Coherent Noise Research Articles

High spatial resolution φ-OFDR based on frequency-shift averaging and rotating vector summation

The coherent fading noise hinders the spatial resolution enhancements of phase optical frequency domain reflectometry (φ-OFDR). To mitigate the coherent fading noise, this study introduces frequency-shift averaging and rotating vector summation for strain measurements. The original Rayleigh backscattering signal was first divided into numerous sub-segments in frequency domain by using the frequency-shift averaging, and each sub-signal segment was transferred into vector signal with both intensity and phase information by using the rotating vector summation. Furthermore, a spatial position correction algorithm is incorporated to counteract the degradation of phase coherent, facilitating large-scale strain measurements. After optimizing the sub-frequency range and overlap ratio of the sub-signal in the frequency domain, we experimentally demonstrated that this method achieves strain measurement with a broad strain range of 2000 με, a high spatial resolution of 0.54 mm, and a high precision of 0.87%.

The impact of receiver arrays on suppressing seismic speckle scattering noise caused by the meter-scale near-surface heterogeneity

Managing seismic scattering noise in geophysical surveying, especially from near-surface heterogeneity, presents significant challenges. We revisit the role of receiver arrays in mitigating such noise in regions with near-surface meter-scale heterogeneity. Moving beyond traditional approaches that focus on suppressing coherent noise from near-surface arrivals and random ambient noise, our research highlights the substantial impact of speckle scattering noise. This noise, resulting from near-ballistic scattering on small-scale heterogeneities, introduces considerable trace-to-trace variability in phase and amplitude, complicating data processing. By integrating a novel model of random multiplicative noise with sophisticated statistical techniques, we determine the efficacy of array stacking in significantly reducing this type of noise and decreasing the dispersion of phase and amplitudes. Our analytical and numerical analyses reveal a notable trend: the reduction in phase and amplitude spread amplifies with the array size. A key finding of our study is the identification of a [Formula: see text] law for phase spread reduction, analogous yet distinct from the well-known [Formula: see text] law observed in amplitude attenuation of additive ambient noise. In our context, this law implies that the standard deviation of residual phase disturbances diminishes in proportion to [Formula: see text], where N denotes the array size. This insight holds particular significance for smaller arrays, such as those with nine receivers commonly used in desert environments.

Multi-mode high resolution TFM imaging of microdefects based on laser ultrasonic full matrix capture

The identification and visualization of submillimeter microdefects have long been a critical focus within the realm of ultrasonic testing. The utilization of high-frequency signals is imperative due to the small acoustic interaction structure. An additional benefit of using lasers is their non-contact nature and ability to utilize multiple imaging modes, effectively reducing false or missed detections and enhancing credibility. This study proposes an advanced optimization algorithm for imaging and quantitative evaluation of submillimeter microdefects using laser ultrasonic full matrix capture (FMC) and total focusing method (TFM). The analysis of acoustic directionality under laser excitation and detection provides a foundation for mode selection. In preprocessing, the multi-scale principal component analysis (MSPCA) method is introduced to suppress coherent noise and blind spots. Moreover, phase coherence methods are implemented to further reduce mode artifacts, background noise, and residual surface acoustic wave (SAW). Additionally, multi-mode imaging and mode fusion are achieved using reflected longitudinal waves, reflected shear waves, converted longitudinal waves, and converted shear waves. Experiments were carried out on two different types of microdefects, namely side drilled holes (SDHs) and blind holes (BHs), resulting in high resolution TFM imaging with a minimum BH size of 0.3 mm. The outcomes demonstrate the efficacy of MSPCA in suppressing coherent noise, with further enhancement in imaging quality after phase coherence weighting. In addition, the −6 dB threshold of multi-mode images also provided the size information. This research is poised to contribute to the resolution of quality assessment challenges in powder metallurgy, welding, and additive manufacturing processes.

Nanomechanical Crystalline AlN Resonators with High Quality Factors for Quantum Optoelectromechanics.

