Estimating Three-Dimensional Resistivity Distribution with Magnetotelluric Data and a Deep Learning Algorithm

In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric/magnetic field response by using a fully connected n...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Remote sensing (Basel, Switzerland) Ročník 16; číslo 18; s. 3400
Hlavní autori: Liu, Xiaojun, Craven, James A., Tschirhart, Victoria, Grasby, Stephen E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2024
Predmet:
Algorithms
British Columbia
computer hardware
Computer peripherals
Data analysis
data collection
Data mining
Datasets
Deep learning
Electric fields
Electric properties
Electrical resistivity
geophysics
inversion problem
Machine learning
Magnetic fields
magnetotelluric
Mathematical models
Methods
Mount Meager
mountains
neural network
Neural networks
Optimization
Parameters
prediction
Random walk
Workflow
Canada
British Columbia
ISSN:2072-4292, 2072-4292
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric/magnetic field response by using a fully connected neural network framework (U-Net). Limited by computer hardware functionality, the resistivity models were generated by using a random walk technique to enlarge the generalization coverage of the neural network model, and 15,000 paired datasets were utilized to train and validate it. Grid search was used to select the optimal configuration parameters. With the optimal model framework from the parameter tuning phase, the metrics showed stable convergence during model training/validation. In the test period, the trained model was applied to predict the resistivity distribution by using both the simulated synthetic and the real MT data from the Mount Meager area, British Columbia. The reliability of the model prediction was verified with noised input data from the synthetic model. The calculated results can be used to reconstruct the position and shape trends of bodies with anomalous resistivity, which verifies the stability and performance of the DL-based 3D inversion algorithm and showcases its potential practical applications.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16183400