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...
Share article
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...
Share article
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...
Share article
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...
Share article
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...
Share article
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...
Share article
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....
Share article
Estimating relative diffusion from 3D micro-CT images using CNNs
December 2023
In recent years, convolutional neural networks (CNNs) have demonstrated their effectiveness in predicting bulk parameters, such as effective diffusion, directly from pore-space geometries. CNNs offer...
Share article
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...
Share article
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,...
Share article
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...
Share article
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...
Share article
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...
Share article
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....
Share article
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...
Share article
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,...
Share article
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...
Share article
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...
Share article
Wavefield solutions from machine learned functions constrained by the Helmholtz equation
December 2021
Solving the wave equation is one of the most (if not the most) fundamental problems we face as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation provides wavefield solutions...
Share article
Exact Conditioning of Regression Random Forest for Spatial Prediction
December 2020
Regression random forest is becoming a widely-used machine learning technique for spatial prediction that shows competitive prediction performance in various geoscience fields. Like other popular machine...
Share article
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...
Share article
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...
Share article
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...
Share article
Optimization of shale gas fracturing parameters based on artificial intelligence algorithm
December 2023
Resource-rich shale gas plays a pivotal role in new energy types. The key to scientifically and efficiently developing shale gas fields is to clarify the main factors that affect the production of shale...
Share article
Seismic swarm intelligence inversion with sparse probability distribution of reflectivity
December 2023
Seismic inversion, such as velocity and impedance, is an ill-posed problem. To solve this problem, swarm intelligence (SI) algorithms have been increasingly applied as the global optimization approach,...
Share article