전기전자탐사에 적용되는 심층 학습 연구 동향 및 적용 사례 분석

논문상세정보
' 전기전자탐사에 적용되는 심층 학습 연구 동향 및 적용 사례 분석' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • dataprocessing
  • deep learning
  • electromagnetic survey
  • interpretation
  • inversion
  • 기계학습
  • 역산
  • 자료처리
  • 자료해석
  • 전기전자탐사
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
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' 전기전자탐사에 적용되는 심층 학습 연구 동향 및 적용 사례 분석' 의 참고문헌

  • 한국의 전자탐사 현황
    조동행 [2006]
  • Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: A MATLAB application
  • Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
    Hornik, K. [1990]
  • U-Net: convolutional networks for biomedical image segmentation
  • Two-dimensional joint inversion of magnetotelluric and dipole-dipole resistivity data
    Sasaki, Y. [1989]
  • The geoelectrical methods in geophysical exploration
  • The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales
    Binley, A. [2015]
  • The changing science of machine learning
    Langley, P. [2011]
  • TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal with Signal-to-Image Transformation
    Chen, K. [2020]
  • Supervised machine learning for lithology estimation using spectral induced polarization data
    Shin, S. [2018]
  • Spectral-induced polarization characterization of rocks from the Handuk iron mine, South Korea
    Shin, S. W. [2016]
  • Spectral induced polarization porosimetry
    Revil, A. [2014]
  • Sinkhole risk assessment by ERT: The case study of Sirino Lake (Basilicata, Italy)
  • Shape and depth solutions from numerical horizontal self-potential gradients
  • Shakedrop regularization
  • Salt delineation from electromagnetic data using convolutional neural networks
    Oh, S. [2019]
  • Reconnaissance electrical resistivity survey of geothermal reservoir at Hamam Faraun area, Sinai Peninsular, Egypt
  • Readings in machine learning
  • Probabilistic inversions of electrical resistivity tomography data with a machine learning‐based forward operator
    Aleardi, M. [2022]
  • Parameter estimation and inverse problems
  • Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks
  • Noise reduction of grounded electrical source airborne transient electromagnetic data using an exponential fitting adaptive Kalman filter
    Ji, Y. [2018]
  • Neural networks, A comprehensive foundation
    Haykin, S. [1994]
  • Mining of massive datasets
  • Mineral discrimination and removal of inductive coupling with multifrequency IP
  • Making massive computational experiments painless
  • Least-squares deconvolution of apparent resistivity pseudosections
    Loke, M.H. [1995]
  • Least squares estimation technique of Cole-Cole parameters from step response
  • Learning representations by back-propagating errors
  • Learning deep CNN denoiser prior for image restoration
    Zhang, K. [2017]
  • Kernel density estimation via diffusion
  • Joint interpretation of geophysical data: Applying machine learning to the modeling of an evaporitic sequence in Villar de Cañas (Spain)
    Marzán, I. [2021]
  • Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks
  • Inversion of DC resistivity data using neural networks
    El-Qady, G. [2001]
  • Inverse problem theory and methods for model parameter estimation
  • Integrated geophysical signatures and structural geometry of the Kabinakagami Lake greenstone belt, Superior Province, Ontario, Canada: Exploration implications for concealed Archean orogenic gold deposits
    Müller, D. [2022]
  • Instantaneous inversion of airborne electromagnetic data based on deep learning
    Wu, S. [2022]
  • Improved Cole parameter extraction based on the least absolute deviation method
    Yang, Y. [2013]
  • Imaging subsurface resistivity structure from airborne electromagnetic induction data using deep neural network
    Noh, K. [2020]
  • Imaging subsurface orebodies with airborne electromagnetic data using a recurrent neural network
    Bang, M. [2021]
  • Imaging groundwater infiltration dynamics in the karst vadose zone with long-term ERT monitoring
    Watlet, A. [2018]
  • IP4DI: A software for time-lapse 2D/3D DC-resistivity and induced polarization tomography
  • Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
    Géron, A [2019]
  • Google’s neural machine translation system: Bridging the gap between human and machine translation
  • Geostatistical methods for reservoir geophysics
    Azevedo, L. [2017]
  • Geophysical anatomy of counter-slope scarps in sedimentary flysch rocks (Outer Western Carpathians)
  • FW2_5D: A MATLAB 2.5-D electrical resistivity modeling code
  • FFDNet: Toward a fast and flexible solution for CNN-based image denoising
    Zhang, K. [2018]
  • Evaluation of the Fate of Nitrate and Analysis of Shallow Soil Water using Geo-electrical Resistivity Survey
    Islami, N. [2017]
  • Electrical characteristics analysis of Mesozoic and Cenozoic evolution mechanisms of basins in the Dachaidan area, Qaidam Basin
    Xu, Z. [2019]
  • Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection
    Ward, W. O. [2014]
  • Dispersion and absorption in dielectrics II. Direct current characteristics
    Cole, K.S. [1942]