High-quality factor (Qm) mechanical resonators are crucial for applications where low noise and long coherence time are required, as mirror suspensions, quantum cavity optomechanical devices, or nanomechanical sensors. Tensile strain in the material enables the use of dissipation dilution and strain engineering techniques, which increase the mechanical quality factor. These techniques have been employed for high-Qm mechanical resonators made from amorphous materials and, recently, from crystalline materials such as InGaP, SiC, and Si. A strained crystalline film exhibiting substantial piezoelectricity expands the capability of high-Qm nanomechanical resonators to directly utilize electronic degrees of freedom. In this work, nanomechanical resonators with Qm up to 2.9 × 107 made from tensile-strained 290 nm-thick AlN are realized. AlN is an epitaxially-grown crystalline material offering strong piezoelectricity. Nanomechanical resonators that exploit dissipation dilution and strain engineering to reach a Qm × fm-product approaching 1013 Hz at room temperature are demonstrated. A novel resonator geometry is realized, triangline, whose shape follows the Al-N bonds and offers a central pad patterned with a photonic crystal. This allows to reach an optical reflectivity above 80% for efficient coupling to out-of-plane light. The presented results pave the way for quantum optoelectromechanical devices at room temperature based on tensile-strainedAlN.

Suppression of intensity noise and phase noise for thulium-holmium co-doped fiber laser by self-injection locking

The present study proposes and demonstrates a narrow-linewidth thulium-holmium co-doped fiber laser based on self-injection locking. The laser with single-longitudinal-mode operation is realized using a fiber Bragg grating as a wavelength-selection component and a dual-ring compound cavity as a mode-selection filter. The linewidth was compressed by increasing the photon lifetime by utilizing delay fibers of different lengths in the feedback cavity. The results show that the optical signal-to-noise ratio of the output laser is 57.2 dB, and the relative intensity noise is -130.43 dB/Hz with self-injection locking. The intrinsic linewidth is compressed from 39.45 kHz to 3.21 kHz, a reduction of nearly an order of magnitude. The linewidth compression mechanism solves the problems associated with deep linewidth compression in fiber lasers with long ring cavities and fosters the development of practical and reliable all-fiber structures. The laser source is characterized by low cost, high coherence, and low noise, which are highly desirable features in coherent optical detection, high-resolution spectrometers, microwave photonics, and optical sensing.

Iterative processing of the noise coherence matrix to determine the acoustic properties of the water area

Abstract An expression is given for the coherence matrix of the acoustic field produced by random sources in an inhomogeneous medium in the Born approximation. An iterative algorithm is proposed to determine the wave number inside the inhomogeneity based on this expression. The reconstruction results are analyzed for the cases of precise input data, data with interference, and incomplete data.

Foundation model of neural activity predicts response to new stimulus types and anatomy.

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

Scattering matrix similarity metric optimization for improved defect characterisation based on dynamic graph attention networks

Ultrasonic scattering matrices contain rich defect information and have great potential for characterising small crack-like defects. However, experimentally measured scattering matrices often exhibit some level of distortions compared to those of the idealised defects, posing challenges for accurate defect characterisation. In this paper, defect characterisation was performed by adopting a nearest neighbour approach based on a scattering matrix database of reference defects, and the test data were contaminated by coherent measurement noise of varying amplitudes. The performance of different similarity metrics on characterisation accuracy was studied, including the Euclidean similarity, cosine similarity, Pearson correlation coefficient, and the structural similarity index. Based on a comprehensive analysis of the strengths and weaknesses of different similarity metrics, we propose a defect characterisation framework by constructing similarity graphs and leveraging advanced graph neural networks. Within the proposed approach, multiple metrics were adopted to quantify the similarity between the scattering matrices of different defects, and an improved dynamic graph attention network was developed based on a customised neighbour sampling strategy to learn the optimal metric from the graph-structured data. Experimental results show that compared to the conventional approach which adopted a globally optimal similarity metric, the proposed method can reduce the root mean squared error for the length and angle predictions by 60.5% and 67.1%, respectively.

Uniformity optimization of galvanometric illumination

Dual-axis galvanometer is used as a common beam steering component in diverse illumination and imaging approaches. In this paper, we propose an uncorrelated uniform illumination approach based on dual-axis galvanometric scanning. Specially, we analyze the mechanism of galvanometric illumination and elucidate three main factors affecting its uniformity properties. In this illumination module, the laser beam is deflected rapidly by the galvanometer which is driven by triangular voltage signals, and then focused by an f-θ lens to illuminate the object at an equal lateral scanning velocity. This method has lower coherent noise than extended laser illumination, higher brightness than partial coherent illumination and higher uniformity than galvanometric Lissajous trajectory driven by sinusoidal signals.

