Bollettino di Geofisica Teorica e Applicata
OGS Website
About the Journal
Statistiche Web
Contacts
To Authors
On-line Submission
Subscriptions
Forthcoming
On-line First
The Historical First Issue
Issues

2023 Vol. 64
1

2022 Vol. 63
1 / 2 / 3 / 4

2021 Vol. 62
1 / 2 / 3 / 4 / Suppl. 1 / Suppl. 2 / Suppl. 3

2020 Vol. 61
1 / 2 / 3 / 4 / Suppl. 1

2019 Vol. 60
1 / 2 / 3 / 4 / Suppl. 1 / Suppl. 2 / Suppl. 3

2018 Vol. 59
1 / 2 / 3 / 4 / Suppl. 1

2017 Vol. 58
1 / 2 / 3 / 4

2016 Vol. 57
1 / 2 / 3 / 4 / Suppl. 1

2015 Vol. 56
1 / 2 / 3 / 4

2014 Vol. 55
1 / 2 / 3 / 4

2013 Vol. 54
1 / 2 / 3 / 4 / Suppl. 1 / Suppl. 2

2012 Vol. 53
1 / 2 / 3 / 4

2011 Vol. 52
1 / 2 / 3 / 4 / Suppl. 1

2010 Vol. 51
1 / 2-3 / 4 / Suppl. 1

2009 Vol. 50
1 / 2 / 3 / 4

2008 Vol. 49
1 / 2 / 3-4 / Suppl. 1

2007 Vol. 48
1 / 2 / 3 / 4

2006 Vol. 47
1-2 / 3 / 4

2005 Vol. 46
1 / 2-3 / 4

2004 Vol. 45
1-2 / 3 / 4 / Suppl. 1 / Suppl. 2

2003 Vol. 44
1 / 2 / 3-4

2002 Vol. 43
1-2 / 3-4

2001 Vol. 42
1-2 / 3-4

2000 Vol. 41
1 / 2 / 3-4

1999 Vol. 40
1 / 2 / 3-4

1998 Vol. 39
1 / 2 / 3 / 4

1997 Vol. 38
1-2 / 3-4

1995 Vol. 37
145 / 146 / 147 / 148 / Suppl. 1

1994 Vol. 36
141-144 / Suppl. 1

1993 Vol. 35
137-138 / 139 / 140

1992 Vol. 34
133 / 134-135 / 136

1991 Vol. 33
129 / 130-131 / 132

 
 

Vol. 63, n.2, June 2022
pp. 189-214

A Convolutional Neural Network-Monte Carlo approach for petrophysical seismic inversion

M. Aleardi and C. De Biasi

Received: 23 September 2021; accepted: 3 December 2021; published online: 22 March 2022

Abstract

We implement a machine-learning inversion approach to infer petrophysical rock properties from pre-stack data that combines a convolutional neural network and a discrete cosine transform of data and model spaces. This transformation is used for model and data compression. The network learns the inverse mapping between the compressed seismic and the compressed petrophysical domain. A theoretical rock-physics model relates elastic and petrophysical properties, while the exact Zoeppritz equations map the elastic properties onto the seismic domain. Training and validation examples are generated under the assumption of a Gaussian variogram model and a non-parametric prior. A Monte Carlo simulation strategy is employed for uncertainty assessment. We present synthetic inversions on a realistic subsurface model and the outcomes of the proposed approach are compared with those achieved by a standard linearised inversion. The network predictions are assessed in case of errors in the calibrated rock-physic model, in the estimated source wavelet, and in the assumed noise statistics. We also demonstrate that transfer learning avoids retraining the network from scratch when the target and training properties differ. Our experiments confirm that the implemented inversion successfully solves the petrophysical seismic inversion, opening the possibility to get instantaneous predictions of reservoir properties and related uncertainties.



Download PDF complete


back to table of contents