Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral
  Projected-Gradient   In this work, an efficient numerical scheme is presented for seismic blind
deconvolution in a multichannel scenario. The proposed method iterate with wo
steps: first, wavelet estimation across all channels and second, refinement of
the reflectivity estimate simultaneously in all channels using sparse
deconvolution. The reflectivity update step is formulated as a basis pursuit
denoising problem and a sparse solution is obtained with the spectral
projected-gradient algorithm - faithfulness to the recorded traces is
constrained by the measured noise level. Wavelet re-estimation has a closed
form solution when performed in the frequency domain by finding the minimum
energy wavelet common to all channels. Nothing is assumed known about the
wavelet apart from its time duration. In tests with both synthetic and real
data, the method yields sparse reflectivity series and stable wavelet estimates
results compared to existing methods with significantly less computational
effort.

22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int