University of Oulu

Bai, P.; Vignoli, G.; Viezzoli, A.; Nevalainen, J.; Vacca, G. (Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network. Remote Sens. 2020, 12, 3440. https://doi.org/10.3390/rs12203440

(Quasi-)Real-time inversion of airborne time-domain electromagnetic data via artificial neural network

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Author: Bai, Peng1; Vignoli, Giulio1,2; Viezzoli, Andrea3;
Organizations: 1Department of Civil and Environmental Engineering and Architecture, University of Cagliari, 09123 Cagliari, Italy
2GEUS—Geological Survey of Denmark and Greenland, 8000 Aarhus, Denmark
3Aarhus Geophysics ApS, 8000 Aarhus, Denmark
4Oulu Mining School, University of Oulu, 90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe20201221101674
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2020
Publish Date: 2020-12-21
Description:

Abstract

The possibility to have results very quickly after, or even during, the collection of electromagnetic data would be important, not only for quality check purposes, but also for adjusting the location of the proposed flight lines during an airborne time-domain acquisition. This kind of readiness could have a large impact in terms of optimization of the Value of Information of the measurements to be acquired. In addition, the importance of having fast tools for retrieving resistivity models from airborne time-domain data is demonstrated by the fact that Conductivity-Depth Imaging methodologies are still the standard in mineral exploration. In fact, they are extremely computationally efficient, and, at the same time, they preserve a very high lateral resolution. For these reasons, they are often preferred to inversion strategies even if the latter approaches are generally more accurate in terms of proper reconstruction of the depth of the targets and of reliable retrieval of true resistivity values of the subsurface. In this research, we discuss a novel approach, based on neural network techniques, capable of retrieving resistivity models with a quality comparable with the inversion strategy, but in a fraction of the time. We demonstrate the advantages of the proposed novel approach on synthetic and field datasets.

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Series: Remote sensing
ISSN: 2072-4292
ISSN-E: 2072-4292
ISSN-L: 2072-4292
Volume: 12
Issue: 20
Article number: 3440
DOI: 10.3390/rs12203440
OADOI: https://oadoi.org/10.3390/rs12203440
Type of Publication: A1 Journal article – refereed
Field of Science: 117 Geography and environmental sciences
Subjects:
Funding: Thisworkwas partially supported: by the initiative PON-RI 2014-2020, Asse I “CapitaleUmano”—Azione I.1 “Dottorati innovativi con caratterizzazione industriale Ciclo XXXIII”—project: “GEOPROBARE: stochastic inversion of time-domain electromagnetic data”; by the INFACT project (https://www.infactproject.eu/) funded by the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 776487); by the initiative POR-FESR Sardegna 2014-2020, Asse I Azione I.1.3 “Creare opportunità di lavoro favorendo la competitività delle imprese”—project: “Tecnologie di CARatterizzazione Monitoraggio e Analisi per il ripristino e la bonifica (CARMA)”; by the project “GETHERE” (RAS/FBS grant-number: F71/17000190002).
EU Grant Number: (776487) INFACT - Innovative, Non-invasive and Fully Acceptable Exploration Technologies
Copyright information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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