Complex conductivity tomography using low-frequency crosswell electromagnetic data

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. E23-E38 ◽  
Author(s):  
Kris MacLennan ◽  
Marios Karaoulis ◽  
André Revil
Keyword(s):  
Oil Spills ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Time Lapse ◽  
Complex Conductivity ◽  
Inversion Algorithm ◽  
Polarization Tomography ◽  
Exploration And Production ◽  
Integral Equation Approach ◽  
Gradient Based

The complex conductivity of partially saturated siliciclastic sediments can now be reasonably predicted through recently developed petrophysical models like the POLARIS model. However, classical crosswell-induced polarization tomography (using a galvanometric approach) is characterized by a poor sensitivity map far from the wells, and thus other methods should be analyzed for potential improvements. The presence of low-frequency ([Formula: see text]) polarization effects in earth porous materials noticeably increases the amplitude and decreases the phase of measured electromagnetic (EM) fields. As such, the quadrature conductivity (directly associated with the low-frequency polarization effect) yields a significant contribution to the EM fields. We demonstrate that these contributions can be observed in crosswell EM data in terms of signal-to-noise ratio. With a realistic amount of noise, we can recover the distribution of the in-phase and quadrature conductivities for crosswell EM tomography. We use an integral equation approach for the forward modeling and a gradient-based approach with Tikhonov regularization for the inverse problem. We also develop a new inversion algorithm to invert time-lapse frequency domain EM data using an active-time-constrain approach. This information may be used in turn to improve our ability to monitor saturation changes in enhanced oil reservoir production, the remediation of oil spills, and the exploration and production of geothermal fields.

High-frequency localized elastic full waveform inversion for time-lapse seismic surveys

Geophysics ◽  
2021 ◽  
pp. 1-55
Author(s):  
Shihao Yuan ◽  
Nobuaki Fuji ◽  
Satish C. Singh
Keyword(s):  
High Frequency ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Time Lapse ◽  
Target Area ◽  
Full Waveform Inversion ◽  
Inversion Algorithm ◽  
Elastic Parameters ◽  
Model Inversion ◽  
Full Waveform

Seismic full waveform inversion is a powerful method to estimate the elastic properties of the subsurface. To mitigate the non-linearity and cycle-skipping problems, in a hierarchical manner, one inverts first low-frequency contents to determine long- and medium-wavelength structures and then increases the frequency contents to obtain detailed information. However, the inversion of higher frequencies can be computationally very expensive, especially when the target of interest, such as oil/gas reservoirs and axial melt lens, is at a great depth, far away from source and receiver arrays. To address this problem, we present a localized full waveform inversion algorithm where iterative modeling is performed locally, allowing us to extend inversions for higher frequencies with little computation effort. Our method is particularly useful for time-lapse seismic, where the changes in elastic parameters are local due to fluid extraction and injection in the subsurface. In our method, both sources and receivers are extrapolated to a region close to the target area, allowing forward modeling and inversion to be performed locally after low-frequency full-model inversion for the background model, which by nature only represents long- to medium-wavelength features. Numerical tests show that the inversion of low-frequency data for the overburden is sufficient to provide an accurate high-frequency estimation of elastic parameters of the target region.

scholarly journals A role for the fusogen eff-1 in epidermal stem cell number robustness in Caenorhabditis elegans

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sneha L. Koneru ◽  
Fu Xiang Quah ◽  
Ritobrata Ghose ◽  
Mark Hintze ◽  
Nicola Gritti ◽  
...  
Keyword(s):  
Stem Cell ◽  
Caenorhabditis Elegans ◽  
Cell Fate ◽  
Single Molecule ◽  
Phenotypic Variability ◽  
Low Frequency ◽  
Time Lapse ◽  
Cell Number ◽  
Seam Cell ◽  
Neuronal Tissues

AbstractDevelopmental patterning in Caenorhabditis elegans is known to proceed in a highly stereotypical manner, which raises the question of how developmental robustness is achieved despite the inevitable stochastic noise. We focus here on a population of epidermal cells, the seam cells, which show stem cell-like behaviour and divide symmetrically and asymmetrically over post-embryonic development to generate epidermal and neuronal tissues. We have conducted a mutagenesis screen to identify mutants that introduce phenotypic variability in the normally invariant seam cell population. We report here that a null mutation in the fusogen eff-1 increases seam cell number variability. Using time-lapse microscopy and single molecule fluorescence hybridisation, we find that seam cell division and differentiation patterns are mostly unperturbed in eff-1 mutants, indicating that cell fusion is uncoupled from the cell differentiation programme. Nevertheless, seam cell losses due to the inappropriate differentiation of both daughter cells following division, as well as seam cell gains through symmetric divisions towards the seam cell fate were observed at low frequency. We show that these stochastic errors likely arise through accumulation of defects interrupting the continuity of the seam and changing seam cell shape, highlighting the role of tissue homeostasis in suppressing phenotypic variability during development.

scholarly journals SAR Oil Spill Detection System through Random Forest Classifiers

2021 ◽  
Vol 13 (11) ◽  
pp. 2044
Author(s):  
Marcos R. A. Conceição ◽  
Luis F. F. Mendonça ◽  
Carlos A. D. Lentini ◽  
André T. C. Lima ◽  
José M. Lopes ◽  
...  
Keyword(s):  
Random Forest ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection System ◽  
Feature Space ◽  
Dark Spot ◽  
Sar Image ◽  
Extensive Search ◽  
Gradient Based ◽  
Biological Films

A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks

2021 ◽  
pp. 1-10
Author(s):  
Hongguang Pan ◽  
Fan Wen ◽  
Xiangdong Huang ◽  
Xinyu Lei ◽  
Xiaoling Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Super Resolution ◽  
Structural Similarity ◽  
Plug And Play ◽  
Attention Networks ◽  
Frequency Information ◽  
Blind Deblurring ◽  
Adaptive Adjustment ◽  
Deep Feature Extraction

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.

