Effects Of Spatial Sampling Research Articles

The effect of spatial sampling on magnetic field modeling and helicity computation

Context. Nonlinear force-free (NLFF) modeling is regularly used to indirectly infer the 3D geometry of the coronal magnetic field, which is not otherwise accessible on a regular basis by means of direct measurements. Aims. We study the effect of binning in time-series NLFF modeling of individual active regions (ARs) in order to quantify the effect of a different underlying spatial sampling on the quality of modeling as well as on the derived physical parameters. Methods. We apply an optimization method to sequences of Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI) vector magnetogram data at three different plate scales for three solar active regions to obtain nine NLFF model time series. From the NLFF models, we deduce active-region magnetic fluxes, electric currents, magnetic energies, and relative helicities, and analyze those with respect to the underlying spatial sampling. We calculate various metrics to quantify the quality of the derived NLFF models and apply a Helmholtz decomposition to characterize solenoidal errors. Results. At a given spatial sampling, the quality of NLFF modeling is different for different ARs, and the quality varies along the individual model time series. For a given AR, modeling at a certain spatial sampling is not necessarily of superior quality compared to that performed with a different plate scale. Generally, the NLFF model quality tends to be higher for larger pixel sizes with the solenoidal quality being the ultimate cause for systematic variations in model-deduced physical quantities. Conclusions. Optimization-based modeling using SDO/HMI vector data binned to larger pixel sizes yields variations in magnetic energy and helicity estimates of ≲30% on overall, given that concise checks ensure the physical plausibility and high solenoidal quality of the tested model. Spatial-sampling-induced differences are relatively small compared to those arising from other sources of uncertainty, including the effects of applying different data calibration methods, those of using vector data from different instruments, or those arising from application of different NLFF methods to identical input data.

Effect of spatial sampling on phylogeography.

Effect of spatial sampling on phylogeography.

IMRT QA and gamma comparisons: The impact of detector geometry, spatial sampling, and delivery technique on gamma comparison sensitivity.

To separately quantify sensitivity differences in patient-specific quality assurance comparisons analyzed with the gamma comparison for different measurement geometries, spatial samplings, and delivery techniques [intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT)]. Error-free calculations for 20 IMRT and 20 VMAT cases were compared to calculations with known induced errors of varying magnitudes, using gamma comparisons. Five error types (MU scaling, three different MLC errors, and collimator errors) were induced in plan calculations on three different detector geometries - ArcCHECK, MapCHECK, and Delta 4. To study detector geometry sensitivity effects alone, gamma comparisons were made with 1mm error-free calculations compared to 1mm error-induced calculations for each device. Effects of spatial sampling were studied by making the same gamma comparisons, but down-sampling the error-induced calculations to the real spatial sampling of each device. Additionally, 1mm vs 1mm comparisons between the IMRT and VMAT cases were compared to investigate sensitivity differences between IMRT and VMAT using IMRT and VMAT cohorts with similar ranges of plan complexity and average aperture size. For each case, induced error type, and device, five different gamma criteria were studied to ensure sensitivity differences between devices, spatial sampling scenarios, and delivery technique were not gamma criterion specific, resulting in over 36,000 gamma comparisons. For IMRT cases, Delta4 and MapCHECK devices had similar error sensitivities for lagging leaf, bank shift, and MU errors, while the ArcCHECK had considerably lower sensitivity than the planar-type devices. For collimator errors and perturbational leaf errors the ArcCHECK had higher error sensitivity than planar-type devices. This behavior was independent of gamma parameters (percent dose difference, distance-to-agreement, and low dose threshold), though use of local normalization resulted in error sensitivites that were markedly similar between all three devices. Differences between detector geometries were less pronounced for VMAT deliveries. Error sensitivity for a given gamma criterion when comparing IMRT and VMAT deliveries on the same devices showed that VMAT plans were more sensitive to some specific error types and less sensitive to others, when compared to IMRT plans. For the ArcCHECK device, the sensitivity of IMRT and VMAT cases was quite similar, whereas this was not the case for the planar-type devices. When comparing error sensitivity between 1mm vs 1mm calculations and 1mm vs the real spatial sampling for each device, results showed that increased spatial sampling did not systematically increase error sensitivity. Noticeable differences in error sensitivity were observed for different detector geometries, but differences were dependent on induced error type, and a particular device geometry did not offer universal improvements in error sensitivity across studied error types. This study demonstrates that the sensitivity of the gamma comparison does not largely hinge on detector spatial sampling. VMAT deliveries were generally less sensitive to errors when compared to IMRT plans for the planar-type devices, while similar sensitivities were observed between delivery techniques for the ArcCHECK device. Results of this work suggest that a universal gamma criterion is inappropriate for IMRT QA and that the percent pixels passing is an insufficient metric for evaluating quality assurance checks in the clinic.

Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models.

Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care.

Diagnostic yield of high-density versus low-density EEG: The effect of spatial sampling, timing and duration of recording

ObjectiveTo investigate the effect of spatial sampling and of recording duration on the diagnostic yield of EEG for identification of interictal epileptiform discharges (IEDs). Previous studies demonstrated that high-density (HD) recordings increased accuracy of localization compared to low-density (LD) recordings. MethodsWe have prospectively evaluated the effect of spatial sampling and of recording duration in patients who had short-term (ST) recordings with a HD array of 256 electrodes following long-term (LT) recordings with a LD array consisting of the standard IFCN array of 25 electrodes. IED clusters were identified in four datasets: LT-LD, ST-LD (spatially down-sampled to the standard IFCN array), ST-HD and a shortened (90 minutes) epoch of LT-LD. ResultsSixty consecutive patients were recruited. We identified 89 IED clusters totally. Two clusters were found by increasing spatial sampling from 25 to 256 electrodes. This modest increase was not statistically significant. Eight clusters were missed by reducing the recording duration to 90 minutes, as compared with the LT recordings (p = 0.003). ConclusionsRecording duration is more important for the diagnostic yield of EEGs than increasing spatial sampling beyond the standard IFCN electrode array. SignificanceThe standard IFCN electrode array provides sufficient spatial sampling for identification of the IEDs.

The relationship between environmental niche breadth and geographic range size across plant species

AbstractAimA positive association between environmental niche breadth and geographic range size across species (RS‐NB association) is considered a major macroecological pattern and a key mechanism explaining differences in commonness and rarity among species. It is typically assumed that niche breadth determines range size. We explore ways in which spurious positive RS‐NB associations can arise through sampling artefacts, in the absence of any ecological or evolutionary link between the two variables.LocationAustralia and Africa.MethodsWe used a process‐based simulation model to explore the influence of spatial autocorrelation in the environment, and the rate of neutral range evolution, on the slope of the RS‐NB association. We quantified niche breadth in four large plant genera (Banksia, Hakea, Protea and Moraea) using up to 12 environmental variables and accounting for variable numbers of occurrence records, and we tested RS‐NB associations using phylogenetic generalized least‐squares (PGLS) models. We compared observed patterns to two null models that break the link between species occurrences and environmental conditions in different ways.ResultsThe simulations show that positive RS‐NB associations are generated even under random diversification and neutral range evolution, when the environment is spatially autocorrelated. There were strong positive interspecific RS‐NB associations in all four plant genera, but in most cases, PGLS slopes for the four genera were similar to those generated by the null models. After accounting for sampling effects under the two the null models by calculating the standardized effect sizes of species’ niche breadths, there was little evidence of general, positive associations between range size and niche breadth.Main ConclusionsPositive RS‐NB associations in our four plant genera do not necessarily result from an ecological or evolutionary link between niche breadth and range size but may largely reflect the historical legacy of speciation and limited dispersal, biotic interactions or other constraints on range expansion. Our results suggest that niche breadth as a general determinant of range extent among species should not be assumed without testing and correcting for spatial sampling effects.

