Comparison between the change in total corneal astigmatism and actual change in refractive astigmatism in transepithelial photorefractive keratectomy (tPRK), laser in situ keratomileusis (LASIK) and femtosecond laser assisted laser in situ keratomileusis (FsLASIK)

Purpose To compare changes in astigmatism by refraction and total corneal astigmatism after tPRK, LASIK and FsLASIK. Setting Specialty Eye Hospital Svjetlost, Zagreb, Croatia. Design Partially masked, semi-randomized, prospective, case-by-case, interventional, clinical study. Methods Patients with a stable refraction (-0.75DS to −8.00DS, astigmatism ≤1.00DC) underwent tPRK, LASIK or FsLASIK without complication. Astigmatism was measured at both corneal surfaces over the central 3.2 mm zone (approximately using Pentacam HRTM) preoperatively and 3 months postoperatively. Pentacam and refraction data were subjected to vector analysis to calculate the surgically induced changes in i) total corneal astigmatism (SIATCA) ii) any astigmatism by refraction (SIAR) and the vectorial difference (DV) between SIATCA and SIAR. Results Reporting key findings (p < .01), there was a significant difference between mean SIATCA and SIAR powers after tPRK (75eyes) but not after LASIK (100eyes) or FsLASIK (100eyes). Mean (±sd,95% CIs) values for DV powers were, tPRK −1.13DC(±0.71, −1.29 to −0.97), LASIK −0.39DC(±0.23,-0.44 to −0.34), FsLASIK −0.55DC(±0.38,-0.62 to −0.47). The differences were significant. For the tPRK and FsLASIK cases, linear regression revealed significant associations between I) SIATCA (x) &DV (z) powers (tPRK z = 1.586x-0.179, r = 0.767, p < .01; FsLASIK z = 0.442x-0.303, r = .484,p < .01), II) sines of SIATCA (x1) &DV (z1) axes (tPRK, z1 = 0.523 × 1 + 0.394, r = .650,p < .01; FsLASIK z1 = 0.460 × 1-0.308, r = .465,p < .01). Conclusions tPRK is more prone to unintended changes in astigmatism. The difference between SIATCA & SIAR after tPRK or FsLASIK is mediated by SIATCA. Photoablating deeper regions of the cornea reduces the gap between SIATCA & SIAR.

Seismic Refraction Data Analysis Using Machine Learning and Numerical Modeling for Characterization of Dam Construction Sites

Seismic refraction is a cost-effective tool to reveal subsurface P-wave velocity. Inversion of travel times for estimating a realistic velocity model is a significant step in the processing of seismic refraction data. The results of the seismic data inversion are stochastic and, thus, using prior information or complementary geophysical data can have a significant role in estimating the structural properties based on observed data. Nevertheless, sufficient prior information or auxiliary data are not available in many geophysical sites. In such situations, developing advanced computational modeling is a vital step in providing primary information and improving the results. To this aim, a new inversion framework through hybrid committee artificial neural networks (CANN) and the flower pollination (FP) optimization algorithm is introduced for inversion of refracted seismic travel times. Synthetic models generated by a forward modeling approach are used to train the machine learning model. Then, model parameters, such as the number of layers, thicknesses, and P-wave velocities, are predicted using a committee machine constructed based on several neural networks, which is achieved by averaging and stack generalization methods where the latter method provides a better result. Then, the CANN results are used in the FP inversion algorithm to estimate the final model as it provides essential prior information on the number of layers and model parameters, which can be used in the FP searching algorithm. The proposed inversion procedure is tested on different synthetic datasets and applied at a dam site to determine the number of layers and their thicknesses. Our findings indicate a successful performance on both synthetic and real data for automatic inversion of seismic refraction data.

Visual and refractive status of children with Down's syndrome and nystagmus

Background/Aims: Children with Down's syndrome (DS) are known to have poorer visual acuity that neurotypical children. One report has shown that children with DS and nystagmus also have poor acuity when compared to typical children with nystagmus. What has not been established, is the extent of any acuity deficit due to nystagmus and whether nystagmus impacts on refractive error is within a population with DS. Methods: Clinical records from The Cardiff University Down's Syndrome Vision Research Unit were examined retrospectively. Binocular visual acuity and refraction data were available for 50 children who had DS and nystagmus (DSN) and 176 children who had DS but no nystagmus. Data were compared between the two groups, and with published data for neurotypical children with nystagmus. Results: The study confirms the deficit in acuity in DS, compared to neurotypical children, of approximately 0.2 LogMAR and shows a further deficit attributable to nystagmus of a further 0.2 logMAR beyond the first year of life. Children with DS and no nystagmus appear to have acuity that mirrors that of typical children with nystagmus, while children with both DS and nystagmus have a significant additional impairment. Children with DS have a wide range of refractive errors, but nystagmus increases the likelihood of myopia. Prevalence and axis direction of astigmatism, on the other hand appears unaffected by nystagmus. Conclusion: Nystagmus confers an additional visual impairment on children with Down's syndrome and must be recognised as such by families and educators. Children with both DS and nystagmus clearly need targeted support.

