Adjacent Traces Research Articles

Distributed vibration and temperature sensing system by multiplexed fiber scattering spectra

A new, to the best of our knowledge, distributed optical fiber vibration and temperature hybrid sensing system is proposed and experimentally demonstrated. The proposed system only employs two signal channels, which is more compact and practical. Based on the structure of the optical time domain reflectometer (OTDR), the Rayleigh scattering light and the Raman anti-Stokes scattering light is extracted for vibration and temperature sensing, respectively. For vibration sensing, a new differential location algorithm based on polarization state analysis of the Rayleigh scattering light is proposed to locate the vibration events. It first rectifies the original OTDR traces by fiber attenuation compensation to make each position in it with the same pulse power level. And then, by difference of adjacent traces and threshold discrimination, the vibration positions are identified and located. For temperature sensing, a temperature calibration unit and algorithm are adopted to dynamically correct the trace data and reduce the temperature measurement error caused by the instability of the pulse laser source. The experiment is conducted with a fiber range of about 12 km and laser pulse width of 60 ns, and the experimental results show that the maximum error range for temperature measurement is −0.7∘C to 1.3°C, with a root mean square (RMS) error of 0.85°C in the entire temperature measurement range. Additionally, the spatial resolution (SR) for both vibration and temperature sensing is 6 m.

Dealiased seismic data interpolation by dynamic matching

Interpolation is a critical step in seismic data processing. Gaps in seismic traces can lead to severe spatial aliasing phenomena in the corresponding f- k spectra. The aliasing caused by regularly spaced gaps has similar f- k spectra as those of the actual data. Existing dealiasing interpolation algorithms generally assume that seismic events are linear and cannot handle nonstationary events. To address this shortcoming, we develop a novel dealiased seismic data interpolation approach using dynamic matching. First, we match two adjacent seismic traces using the local affine regional dynamic time-warping algorithm. Subsequently, we calculate the local slope between two seismic traces. Finally, we perform linear interpolation on the regularly missing seismic data using local slope information. Our approach is tested on synthetic and field seismic data sets. The interpolation results indicate that our approach has a better antialiasing ability and computational efficiency than the traditional Spitz and seislet-based approaches. In addition, this method can be applied to interpolate irregularly sampled seismic data and for simultaneous seismic data interpolation and denoising.

Hybrid Printing of Conductive Traces from Bulk Metal for Digital Signals in Intelligent Devices.

In this article, we explore multi-material additive manufacturing (MMAM) for conductive trace printing using molten metal microdroplets on polymer substrates to enhance digital signal transmission. Investigating microdroplet spread informs design rules for adjacent trace printing. We studied the effects of print distance on trace morphology and resolution, noting that printing distance showed almost no change in the printed trace pitch. Crosstalk interference between adjacent signal traces was analyzed across frequencies and validated both experimentally and through simulation; no crosstalk was visible for printed traces at input frequencies below 600 kHz. Moreover, we demonstrate printed trace reliability against thermal shock, whereby no discontinuation in conductive traces was observed. Our findings establish design guidelines for MMAM electronics, advancing digital signal transmission capabilities.

Regularized deep learning for unsupervised random noise attenuation in poststack seismic data

Abstract Deep learning methods achieve excellent noise reduction performances in seismic data processing compared with traditional methods. However, deep learning usually requires a large number of pairwise noisy-clean training data, which is an extremely challenging task. In this paper, an unsupervised approach without clean seismic data is proposed to suppress random noise. Seismic data is divided into odd and even traces, which serve as the input and output of the depth network, so that the proposed algorithm can be trained directly on the original data. What is more, the proposed method introduces two regularization terms to solve the over-smoothing problem caused by reconstruction of adjacent traces. The first term considers an ideal denoising network that does not cause oversmooth as a constraint, while the second term considers the structural information existing in seismic data. Experiments on synthetic post-stack data illustrate that the proposed method obtain a higher signal-to-noise ratio than the comparison methods. In the application of field post-stack seismic data, the proposed method can effectively maintain the seismic amplitude and generate good spectral characteristics.