The effects of spatial sampling on random noise for gyrokinetic PIC simulations in real space

We study the effects of cloud-in-cell sampling and gyroaveraging on random noise in real space (as opposed to the common Fourier space presentation), and show that together, these can reduce the noise by a factor of 3 compared to nearest grid point sampling without gyroaveraging. Hence an order of magnitude less test particles are needed for the given noise level. We derive equations for noise level as a function of Larmor radius and also investigate the effect of gyroaveraging on noise in local gradients. The effect of number of gyropoints on noise is also discussed.

Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks

AbstractEmpirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input‐output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m−2 d−1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7–1.41 gC m−2 d−1), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8–2.09 gC m−2 d−1). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty.

EEG source localization: Sensor density and head surface coverage

BackgroundThe accuracy of EEG source localization depends on a sufficient sampling of the surface potential field, an accurate conducting volume estimation (head model), and a suitable and well-understood inverse technique. The goal of the present study is to examine the effect of sampling density and coverage on the ability to accurately localize sources, using common linear inverse weight techniques, at different depths. Several inverse methods are examined, using the popular head conductivity. New methodSimulation studies were employed to examine the effect of spatial sampling of the potential field at the head surface, in terms of sensor density and coverage of the inferior and superior head regions. In addition, the effects of sensor density and coverage are investigated in the source localization of epileptiform EEG. ResultsGreater sensor density improves source localization accuracy. Moreover, across all sampling density and inverse methods, adding samples on the inferior surface improves the accuracy of source estimates at all depths. Comparison with existing methodsMore accurate source localization of EEG data can be achieved with high spatial sampling of the head surface electrodes. ConclusionsThe most accurate source localization is obtained when the voltage surface is densely sampled over both the superior and inferior surfaces.

On the effect of spatial sampling in damage detection of cracked beams by continuous wavelet transform

Modern measurement techniques are improving in capability to capture spatial displacement fields occurring in deformed structures with high precision and in a quasi-continuous manner. This in turn has made the use of vibration-based damage identification methods more effective and reliable for real applications. However, practical measurement and data processing issues still present barriers to the application of these methods in identifying several types of structural damage. This paper deals with spatial Continuous Wavelet Transform (CWT) damage identification methods in beam structures with the aim of addressing the following key questions: (i) Can the cost of damage detection be reduced by down-sampling? (ii) What is the minimum number of sampling intervals required for optimal damage detection? The first three free vibration modes of a cantilever and a simple supported beam with an edge open crack are numerically simulated. A thorough parametric study is carried out by taking into account the key parameters governing the problem, including the wavelet mother function, level of noise, crack depth and location, mechanical and geometrical parameters of the beam and the padding method to reduce border distortions. The results are employed to assess the optimal number of sampling intervals for effective damage detection.

Real-time temporal shaping of high-speed video streams

Digital movie cameras only perform a discrete sampling of real-world imagery. While spatial sampling effects are well studied in the literature, there has not been as much work in regards to temporal sampling. As cameras get faster and faster, the need for conventional frame-rate video that matches the abilities of human perception remains. In this article, we introduce a system with controlled temporal sampling behavior. It transforms a high fps input stream into a conventional speed output video in real-time. We investigate the effect of different temporal sampling kernels and demonstrate that extended, overlapping kernels can mitigate aliasing artifacts. Furthermore, NPR effects, such as enhanced motion blur, can be achieved. By applying Fourier transforms in the temporal domain, we can also obtain novel tools for analyzing and visualizing time dependent effects. We study the properties of both contemporary and idealized display devices and demonstrate the effect of different sampling kernels in creating enhanced movies and stills of fast motion.

