Bayesian evidential learning of soil-rock interface identification using boreholes

HQ Yang, J Chu, X Qi, S Wu, K Chiam - Computers and Geotechnics, 2023 - Elsevier
Identification of the soil-rock interface of geological profiles has been a challenging task for
underground construction because of lack of sufficient borehole data. Traditional spatial …

[HTML][HTML] 3D modeling of detachment faults in the Jiaodong gold province, eastern China: A Bayesian inference perspective and its exploration implications

J Huang, H Deng, X Mao, G Chen, S Yu, Z Liu - Ore Geology Reviews, 2023 - Elsevier
The world-class Jiaodong gold province, which is located in the Jiaodong Peninsula,
eastern North China Craton, hosts> 5,000 t of gold resources. The major gold mineralization …

Multiple-point geostatistics-based three-dimensional automatic geological modeling and uncertainty analysis for borehole data

J Guo, Z Wang, C Li, F Li, MW Jessell, L Wu… - Natural Resources …, 2022 - Springer
The three-dimensional characterization of geological structures is important for determining
the distribution of subsurface mineral resources. However, geological structures and …

[HTML][HTML] Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

HKH Olierook, R Scalzo, D Kohn, R Chandra… - Geoscience …, 2021 - Elsevier
Traditional approaches to develop 3D geological models employ a mix of quantitative and
qualitative scientific techniques, which do not fully provide quantification of uncertainty in the …

Modeling uncertain data using Monte Carlo integration method for clustering

KK Sharma, A Seal - Expert systems with applications, 2019 - Elsevier
Nowadays, data clustering is an important task to the mining research community since the
availability of uncertain data is increasing rapidly in many applications such as weather …

[HTML][HTML] Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0

M Jessell, V Ogarko, Y De Rose… - Geoscientific Model …, 2021 - gmd.copernicus.org
At a regional scale, the best predictor for the 3D geology of the near-subsurface is often the
information contained in a geological map. One challenge we face is the difficulty in …

Into the Noddyverse: A massive data store of 3D geological models for Machine Learning & inversion applications

M Jessell, J Guo, Y Li, M Lindsay… - Earth System …, 2021 - essd.copernicus.org
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 …

[HTML][HTML] Disjoint interval bound constraints using the alternating direction method of multipliers for geologically constrained inversion: Application to gravity data

V Ogarko, J Giraud, R Martin, M Jessell - Geophysics, 2021 - pubs.geoscienceworld.org
To reduce uncertainties in reconstructed images, geologic information must be introduced in
a numerically robust and stable way during the geophysical data inversion procedure. In the …

[HTML][HTML] Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization

J Giraud, M Lindsay, V Ogarko, M Jessell, R Martin… - Solid Earth, 2019 - se.copernicus.org
We introduce a workflow integrating geological modelling uncertainty information to
constrain gravity inversions. We test and apply this approach to the Yerrida Basin (Western …

Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis

J Giraud, V Ogarko, M Lindsay… - Geophysical Journal …, 2019 - academic.oup.com
The integration of petrophysical data and probabilistic geological modelling in geophysical
joint inversion is a powerful tool to solve exploration challenges. Models obtained from …