Salt body detection from seismic data via sparse representation

Abstract

A B S T R A C TIn seismic interpretation and seismic data analysis, it is of critical importance to effectively identify certain geologic formations from very large seismic data sets. In particular, the problem of salt characterization from seismic data can lead to important savings in time during the interpretation process if solved efficiently and in an automatic manner. In this work, we present a novel numerical approach that is able to automatically segmenting or identifying salt structures from a post-stack seismic data set with a minimum intervention from the interpreter. The proposed methodology is based on the recent theory of sparse representation and consists in three major steps: first, a supervised learning assisted by the user which is performed only once, second a segmentation process via unconstrained 1 optimization, and finally a post-processing step based on signal separation. Furthermore, since the second step only depends upon local information at each time, the whole process greatly benefits from parallel computing platforms. We conduct numerical experiments in a synthetic 3D seismic data set demonstrating the viability of our method. More specifically, we found that the proposed approach matches up to 98.53% with respect to the corresponding 3D velocity model available in advance. Finally, in appendixes A and B, we present a convergence analysis providing theoretical guarantees for the proposed method.

Structure label prediction using similarity-based retrieval and weakly supervised label mapping

“…In recent years, there has been a significant interest in machine learning-based techniques for various seismic interpretation applications such as salt body delineation, fault and fracture detection, horizon extraction, and facies classification (e.g., Coléou et al, 2003;Barnes and Laughlin, 2005;Wang et al, 2015a;Guillen et al, 2015;Zhao et al, 2015;Wang et al, 2015b;Figueiredo et al, 2015;Qi et al, 2016;Ramirez et al, 2016;Lin et al, 2017). Supervised machine learning has proven to be one of the most successful machine learning paradigms.…”

Multi-attribute k-means clustering for salt-boundary delineation from three-dimensional seismic data

“…Similarly, Wu (2016) incorporates discrete pickings by an interpreter into the detection process to guide accurate delineation of salt boundaries, especially in complicated zones with gaps or outliers. To avoid interpreter bias, Ramirez et al (2016) adopt the theory of sparse representation (Donoho et al 1998) to minimize intervention from interpreters while automatically segmenting salt structures from 3-D seismic data set. Wu et al (2017) applies the optimal path picking algorithm for salt-boundary delineation from a limited number of key points defined by an interpreter.…”

Why using CNN for seismic interpretation? An investigation

“…Meanwhile, from the perspective of identifying seismic structures by involving computer graphics and image processing techniques, (semi-)automatic interpretation has become a research focus in the past decade to improve the interpretation efficiency and accuracy. The available computer-aided interpretation methods include ant tracking (Pedersen et al, 2002), normalized cuts (e.g., Lomask et al, 2007), dynamic time wrapping (Hale, 2013), the Hough transform (Wang and AlRegib, 2014), active contour models (e.g., Shafiq et al, 2015), optimal path picking (Wu, 2016), sparse representation (Ramirez et al, 2016), and more.…”