coherent Noise Suppression in DAS-VSP Data by Using a Supervised Deep Learning Method

Distributed fiber-optical acoustic sensing (DAS) is a new and booming technology in seismic exploration. DAS technology has been gradually applied to the exploration of vertical seismic profile (VSP) due to its strong resistance to high temperature and pressure, high sensitivity, high precision (trace interval can be accurate to about 1 m), and so on. However, real DAS-VSP data are always contaminated by both random and coherent noises, which greatly affects the quality of DAS-VSP data. In order to suppress the background noise and increase the signal-to-noise ratio (SNR), a convolutional neural network (CNN) based on leaky rectifier linear unit (ReLU) and forward modeling is proposed and named L-FM-CNN. In terms of network architecture, Leaky ReLU is adopted as the activation function of CNN, which can enhance the recovery ability of trained CNN denoising model to the weak effective signals. As for the training data set, we construct a high-authenticity theoretical pure seismic data set for DAS-VSP data through the complexity of forward models and the diversification of physical parameters. In addition, we propose a new mean square error (MSE) loss function combined with an energy ratio matrix (ERM). The ERM can adjust the SNR between the signal patch and noise patch during the network training and thus increase the robustness of trained CNN denoising model for the DAS-VSP data with different SNRs, especially the DAS-VSP data with extremely low SNR. Both synthetic and real experiments prove the effectiveness of the proposed L-FM-CNN.

Noise suppression in 2D and 3D seismic data with data-driven sifting algorithms

We have developed an empirical-mode decomposition (EMD) algorithm for effective suppression of random and coherent noise in 2D and 3D seismic amplitude data. Unlike other EMD-based methods for seismic data processing, our approach does not involve the time direction in the computation of the signal envelopes needed for the iterative sifting process. Instead, we apply the sifting algorithm spatially in the inline-crossline plane. At each time slice, we calculate the upper and lower signal envelopes by means of a filter whose length is adapted dynamically at each sifting iteration according to the spatial distribution of the extrema. The denoising of a 3D volume is achieved by removing the most oscillating modes of each time slice from the noisy data. We determine the performance of the algorithm by using three public-domain poststack field data sets: one 2D line of the well-known Alaska 2D data set, available from the US Geological Survey; a subset of the Penobscot 3D volume acquired offshore by the Nova Scotia Department of Energy, Canada; and a subset of the Stratton 3D land data from South Texas, available from the Bureau of Economic Geology at the University of Texas at Austin. The results indicate that random and coherent noise, such as footprint signatures, can be mitigated satisfactorily, enhancing the reflectors with negligible signal leakage in most cases. Our method, called empirical-mode filtering (EMF), yields improved results compared to other 2D and 3D techniques, such as [Formula: see text] EMD filter, [Formula: see text] deconvolution, and [Formula: see text]-[Formula: see text]-[Formula: see text] adaptive prediction filtering. EMF exploits the flexibility of EMD on seismic data and is presented as an efficient and easy-to-apply alternative for denoising seismic data with mild to moderate structural complexity.