A Multi-Strategy Hybrid Sparse Reconstruction Method Based on Spatial-Temporal Sparse Wave Number Analysis for Enhancing Pipe Ultrasonic-Guided Wave Anomaly Imaging.

Ultrasonic-guided waves (UGWs) in defective pipes are subject to severe coherent noise caused by imperfect detection conditions, mode conversion, and intrinsic characteristics (dispersion and multiple modes), inducing the limited performance of anomaly imaging. To achieve the high resolution and accuracy of anomaly imaging, a multi-strategy hybrid sparse reconstruction (MHSR) method based on spatial-temporal sparse wavenumber analysis (ST-SWA) is proposed. MHSR leverages the capability of ST-SWA to extract the wavenumber dispersion curves, thereby providing a more refined and precise search space for MHSR. Furthermore, it mitigates the impact of coherent noise by conducting dispersion compensation on the reconstructed signal. The sparse compensated signals through MHSR are employed for sparse reconstruction imaging. To validate the efficacy of the proposed method, UGW testing is performed on the defective steel pipe, and the results demonstrate the significant enhancement of anomaly imaging in defect resolution and positioning accuracy. The lowest estimated errors for axial and circumferential defect positions are 10 mm and 4 mm, respectively.

AI-driven pseudo-light source for achieving high coherence and low speckle noise simultaneously in dual-wavelength digital holographic microscopy

Digital holography, a promising technology for optical imaging, is limited by the fundamental challenge of speckle noise when based on coherent lasers. These approaches often compromise either coherence, affecting surface depth measurement capabilities, or introduce excessive noise, degrading image clarity. To eliminate this trade-off, this study introduces a novel solution based on an AI-driven pseudo-light source that simultaneously achieves high coherence and low speckle noise in holographic imaging. Consisting of conditional generative adversarial networks, the AI model was trained on paired holograms from both highly coherent laser light and partially coherent quantum dot (QD)-based light sources to generate holograms that mimicked the low-noise characteristics of QD-based light while retaining the high coherence length of laser. The effectiveness of the pseudo-light source was validated through holographic observations on a reflective 8.0-µm-deep specimen. Compared to the laser, the AI-driven pseudo-light source achieved substantial improvement in interference pattern clarity and reduced speckle noise contrast from 0.602 to 0.0873. Moreover, the standard deviation of the surface depth distribution was notably reduced from 215.3 nm to 44.7 nm. Quantitative phase evaluations further confirmed the preservation of accurate phase information in the generated holograms, verifying the successful reconstruction of the three-dimensional specimen structure.

Characterization of entanglement on superconducting quantum computers of up to 414 qubits

As quantum technology advances and the size of quantum computers grow, it becomes increasingly important to understand the extent of quality in the devices. As large-scale entanglement is a quantum resource crucial for achieving quantum advantage, the challenge in its generation makes it a valuable benchmark for measuring the performance of universal quantum devices. In this paper, we study entanglement in Greenberger-Horne-Zeilinger (GHZ) and graph states prepared on the range of IBM Quantum devices. We generate GHZ states and investigate their coherence times with respect to state size and dynamical decoupling techniques. A GHZ fidelity of 0.519±0.014 is measured on a 32-qubit GHZ state, certifying its genuine multipartite entanglement (GME). We show a substantial improvement in GHZ decoherence rates for a seven-qubit GHZ state after implementing dynamical decoupling, and observe a linear trend in the decoherence rate of α=(7.13N+5.54)×10−3µs−1 for up to N=15 qubits, confirming the absence of superdecoherence. Additionally, we prepare and characterize fully bipartite entangled native-graph states on 22 superconducting quantum devices with qubit counts as high as 414 qubits, all active qubits of the 433-qubit IBM Osprey device. Analysis of the decay of two-qubit entanglement within the prepared states shows suppression of coherent noise signals with the implementation of dynamical decoupling techniques. Additionally, we observe that the entanglement in some qubit pairs oscillates over time, which is likely caused by residual ZZ-interactions. Characterizing entanglement in native-graph states, along with detecting entanglement oscillations, can be an effective approach to low-level device benchmarking that encapsulates two-qubit error rates along with additional sources of noise, with possible applications to quantum circuit compilation. We develop several tools to automate the preparation and entanglement characterization of GHZ and graph states. In particular, a method to characterize graph state bipartite entanglement using just 36 circuits, constant with respect to the number of qubits. Published by the American Physical Society 2024

