scholarly journals Joint Inversion of GPR and ER Data

2020 ◽  
Author(s):  
Diego Domenzain
Keyword(s):  
Spatial Frequency ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Joint Inversion ◽  
A Priori ◽  
Waveform Inversion ◽  
High Spatial Frequency ◽  
Seismic Exploration ◽  
Frequency Content ◽  
Resource Monitoring

Imaging the subsurface can shed knowledge on important processes needed in a modern day human's life such as ground-water exploration, water resource monitoring, contaminant and hazard mitigation, geothermal energy exploration and carbon dioxide storage. As computing power expands, it is becoming ever more feasible to increase the physical complexity of Earth's exploration methods, and hence enhance our understanding of the subsurface. We use non-invasive geophysical active source methods that rely on electromagnetic fields to probe the depths of the Earth. In particular, we use Ground penetrating radar (GPR) and Electrical resistivity (ER). Both methods are sensitive to electrical conductivity while GPR is also sensitive to electrical permittivity. We combine both types of data and let the different physical sensitivities of both methods cooperate in order to account for non-uniqueness of the subsurface image. Full-waveform inversion (FWI) of GPR is a promising technique for recovering permittivity and conductivity of the subsurface by using the full response of the electromagnetic wave. While many advances have been made to FWI by the seismic exploration community, using FWI on GPR surface acquired data is a young and growing field of research. Using the full response of ER data is a more common practice in the geophysical community. However, the spatial resolution of the recovered conductivity lacks high spatial-frequency content due to the inherent sensitivity of the data. Fortunately, the sensitivities of GPR and ER are complimentary. GPR is sensitive to conductivity through reflection and attenuation while ER is directly sensitive to conductivity. GPR is sensitive to high spatial-frequency content while ER is sensitive to low spatial-frequency content. We present a novel non-linear joint inversion that iteratively combines the sensitivities of both GPR and ER surface acquired data. Our algorithm uses both GPR and ER sensitivities in order to effectively alleviate the non-uniqueness of the recovered electrical parameters. We join GPR and ER sensitivities within the same computational grid and without the need of petrophysical relationships. By further assuming structural similarities between permittivity and conductivity, we are able to relax a priori assumptions about the subsurface and accurately recover parameters in regions where the GPR data has a signal-to-noise ratio close to one. Furthermore, assuming a good initial model is available our algorithm makes no assumption of the underlying geometry. The demanding computing requirements of GPR-FWI entail an unfeasible amount of memory for existing ER inversion methods. This is due to the very fine discretization of the subsurface required by GPR-FWI. We develop a 2.5d ER adjoint method inversion that is capable of recovering accurate subsurface conductivity from field data and relaxes the amount of required memory. We test our method on field data from an alluvial aquifer site and find agreeable results with existing measurements in the literature. Having feasible computational methods for both GPR and ER inversions is an important step for using our joint inversion algorithms on field data.

Laplace-domain full-waveform inversion of seismic data lacking low-frequency information

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. R199-R206 ◽  
Author(s):  
Wansoo Ha ◽  
Changsoo Shin
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Single Frequency ◽  
Frequency Content ◽  
Laplace Domain ◽  
Frequency Information ◽  
Velocity Models ◽  
Gaussian Source

The lack of the low-frequency information in field data prohibits the time- or frequency-domain waveform inversions from recovering large-scale background velocity models. On the other hand, Laplace-domain waveform inversion is less sensitive to the lack of the low frequencies than conventional inversions. In theory, frequency filtering of the seismic signal in the time domain is equivalent to a constant multiplication of the wavefield in the Laplace domain. Because the constant can be retrieved using the source estimation process, the frequency content of the seismic data does not affect the gradient direction of the Laplace-domain waveform inversion. We obtained inversion results of the frequency-filtered field data acquired in the Gulf of Mexico and two synthetic data sets obtained using a first-derivative Gaussian source wavelet and a single-frequency causal sine function. They demonstrated that Laplace-domain inversion yielded consistent results regardless of the frequency content within the seismic data.

scholarly journals Neuronal basis of perceptual learning in striate cortex

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Zhen Ren ◽  
Jiawei Zhou ◽  
Zhimo Yao ◽  
Zhengchun Wang ◽  
Nini Yuan ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Perceptual Learning ◽  
Contrast Sensitivity ◽  
Striate Cortex ◽  
Signal To Noise Ratio ◽  
High Spatial Frequency ◽  
Sensitivity Training ◽  
Spatial Frequencies ◽  
Cortex Area ◽  
Adult Cats