Low-frequency multiplex CARS microscopy with a high-repetition near-infrared supercontinuum laser

2021 ◽  
Author(s):  
Yusuke Arashida ◽  
Atsushi Taninaka ◽  
Takayuki Ochiai ◽  
Hiroyuki Mogi ◽  
Shoji YOSHIDA ◽  
...  
Keyword(s):  
Near Infrared ◽  
High Efficiency ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
High Sensitivity ◽  
High Repetition ◽  
Spectrum Shaping ◽  
Crystal Phase Transition ◽  
Wavenumber Region ◽  
Anti Stokes

Abstract We have developed a multiplex Coherent anti-Stokes Raman scattering (CARS) microscope effective for low-wavenumber measurement by combining a high-repetition supercontinuum light source of 1064 nm and an infrared high-sensitivity InGaAs diode array. This system could observe the low-wavenumber region down to 55 cm-1 with high sensitivity. In addition, using spectrum shaping and spectrum modulation techniques, we simultaneously realized a wide bandwidth (<1800 cm-1), high wavenumber resolution (9 cm-1), high efficiency, and increasing signal to noise ratio by reducing the effect of the background shape in low-wavenumber region. Spatial variation of a sulfur crystal phase transition with metastable states was visualized.

scholarly journals An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3721 ◽  
Author(s):  
Usman Rashid ◽  
Imran Niazi ◽  
Nada Signal ◽  
Denise Taylor
Keyword(s):  
Experimental Study ◽  
Mixed Models ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Low Frequency ◽  
Low Power Consumption ◽  
Motor Tasks ◽  
Negative Peak ◽  
Eeg Data ◽  
Healthy Participants

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

Research of Wavelet Packet Analysis in the De-Noising of Ultrasonic Echo Signal

2011 ◽  
Vol 403-408 ◽  
pp. 1817-1822
Author(s):  
Xi Feng Zhou ◽  
Xiao Wu ◽  
Qian Gang Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Random Noise ◽  
Flaw Detection ◽  
Low Frequency ◽  
Local Analysis ◽  
Echo Signal ◽  
Wavelet Packet Analysis ◽  
Ultrasonic Flaw

The quality of ultrasonic flaw echo signal is the foundation of achieving qualitative and quantitative analysis in the in ultrasonic flaw detection. In practice, the flaw echo signals are often contaminated or even annihilation by random noise. According to the characteristics of ultrasonic flaw echo signal, wavelet packet has more accurate local analysis ability in low frequency and high frequency part. This paper discusses de-noising in ultrasonic signals based on wavelet packet analysis, and proposes an improved threshold approach for de-noising. The results show that: It remarkably raises the signal-to-noise ratio of ultrasonic flaw echo signal and improves the quality of signal with improved wavelet packet threshold.

scholarly journals Signal-to-noise ratio in the membrane potential of the owl's auditory coincidence detectors

2012 ◽  
Vol 108 (10) ◽  
pp. 2837-2845 ◽  
Author(s):  
Go Ashida ◽  
Kazuo Funabiki ◽  
Paula T. Kuokkanen ◽  
Richard Kempter ◽  
Catherine E. Carr
Keyword(s):  
Neural Circuit ◽  
Signal To Noise Ratio ◽  
Phase Locking ◽  
Low Frequency ◽  
Membrane Potentials ◽  
Signal To Noise ◽  
Theoretical Predictions ◽  
Noise Ratio ◽  
Band Limited

Owls use interaural time differences (ITDs) to locate a sound source. They compute ITD in a specialized neural circuit that consists of axonal delay lines from the cochlear nucleus magnocellularis (NM) and coincidence detectors in the nucleus laminaris (NL). Recent physiological recordings have shown that tonal stimuli induce oscillatory membrane potentials in NL neurons (Funabiki K, Ashida G, Konishi M. J Neurosci 31: 15245–15256, 2011). The amplitude of these oscillations varies with ITD and is strongly correlated to the firing rate. The oscillation, termed the sound analog potential, has the same frequency as the stimulus tone and is presumed to originate from phase-locked synaptic inputs from NM fibers. To investigate how these oscillatory membrane potentials are generated, we applied recently developed signal-to-noise ratio (SNR) analysis techniques (Kuokkanen PT, Wagner H, Ashida G, Carr CE, Kempter R. J Neurophysiol 104: 2274–2290, 2010) to the intracellular waveforms obtained in vivo. Our theoretical prediction of the band-limited SNRs agreed with experimental data for mid- to high-frequency (>2 kHz) NL neurons. For low-frequency (≤2 kHz) NL neurons, however, measured SNRs were lower than theoretical predictions. These results suggest that the number of independent NM fibers converging onto each NL neuron and/or the population-averaged degree of phase-locking of the NM fibers could be significantly smaller in the low-frequency NL region than estimated for higher best-frequency NL.

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