Zobrazeno 1 - 10
of 462
pro vyhledávání: '"J, Giraud"'
Publikováno v:
Geoscientific Model Development, Vol 17, Pp 2325-2345 (2024)
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that incorporates several functionalities enhancing its performance and applicability for both industrial and academic studies. The code has been re-designed
Externí odkaz:
https://doaj.org/article/cbef1ae2824045fdbc7431aeb3c0ddc8
Publikováno v:
Solid Earth, Vol 15, Pp 63-89 (2024)
We propose and evaluate methods for the integration of automatic implicit geological modelling into the geophysical (potential field) inversion process. The objective is to enforce structural geological realism and to consider geological observations
Externí odkaz:
https://doaj.org/article/7493587a64454820a13af8c034740a9e
Publikováno v:
Solid Earth, Vol 14, Pp 43-68 (2023)
We propose, test and apply a methodology integrating 1D magnetotelluric (MT) and magnetic data inversion, with a focus on the characterisation of the cover–basement interface. It consists of a cooperative inversion workflow relying on standalone in
Externí odkaz:
https://doaj.org/article/c418e3913dbf497b9b5daaf502a91f87
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 4689-4708 (2022)
To support the needs of practitioners regarding 3D geological modelling and uncertainty quantification in the field, in particular from the mining industry, we propose a Python package called loopUI-0.1 that provides a set of local and global indicat
Externí odkaz:
https://doaj.org/article/84b35c5576c14be99117ec345c92ecab
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 3641-3662 (2022)
Parametric geological models such as implicit or kinematic models provide low-dimensional, interpretable representations of 3-D geological structures. Combining these models with geophysical data in a probabilistic joint inversion framework provides
Externí odkaz:
https://doaj.org/article/6a491a6aff7a45728d3e861c9acebb82
Publikováno v:
Earth System Science Data, Vol 14, Pp 381-392 (2022)
Unlike some other well-known challenges such as facial recognition, where machine learning and inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled data sets that can be used to validate or train robust mac
Externí odkaz:
https://doaj.org/article/b74969ceb6c642ffbf49671e446a0f83
Publikováno v:
Geoscientific Model Development, Vol 14, Pp 6681-6709 (2021)
The quantitative integration of geophysical measurements with data and information from other disciplines is becoming increasingly important in answering the challenges of undercover imaging and of the modelling of complex areas. We propose a review
Externí odkaz:
https://doaj.org/article/a11b58f92f0d4fb9b1120143e4eb81d0
Publikováno v:
Solid Earth, Vol 12, Pp 2387-2406 (2021)
One of the main tasks in 3D geological modeling is the boundary parametrization of the subsurface from geological observations and geophysical inversions. Several approaches have been developed for geometric inversion and joint inversion of geophysic
Externí odkaz:
https://doaj.org/article/725bf62b8949482584876f12b262d3e4
Publikováno v:
Solid Earth, Vol 11, Pp 419-436 (2020)
We propose a methodology for the recovery of lithologies from geological and geophysical modelling results and apply it to field data. Our technique relies on classification using self-organizing maps (SOMs) paired with geoscientific consistency chec
Externí odkaz:
https://doaj.org/article/2993d65b18824a01aa0ac67d9e53d66c
Publikováno v:
Solid Earth, Vol 10, Pp 1663-1684 (2019)
This paper proposes and demonstrates improvements for the Monte Carlo simulation for uncertainty propagation (MCUP) method. MCUP is a type of Bayesian Monte Carlo method aimed at input data uncertainty propagation in implicit 3-D geological modeling.
Externí odkaz:
https://doaj.org/article/3efe3934a58042e785ad4252c954da83