Optical coherence tomography imaging and noise characterization based on 1-μm microresonator frequency combs

Optical coherence tomography imaging and noise characterization based on 1-<i>μ</i>m microresonator frequency combs

Polarization analysis and filtering of undersampled 3C seismic data in the frequency-slowness domain

Polarization filters have emerged as a useful tool for coherent noise denoising in multicomponent seismic data. By exploiting the polarization properties of seismic waves, these filters can significantly improve wave-type separation, which is a crucial step in seismic data processing. However, their effectiveness in land seismic data in complex areas can be impacted by the presence of scattering noise, and their applicability is limited by irregular spatial sampling, which hinders velocity information for wave-type discrimination. To address these limitations, we introduce a novel two-step method for distinguishing and separating polarized wave types in shot gathers captured from a spatially undersampled linear array of 3C vector sensors. The first step involves conducting a polarization analysis in the frequency-slowness domain using elliptical elements derived from the linear Radon transform of the seismic data. The second step isolates specific polarized waves using the 3C frequency-slowness polarization filter (3C-FSPF), a technique that uses tailored taper functions based on polarization analysis. Our method stands out from single-station filters that cannot use velocity information for wave-type discrimination and existing multistation filters that require regularly sampled data. To evaluate our method, comprehensive tests are conducted on synthetic and real data. The results consistently demonstrate the effectiveness of 3C-FSPF in separating diverse polarized wave types under conditions of irregular and sparse spatial sampling and noise. Our findings underscore the potential of this method for advancing exploration geophysics by enhancing the quality of seismic data, particularly in land regions with complex near-surface structures.

Interpretation and use of coherence in multi-channel measurements

The coherence function can be a powerful tool when analyzing data from multi-channel measurements. Often, coherence is used to describe the amount of incoherent noise in an output signal, though more generally the coherence between two signals is a representation of the linearity of the relationship between the signals as a function of frequency. Such degradation in the coherence then can be understood to not only be caused by potential incoherent noise, but also by moving sensors or a changing acoustic environment. Aside from its use as a diagnostic tool, coherence can also be used to condition measured data. For example, a sensor can be placed on a source of unwanted sound, and the portion of the desired signal coherent with the sensor's output (the "coherent output power") can be removed. In this paper, methods for coherent noise removal and signal enhancement are presented in both the frequency and time domains. Example measurements are used to demonstrate the effectiveness of the techniques for situations where separating noise sources is otherwise difficult.

Self-supervised denoising at low signal-to-noise ratios: A seismic-while-drilling application

Self-supervised blind-mask denoising networks overcome the challenge of requiring clean training targets by employing a mask on raw noisy data to form the input for training while using the unmasked version as the network's target. The application of such networks has shown considerable strength in suppressing both random and coherent noise in seismic data. However, because such networks need to figure out the target (clean signal) on their own, they struggle at low signal-to-noise ratios. Seismic-while-drilling acquisitions result in seismic data of very low quality because the drilling operation introduces significant noise propagating from the rig site. Due to its consistent and low-frequency nature, it is hard to design a noise mask to hide the rig noise from the network without also hiding useful information required for predicting the signal. However, by reframing the task from a noise suppression to a noise prediction task and utilizing a mask to hide the signal from the network, the rig noise can be predicted accurately. Therefore, the difference between the network's prediction and the raw data results in common bit (equivalent to shot, but continuous) gathers with a significantly higher signal-to-noise ratio due to the removal of rig noise. Illustrated on six common bit gathers, this reversed methodology is shown to separate the rig noise and signal, even in their shared bandwidth. The additional use of explainable artificial intelligence is investigated as a means of avoiding the manual step of creating the signal mask, providing promising results. This study lays the ground work for suppression of high-amplitude, consistent noises, such as those arising from well site operations like fluid injection procedures for carbon sequestration or geothermal energy production purposes.