Abstract It is well known that, in humans, contrast sensitivity training at high spatial frequency (SF) not only leads to contrast sensitivity improvement, but also results in an improvement in visual acuity as assessed with gratings (direct effect) or letters (transfer effect). However, the underlying neural mechanisms of this high spatial frequency training improvement remain to be elucidated. In the present study, we examined four properties of neurons in primary visual cortex (area 17) of adult cats that exhibited significantly improved acuity after contrast sensitivity training with a high spatial frequency grating and those of untrained control cats. We found no difference in neuronal contrast sensitivity or tuning width (Width) between the trained and untrained cats. However, the trained cats showed a displacement of the cells’ optimal spatial frequency (OSF) to higher spatial frequencies as well as a larger neuronal signal-to-noise ratio (SNR). Furthermore, both the neuronal differences in OSF and SNR were significantly correlated with the improvement of acuity measured behaviorally. These results suggest that striate neurons might mediate the perceptual learning-induced improvement for high spatial frequency stimuli by an alteration in their spatial frequency representation and by an increased SNR.

scholarly journals High-resolution coherency functionals for velocity analysis: An application for subbasalt seismic exploration

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. U53-U63 ◽  
Author(s):  
Andrea Tognarelli ◽  
Eusebio Stucchi ◽  
Alessia Ravasio ◽  
Alfredo Mazzotti
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Synthetic Seismograms ◽  
Seismic Exploration ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Geologic Setting ◽  
Analytic Signals

We tested the properties of three different coherency functionals for the velocity analysis of seismic data relative to subbasalt exploration. We evaluated the performance of the standard semblance algorithm and two high-resolution coherency functionals based on the use of analytic signals and of the covariance estimation along hyperbolic traveltime trajectories. Approximate knowledge of the wavelet was exploited to design appropriate filters that matched the primary reflections, thereby further improving the ability of the functionals to highlight the events of interest. The tests were carried out on two synthetic seismograms computed on models reproducing the geologic setting of basaltic intrusions and on common midpoint gathers from a 3D survey. Synthetic and field data had a very low signal-to-noise ratio, strong multiple contamination, and weak primary subbasalt signals. The results revealed that high-resolution coherency functionals were more suitable than semblance algorithms to detect primary signals and to distinguish them from multiples and other interfering events. This early discrimination between primaries and multiples could help to target specific signal enhancement and demultiple operations.

scholarly journals Quantitative imaging of water, ice and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data

2019 ◽  
Vol 219 (3) ◽  
pp. 1866-1875 ◽  
Author(s):  
F M Wagner ◽  
C Mollaret ◽  
T Günther ◽  
A Kemna ◽  
C Hauck
Keyword(s):  
Electrical Resistivity ◽  
Liquid Water ◽  
Field Data ◽  
Joint Inversion ◽  
A Priori ◽  
Seismic Refraction ◽  
Rock Matrix ◽  
Data Set ◽  
Water Ice ◽  
Borehole Temperatures

SUMMARY Quantitative estimation of pore fractions filled with liquid water, ice and air is crucial for a process-based understanding of permafrost and its hazard potential upon climate-induced degradation. Geophysical methods offer opportunities to image distributions of permafrost constituents in a non-invasive manner. We present a method to jointly estimate the volumetric fractions of liquid water, ice, air and the rock matrix from seismic refraction and electrical resistivity data. Existing approaches rely on conventional inversions of both data sets and a suitable a priori estimate of the porosity distribution to transform velocity and resistivity models into estimates for the four-phase system, often leading to non-physical results. Based on two synthetic experiments and a field data set from an Alpine permafrost site (Schilthorn, Bernese Alps and Switzerland), it is demonstrated that the developed petrophysical joint inversion provides physically plausible solutions, even in the absence of prior porosity estimates. An assessment of the model covariance matrix for the coupled inverse problem reveals remaining petrophysical ambiguities, in particular between ice and rock matrix. Incorporation of petrophysical a priori information is demonstrated by penalizing ice occurrence within the first two meters of the subsurface where the measured borehole temperatures are positive. Joint inversion of the field data set reveals a shallow air-rich layer with high porosity on top of a lower-porosity subsurface with laterally varying ice and liquid water contents. Non-physical values (e.g. negative saturations) do not occur and estimated ice saturations of 0–50 per cent as well as liquid water saturations of 15–75 per cent are in agreement with the relatively warm borehole temperatures between −0.5  and 3 ° C. The presented method helps to improve quantification of water, ice and air from geophysical observations.

Simulation Research on Synthesis Algorithm of Directional Illumination Data

2011 ◽  
Vol 403-408 ◽  
pp. 2337-2340
Author(s):  
Shu Cong Liu ◽  
Yan Xing Song ◽  
Jing Song Yang
Keyword(s):  
Seismic Data ◽  
Seismic Waves ◽  
Seismic Analysis ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Effective Means ◽  
Seismic Signal ◽  
Geological Structure ◽  
Seismic Exploration ◽  
The Impact

Seismic illumination analysis was an effective means of recognizing and studying the energy distributions in the underground geological structure in seismic data acquisition. Effective seismic illumination analysis to a priori targeted-geological model to identify the energy distribution of seismic waves, can apply to seismic analysis and amplitude compensation analysis. To increase the signal to noise ratio and resolution of seismic data when vibrator seismic exploration, it was necessary to strengthen the energy of a certain direction to get the High-Precision imaging and the best illumination of the target areas.Simulation research were done on single source directional illumination seismic technology, with seismic illumination analysis, and the impact of source number, spacing change on directional illumination seismic technology were also analyzed. Simulation results showed that the directional seismic technology could improved SNR of seismic data, and could be used for seismic signal processing.