A computer program to simulate multilocus genotype data with spatially autocorrelated allele frequencies

Many models for inference of population genetic parameters are based on the assumption that the data set at hand consists of groups displaying within-group Hardy-Weinberg equilibrium at individual loci and linkage equilibrium between loci. This assumption is commonly violated by the presence of within-group spatial structure arising from nonrandom mating of individuals due to isolation by distance (IBD). This paper proposes a model and simulation method implemented in a computer program to flexibly simulate data displaying such patterns. The program permits displaying of smooth spatial variations of allele frequencies due to IBD and more abrupt variations due to presence of strong barriers to gene flow. It is useful in assessing performance of various statistical inference methods and in designing spatial sampling schemes. This is shown by a simulation study aimed at assessing the extent to which IBD patterns affect accuracy of cluster inferences performed in models assuming panmixia. The program is also used to study the effects of spatial sampling scheme (e.g. sampling individuals in clumps or uniformly across the spatial domain). The accuracy of such inferences is assessed in terms of number of inferred populations, assignment of individuals to populations and location of borders between populations. The effect of spatial sampling was weak while the effect of IBD may be substantial, leading to the inference of spurious populations, especially when IBD was strong with respect to the size of the sampling domain. The model and program are new and have been embedded in the R package Geneland, for user convenience and compliance with existing data formats.

The impact of sampling schemes on the site frequency spectrum in nonequilibrium subdivided populations.

Using coalescent simulations, we study the impact of three different sampling schemes on patterns of neutral diversity in structured populations. Specifically, we are interested in two summary statistics based on the site frequency spectrum as a function of migration rate, demographic history of the entire substructured population (including timing and magnitude of specieswide expansions), and the sampling scheme. Using simulations implementing both finite-island and two-dimensional stepping-stone spatial structure, we demonstrate strong effects of the sampling scheme on Tajima's D (D(T)) and Fu and Li's D (D(FL)) statistics, particularly under specieswide (range) expansions. Pooled samples yield average D(T) and D(FL) values that are generally intermediate between those of local and scattered samples. Local samples (and to a lesser extent, pooled samples) are influenced by local, rapid coalescence events in the underlying coalescent process. These processes result in lower proportions of external branch lengths and hence lower proportions of singletons, explaining our finding that the sampling scheme affects D(FL) more than it does D(T). Under specieswide expansion scenarios, these effects of spatial sampling may persist up to very high levels of gene flow (Nm > 25), implying that local samples cannot be regarded as being drawn from a panmictic population. Importantly, many data sets on humans, Drosophila, and plants contain signatures of specieswide expansions and effects of sampling scheme that are predicted by our simulation results. This suggests that validating the assumption of panmixia is crucial if robust demographic inferences are to be made from local or pooled samples. However, future studies should consider adopting a framework that explicitly accounts for the genealogical effects of population subdivision and empirical sampling schemes.

Thresholds for Sampled Sloan Letters Are Smaller Than Sample Spacing

Although the effect of spatial sampling on the visibility of grating stimuli is well described and well understood, little research has been conducted into the effects of spatial sampling on the visibility of letter optotypes. The purpose of this study was to investigate whether thresholds for spatially sampled Sloan letters were equal to or significantly smaller than the spacing between spatial samples. For four visually normal subjects, we measured Sloan letter acuity thresholds, presented on a computer monitor, after the letters had been sampled by sampling arrays with 6.285 elements per square degree, (23.9 min of arc between samples for a square packed array). We used four different sampling arrays: square packed; hexagonally packed; a cone-like array with positive contrast; and a cone-like array with negative contrast. Thresholds were assessed using letter-counting rules and also Probit analysis. Although results depended on array type, and the definition of sample spacing, letter acuity thresholds were substantially less than estimates of sample spacing by between 0.290 log units (49% less) (hexagonally sampled Probit thresholds compared with spacing between hexagonal samples) to 0.136 log units (27% less) (positive contrast cone-like sampled letter-counting thresholds compared with spacing between rows of hexagonal samples). Sample spacing is not an absolute limit for Sloan letter thresholds. By comparison with previous measurements of human foveal cone sampling of space, our findings suggest that cone sampling limits for Sloan letters could be as small as 20/4.

