Estimating Primaries by Sparse Inversion with Cost-Effective Computation

Estimation of primaries by sparse inversion incuding the ghost

Today, the problem of surface-related multiples, especially in shallow water, is not fully solved. Although surface-related multiple elimination (SRME) method has proved to be successful on a large number of data cases, the involved adaptive subtraction acts as a weak link in this methodology, where primaries can be distorted due to their interference with multiples. Therefore, recently, SRME h...

Robust estimation of primaries by sparse inversion via one-norm minimization

Even though contemporary methods for the removal of multiple events in seismic data due to a free-surface are built upon reciprocity relationships between wavefields, they are often still implemented as prediction-subtraction processes. The subtraction process does not always compensate for imperfect prediction of multiple events, and itself often leads to distortion of primary events. A recent...

Sparse Computation with PEI

Pei formalism has been designed to reason and develop parallel programs in the context of data parallelism. In this paper, we focus on the use of Pei to transform a program involving dense matrices into a new program involving sparse matrices, using the example of the matrix-vector product.

Sparse Computation with PEI

Sparse matrix inversion with scaled Lasso

We propose a new method of learning a sparse nonnegative-definite target matrix. Our primary example of the target matrix is the inverse of a population covariance or correlation matrix. The algorithm first estimates each column of the target matrix by the scaled Lasso and then adjusts the matrix estimator to be symmetric. The penalty level of the scaled Lasso for each column is completely dete...