TY - JOUR
T1 - An automatic geological 3D cross-section generator
T2 - Geopropy, an open-source library
AU - Hassanzadeh, Ashkan
AU - Vázquez-Suñé, Enric
AU - Corbella, Mercè
AU - Criollo, Rotman
N1 - Funding Information:
The authors acknowledge Miguel López Blanco, Patricia Cabello, Carlos Ayora and two anonymous reviewers that helped us to improve this article. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. IDAEA-CSIC is Centre of Excellence Severo Ochoa (Project CEX2018-000794-S). R. Criollo gratefully acknowledges the financial support from the Balearic Island Government through the Margalida Comas postdoctoral fellowship programme (PD/036/2020).
PY - 2022/3
Y1 - 2022/3
N2 - Geological modelling is an essential aspect of underground investigations, with cross-sections being one of the key aspects. This modelling can be done by experienced geologists or using mathematical methods. We present Geopropy, an open-source decision-making algorithm implemented in Python, that generates 3D cross-sections (the boreholes do not have to be aligned). It performs as an intelligent agent that simulates the steps taken by the geologist in the process of creating the cross-section, coupled with data-driven decisions. The algorithm detects zones with more than one possible outcome and, based on the level of complexity (or user preference), proceeds to automatic, semiautomatic or manual stages. Geopropy could be the basis of a new, simpler, more comprehensible way of looking at geological models in industry and academia while at the same time creating the potential for using novel machine learning algorithms in geological modelling.
AB - Geological modelling is an essential aspect of underground investigations, with cross-sections being one of the key aspects. This modelling can be done by experienced geologists or using mathematical methods. We present Geopropy, an open-source decision-making algorithm implemented in Python, that generates 3D cross-sections (the boreholes do not have to be aligned). It performs as an intelligent agent that simulates the steps taken by the geologist in the process of creating the cross-section, coupled with data-driven decisions. The algorithm detects zones with more than one possible outcome and, based on the level of complexity (or user preference), proceeds to automatic, semiautomatic or manual stages. Geopropy could be the basis of a new, simpler, more comprehensible way of looking at geological models in industry and academia while at the same time creating the potential for using novel machine learning algorithms in geological modelling.
KW - 3D geological modelling
KW - Cross-section
KW - Decision making algorithm
KW - Open-source
KW - Python
UR - http://www.scopus.com/inward/record.url?scp=85123320863&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2022.105309
DO - 10.1016/j.envsoft.2022.105309
M3 - Article
AN - SCOPUS:85123320863
SN - 1364-8152
VL - 149
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 105309
ER -