Another old paper is added to the collection of reproducible documents:
A prospect for super resolution

Wouldn’t it be great if I could take signals of 10-30 Hz bandwidth from 100 different offsets and construct a zero-offset trace with 5-100 Hz bandwidth? This would not violate Shannon’s sampling theorem which theoretically allows us to have a transform from 100 signals of 20 Hz bandwidth to one signal at 2000 Hz bandwidth. The trouble is that simple NMO is not such a transformation. Never-the-less, if the different offsets really did give us any extra information, we should be able to put the information into extra bandwidth. Let us consider noise free synthetic data and see if we can come up with a model where this could happen.

Another old paper is added to the collection of reproducible documents:
Earthquake stacks at constant offset

I show Shearer’s earthquake stacks over all source-receiver locations at constant offset and compare them to exploration seismic data. This electronic document simply reads the stacks and plots them.

A new paper is added to the collection of reproducible documents:
Adaptive prediction filtering in t-x-y domain for random noise attenuation using regularized nonstationary autoregression

Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary. They may exhibit stationarity on a short timescale but eventually alter their behavior in time and space. We propose a 2D t-x adaptive prediction filter (APF) and further extend this to a 3D t-x-y version for random noise attenuation based on regularized nonstationary autoregression (RNA). Instead of using patching, a popular method for handling nonstationarity, we obtain smoothly nonstationary APF coefficients by solving a global regularized least-squares problem. We use shaping regularization to control the smoothness of the coefficients of APF. 3D space-noncausal t-x-y APF uses neighboring traces around the target traces in the 3D seismic cube to predict noise-free signal, so it provides more accurate prediction results than the 2D version. In comparison with other denoising methods, such as frequency-space deconvolution, time-space prediction filter, and frequency-space RNA, we test the feasibility of our method in reducing seismic random noise on three synthetic datasets. Results of applying the proposed method to seismic field data demonstrate that nonstationary t-x-y APF is effective in practice.

This reproducible paper is the first direct contribution from Jilin University, China.

Another old paper is added to the collection of reproducible documents:
The time and space formulation of azimuth moveout

Azimuth moveout (AMO) transforms 3-D prestack seismic data from one common azimuth and offset to different azimuths and offsets. AMO in the time-space domain is represented by a three-dimensional integral operator. The operator components are the summation path, the weighting function, and the aperture. To determine the summation path and the weighting function, we derive the AMO operator by cascading dip moveout (DMO) and inverse DMO for different azimuths in the time-space domain. To evaluate the aperture, we apply a geometric approach, defining AMO as the result of cascading prestack migration (inversion) and modeling. The aperture limitations provide a consistent description of AMO for small azimuth rotations (including zero) and justify the economic efficiency of the method.