DOWNLOAD PRESENTATIONWATCH VIDEOReverse-Time Migration (RTM) is a compute-intensive step in processing seismic data for the purpose of petroleum exploration. Because complex geologies (e.g., folds, faults, domes) introduce unwanted signal (aka. noise) into recorded seismic traces, RTM is also an essential step in all upstream-processing workflows. The need to apply numerically intensive algorithms for wave-equation migration against extremely large volumes of seismic data is a well-established industry requirement. Not surprisingly then, providers of processing services for seismic data continue to make algorithm development an ongoing area of emphasis. With implementations making use of the Message Passing Interface (MPI), and variously CUDA for programming GPUs, RTM algorithms routinely exploit the processing of large volumes of seismic data in parallel. Given its innate ability to topologically align data with compute, through the combination of a parallel, distributed high volume filesystem (HDFS or Lustre) and workload manager (YARN), RTM algorithms could make use of Hadoop. Given the current level of convergence between High Performance Computing (HPC) and Big Data Analytics, the barrier for entry has never been lower. At the outset then, this presentation reviews the opportunities and challenges for Hadoop’izing RTM. Because recontextualizing RTM for Big Data Analytics will be a significant undertaking for organizations of any size, the analytics upside of using Hadoop applications as well as Apache Spark will be also considered. Although the notion of Hadoop’izing RTM is at the earliest of stages, the platform provided by Big Data Analytics has already delivered impressive results in processing large-scale seismic event data via waveform cross correlation (e.g., Addair et al., 2014, http://dx.doi.org/10.1016/j.cageo.2014.01.014).