Integration of gravity, magnetic, and seismic data for subsalt modeling in the Northern Red Sea

Rifts and rifted passive margins are often associated with thick evaporite layers, which challenge seismic reflection imaging in the subsalt domain. This makes understanding the basin evolution and crustal architecture difficult. An integrative, multidisciplinary workflow has been developed using the exploration well, gravity and magnetics data, together with seismic reflection and refraction data sets to build a comprehensive 3D subsurface model of the Egyptian Red Sea. Using a 2D iterative workflow first, we have constructed cross sections using the available well penetrations and seismic refraction data as preliminary constraints. The 2D forward model uses regional gravity and magnetic data to investigate the regional crustal structure. The final models are refined using enhanced gravity and magnetic data and geologic interpretations. This process reduces uncertainties in basement interpretation and magmatic body identification. Euler depth estimates are used to point out the edges of high-susceptibility bodies. We achieved further refinement by initiating a 3D gravity inversion. The resultant 3D gravity model increases precision in crustal geometries and lateral density variations within the crust and the presalt sediments. Along the Egyptian margin, where data inputs are more robust, basement lows are observed and interpreted as basins. Basement lows correspond with thin crust ([Formula: see text]), indicating that the evolution of these basins is closely related to the thinning or necking process. In fact, the Egyptian Northern Red Sea is typified by dramatic crustal thinning or necking that is occurring over very short distances of approximately 30 km, very proximal to the present-day coastline. The integrated 2D and 3D modeling reveals the presence of high-density magnetic bodies that are located along the margin. The location of the present-day Zabargad transform fault zone is very well delineated in the computed crustal thickness maps, suggesting that it is associated with thin crust and shallow mantle.

What does the 2A Event observed in the oceanic crust actually represent?

&lt;p&gt;The transition zone between the more porous upper extrusive layer (2A) and the less porous lower dyke layer (2B) within the oceanic crust is characterised by a high velocity gradient based on inversion of controlled-source, long-offset refraction data. In these data the phase associated with this high velocity gradient, termed the 2A Event, has an anomalously high amplitude over a limited range of offsets and appears to form a triplication with refractions from layer 2A above and 2B below. These characteristics fit the accepted model that this event is a caustic or retrograde phase, generated by a distinct layer whose thickness and velocity gradient can be determined by ray-trace modelling. Hence, a velocity model for Layer 2 (derived from seismic data acquired near ODP 504B) consists of a ~500 m-thick 2A with a velocity gradient of ~1.0 s&lt;sup&gt;-1&lt;/sup&gt;; a ~200 m-thick transition zone with a high velocity gradient of ~4.0 s&lt;sup&gt;-1&lt;/sup&gt;; and a ~1300 m-thick 2B with a velocity gradient of ~0.3 s&lt;sup&gt;-1&lt;/sup&gt;. However, this model is at odds with observation of Layer 2 lithology obtained from coring and ophiolites where the 2A is composed of a mixture of higher velocity basalt flows and lower velocity pillow lavas and breccia, with the transition zone represented by an increasing number of dykes which eventually make up 100% of the section in layer 2B combined and the effects of high-temperature alteration. Starting with a simplified but plausible geologically-based model, we show that it is possible to synthetically generate the observed 2A Event, and gain insight into what controls its visibility and variability in refraction data. Our primary findings show that the 2A Event will only form and propagate in the base of layer 2A, above the level where the higher velocities dominate. We also show that the amplitude of the 2A Event is sensitive to the local velocity structure of the extrusive layer and is most visible when seismic energy is focused by a low velocity layer. Hence, we conclude that the 2A Event is not a simple caustic, as defined by geometrical optics, but instead caused by the incident seismic energy being briefly concentrated in a leaky waveguide close to, but above, the mean depth of the dykes and the onset of high temperature alteration.&lt;/p&gt;

Improved interpretation of SAGEEP 2011 blind refraction data using Frequency-Dependent Traveltime Tomography

