Leiv-J Gelius

Jing Sun, Sigmund Slang, Thomas Elboth et al.

For economic and efficiency reasons, blended acquisition of seismic data is becoming more and more commonplace. Seismic deblending methods are always computationally demanding and normally consist of multiple processing steps. Besides, the parameter setting is not always trivial. Machine learning-based processing has the potential to significantly reduce processing time and to change the way seismic deblending is carried out. We present a data-driven deep learning-based method for fast and efficient seismic deblending. The blended data are sorted from the common source to the common channel domain to transform the character of the blending noise from coherent events to incoherent distributions. A convolutional neural network (CNN) is designed according to the special character of seismic data, and performs deblending with comparable results to those obtained with conventional industry deblending algorithms. To ensure authenticity, the blending was done numerically and only field seismic data were employed, including more than 20000 training examples. After training and validation of the network, seismic deblending can be performed in near real time. Experiments also show that the initial signal to noise ratio (SNR) is the major factor controlling the quality of the final deblended result. The network is also demonstrated to be robust and adaptive by using the trained model to firstly deblend a new data set from a different geological area with a slightly different delay time setting, and secondly deblend shots with blending noise in the top part of the data.

Jing Sun, Song Hou, Vetle Vinje et al.

To streamline fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for generating high-quality training data along with a list of data conditioning techniques to improve performance of the data-driven model. We make use of unblended shot gathers acquired at the end of each sail line, to which the access requires no additional time or labor costs beyond the blended acquisition. By manually blending these data we obtain training data with good control of the ground truth and fully adapted to the given survey. Furthermore, we train a deep neural network using multi-channel inputs that include adjacent blended shot gathers as additional channels. The prediction of the blending noise is added in as a related and auxiliary task with the main task of the network being the prediction of the primary-source events. Blending noise in the ground truth is scaled down during the training and validation process due to its excessively strong amplitudes. As part of the process, the to-be-deblended shot gathers are aligned by the blending noise. Implementation on field blended-by-acquisition data demonstrates that introducing the suggested data conditioning steps can considerably reduce the leakage of primary-source events in the deep part of the blended section. The complete proposed approach performs almost as well as a conventional algorithm in the shallow section and shows great advantage in efficiency. It performs slightly worse for larger traveltimes, but still removes the blending noise efficiently.