WATCH THE PRESENTATIONThe need to cross-correlate two wavefields in the application of Reverse Time Migration’s imaging condition remains one of two fundamental challenges with use of the method in practice (e.g., Liu et al., Computers & Geosciences 59, 17–23, 2013). In a significant departure from previous approaches, this computational challenge is addressed here through the introduction of Resilient Distributed Datasets (RDDs) for RTM’s precomputed source wavefields. RDDs are a relatively recent abstraction for in-memory computing ideally suited to distributed computing environments like clusters (Zaharia et al., NSDI 2012, http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf). Originally introduced for Big Data Analytics and popularized (e.g., Lumb, “8 Reasons Apache Spark is So Hot”, insideBIGDATA, http://insidebigdata.com/2015/03/06/8-reasons-apache-spark-hot/, 2015) through the open-source implementation known as Apache Spark (https://spark.apache.org/), RDDs also appear promising in recontextualizing RTM’s imaging condition.
Recent work has already indicated that seismic reflection data in accepted industry formats can be distributed in memory across a cluster using Apache Spark (Yan et al., “A Spark-based Seismic Data Analytics Cloud”, 2015 Rice Oil & Gas Workshop, Houston, TX, http://rice2015.og-hpc.org/technical-program/). And although Lumb (“RTM Using Hadoop Is There a Case for Migration?”, 2015 Rice Oil & Gas Workshop, Houston, TX, http://rice2015.og-hpc.org/technical-program/) has indicated that RDDs and Spark appear promising for impacting RTM in a number of ways (e.g., in allowing for the implementation of imaging conditions using alternatives to cross-correlation), attention here focuses on use of RDDs for facilitating the assessment of coherence between seismic-reflection wavefields in memory. More specifically an algorithm that significantly reduces the impact of disk I/O, in the wavefield manipulations required by RTM, is proposed based on RDDs and subsequently implementation-prototyped using open-source Thunder (http://thunder-project.org/) via Apache Spark.
