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Design and implementation of an Automatic Deep Stacked Sparsely Connected Convolutional Autoencoder (ADSSCCA) neural network for remote sensing lithological map** using calculated dropout
The accurate map** of lithological units in tropical environments characterised by dense forest, persistent cloud cover, and limited bedrock...
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Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest
According to the characteristic that mineralized-anomaly samples have larger reconstruction errors, traditional autoencoder networks have been...
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Sparse subspace clustering incorporated deep convolutional transform learning for hyperspectral band selection
This work delves into a research area with a limited number of studies, that of convolutional filter-learning based clustering. Since clustering is...
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Unsupervised seismic data deblending based on the convolutional autoencoder regularization
Simultaneous source technology can provide high-quality seismic data with lower acquisition costs. However, a deblending algorithm is needed to...
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Bridging Deep Convolutional Autoencoders and Ensemble Smoothers for Improved Estimation of Channelized Reservoirs
One of the main problems associated with applying data assimilation methods for facies models is the lack of geological plausibility in updates. This...
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Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest
The convolutional neural networks used widely in mineral prospectivity prediction usually perform mixed feature extraction for multichannel inputs....
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Attention mechanism-based deep denoiser for desert seismic random noise suppression
Seismic data collected from desert areas contain a large amount of low-frequency random noise with similar waveforms to the effective signals. The...
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A new framework for damage detection of steel frames using burg autoregressive and stacked autoencoder-based deep neural network
In civil engineering, monitoring the structural damage becomes critically important to ensure safety and avoid sudden failures of structures....
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Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder
Deep neural networks perform very well in learning high-level representations in support of multivariate geochemical anomaly recognition. Geochemical...
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A hyperspectral unmixing model using convolutional vision transformer
Hyperspectral imaging technology has impacted computer vision and remote sensing applications. By capturing continuous spectral information, fine...
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Variational Autoencoder or Generative Adversarial Networks? A Comparison of Two Deep Learning Methods for Flow and Transport Data Assimilation
Groundwater modeling is an important tool for water resources management and aquifer remediation. However, the inherent strong heterogeneity of the...
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Underwater Acoustic Signal Noise Reduction Based on a Fully Convolutional Encoder-Decoder Neural Network
Noise reduction analysis of signals is essential for modern underwater acoustic detection systems. The traditional noise reduction techniques...
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A Geologically Constrained Variational Autoencoder for Mineral Prospectivity Map**
Deep learning algorithms (DLAs) are becoming popular tools for mineral prospectivity map**. However, purely data-driven DLAs frequently ignore...
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Deep learning-based 1-D magnetotelluric inversion: performance comparison of architectures
The study compares the three deep learning approaches and assesses their relative performance solving the 1-D magnetotellurics (MT) inverse problem....
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Metallogenic-Factor Variational Autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms
Deep learning algorithms (DLAs) are becoming hot tools in processing geochemical survey data for mineral exploration. However, it is difficult to...
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Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys
Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern...
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Generating subsurface earth models using discrete representation learning and deep autoregressive network
Subsurface earth models (referred as geomodels) are crucial for characterizing complex subsurface systems. Multiple-point statistics is commonly used...
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Deep learning with autoencoders and LSTM for ENSO forecasting
El Niño Southern Oscillation (ENSO) is the prominent recurrent climatic pattern in the tropical Pacific Ocean with global impacts on regional...
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Recognizing Multivariate Geochemical Anomalies Related to Mineralization by Using Deep Unsupervised Graph Learning
The spatial structure of geochemical patterns is influenced by various geological processes, one of which may be mineralization. Thus, analysis of...
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Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability
Effective geochemical anomaly identification is crucial in mineral exploration. Recent trends have favored deep learning (DL) to decipher geochemical...
