Deep learning prior models from seismic images for full-waveform inversion

Full-waveform inversion (FWI) is now a mature technology that is routinely used in exploration around the world to obtain high resolution earth models. In geological areas such as the Gulf of Mexico, however, reconstructing complex salt geobodies poses a huge challenge to FWI due to the absence of low frequencies in the data needed to resolve such features. A skilled seismic interpreter has to interpret these geobodies and manually insert them into the earth model and repeat this process several times in the earth model building workflow. Deep learning algorithms have gained a lot of interest in recent years by obtaining state-of-the art results in various problems arising in the fields of computer vision, automatic speech recognition and natural language processing. We investigate the use of these algorithms to generate useful prior models for full-waveform inversion by learning features relevant to earth model building from a seismic image. We test this methodology in full-waveform inversion by generating a probability map of salt bodies in the migrated image along with a prior model and incorporating it in the FWI objective function. This approach is shown to be promising in enabling an automated salt body reconstruction using FWI. Presentation Date: Thursday, September 28, 2017 Start Time: 10:10 AM Location: 361F Presentation Type: ORAL