Phase noise cancellation for digital holographic microscopy based on compressed sensing iterative adaptive sparse dictionary

Digital holographic microscopy (DHM), like other digital imaging techniques, will introduce severe coherent noise, which is called random noise, due to the high coherence of the required light source and the measurement experimental environment. Random noise seriously affects the accuracy of DHM surface topography measurements. To overcome this problem, this paper proposes a DHM phase noise cancellation method based on compressed sensing (CS) iterative adaptive sparse dictionary. The holographic image is first acquired using the proposed recording method. Then a sparse dictionary more suitable for the sample is trained by dictionary learning to sparsely encode the holographic image. Finally, reconstruction is performed using the improved orthogonal matching pursuit (IOMP), which can obtain almost noise-free holographic images. The experimental results show that the reconstructed phase peak signal-to-noise ratio (PSNR) is 33.2871, which is improved by 65.01 %. The sample feature similarity is 0.9553, which is improved by 45.41 %. The proposed method can also be used with different learning modes and learning objects. On top of the original results, the reconstructed phase PSNR was able to continue to improve by 5.99 % and its reconstruction time was also accelerated by 29.57 %.

Coherent Noise (Ground roll) Consideration: Local Wave-field Decomposition (LWD) Based Approaches to Suppression

There is always a signal and a noise component in seismic data. Noise can be defined as any recorded energy that tampers with the desired signal of interest. Although it might be difficult to separate signal from noise due to the variety of noise kinds, effective noise suppression is crucial for high-quality imaging. Surface waves or ground roll are a prominent source-generated coherent noise in exploration seismology. Ground roll contains little to no information about the deeper subsurface as it moves laterally through the earth’s near surface. The low-frequency contents and strong amplitudes of these arrivals often mask recordings of body waves reflected by deeper subsurface targets. This discussion paper will describe ground rolls, a significant form of seismic coherent noise, as well as their unique characteristics, how they appear on acquired seismic data, the significance of suppressing them in seismic processing applications, and an examination of local wave-field decomposition (LWD) techniques and their applications.

Generation of reverse time migration dip gathers with the stabilized Poynting vector and their application in the improvement of subsalt images

Subsalt imaging is challenging with coherent noise prevalent beneath complex salt structures. The coherent noise degrades the imaging quality, making image enhancement and interpretation difficult and potentially erroneous. The dip gathers of reverse time migration (RTM) serve as an ideal domain for separating signals from coherent noise due to their distinct distributions in the dip domain. Methods utilizing the Poynting vector offer an efficient and cost-effective means to produce dip gathers. However, the presence of zero points in the Poynting vector causes instabilities in direction or angle estimation, leading to the leakage of reflection energy into false dip angles. This issue complicates the separation of desired signals from coherent noise. We address this instability by employing a stabilized Poynting vector to produce high-quality RTM dip gathers. The stabilized Poynting vector does not contain zero points within the range of wave propagation, thus mitigating the instability problem. The dip gathers generated using the stabilized Poynting vector provide a clearer and more precise depiction of signal and noise distributions, allowing us to identify the boundaries of desired signals and mute all noise outside these boundaries. Two numerical examples with a synthetic dataset and a field dataset are used to demonstrate our method's effectiveness in reducing coherent noise and improving quality of subsalt images.

Theoretical analysis and experimental verification of spatial coherence of acoustic cavitation noise from bubble clusters under ultrasonic horn

Acoustic cavitation bubbles under ultrasonic horn in water emit acoustic cavitation noise, which consists of spherical shockwaves. This study theoretically derived the spatial coherence of acoustic cavitation noise or, more precisely, the spectral degree of coherence. The acoustic cavitation noise was found to have spatial coherence characteristics similar to the “thermal light” in optics, unlike ultrasound generated by general transducers, which are analogous to “laser” with high coherence. The experiments validated the derived theory and showed that the spectral degree of coherence of the acoustic cavitation noise depends on the product between the distribution width of the shockwave origin, proportional to the horn diameter, and the angle between the hydrophones viewed from the horn. The lower the product gives, the higher the spectral degree of coherence at a higher frequency range.