云南高校图书馆联盟文献共享服务平台- Artificial Intelligence in Geosciences

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Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin,Western Himalaya:Using Markov Chain and LSTM approaches: 《Artificial Intelligence in Geosciences》2024年第1期114-136,共23页Pankaj Chauhan Muhammed Ernur Akiner Rajib Shaw Kalachand Sain; This research work was carried out during the SERB,SIRE fellowship (File No.SIR/2022/000972)tenure at Keio University,Japan.; 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 method assesses persi...; 关键词：Forecast disasters Western Himalaya Hydro-meteorological hazards LSTM Markov chain Yamuna river basin

The 3-billion fossil question:How to automate classification of microfossils: 《Artificial Intelligence in Geosciences》2024年第1期137-145,共9页Iver Martinsen David Wade Benjamin Ricaud Fred Godtliebsen; supported by the Research Council of Norway,through its Centre for Research-based Innovation funding scheme (grant no.309439),and Consortium Partners.; Microfossil classification is an important discipline in subsurface exploration,for both oil&gas and Carbon Capture and Storage(CCS).The abundance and distribution of species found in sedimentary rocks provide valuabl...; 关键词：Self-supervised learning PALYNOLOGY Deep learning MICROFOSSILS

Application of ChatGPT in soil science research and the perceptions of soil scientists in Indonesia: 《Artificial Intelligence in Geosciences》2024年第1期146-153,共8页Destika Cahyana Agus Hadiarto Irawan Diah Puspita Hati Mira Media Pratamaningsih Vicca Karolinoerita Anny Mulyani Sukarman Muhammad Hikmat Fadhlullah Ramadhani Rachmat Abdul Gani Edi Yatno R.Bambang Heryanto Suratman Nuni Gofar Abraham Suriadikusumah; 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 approach.ChatGPT-...; 关键词：Artificial intelligence ChatGPT Soil science TOOLS PARADIGM

The role of artificial intelligence and IoT in prediction of earthquakes:Review被引量：1: 《Artificial Intelligence in Geosciences》2024年第1期154-172,共19页Joshua Pwavodi Abdullahi Umar Ibrahim Pwadubashiyi Coston Pwavodi Fadi Al-Turjman Ali Mohand-Said; 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 prediction of earthqu...; 关键词：EARTHQUAKES SEISMICITY Artificial intelligence Internet of things PREDICTION

Enhancing economic sustainability in mature oil fields:Insights from the clustering approach to select candidate wells for extended shut-in: 《Artificial Intelligence in Geosciences》2024年第1期173-188,共16页B.Lobut E.Artun; support from research grants MGA-2021-42991 and MYL-2022-43726,funded by Istanbul Technical University-Scientific Research Projects,Turkey.Thissupportis gratefully acknowledged.; Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts.In periods of significant price drops,companies may consider exte...; 关键词：Unsupervised learning CLUSTERING Mature oil fields Extended shut-in Well classification

High-resolution seismic inversion method based on joint data-driven in the time-frequency domain: 《Artificial Intelligence in Geosciences》2024年第1期189-201,共13页Yu Liu Sisi Miao; Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydo...; 关键词：Time-frequency domain Joint dictionary learning DATA-DRIVEN High-resolution inversion

Exploring emerald global geochemical provenance through fingerprinting and machine learning methods: 《Artificial Intelligence in Geosciences》2024年第1期202-219,共18页Raquel Alonso-Perez James M.D.Day D.Graham Pearson Yan Luo Manuel A.Palacios Raju Sudhakar Aaron Palke; Emeralds-the green colored variety of beryl-occur as gem-quality specimens in over fifty deposits globally.While digital traceability methods for emerald have limitations,sample-based approaches offer robust alterna-t...; 关键词：BERYL PROVENANCE LA-ICP-MS Machine learning Multivariate analysis Trace elements

Water resource forecasting with machine learning and deep learning:A scientometric analysis: 《Artificial Intelligence in Geosciences》2024年第1期220-231,共12页Chanjuan Liu Jing Xu Xi’an Li Zhongyao Yu Jinran Wu; The funding for this study was provided by the Ministry of Ed-ucation of Humanities and Social Science project in China (Project No.22YJC630083);the 2022 Shanghai Chenguang Scholars Program (Project No.22CGA82);the Belt and Road Special Foundation of The National Key Laboratory of Water Disaster Prevention (2021491811);the National Social Science Fund of China (Project No.23CGL077).; Water prediction plays a crucial role in modern-day water resource management,encompassing both logical hydro-patterns and demand forecasts.To gain insights into its current focus,status,and emerging themes,this study...; 关键词：Water forecasting Machine learning/deep learning Web of Science VISUALIZATION

When linear inversion fails:Neural-network optimization for sparse-ray travel-time tomography of a volcanic edifice: 《Artificial Intelligence in Geosciences》2024年第1期232-243,共12页Abolfazl Komeazi Georg Rümpker Johannes Faber Fabian Limberger Nishtha Srivastava; In this study,we present an artificial neural network(ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage.We employ ray tracing to simulate the propagation of seismic waves t...; 关键词：Volcanic edifice Neural network Deep learning Magma chamber TOMOGRAPHY INVERSION

Wide & deep learning for predicting relative mineral compositions of sediment cores solely based on XRF scans, a case study from Pleistocene Paleolake Olduvai, Tanzania: 《Artificial Intelligence in Geosciences》2024年第1期244-256,共13页Gayantha R.L.Kodikara Lindsay J.McHenry Ian G.Stanistreet Harald Stollhofen Jackson K.Njau Nicholas Toth Kathy Schick; supported by the National Science Foundation (BCS grant#1623884 to Njau and McHenry);Computational work was also supported by NASA SSW grant NNH20ZDA001N.; This study develops a method to use deep learning models to predict the mineral assemblages and their relative abundances in paleolake cores using high-resolution XRF core scan elemental data and X-ray diffraction(XRD...; 关键词：Paleo PLEISTOCENE XRF

《Artificial Intelligence in Geosciences》