Causes of differing temperature trends in radiosonde upper air data sets

Differences between trends in different radiosonde temperature products resulting from the varying choices made by the developers of the data sets create obstacles for use of those products in climate change detection and attribution. To clarify the causes of these differences, one must examine results using a common subset of locations to minimize spatial sampling effects. When this is done for the Lanzante‐Klein‐Seidel (LKS) and Hadley Center (HadRT) radiosonde data sets, differences are reduced by at least one third. Differing homogeneity adjustment methods and differences in the source data are both important factors contributing to the remaining discrepancies. In contrast, subsampling the microwave sounding unit (MSU) satellite data sets according to the radiosonde coverage does not generally bring the trends in the satellite data closer to those in the radiosonde data so that adjustments and other processing differences appear to be the predominant sources of satellite‐radiosonde discrepancies. Experiments in which we subsample globally complete data sets provide additional insight into the role of sampling errors. In the troposphere, spatial sampling errors are frequently comparable to the trends for 1979–1997, while in the stratosphere the errors are generally small relative to the trends. Sampling effects estimated from National Centers for Environmental Prediction reanalysis and MSU satellite data for seven actual radiosonde networks show little consistent relation between sampling error and network size. These results may have significant implications for the design of future climate monitoring networks. However, estimates of sampling effects using the reanalysis and the satellite data sets differ noticeably from each other and from effects estimated from actual radiosonde data, suggesting that these globally complete data sets may not fully reproduce actual sampling effects.

The effects of spatial sampling on refraction statics

The use of seismic refraction data to determine the longwavelength statics correction not adequately addressed by residual statics routines, is widespread. While the importance of accurate traveltimes is well known, the effects of the geophone array on the inversion process are not.This study used two coincident sets of data recorded with two recording systems at Cobar in southeastern Australia. One system used a 60 m trace interval with end-to-end arrays consisting of 16 detectors, while the other recording system used single detectors, which were 10 m apart.The temporal resolution achieved with the 60 m groups is poor, and does not accurately measure the thickness of weathering as determined with the data using the 10 m trace spacing. One factor limiting the achieved resolution is the finite length of the geophone array. With extended arrays, the location of the effective receiving point, depends upon the direction from which the seismic signal arrives. For signals traveling in the forward direction, the effective receiving point is the first detector in the array, while for signals traveling in the reverse direction, it is the last detector. For the data used in this study, the forward and reverse traveltimes which are referenced to the same station number, represents a lateral interval of the sub-weathering interface of approximately 100 m, when both the length of the geophone array and the offset distances are added. The traveltime differences of the refracted arrivals over this interval can be up to 20 milliseconds, which implies that an accuracy of only about 10 milliseconds is meaningful with model based inversion methods, unless the effect of the length of the array is accommodated.An analysis using the generalized reciprocal method, which uses refraction migration in multiples of the array length, shows that a migration distance of one station interval essentially removes the effect of the finite array length, when end-to-end arrays are employed. However, the static then applies to each end of the array, and in the study area, there can be a variation of up to 7 milliseconds in the weathering correction from one end of the array to the other. Therefore, it is necessary to further increase the refraction migration by an additional array length, in order to determine the static in the central region of the geophone array, which is then taken as the static correction for the entire array. Although there is an improvement in the resolution achieved with the 60 m groups, it is still poor, when compared with that achieved using the 10 m single detectors.Further analysis of the data will be carried out to confirm that the finite length of the 60 m geophone arrays is the significant factor limiting the resolution of the refraction data, rather than the variable signal-to-noise ratios of the data.

