The role of artificial intelligence and IoT in prediction of earthquakes: Review
December 2024
Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment, lives, and properties. There has been an increasing interest in the...
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Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines
December 2023
Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource...
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A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India
December 2024
In recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study involved...
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Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches
December 2024
This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This...
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Reservoir evaluation using petrophysics informed machine learning: A case study
December 2024
We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical information...
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MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning
December 2022
Among the biggest challenges we face in utilizing neural networks trained on waveform (i.e., seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement for accurate...
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Deriving big geochemical data from high-resolution remote sensing data via machine learning: Application to a tailing storage facility in the Witwatersrand goldfields
December 2023
Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues...
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Machine learning in petrophysics: Advantages and limitations
December 2022
Machine learning provides a powerful alternative data-driven approach to accomplish many petrophysical tasks from subsurface data. It can assimilate information from large and rich data bases and infer...
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Improved frost forecast using machine learning methods
December 2023
Frosts are one of the atmospheric phenomena with one of the larger negative effects on the agricultural sector in the southern region of Brazil, therefore, an earlier forecast can minimize their impacts....
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Models of plate tectonics with the Lattice Boltzmann Method
December 2023
Modern geodynamics is based on the study of a large set of models, with the variation of many parameters, whose analysis in the future will require Machine Learning to be analyzed. We introduce here...
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Blockly earthquake transformer: A deep learning platform for custom phase picking
December 2023
Deep-learning (DL) algorithms are increasingly used for routine seismic data processing tasks, including seismic event detection and phase arrival picking. Despite many examples of the remarkable performance...
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Toward earthquake early warning: A convolutional neural network for rapid earthquake magnitude estimation
December 2023
Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake magnitude,...
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The benefits and dangers of using artificial intelligence in petrophysics
December 2021
Artificial Intelligence, or AI, is a method of data analysis that learns from data, identify patterns and makes predictions with the minimal human intervention. AI is bringing many benefits to petrophysical...
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Robust high frequency seismic bandwidth extension with a deep neural network trained using synthetic data
December 2024
Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features. Various deterministic...
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Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale
December 2020
Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. The wealth of publicly-available...
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Flood susceptibility assessment using artificial neural networks in Indonesia
December 2021
Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments,...
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Reconstruction of lithofacies using a supervised Self-Organizing Map: Application in pseudo-wells based on a synthetic geologic cross-section
December 2024
Recently, machine learning (ML) has been considered a powerful technological element of different society areas. To transform the computer into a decision maker, several sophisticated methods and algorithms...
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The potential of self-supervised networks for random noise suppression in seismic data
December 2021
Noise suppression is an essential step in many seismic processing workflows. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural networks...
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Synthetic shear sonic log generation utilizing hybrid machine learning techniques
December 2022
Compressional and shear sonic logs (DTC and DTS, respectively) are one of the effective means for determining petrophysical/geomechanical properties. However, the DTS log has limited availability mainly...
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Uncertainty and explainable analysis of machine learning model for reconstruction of sonic slowness logs
December 2023
Logs are valuable information for oil and gas fields as they help to determine the lithology of the formations surrounding the borehole and the location and reserves of subsurface oil and gas reservoirs....
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PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
December 2022
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming...
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Deep convolutional autoencoders as generic feature extractors in seismological applications
December 2021
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea...
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Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager
December 2022
Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geochemical...
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Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities
December 2023
Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage...
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2D magnetotelluric inversion based on ResNet
December 2023
In this study, a deep learning algorithm was applied to two-dimensional magnetotelluric (MT) data inversion. Compared with the traditional linear iterative inversion methods, the MT inversion method...
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