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ISSN: 2666-5441

Reservoir evaluation using petrophysics informed machine learning: A case study

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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Robust high frequency seismic bandwidth extension with a deep neural network trained using synthetic data

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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Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches

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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Reconstruction of lithofacies using a supervised Self-Organizing Map: Application in pseudo-wells based on a synthetic geologic cross-section

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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A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India

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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Thank you reviewers!

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The role of artificial intelligence and IoT in prediction of earthquakes: Review

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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Prediction of seismic-induced bending moment and lateral displacement in closed and open-ended pipe piles: A genetic programming approach

Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment, typically achieved through complex numerical...

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ASTER data processing and fusion for alteration minerals and silicification detection: Implications for cupriferous mineralization exploration in the western Anti-Atlas, Morocco

Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral exploration....

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Application of ChatGPT in soil science research and the perceptions of soil scientists in Indonesia

Since its arrival in late November 2022, ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned, conducted, and published using a generative artificial intelligence...

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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

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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Seismic swarm intelligence inversion with sparse probability distribution of reflectivity

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,...

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Unsupervised pre-stack seismic facies analysis constrained by spatial continuity

Seismic facies analysis plays important roles in geological research, especially in sedimentary environment identification. Traditional method is mainly based on seismic waveform or attributes of a...

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Research on microseismic denoising method based on CBDNet

Noise suppression is an important part of microseismic monitoring technology. Signal and noise can be separated by denoising and filtering to improve the subsequent analysis. In this paper, we propose...

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Toward earthquake early warning: A convolutional neural network for rapid earthquake magnitude estimation

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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Models of plate tectonics with the Lattice Boltzmann Method

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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Machine learning elucidates the anatomy of buried carbonate reef from seismic reflection data

A carbonate build-up or reef is a thick carbonate deposit consisting of mainly skeletal remains of organisms that can be large enough to develop a favourable topography. Delineation of such geologic...

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Developing soft-computing regression model for predicting bearing capacity of eccentrically loaded footings on anisotropic clay

In this investigation, the bearing capacity solution of a strip footing in anisotropic clay under inclined and eccentric load is analyzed using the numerical simulation model. The lower and upper bound...

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Blockly earthquake transformer: A deep learning platform for custom phase picking

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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Improving patch-based simulation using Generative Adversial Networks

Multiple-Point Simulation (MPS) is a geostatistical simulation technique commonly used to model complex geological patterns and subsurface heterogeneity. There have been a great variety of implementation...

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Optimization of shale gas fracturing parameters based on artificial intelligence algorithm

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...

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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

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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2D magnetotelluric inversion based on ResNet

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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Automated stratigraphic correlation of well logs using Attention Based Dense Network

The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs. However, it suffers from a small amount of training data and expensive computing time. In...

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Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

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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