A CMOS Hall Sensors Array With Integrated Readout Circuit Resilient to Local Magnetic Interference From Current-Carrying Traces

This paper proposes a CMOS Hall sensors array with readout circuitry to counteract the magnetic interference from nearby conductors carrying high currents. The 3×3 Hall sensors array with current-spinning collects the spatial magnetic field information in a time-interleaved manner while the readout circuitry, formed by a capacitively-coupled instrumentation amplifier (CCIA) with T-capacitor feedback network, amplifies the transduced signals for subsequent processing. The calibration mechanism analyzes the magnetic field distribution on the array and separates the magnetic field component due to the environment and local perturbation from adjacent traces, enabling accurate detection of the environmental magnetic field. Prototyped in a 0.35-μm CMOS process, the Hall sensors array and readout circuit occupy an area of 2.2 × 2.2 mm2 and consume 3.5 mA of current under a 3.3-V supply. The hall sensors achieve an average sensitivity of 100 V/(A·T). The input-referred noise of the entire circuit is 0.128 μT/√Hz. With the interference cancellation scheme, the error in the magnetic field readings caused by a conductor 5 mm from the center of the chip carrying 3 A of current reduced from 120 to 5 μT, corresponding to a 24× improvement. Overall, the average error of the captured magnetic field after calibration reaches 0.9%.

Automatic first break picking with structured random forests

AbstractIn the seismic wave exploration field, first break picking is an important underlying task. With the emergence of massive seismic data, it is urgent to develop automatic and accurate picking algorithms to relieve the huge workload of manual picking. Although many traditional and machine learning based techniques have been developed, it is still a challenging task to obtain satisfactory picking results in practice because of complex subsurface structures and low signal‐to‐noise ratio. Ensemble learning has been demonstrated to be a powerful tool to produce good prediction results in many fields. Its core idea is to create multiple models with some special techniques and then to combine the prediction results of each model with a fusion strategy. Structured random forests , a type of ensemble learning method, have been proven to be quite effective in implementing structured learning tasks. Based on the observation that adjacent traces can provide useful information to pick the first arrival time for a specific trace, we propose in this paper a novel structured random forest based first break picking framework. In particular, a multi‐scale normalization technique is presented to make full use of the amplitude information at different scales. To capture local features of first breaks, an enhanced feature map based on the short‐term/long‐term average ratio method is also computed. By extracting patches from two channel feature maps, we construct a structured random forest to predict the locations of the first breaks. On the basis of the probability score map produced by the structured random forest, an effective post‐processing strategy is proposed to further deal with the detected results. By conducting experiments with synthetic and field data, the proposed method is shown to be effective in identifying first breaks. It is significantly superior to the short‐term/long‐term average ratio method and support vector machine in terms of picking accuracy. With the advantage of parallel computing, the computational cost of structured random forest is acceptable, that is, it is much lower than the support vector machine while being higher than the short‐term/long‐term average ratio method. In addition, the experiments also confirm that the multi‐scale normalization plays an important role in improving the picking performance of the structured random forest.

Joint denoising method of seismic velocity signal and acceleration signals based on independent component analysis

The signal-to-noise ratio (SNR) of seismic data is the key to seismic data processing, and it also directly affects interpretation of seismic data results. The conventional denoising method, independent variable analysis, uses adjacent traces for processing. However, this method has problems, such as the destruction of effective signals. The widespread use of velocity and acceleration geophones in seismic exploration makes it possible to obtain different types of signals from the same geological target, which is fundamental to the joint denoising of these two types of signals. In this study, we propose a joint denoising method using seismic velocity and acceleration signals. This method selects the same trace of velocity and acceleration signal for Independent Component Analysis (ICA) to obtain the independent initial effective signal and separation noise. Subsequently, the obtained effective signal and noise are used as the prior information for a Kalman filter, and the final joint denoising results are obtained. This method combines the advantages of low-frequency seismic velocity signals and high-frequency and high-resolution acceleration signals. Simultaneously, this method overcomes the problem of inconsistent stratigraphic reflection caused by the large spacing between adjacent traces, and improves the SNR of the seismic data. In a model data test and in field data from a work area in the Shengli Oilfield, the method increases the dominate frequency of the signal from 20 to 40 Hz. The time resolution was increased from 8.5 to 6.8 ms. The test results showed that the joint denoising method based on seismic velocity and acceleration signals can better improve the dominate frequency and time resolution of actual seismic data.

