Machine Learning Using U-Net Convolutional Neural Networks for the Imaging of Sparse Seismic Data

Machine learning using convolutional neural networks (CNNs) is investigated for the imaging of sparsely sampled seismic reflection data. A limitation of traditional imaging methods is that they often require seismic data with sufficient spatial sampling. Using CNNs for imaging, even if the spatial sampling of the data is sparse, good imaging results can still be obtained. Therefore, CNNs applied to seismic imaging have the potential of producing improved imaging results when spatial sampling of the data is sparse. The imaged model can then be used to generate more densely sampled data and in this way be used to interpolate either regularly or irregularly sampled data. Although there are many approaches for the interpolation of seismic data, here seismic imaging is performed directly with sparse seismic data once the CNN model has been trained. The CNN model is found to be relatively robust to small variations from the training dataset. For greater deviations, a larger training dataset would likely be required. If the CNN is trained with a sufficient amount of data, it has the potential of imaging more complex seismic profiles

Analysis of Local Seismic Events near a Large-N Array for Moho Reflections

Local seismic events recorded by the large-N IRIS Community Wavefield Experiment in Oklahoma are used to estimate Moho reflections near the array. For events within 50 km of the center of the array, normal moveout corrections and receiver stacking are applied to identify the PmP and SmS Moho reflections on the vertical and transverse components. Corrections for the reported focal depths are applied to a uniform event depth. To stack signals from multiple events, further static corrections of the envelopes of the Moho reflected arrivals from the individual event stacks are applied. The multiple-event stacks are then used to estimate the pre-critical PmP and SmS arrivals, and an average Poisson\u27s ratio of 1.77±.02 was found for the crust near the array. Using a modified Oklahoma Geological Survey (OGS) velocity model with this Poisson\u27s ratio, the time-to-depth converted PmP and SmS arrivals resulted in a Moho depth of 41±.6 km. The modeling of wide-angle Moho reflections for selected events at epicenter-to-station distances of 90 to 135 km provides additional constraints, and assuming the modified OGS model, a Moho depth of 40±1 km was inferred. The difference between the pre-critical and wide-angle Moho estimates could result from some lateral variability between the array and the wide-angle events. However, both estimates are slightly shallower than the original OGS model Moho depth of 42 km, and this could also result from a somewhat faster lower crust. This study shows that local seismic events, including induced events, can be utilized to estimate properties and structure of the crust, which in turn can be used to better understand the tectonics of a given region. The recording of local seismicity on large-N arrays provides increased lateral phase coherence for the better identification of pre-critical and wide-angle reflected arrivals

Seismic Interferometry using Seismic Noise from Wind Turbines and other Anthropogenic Sources

We investigate seismic noise from anthropogenic sources, in particular wind turbines, for seismic interferometry. The data is from the 17-station Autocorr Seismic Array located in the Midwestern United States. The array has a linear component that extends about 30 km from north to south and a subarray to the south with a diameter of 10 km. The array was deployed from August 2019 to July 2020, which included the initial months of the Covid-19 pandemic. The northernmost seismic stations of the array are located within the southern end of one of the largest onshore wind farms in the world. To the south of the array there are regularly occurring east-west running trains. However even during times when trains are present, the frequency signatures of the wind turbines are dominant over much of the array, including seismic stations well to the south of the wind farm. Although there is vehicle traffic in the region, time windows in the late evening and early morning were chosen to reduce its effect. Shallow refraction data are available nearby individual seismic stations of the array, and since the spectral peaks do not vary for stations with differing basement depths, they are inferred to be source effects of wind turbines. When utilizing seismic interferometry, coherent Rayleigh wave signals are observed for time windows of seismic noise as short as 15 minutes. There are also concurrent estimates of average hourly wind speeds and wind gusts at the locations of the seismic stations. These data show that for ambient noise correlations, clear south propagating Rayleigh waves are observed for moderate to large average hourly wind speeds. For lower wind speeds, less coherent Rayleigh wave signals are observed in the one-hour ambient noise correlations. For seismic stations within the wind farm, both north and south propagating Rayleigh waves are observed in the correlations. However, for seismic stations to the south of the wind farm, only south propagating waves are observed, which are inferred to be coming from the wind farm

Wave propagation in laterally varying media and iterative inversion for velocity

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric and Planetary Sciences, 1985.Microfiche copy available in Archives and Science.Includes bibliographies.by Robert L. Nowack.Ph.D