Predictions of U-Shaped Backward Pattern Masking from Considerations of the Spatio-Temporal Frequency Response

Perception ◽  
1975 ◽  
Vol 4 (3) ◽  
pp. 297-304 ◽  
Author(s):  
Bruno G Breitmeyer
Keyword(s):  
Spatial Frequency ◽  
Temporal Frequency ◽  
High Spatial Frequency ◽  
Stimulus Onset ◽  
Masking Effect ◽  
Time Interval ◽  
Sinusoidal Grating ◽  
Frequency Content ◽  
Pattern Masking ◽  
Spatio Temporal

The threshold detectability of a briefly presented target stimulus consisting of a vertical sinusoidal grating was affected not only by the spatial frequency content of an equally briefly presented, two-octave-wide masking noise, but also by the time interval separating the onsets of the target and its mask. Over a range of stimulus onset asynchronies, in which the mask onset either preceded, coincided with, or followed the target onset, a mask with a low spatial frequency content had its greatest masking effect on a high spatial frequency target grating when the mask followed the target by 120–180 ms. When the mask had a high spatial frequency content and the target was of low spatial frequency, or when the target was entered on the mask frequency band, optimal masking effects occurred when the onsets of the mask and target coincided. The results are discussed in relation to previous masking studies, particuarly those in which U-shaped backward pattern masking functions are obtained.

scholarly journals Hybrid motion illusions as examples of perceptual conflict

2021 ◽  
Vol 2 ◽  
Author(s):  
Arthur Shapiro
Keyword(s):  
Spatial Frequency ◽  
Spatial Scales ◽  
High Spatial Frequency ◽  
Natural World ◽  
Fine Scale ◽  
Frequency Content ◽  
Coarse Scale ◽  
Trade Offs ◽  
Spatial Frequencies ◽  
Contrast Information

Shapiro and Hedjar (2019) proposed a shift in the definition of illusion, from ‘differences between perception and reality’ to ‘conflicts between possible constructions of reality’. This paper builds on this idea by presenting a series of motion hybrid images that juxtapose fine scale contrast (high spatial frequency content) with coarse scale contrast-generated motion (low spatial frequency content). As is the case for static hybrid images, under normal viewing conditions the fine scale contrast determines the perception of motion hybrid images; however, if the motion hybrid image is blurred or viewed from a distance, the perception is determined by the coarse scale contrast. The fine scale contrast therefore masks the perception of motion (and sometimes depth) produced by the coarser scale contrast. Since the unblurred movies contain both fine and coarse scale contrast information, but the blurred movies contain only coarse scale contrast information, cells in the brain that respond to low spatial frequencies should respond equally to both blurred and unblurred movies. Since people undoubtedly differ in the optics of their eyes and most likely in the neural processes that resolve conflict across scales, the paper suggests that motion hybrid images illustrate trade-offs between spatial scales that are important for understanding individual differences in perceptions of the natural world.

scholarly journals Effects of Spatial Frequency Filtering Choices on the Perception of Filtered Images

Vision ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 29
Author(s):  
Sabrina Perfetto ◽  
John Wilder ◽  
Dirk B. Walther
Keyword(s):  
Spatial Frequency ◽  
Behavioral Outcomes ◽  
High Spatial Frequency ◽  
Frequency Content ◽  
Frequency Filtering ◽  
Image Content ◽  
Specific Choice ◽  
Spatial Frequencies ◽  
Spatial Frequency Filtering ◽  
Frequency Components

The early visual system is composed of spatial frequency-tuned channels that break an image into its individual frequency components. Therefore, researchers commonly filter images for spatial frequencies to arrive at conclusions about the differential importance of high versus and low spatial frequency image content. Here, we show how simple decisions about the filtering of the images, and how they are displayed on the screen, can result in drastically different behavioral outcomes. We show that jointly normalizing the contrast of the stimuli is critical in order to draw accurate conclusions about the influence of the different spatial frequencies, as images of the real world naturally have higher contrast energy at low than high spatial frequencies. Furthermore, the specific choice of filter shape can result in contradictory results about whether high or low spatial frequencies are more useful for understanding image content. Finally, we show that the manner in which the high spatial frequency content is displayed on the screen influences how recognizable an image is. Previous findings that make claims about the visual system’s use of certain spatial frequency bands should be revisited, especially if their methods sections do not make clear what filtering choices were made.

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