A deep learning framework based on improved self‐supervised learning for ground‐penetrating radar tunnel lining inspection

AbstractIt is not practical to obtain a large number of labeled data to train a supervised learning network in tunnel lining nondestructive testing with ground‐penetrating radar (GPR). To decrease the dependence of supervised learning on the number of labeled data, an improved self‐supervised learning algorithm—self‐attention dense contrastive learning (SA‐DenseCL)—is proposed and incorporated with a mask region‐convolution neural network (Mask R‐CNN), which is trained by unlabeled and labeled GPR data. The proposed SA‐DenseCL adds a self‐attention‐based relevant projection head to the DenseCL architecture of self‐supervised learning, capturing the spatially continuing information between adjacent GPR traces. In the workflow, some unlabeled GPR images are used to pre‐train the SA‐DenseCL network for feature extraction and obtaining the backbone weights, which is superior to the conventional pre‐training methods of supervised learning pre‐trained by ImageNet images. The weights of the pre‐trained backbone are then used to initialize the Mask R‐CNN through transfer learning. Subsequently, a limited number of labeled GPR images are used to fine‐tune the Mask R‐CNN for automatically identifying the locations of the reinforcement bars and voids and estimating the secondary lining thickness. The experimental results show that the average precision reaches 96.70%, 81.04%, and 94.67% in identifying reinforcement bar locations, detecting void defects, and estimating secondary lining thickness, respectively, which outperform the conventional methods that use ImageNet‐based supervised learning or GPR image‐based DenseCL for initializing the Mask R‐CNN backbone weights. It is observed that the improved self‐supervised learning‐based framework can improve the detection and estimation accuracy in GPR tunnel lining inspection.

Surrogate Modeling for Creep Strain-Based Fatigue Prediction of a Ball Grid Array Component

AbstractIn the past years, the finite element analysis (FEA) has proven to be a suitable way for fatigue prediction of electronic equipment based on the physics-of-failure-approach. For this, inelastic strain parameters like creep strain or creep energy density are evaluated in crack susceptible regions of solder joints. Due to the nonlinearity of the creep behavior, which is the basis for these simulations, the computational effort can be significant. This mostly leads to a component-focused approach. Global influences on components like local stiffness variations due to adjacent components, copper traces, or fixations of the printed circuit board (PCB) are often ignored. To make creep-based fatigue predictions suitable for complex printed circuit board assemblies (PCBA), a method for reducing computational effort needs to be established. For this matter, a machine learning-based approach for solder joints has been developed. First, the process for data generation and model training has been established. Thereafter, several methods for input parameter reduction are discussed. Finally, a model is being trained based on the generated simulation data.

Research on seismic hydrocarbon prediction based on a self-attention semi-supervised model

Predictions of oil and gas reservoirs play an essential role in hydrocarbon reservoir exploration. Multi-attribute seismic data can provide abundant reservoir information. However, traditional reservoir prediction models only reflect the characteristics of local seismic channels, ignoring information on global geological spatial structures. Thus, a semi-supervised prediction model based on self-attention is proposed to extract reservoirs' oil and gas characteristics. With the high cost of labeled samples, neighborhood similarity measurement based on Grey Relation Analysis is applied to determine the homogeneity of adjacent seismic traces for the first time to obtain pseudo-labels while considering geological spatial structure. As a semi-supervised prediction model, a multilayer convolutional neural network with a multi-head self-attention mechanism is utilized, which can extract features from multiple dimensions and investigate global structural information. The experimental results demonstrate that the algorithm in this paper's reservoir oil and gas prediction is more accurate than that of conventional approaches and is consistent with real drilling data.