  • Discrete cosines transform for parameter space reduction in Bayesian ERT inversion
  • Direct inversion of the apparent complex-resistivity spectrum
    Xiang, J. [2001]
  • Developments in a model to describe low-frequency electrical polarization of rocks
    Dias, C.A. [2000]
  • Determination of Cole–Cole parameters using only the real part of electrical impedivity measurements
  • Derivative analysis of SP anomalies
  • Denoising stacked autoencoders for transient electromagnetic signal denoising
    Lin, F. [2019]
  • Denoising of Photographic Images and Video
    Delon, J. [2018]
  • Deep residual learning for image recognition
    He, K. [2016]
  • Deep neural network-based airborne EM data inversion suitable for mountainous field sites
    Bang, M. [2022]
  • Deep learning inversion of electrical resistivity data
    Liu, B. [2020]
  • Deep learning for geophysics: Current and future trends
    Yu, S. [2021]
  • Deep learning electromagnetic inversion with convolutional neural networks
    Puzyrev, V. [2019]
  • Deep learning audio magnetotellurics inversion using residual-based deep convolution neural network
    Liu, Z. [2021]
  • Deep learning
    LeCun Y [2015]
  • Deep learning
  • Deep image prior
    Ulyanov, D. [2018]
  • Cooperative deep learning inversion of controlled-source electromagnetic data for salt delineationCooperative DL inversion
    Oh, S. [2020]
  • Cooperative Deep Learning Inversion of Electromagnetic Data with Seismic Constraint
    Oh, S. [2020]
  • Convolutional neural networks with SegNet architecture applied to three-dimensional tomography of subsurface electrical resistivity: CNN-3D-ERT
    Vu, M.T. [2021]
  • Convolutional neural network inversion of airborne transient electromagnetic data
    Wu, S. [2021]
  • Cole-Cole model parameter estimation from multi-frequency complex resistivity spectrum based on the artificial neural network
    Liu, W. [2021]
  • Beyond a gaussian denoiser : Residual learning of deep cnn for image denoising .
    Zhang , K. [2017]
  • Bayesian inference of the Cole–Cole parameters from time‐and frequency‐domain induced polarization
  • Bayesian inference of spectral induced polarization parameters for laboratory complex resistivity measurements of rocks and soils
  • Bayesian image denoising with multiple noisy images
    Kataoka, S. [2019]
  • Batch normalization: accelerating deep network training by reducing internal covariate shift
    Ioffe, S. [2015]
  • Automatic processing of time domain induced polarization data using supervised artificial neural networks
  • Automatic inversion of self-potential anomalies of sheet-like bodies
    Rao, S. J. [1993]
  • Automatic early stopping using cross validation : quantifying the criteria
  • Automated fault detection without seismic processing
  • Application of joint inversion and fuzzy c-means cluster analysis for road pre-investigations
    Hellman, K. [2012]
  • Application of geophysics in metalliferous mines
  • And the geophysicist replied:“Which model do you want?”
  • Airborne Electromagnetic Data and Processing Within Leech Lake Basin
  • Adam:A method for stochastic optimization
  • A review of block Krylov subspace methods for multisource electromagnetic modelling
    Puzyrev, V. [2015]
  • A new method of interpreting self-potential anomalies of twodimensional inclined sheets
  • A linked geomorphological and geophysical modelling methodology applied to an active landslide
    Boyd, J. [2021]
  • A leastsquares approach to depth determination from self-potential anomalies caused by horizontal cylinders and spheres
  • A least-squares approach to shape determination from residual self-potential anomalies
  • A denoising method based on principal component analysis for airborne transient electromagnetic data
    Wu, Y. [2014]
  • A de-noising algorithm based on wavelet threshold-exponential adaptive window width-fitting for ground electrical source airborne transient electromagnetic signal
    Ji, Y. [2016]
  • A convolutional neural network approach to electrical resistivity tomography
    Aleardi, M. [2021]
  • A comparison between Gauss-Newton and Markov-chain Monte Carlo–based methods for inverting spectral induced-polarization data for Cole-Cole parameters
    Chen, J. [2008]
  • A Study on the Modified Electrode Arrays in Two-Dimensional Resistivity Survey
    Kim, J. H. [2001]
  • A State-of-the-Art Survey on Deep Learning Theory and Architectures
    Alom, M. Z. [2019]
  • A Fourier transform method for the interpretation of self-potential anomalies due to two-dimensional inclined sheets of finite depth extent
    Rao, D. A. [1982]
  • 4D time‐lapse ERT inversion: introducing combined time and space constraints
  • 3D electrical resistivity tomography to locate DNAPL contamination in an urban environment
    Naudet, V. [2011]
  • 3-D inversion of borehole-to-surface electrical data using a back-propagation neural network
    Ho, T. L. [2009]
  • 2‐D Resistivity Inversion Using the Neural Network Method
    Xu, H.L. [2006]
  • 2-D and 3-D resistivity image reconstruction using crosshole data
    Shima, H. [1992]