&lt;p&gt;We demonstrate improved resolution in P-wave velocity tomograms obtained by inversion of the synthetic SAGEEP 2011 refraction traveltime data (Zelt 2010) using Wavepath-Eikonal Traveltime Inversion (WET; Schuster 1993) and Wavelength-Dependent Velocity Smoothing (WDVS; Zelt and Chen 2016). We use a multiscale inversion approach and a Conjugate-Gradient based search method. Our default starting model is a 1D-gradient model obtained directly from the traveltime first arrivals assuming diving waves (Sheehan, 2005). As a second approach, we map the first breaks to assumed refractors and obtain a layered starting model using the Plus-Minus refraction method (Hagedoorn, 1959). We compare tomograms obtained using WDVS to smooth the current velocity model grid before forward modeling traveltimes vs. tomograms obtained without WDVS. Results show that WET images velocity layer boundaries more sharply when engaging WDVS. We determine the optimum WDVS frequency iteratively by trial-and-error. We observe that the lower the used WDVS frequency, the stronger the imaged velocity contrast at the top-of-basement. Using a WDVS frequency that is too low makes WDVS based WET inversion unstable exhibiting increasing RMS error, too high modeled velocity contrast and too shallow imaged top-of-basement. To speed up WDVS, we regard each nth node only when scanning the velocity along straight scan lines radiating from the current velocity grid node. Scanned velocities are weighted with a Cosine-Squared function as described by (Zelt and Chen, 2016). We observe that activating WDVS allows decreasing WET regularization (smoothing and damping) to a higher degree than without WDVS.&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;&lt;span&gt;Hagedoorn, J.G., 1959, &lt;/span&gt;&lt;span&gt;The Plus-Minus method of interpreting seismic refraction sections, Geophysical Prospecting&lt;/span&gt;&lt;span&gt;, Volume 7, 158-182.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Rohdewald, S.R.C., 2021, SAGEEP11 data interpretation, https://rayfract.com/tutorials/sageep11_16.pdf.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Schuster, G.T., Quintus-Bosz, A., 1993, &lt;span&gt;Wavepath eikonal traveltime inversion: Theory&lt;/span&gt;. Geophysics, Volume 58, 1314-1323.&lt;/p&gt;&lt;p&gt;&lt;span&gt;Sheehan, J.R., Doll, W.E., Mandell, W., 2005, &lt;/span&gt;&lt;span&gt;An evaluation of methods and available software for seismic refraction tomography analysis&lt;/span&gt;&lt;span&gt;, JEEG, Volume 10(1), 21-34.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Shewchuk, J.R., 1994, An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, &lt;span&gt;http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;Zelt, C.A., 2010, Seismic refraction shootout: blind test of methods for obtaining velocity models from first-arrival travel times, &lt;span&gt;http://terra.rice.edu/department/faculty/zelt/sageep2011&lt;/span&gt;.&lt;/p&gt;&lt;p&gt;&lt;span&gt;Zelt, C.A., Haines, S., Powers, M.H. et al. 2013, &lt;/span&gt;&lt;span&gt;Blind Test of Methods for Obtaining 2-D Near-Surface Seismic Velocity Models from First-Arrival Traveltimes&lt;/span&gt;&lt;span&gt;, JEEG, Volume 18(3), 183-194. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Zelt, C.A., Chen, J., 2016, &lt;/span&gt;&lt;span&gt;Frequency-dependent traveltime tomography for near-surface seismic refraction data&lt;/span&gt;&lt;span&gt;, Geophys. J. Int., Volume 207, 72-88. &lt;/span&gt;&lt;/p&gt;

Seismic velocity structure of the Queen Charlotte terrace off western Canada in the region of the 2012 Haida Gwaii Mw 7.8 thrust earthquake

A well-recorded Mw 7.8 megathrust earthquake occurred on 27 October 2012 under the Queen Charlotte terrace off the west coast of Haida Gwaii, western Canada. In this study, we supplement limited earlier seismic refraction work on the offshore velocity structure off Haida Gwaii with data from ocean bottom seismometers (OBS) operating between 6 December 2012 and 5 January 2013. The OBS recorded a portion of the aftershock sequence, and an active-source seismic survey was conducted in January 2013 to acquire seismic refraction data in the region of the Haida Gwaii earthquake across the Queen Charlotte terrace. P-wave velocity analyses using first-arrival tomography showed relatively shallow (2.0–3.0 km below seafloor) high-velocity material with values up to 4.0 km/s beneath the terrace. At the one OBS station seaward of the deformation front on the abyssal plain, refraction velocities of ~4.5 km/s indicated the top of the oceanic plate at ~1–2 km below the seafloor. At sev­eral OBS stations, converted shear-wave velocities were determined within the sediment cover using reflected arrivals. The S-wave velocities ranged from 0.5 to 1.5 km/s, and the corresponding P/S velocity ratio was between 3.0 and 4.2. The new refraction data confirm earlier interpretations of high-velocity material in the shallow terrace that may indicate fractured oceanic crustal material lies significantly above the location where a sub­ducted slab is thought to occur under the terrace. Transpressive deformation of the Pacific plate may explain these observations.