High cycle fatigue behaviour of Invar 36 alloy fabricated by laser powder bed fusion

ABSTRACT The Invar 36 alloy was additively manufactured by laser powder bed fusion (PBF-LB), and systematical observations and experiments for microstructure, defects, metallography, especially high cycle fatigue behaviour and fractography were conducted. Inadequate laser energy density results in hardly overlapping melting traces, generating numerous defects. Accordingly, the fabricated Invar 36 alloy presents an inferior high cycle fatigue life, as it failures from the rapid aggregation of the defects. In contrast, an adequate laser energy density remarkably enlarges the overlapping between adjacent melting traces. The large molten pools with steady boundaries are beneficially to generate favourable microstructures and low porosity. Consequently, the Invar 36 alloy shows superior high cycle fatigue life, completely generated from small crack propagation, long crack propagation and final fracture stages. Above experimental results and analysis primarily link up the PBF-LB process, microstructures (defects) and high cycle fatigue performance for PBF-LB Invar 36 alloy.

Fast pre-stack multi-channel inversion constrained by seismic reflection features

Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set.

Attenuating coherent environmental noise in seismic data via the U-net method

Noise attenuation is a key step in seismic data processing to enhance desired signal features, minimize artifacts, and avoid misinterpretation. However, traditional attenuation methods are often time-consuming and require expert parameter selection. Deep learning can successfully suppress various types of noise via a trained neural network, potentially saving time and effort while avoiding mistakes. In this study, we tested a U-net method to assess its usefulness in attenuating repetitive coherent events (e.g., pumpjack noise) and to investigate the influence of gain methods on denoising quality. We used the U-net method because it preserves fine-scale information during training. Its performance is controlled by network parameters and improved by minimizing misfits between true data and network estimates. A gain method is necessary to avoid the network’s parameter optimization being biased toward large values in data. We first generated synthetic seismic data with added noise for training. Next, we recovered amplitudes using an automatic gain control (AGC) or a 2D AGC (using adjacent traces’ amplitudes). Then, a back-propagation algorithm minimized the Euclidean norm cost function to optimize the network parameters for better performance. The updating step size and direction were determined using an adaptive momentum optimization method. Finally, we removed the gain effect and evaluated the denoising quality using a normalized root-mean-square error (RMSE). Based on RMSE, the data pre-processed by the 2D AGC performed better with RMSE decreasing from 0.225 to 0.09. We also assessed the limitations of the network when source wavelets or noise differed from the training set. The denoising quality of the trained network was sensitive to the change in the wavelet and noise type. The noisy data in the limitation test set were not substantially improved. The trained network was also tested on the seismic field data collected at Hussar, Alberta, by the CREWES Project. The data had not only excellent reflection events but also substantial pumpjack noise on some shot gathers. We were able to significantly reduce the noise (favorably in comparison to traditional techniques) to considerably allow greater reflection continuity. U-net noise reduction techniques show considerable promise in seismic data processing.

Structure-adapted Multichannel Matching Pursuit for Seismic Trace Decomposition

While the single-channel matching pursuit decomposes a seismic trace into a series of wavelets, the multichannel matching pursuit examines the lateral coherence of the seismic traces as a constraint to improve the lateral continuity of the decomposition. However, the presence of structures in the subsurface negatively affects the performance of the multichannel matching pursuit. We proposed a structure-adapted matching pursuit method that combines with a dynamic time-warping (DTW) algorithm to estimate the similarity between adjacent seismic traces and extract an optimal wavelet along the dip plane. This structure-adapted implementation would significantly speed up the convergence of the decomposition process. We modified the DTW algorithm by combining the Euclidean distance of the seismic trace and the first-order temporal derivative of the seismic trace. We also updated the amplitudes of all extracted wavelets simultaneously based on the least-squares principle. This DTW-based, structure-adapted, least-squares, multichannel matching pursuit method would improve the robustness and accuracy of seismic trace decomposition.

