A new paper is added to the collection of reproducible documents: Nonstationary pattern-based signal-noise separation using adaptive prediction-error filter

Complex field conditions always create different interferences during seismic data acquisition, and there exist several types of noise in the recorded data, which affect the subsequent data processing and interpretation. To separate an effective signal from the noisy data, we adopted a pattern-based method with a two-step strategy, which involves two adaptive prediction-error filters (APEFs) corresponding to a nonstationary data pattern and noise pattern. By introducing shaping regularization, we first constructed a least-squares problem to estimate the filter coefficients of the APEF. Then, we solved another constrained least-square problem corresponding to the pattern-based signal-noise separation, and different pattern operators are adopted to characterize random noise and ground-roll noise. In comparison with traditional denoising methods, such as FXDECON, curvelet transform and local time-frequency (LTF) decomposition, we examined the ability of the proposed method by removing seismic random noise and ground-roll noise in several examples. Synthetic models and field data demonstrate the validity of the strategy for separating nonstationary signal and noise with different patterns.

A new paper is added to the collection of reproducible documents: Automatic channel detection using deep learning

We propose a method based on an encoder-decoder convolutional neural network for automatic channel detection in 3D seismic volumes. We use two architectures borrowed from computer vision which are SegNet for image segmentation together with Bayesian SegNet for uncertainty measurement. We train the network on 3D synthetic volumes and then apply it to field data. We test the proposed approach on a 3D field dataset from the Browse Basin, offshore Australia and a 3D Parihaka seismic data in New Zealand. Applying the weights estimated from training on 3D synthetic volumes to a 3D field dataset accurately identifies channel geobodies without the need for any human interpretation on seismic attributes. Our proposed method also produces uncertainty volumes to quantify the trustiness of detection model.

The example in rsf/tutorials/timefreq reproduces the tutorial from Matt Hall on time-frequency decomposition.

The tutorial was published in the June 2018 issue of The Leading Edge.

The Madagascar version was created by Sarah Greer. Madagascar users are encouraged to try improving the results.

The example in rsf/tutorials/interp reproduces the tutorial from Matt Hall on interpolation methods.

The tutorial was published in the April 2016 issue of The Leading Edge.

Madagascar users are encouraged to try improving the results.

The example in rsf/tutorials/lineaments reproduces the tutorial from Matteo Niccoli on mapping and validating lineaments.

The tutorial was published in the August 2015 issue of The Leading Edge.

Madagascar users are encouraged to try improving the results.

The example in rsf/tutorials/wavelet reproduces the tutorial from Evan Bianco on wavelet estimation for well ties.

The tutorial was published in the June 2016 issue of The Leading Edge.

Madagascar users are encouraged to try improving the results.

The example in rsf/tutorials/inv reproduces the tutorial from Matt Hall on linear inversion. The tutorial was published in the December 2016 issue of The Leading Edge.

Madagascar users are encouraged to try improving the results.

The example in rsf/tutorials/nmo reproduces the tutorial from Leonardo Uieda on NMO correction. The tutorial was published in the February 2017 issue of The Leading Edge.

Madagascar users are encouraged to try improving the results.

The example in rsf/tutorials/semblance reproduces the tutorial from Joe Kington on semblance, coherence, and other discontinuity attributes. The tutorial was published in the December 2015 issue of The Leading Edge.

Madagascar users are encouraged to try improving the results.

The example in rsf/tutorials/cs reproduces the tutorial from Ben Bougher on compressed sensing. The tutorial was published in the October 2015 issue of The Leading Edge.

Madagascar users are encouraged to try improving the results.