Search Results - "Computers and Geosciences"

Acycle: Time-series analysis software for paleoclimate research and education by Li, Mingsong, Hinnov, Linda, Kump, Lee

“…Recognition and interpretation of paleoclimate signals in sedimentary proxy datasets are time consuming and subjective. Acycle is a comprehensive and…”
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Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping by Fang, Zhice, Wang, Yi, Peng, Ling, Hong, Haoyuan

“…Landslides are regarded as one of the most common geological hazards in a wide range of geo-environment. The aim of this study is to assess landslide…”
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Comparative study of landslide susceptibility mapping with different recurrent neural networks by Wang, Yi, Fang, Zhice, Wang, Mao, Peng, Ling, Hong, Haoyuan

“…This paper aims to use recurrent neural networks (RNNs) to perform landslide susceptibility mapping in Yongxin County, China. The two main contributions of…”
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Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China by Zhou, Chao, Yin, Kunlong, Cao, Ying, Ahmed, Bayes, Li, Yuanyao, Catani, Filippo, Pourghasemi, Hamid Reza

“…Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir…”
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PINNeik: Eikonal solution using physics-informed neural networks by Waheed, Umair bin, Haghighat, Ehsan, Alkhalifah, Tariq, Song, Chao, Hao, Qi

“…The eikonal equation is utilized across a wide spectrum of science and engineering disciplines. In seismology, it regulates seismic wave traveltimes needed for…”
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Segmentation of digital rock images using deep convolutional autoencoder networks by Karimpouli, Sadegh, Tahmasebi, Pejman

“…Segmentation is a critical step in Digital Rock Physics (DRP) as the original images are available in a gray-scale format. Conventional methods often use…”
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Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling by Goetz, J.N., Brenning, A., Petschko, H., Leopold, P.

“…Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these…”
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pyGIMLi: An open-source library for modelling and inversion in geophysics by Rücker, Carsten, Günther, Thomas, Wagner, Florian M.

“…Many tasks in applied geosciences cannot be solved by single measurements, but require the integration of geophysical, geotechnical and hydrological methods…”
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ResIPy, an intuitive open source software for complex geoelectrical inversion/modeling by Blanchy, Guillaume, Saneiyan, Sina, Boyd, Jimmy, McLachlan, Paul, Binley, Andrew

“…Electrical resistivity tomography (ERT) and induced polarization (IP) methods are now widely used in many interdisciplinary projects. Although field surveys…”
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A review of Earth Artificial Intelligence by Sun, Ziheng, Sandoval, Laura, Crystal-Ornelas, Robert, Mousavi, S. Mostafa, Wang, Jinbo, Lin, Cindy, Cristea, Nicoleta, Tong, Daniel, Carande, Wendy Hawley, Ma, Xiaogang, Rao, Yuhan, Bednar, James A., Tan, Amanda, Wang, Jianwu, Purushotham, Sanjay, Gill, Thomas E., Chastang, Julien, Howard, Daniel, Holt, Benjamin, Gangodagamage, Chandana, Zhao, Peisheng, Rivas, Pablo, Chester, Zachary, Orduz, Javier, John, Aji

“…In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and…”
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Driving digital rock towards machine learning: Predicting permeability with gradient boosting and deep neural networks by Sudakov, Oleg, Burnaev, Evgeny, Koroteev, Dmitry

“…We present a research study aimed at testing of applicability of machine learn-ing techniques for permeability prediction. We prepare a training set…”
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Evaluation of machine learning methods for lithology classification using geophysical data by Bressan, Thiago Santi, Kehl de Souza, Marcelo, Girelli, Tiago J., Junior, Farid Chemale

“…Specific computational tools assist geologists in identifying and sorting lithologies in well surveys and reducing operational costs and practical working…”
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Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data by Cunha, Augusto, Pochet, Axelle, Lopes, Hélio, Gattass, Marcelo

“…The challenging task of automatic seismic fault detection recently gained in quality with the emergence of deep learning techniques. Those methods successfully…”
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ModEM: A modular system for inversion of electromagnetic geophysical data by Kelbert, Anna, Meqbel, Naser, Egbert, Gary D., Tandon, Kush

“…We describe implementation of a modular system of computer codes for inversion of electromagnetic geophysical data, referred to as ModEM. The system is…”
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dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport by Hyman, Jeffrey D., Karra, Satish, Makedonska, Nataliia, Gable, Carl W., Painter, Scott L., Viswanathan, Hari S.

“…dfnWorks is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los…”
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A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS by Pradhan, Biswajeet

“…The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine…”
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Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China by Xu, Shiluo, Niu, Ruiqing

“…Every year, landslides pose huge threats to thousands of people in China, especially those in the Three Gorges area. It is thus necessary to establish an early…”
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Information extraction and knowledge graph construction from geoscience literature by Wang, Chengbin, Ma, Xiaogang, Chen, Jianguo, Chen, Jingwen

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Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information by Cracknell, Matthew J., Reading, Anya M.

“…Machine learning algorithms (MLAs) are a powerful group of data-driven inference tools that offer an automated means of recognizing patterns in…”
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MAX UnMix: A web application for unmixing magnetic coercivity distributions by Maxbauer, Daniel P., Feinberg, Joshua M., Fox, David L.

“…It is common in the fields of rock and environmental magnetism to unmix magnetic mineral components using statistical methods that decompose various types of…”
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