Attention and Hybrid Loss Guided 2-D Network for Seismic Impedance Inversion

Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), achieve state-of-the-art performance on seismic impedance inversion. Most of the methods are based on one-dimensional (1D) convolution, tending to yield lateral discontinuities of impedance on field data applications. To alleviate this problem, we design a network equipped with two-dimensional (2D) convolutions and a coordinate attention (CA) block. The former can take the relationship between adjacent traces into consideration. The latter can capture the positional relationship of the geological structure, both horizontally and vertically. At the same time, we use a hybrid loss combined with edge operator and Mean Square Error (MSE) to further improve the stability of the designed network. Comparison experiments on synthetic SEAM model and field seismic data demonstrate the effectiveness of the adopted components, 2D convolution, CA and hybrid loss function, in improving the lateral continuity of inverted impedance. For field seismic data, the impedance predicted by proposed method shows improved lateral continuity and high resolution compared with the 1D network and Constrained Sparse Spike Inversion (CSSI) method using commercial software (InverTrace Plus module in Jason).

Seismic First Break Picking Through Swin Transformer Feature Extraction

The application of neural networks to seismic first breaks (FBs) picking research has been developed for many years. Numerous multi-trace FBs picking methods based on convolutional neural networks have been proposed. Among them, the pickup method of semantic segmentation networks based on fully convolutional networks (FCNs) is proven to have stronger noise immunity. However, when in the data with drastic variations in the FBs between local adjacent traces, because of the feature extraction method of FCNs for convergence information around the data, the network output feature edge tends to smooth, this leads to a flattening of the FBs of the multi-trace pickup, which is not conducive to picking traces with drastic inter-trace FBs variation. Therefore, we use Transformer, a weight-transfer model that relies on the self-attention mechanism to calculate weights between input and output data, to extract FBs features. The 2D seismic data are flattened into a 1D sequence along the time dimension of the trace and input to the network, the spatial information of the FBs of the adjacent traces is considered without disrupting the time-series semantic information. The correlation between any element of the sequence and other elements is calculated to obtain the sequence feature weights. We use Swin Transformer as the backbone and combine the features of U-shaped networks to design an end-to-end FBs picking network – STUNet. The results show that STUNet has higher FBs picking accuracy than current FCNs, and is more effective in local adjacent traces where the FBs time variation is drastic.

Designing a Spintronic Based Magnetoresistive Bridge Sensor for Current Measurement and Low Field Sensing

An exchanged-biased anisotropic magnetoresistance bridge sensor for low currents measurement is designed and implemented. The sensor has a simple construction (single mask) and is based on results from micromagnetic simulations. For increasing the sensitivity of the sensor, the magnetic field generated by the measurement current passing through the printed circuit board trace is determined through an analytical method and, for comparative analysis, finite elements method simulations are used. The sensor performance is experimentally tested with a demonstrator chip. Four case studies are considered in the analytical method: neglecting the thickness of the trace, dividing the thickness of the trace in several layers, and assuming a finite or very long conductive trace. Additionally, the influence of several adjacent traces in the sensor area is evaluated. The study shows that the analytical design method can be used for optimizing the geometric selectivity of a non-contacting magnetoresistive bridge sensor setup in single trace, differential, and multi-trace (planar coil) configurations. Further, the results can be applied for developing highly performant magnetoresistance sensors and optimizations for low field detection, small dimensions, and low costs.

High‐efficiency and high‐precision seismic trace interpolation for irregularly spatial sampled data by combining an extreme gradient boosting decision tree and principal component analysis

AbstractIn seismic data acquisition, because of several factors, such as surface barriers, receiver failure, noise contamination and budget control, seismic records often exhibit irregular sampling in the space domain. As corrupted seismic records have a negative effect on seismic migration, inversion and interpretation, seismic trace interpolation is a key step in seismic data pre‐processing. In this paper, we propose a high‐efficiency and high‐precision seismic trace interpolation method for irregularly spatially sampled data by combining an extreme gradient boosting decision tree and principal component analysis in a semi‐supervised learning method. The adjacent trace number, sampling number and amplitudes of the effective seismic data were taken as features to build the training data set for the extreme gradient boosting decision tree. Principal component analysis is applied to remove redundant information and accelerate the training speed. This is different from the traditional trace interpolation method in that the proposed method is data‐driven; therefore, it does not require any assumptions. Compared with other deep learning‐based trace interpolation methods, the proposed method has fewer control parameters and learning labels and a smaller training cost. Experiments using synthetic and field data demonstrated the validity and flexibility of this trace interpolation method.