Biases of the observed atmospheric water budgets over the central United States

Analysis of the mean annual cycle of the observed water budget based on radiosonde data for the 20 year period 1973–1992 reveals a negative bias in the atmospheric estimate of mean moisture flux convergence over the central United States when compared to mean river discharge for the same period. Several possible sources of this bias are examined through the analysis of radiosonde, wind profiler, and river discharge data as well as water budget estimates from NCEP's mesoscale Eta Model. The analysis suggests that 20 years of radiosonde observations are sufficient to get stable mean water vapor flux convergence estimates. The analysis also shows that the bias between averaged vapor flux convergence and river discharge likely does not depend on changes in the locations of upper‐air observations or on changes in the radiosondes observational technique. Components of the atmospheric water budget diurnal cycle are estimated from hourly wind profiler data and from 3‐hourly Eta Model output. The analyses show that monthly mean atmospheric water budget quantities calculated from twice a day sampling, on average, significantly underestimate magnitudes of these quantities compared to estimates based on hourly or 3‐hourly analyses. The magnitude of the underestimation is more than 60% of the difference between mean observed terrestrial and atmospheric components of the hydrological cycle over the central United States, in agreement with earlier studies suggesting that temporal sampling may account for part of this bias. The effect of spatial sampling was evaluated using differences between atmospheric water budget terms from the Eta Model at its full 48 km resolution compared to Eta Model water budget estimates sampled at the radiosonde spatial resolution. These analyses show that the magnitude of the effect of spatial resolution is about 40% of the magnitude of the mean bias between the average moisture flux convergence and river discharge. The combined effect of temporal and spatial undersampling by radiosondes over the central United States is estimated to be 0.44 mm d−1. This value is within 8% of the estimated mean difference between river discharge and moisture flux convergence for the central United States.

Effect of noise contrast polarity and temporal asynchrony on visual sensitivity.

We evaluated the effect of substitutive noise on contrast sensitivity within the context of linear (Fourier) and nonlinear (non-Fourier) visual processes. Orientation judgments for D6 (sixth spatial derivative of Gaussian) patterns were obtained from three visually normal subjects when random regions of the target and background were occluded by small (1.7 arc min) pixel arrays that were either all of the same contrast polarity or a mixture of equal percentages of negative and positive contrast. The target was presented either synchronously or asynchronously with the occluding elements. Our results indicate that the manipulation of noise characteristics in this way can bias performance either toward a nonlinear process that is insensitive to noise contrast polarity but sensitive to temporal asynchrony or toward a quasi-linear process that is sensitive to noise contrast polarity but insensitive to temporal asynchrony. These findings have relevance to models of the effect of spatial sampling on the visual performance of persons with retinal disease.

Seismic system analysis—A case study from the Gulf of Mexico

It is often difficult to predict the effects of change in seismic acquisition or processing on data quality. To address this difficulty, ensemble averaging can be used to estimate the final stacked‐trace effects of altering aspects of the seismic acquisition or processing. This method of studying the seismic system begins by modeling of an ensemble of data gathers based on some geological region of interest. The model gathers are subsequently processed to create stacked traces that are compared with related reference traces to make signal‐to‐noise ratio (SNR) or bandwidth measurements. The ensemble average of these stacked‐trace measurements can then be examined while some aspect of the simulated acquisition or processing is adjusted. The result of this analysis is a plot illustrating the sensitivity of the seismic system (i.e., the stacked‐trace quality) to the selected parameter under investigation. This ensemble averaging approach was used to study system sensitivities for a data set collected in the Gulf of Mexico. The specific issues examined in this analysis include source and receiver parameters, ambient noise levels, spatial sampling, velocity picking, velocity errors, and stretch muting. Of the parameters studied, the final system output was most sensitive to velocity errors. Even differences of 1–2% in the stacking velocity led to noticeable degradation of the stacked‐trace bandwidth and SNR. This velocity sensitivity was evident in both the model and field data. Certain parameters, such as gun volumes, were less important for typical values. The ambient noise level and spatial sampling effects were similarly less important except in the deeper portions of the data (where the unstacked SNR was fairly low). These insensitivities are interesting because they imply potential cost savings. The percent stretch‐mute study was interesting because SNR and bandwidth were optimized with different mutes. All study results by design, are, tied to a specific data area. Nonetheless, these findings may provide an initial direction for system studies in other areas.

Effects of spatial sampling of marine 3-D seismic data: A case study

The ability to tow many streamers and source arrays has dramatically increased the productivity of marine 3-D surveys in recent years. Although it is now economically feasible to acquire densely spaced data above oil fields, it is still too expensive to do so in frontier exploration.