Seismic attribute inversion for velocity and attenuation structure using data from the GLIMPCE Lake Superior experiment

A simultaneous inversion for velocity and attenuation structure using multiple seismic attributes has been applied to refraction data from the 1986 GLIMPCE Lake Superior experiment. The seismic attributes considered include envelope amplitude, instantaneous frequency, and travel time of first arrival data. Instantaneous frequency is converted to t* using a matching procedure which approximately removes the effects of the source spectra. The derived seismic attributes are then used in an iterative inversion procedure referred to as AFT inversion for amplitude, (instantaneous) frequency, and time. Uncertainties and resolution of the velocity and attenuation models are estimated using covariance calculations and checkerboard resolution maps. A simultaneous inversion of seismic attributes from the GLIMPCE data results in a velocity model similar to that of previous studies across Lake Superior. A central rift basin and a northern basin are the most prominent features with an increase in velocity near the Isle Royale fault. Although there is an indication of the central and northern basins in the attenuation model for depths greater than 4 km, the separation is not evident for shallower depths. This may result from microstructures masking compositional variations in the attenuation model for shallower depths. Attenuation Q values range from approximately 60 near the surface to ear 500 at 10 km depth. A relationship between inverse Q and velocity of Q¯¹=0.0210-0.0028*v was found between Q¯¹ and velocity beneath Lake Superior which supports previous laboratory results. The invereted velocity and attenuation models provide important constraints on the lithology and physical properties of the Midcontinent rift beneath Lake Superior.Copyrighted by American Geophysical Union

Epilepsy Caused by an Abnormal Alternative Splicing with Dosage Effect of the SV2A Gene in a Chicken Model

Photosensitive reflex epilepsy is caused by the combination of an individual's enhanced sensitivity with relevant light stimuli, such as stroboscopic lights or video games. This is the most common reflex epilepsy in humans; it is characterized by the photoparoxysmal response, which is an abnormal electroencephalographic reaction, and seizures triggered by intermittent light stimulation. Here, by using genetic mapping, sequencing and functional analyses, we report that a mutation in the acceptor site of the second intron of SV2A (the gene encoding synaptic vesicle glycoprotein 2A) is causing photosensitive reflex epilepsy in a unique vertebrate model, the Fepi chicken strain, a spontaneous model where the neurological disorder is inherited as an autosomal recessive mutation. This mutation causes an aberrant splicing event and significantly reduces the level of SV2A mRNA in homozygous carriers. Levetiracetam, a second generation antiepileptic drug, is known to bind SV2A, and SV2A knock-out mice develop seizures soon after birth and usually die within three weeks. The Fepi chicken survives to adulthood and responds to levetiracetam, suggesting that the low-level expression of SV2A in these animals is sufficient to allow survival, but does not protect against seizures. Thus, the Fepi chicken model shows that the role of the SV2A pathway in the brain is conserved between birds and mammals, in spite of a large phylogenetic distance. The Fepi model appears particularly useful for further studies of physiopathology of reflex epilepsy, in comparison with induced models of epilepsy in rodents. Consequently, SV2A is a very attractive candidate gene for analysis in the context of both mono- and polygenic generalized epilepsies in humans

Measurement of associated Z plus charm production in proton-proton collisions at root s=8TeV

A study of the associated production of a Z boson and a charm quark jet (Z + c), and a comparison to production with a b quark jet (Z + b), in pp collisions at a centre-of-mass energy of 8 TeV are presented. The analysis uses a data sample corresponding to an integrated luminosity of 19.7 fb(-1), collected with the CMS detector at the CERN LHC. The Z boson candidates are identified through their decays into pairs of electrons or muons. Jets originating from heavy flavour quarks are identified using semileptonic decays of c or b flavoured hadrons and hadronic decays of charm hadrons. The measurements are performed in the kinematic region with two leptons with pT(l) > 20 GeV, vertical bar eta(l)vertical bar 25 GeV and vertical bar eta(jet)vertical bar Z + c + X) B(Z -> l(+)l(-)) = 8.8 +/- 0.5 (stat)+/- 0.6 (syst) pb. The ratio of the Z+c and Z+b production cross sections is measured to be sigma(pp -> Z+c+X)/sigma (pp -> Z+b+X) = 2.0 +/- 0.2 (stat)+/- 0.2 (syst). The Z+c production cross section and the cross section ratio are also measured as a function of the transverse momentum of theZ boson and of the heavy flavour jet. The measurements are compared with theoretical predictions.Peer reviewe