The Development of Monolithic Silicon Carbide Intracortical Neural Interfaces for Long-Term Human Implantation

Silicon Carbide (SiC) has been demonstrated as both a bio- and neuro-compatible wide-band-gap semiconductor with a high thermal conductivity and magnetic susceptibility and may be potentially compatible with human brain tissue. Two single-crystal, solid-state forms of SiC have been used to create monolithic intracortical neural implants (INI) without using physiologically exposed metals or polymers, thus eliminating many known reliability challenges in-vivo through a single, homogenous material. Amorphous SiC (a-SiC) was used to insulate 16-channel functional INI and the electrochemical and MRI compatibility (7T) performance were measured. 4H-SiC interfaces were fabricated using homoepitaxy,alternating epitaxial films of n-type and p-type forming an isolating PN junction which prevents substrate leakage current between the 16 adjacent electrodes and traces fabricated which were formed using deep-reactive ion etching (DRIE). 3C-SiC interfaces were fabricated in a similar fashion, but the epitaial conductive layers were grown on on both bulk crystalline (100) silicon and SOI wafers. In both cases a conformal coating of a-SiC was used as the top-side insulator and windows opened using RIE to allow electrochemical interaction. Electrochemical charaterization achieved through electrochemcial impedance spectroscopy (EIS) and cyclic voltammetry (CV) indicates performance on par, or exceeding, that of Pt reference electrodes with the same form fit. While magnetic resonance imaging (MRI) is an essential, non-contact method used to investigate issues with the nervous system, the high field MRI (e.g., 3 T and higher) necessary for proper diagnosis can be a safety issue for patients with INI due to inductive coupling between the powerful electromagnetic fields and the implanted device. This results in having to use lower electromagnetic field power (less than 1.5T), and therefore lower resolution, which hinders diagnostic prognosis for these patients. In this work the MRI compliance of epitaxial, monolithic SiC INI was studied. The specific absorption rate (SAR), induced heating, and image artifacts caused by the portion of the implant within a brain tissue phantom located in a 7 T small animal MRI machine were estimated and measured via finite element method (FEM) and Fourier-based simulations. Both the simulation and experimental results revealed that free-standing 3C-SiC films had no observable image artifacts compared to silicon and platinum reference materials inside the MRI at 7 T while FEM simulations predicted an ~30% SAR reduction for 3C-SiC compared to Pt. These initial simulations and experiments indicate a SiC monolithic INI may effectively reduce MRI induced heating and image artifacts in high field MRI.

Wave-equation migration velocity analysis via the optimal-transport-based objective function

Wave-equation migration velocity analysis builds a kinematically accurate macrovelocity model containing the large-scale structures of the model for seismic imaging or full-waveform inversion (FWI). Differential semblance optimization formulates the misfit function in the subsurface domain by applying a penalty to the unfocused subsurface-offset gathers or the nonflattened angle gathers. Such a penalty leads to gradients with strong artifacts and spurious oscillations, which then leads to slow convergence. We formulate the misfit function using the optimal transport (OT) among neighboring traces in angle-domain common-image gathers (CIGs). Specifically, we measure the unflatness of the gathers by comparing the Wasserstein distance of adjacent traces. Because CIGs are ideal to form a probability distribution, which is required by OT, it is natural to use OT to measure the time shifts between two distributions. Our objective function exhibits well-behaved convex and unimodal properties in a simple model with a single interface, and it is expected to be minimum for the correct velocity when the angle gathers are flat. A synthetic data set example on the Marmousi model indicates the ability of our method to provide an initial model for FWI that allows FWI to converge. We also apply the approach on a marine field data set to further demonstrate its effectiveness in estimating the macrovelocity model.