Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Dramsch, Jesper Soeren"'
Autor:
Dramsch, Jesper Sören
This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the co-developments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to develop
Externí odkaz:
http://arxiv.org/abs/2006.13311
Deep learning has become an area of interest in most scientific areas, including physical sciences. Modern networks apply real-valued transformations on the data. Particularly, convolutions in convolutional neural networks discard phase information e
Externí odkaz:
http://arxiv.org/abs/1905.12321
Geoscience data often have to rely on strong priors in the face of uncertainty. Additionally, we often try to detect or model anomalous sparse data that can appear as an outlier in machine learning models. These are classic examples of imbalanced lea
Externí odkaz:
http://arxiv.org/abs/1904.02254
Publikováno v:
In Computers and Geosciences January 2021 146
Autor:
Dramsch, Jesper Soeren
Presentation for PhD Defence of Jesper Dramsch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c8dde856644320db8e364c72d293d2e
Including robust insights from signal processing, physics and geoscience improves key metrics in deep neural network training and inference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::722e8cdb65ecde72602d5682fb1b59f3
Autor:
Dramsch, Jesper Soeren, Lüthje, Mikael
Recent advances in machine learning relies on convolutional deep neural networks. These are often trained on cropped image patches. Pertaining to non-stationary seismic signals this may introduce low frequency noise and non-generalizability.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c6fa55bada144b7f72bde70822ddb8b
EAGE Annual Meeting 2011 Vienna poster.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8150e62c5a6b2fc8e3f2637b51faa6c8
Autor:
Dramsch, Jesper Soeren, Lüthje, Mikael
4D Seismic data has proven invaluable in O&G asset management, however, it’s engineering challenges are still plentiful. These challenges include non-repeatable noise, tie-in and match with production curves, as well as, separation of imaging, pres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0607a797000c71302bdc566c6fcdd722