Nasser Kazemi

Wetting at the nanoscale: A molecular dynamics study

A novel method to calculate the solid-liquid contact angle is introduced in this study. Using the 3D configuration of a liquid droplet on a solid surface, this method calculates the contact angle along the contact line and provides an angular distribution. Although this method uses the 3D configuration of liquid droplets, it does not require the calculation of the 3D density profile to identify the boundaries of the droplet. This decreases the computational cost of the contact angle calculation greatly. Moreover, no presumption about the shape of the liquid droplet is needed when using the method introduced in this study. Using this method, the relationship between the size and the contact angle of water nano-droplets on a graphite substrate was studied. It is shown that the contact angle generally decreases by increasing the size of the nano-droplet. The microscopic contact angle of 83.0° was obtained for water on graphite which is in a good agreement with previous experimental and numerical studies. Neglecting other nanoscale effects which may influence the contact angle, the line tension of SPC/E (extended simple point charge model) water was calculated to be 3.6×10-11 N, which is also in good agreement with the previously calculated values.

Surface-Consistent Sparse Multichannel Blind Deconvolution of Seismic Signals

We describe a method that allows for blind surface consistent estimation of the source and receiver wavelets of seismic signals. This is very relevant for surface-consistent deconvolution where current processing standards focus on the removal of the source and receiver effects under the minimum phase assumption. The proposed method, which is an extension of the Euclid deconvolution method, employs an iterative algorithm that simultaneously estimates the source and receiver wavelets that are consistent with the data. Unlike most deconvolution methods, the algorithm requires no prior phase assumptions. Another important feature of the algorithm is that we questioned the Gaussian density assumption of the reflectivity series and instead implemented a sparse regularizer to constrain the solution space of our desired reflectivity series. In other words, we assume that the reflectivity series can be cast as a sparse vector with few nonzero coefficients.

An algorithm for quantitatively modeling reflected ultrasonic bounded pulses and beams.

HIGHLIGHTSThis algorithm provides a quantitative solution to the reflectivity of the bounded beam.The measured reflectivity from water‐Aluminum and the Copper matches with results from the proposed algorithm.The longitudinal critical reflection and bounded beam effects at Rayleigh angle are discussed from water‐copper alloy and water‐aluminum boundaries. ABSTRACT The study of the reflected acoustic waves plays an important role in our understanding of media. We provide an algorithm to propagate the ultrasonic bounded beam source and study its reflection from any horizontal and homogenous water‐solid boundary. This algorithm implements a hybrid combination of the phase‐advance wavefield continuation in the frequency domain and the complex analytic solution for the acoustic reflectivity. The peak amplitude of the specularly reflected beam is in agreement with the laboratory measured acoustic reflection from water‐Aluminum and water‐Copper alloy boundaries. The algorithm is able to model the observed critical reflection as well as the null in the reflected amplitude at the Rayleigh critical angle from the acoustic wave. This algorithm is a crucial tool to understand the full reflected wave from material immersed in water in any azimuthal or incidental angles. The software of this algorithm and acoustic reflectivity from both solid materials are provided.

Least-squares Migration via a Gradient Projection Method - Application to Seismic Data Deblending

We propose a gradient projection method that is applicable to least-squares migration for separation of simultaneous source seismic data. Using shot-profile split-step migration and de-migration operators, we notice that, in shot-index image domain, the simultaneous source interferences appear random whereas the desired signal is coherent. The latter is used as a coherency constraint for least-squares migration. We incorporate a projection operator, which is the Singular Spectrum Analysis (SSA) filter in shot-index domain, into gradient projection method to solve for a volume of artifacts-reduced shot-index gathers that honors the observed data. The method effectively suppresses simultaneous source crosstalk and improves the quality of shot-index image gathers. The outputs of our method can be a crosstalk-free migrated image and the deblended data set that can be used in conventional processing workflows.

Modified Sparse Multichannel Blind Deconvolution

Euclid deconvolution is a multichannel algorithm that leads to the estimation of the multichannel seismic reflectivity via the solution of homogeneous system of equations. In the ideal case, the eigenvector associated to the minimum nonzero eigenvalue of the homogenous system of equations is an estimator of the multichannel reflectivity. However, small amounts of noise impinge on the identification of the eigenvector associated to the impulse response. Recently, we proposed a method called SMBD that solves the homogeneous system of equations arising in Euclid deconvolution by imposing sparsity on the unknown multichannel impulse response. The method can accurately estimate the seismic reflectivity and wavelet in the presence of a moderate amount of noise. However, it does not model the noise properly and there is no automatic way for defining the regularization parameter. In this abstract, we tried to improve the SMBD algorithm by including an extra term to handle additive noise. Moreover, in our new algorithm the regularization parameters can be automatically estimated via line search and cross validation procedures. The method is successfully tested on a realistic synthetic example and on marine and land real datasets.

Seismic Data Analysis in Julia

A new programming language for scientific computing has undergone rapid development since 2012. The language is named Julia– perhaps a reference to the beautiful fractal patterns of the Julia set. Julia is a high level programming language with an extensive library of mathematical functions that is easy to code and share with others. However, unlike other high level languages it offers C-like performance. It is an open source language with a large community of users and developers with a built-in package manager. The Signal Analysis and Imaging Group (SAIG) has recently released a seismic data processing package named Seismic.jl that contains utilities for reading and manipulating seismic data. We believe Julia is a great new language for research and teaching in the geosciences.

Data Reconstruction and Denoising of Different Wavefield Components Using Green’s Theorem

Multicomponent technology is likely to become the standard in future towed streamer surveys. Among reasons to acquire multicomponent data is to increase efficiency (deep tow) in acquisition, increase resolution (deghost) of pressure data, interpolate to a fine grid (using crossline directional measurements). One of the challenges of working with towed streamer multicomponent data is the fact that the measured low frequencies in the directional components are rather noisy mostly due to practical aspects/limitations. We present a formalism based on Green’s theorem that provides tools to reconstruct, redatum, and/or denoise these components, and/or for deghosting.

Transform-domain Sparsity Regularization in Reconstruction of Channelized Facies

SUMMARY Petrophysical reservoir properties, such as porosity and permeability that determine fluid flow displacement within rocks and oil production, are highly correlated spatial parameters. These parameters are very important for future prediction scenarios. Identification of these input parameters using available knowledge (Library) and observations (data) is essential for optimum characterization of reservoir and proper development of the resources in the region. However, library (Geology, etc) and observations may be among available sources of information that can be used for identifying the high energy bearing basis vectors and estimating their corresponding magnitudes. In this study a few examples are used to show how these information sources can be combined with a reduced representation to provide an efficient reconstruction method. In this paper, compressive sensing is studied for inverse modeling of such ill-posed problems with spatially continuous parameters (channelized features) that are sparse in an incoherent basis (e.g. DCT). Compressive sensing is a recently introduced alternative for exact reconstruction of sparse signals from partial linear observations in an incoherent basis. The suitability of the inversion method is illustrated through synthetic examples in characterization of channelized reservoirs.

Local Stretch Zeroing NMO Correction

We present a method of normal moveout (NMO) correction called local stretch zeroing (LSZ) method that avoids NMO stretch. This is done by eliminating those theoretical curves that generate interpolated data samples responsible for NMO stretch. The original sampling interval is preserved by zero padding and reassigning of true data samples. The LSZ method eliminates all interfering reflection events at far offsets by optimally selection of mute zone. The results are generally higher frequency than a normal stack and contain less noise. The method loses its efficiency when CMP data is over- or under-NMO corrected. Performance of the method is compared to conventional NMO correction Taner et al. (1969) using both synthetic and real data.