Title:
Human and Machine Listening of Seismic Data
Human and Machine Listening of Seismic Data
| dc.contributor.author | Paté, Arthur | |
| dc.contributor.author | Holtzman, Benjamin | |
| dc.contributor.author | Waldhauser, Felix | |
| dc.contributor.author | Repetto, Douglas | |
| dc.contributor.author | Paisley, John | |
| dc.contributor.corporatename | International Community for Auditory Display | |
| dc.contributor.corporatename | Columbia University. Lamont-Doherty Earth Observatory | |
| dc.contributor.corporatename | Columbia University. Department of Electrical Engineering | |
| dc.date.accessioned | 2017-06-15T17:48:03Z | |
| dc.date.available | 2017-06-15T17:48:03Z | |
| dc.date.issued | 2017-06 | |
| dc.description | Presented at the 23rd International Conference on Auditory Display (ICAD 2017) in Pennsylvania, USA. | |
| dc.description.abstract | Geothermal energy mining consists of injecting cold water into hot rocks in order to create micro-fractures allowing heat to be extracted and converted into electrical energy. This water injection can trigger several rock fracture processes. Seismologists are facing the challenge of identifying and understanding these fracture processes in order to maximize heat extraction and minimize induced seismicity. Our assumption is that each fracture process is characterized by spectro-temporal features and patterns that are not picked up by current signal processing methods used in seismology, but can be identified by the human auditory system and/or by machine learning. We present here a pluridisciplinary methodology aimed at addressing this problem, combining machine learning, auditory display and sound perception. | en_US |
| dc.identifier.citation | Paté, A., et al. "Human and Machine Listening of Seismic Data" Presented at the 23rd International Conference on Auditory Display (ICAD2017), June 20-23, 2017, Pennsylvania State University, State College, PA, USA. | en_US |
| dc.identifier.doi | https://doi.org/10.21785/icad2017.047 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/58368 | |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher | Georgia Institute of Technology | |
| dc.publisher.original | International Community on Auditory Display | |
| dc.publisher.original | International Community for Auditory Display (ICAD) | |
| dc.relation.ispartofseries | International Conference on Auditory Display (ICAD) | |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Auditory display | en_US |
| dc.subject | Geothermal energy | en_US |
| dc.subject | Seismic audification | en_US |
| dc.subject | Signal processing | en_US |
| dc.title | Human and Machine Listening of Seismic Data | en_US |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Sonification Lab | |
| local.relation.ispartofseries | International Conference on Auditory Display (ICAD) | |
| relation.isOrgUnitOfPublication | 2727c3e6-abb7-4df0-877f-9f218987b22a | |
| relation.isSeriesOfPublication | 6cb90d00-3311-4767-954d-415c9341a358 |