An Improved Small-Sample Method Based on APSO-LSSVM for Gas-Bearing Probability Distribution Prediction From Multicomponent Seismic Data-Reference-Cited by-同舟云学术

An Improved Small-Sample Method Based on APSO-LSSVM for Gas-Bearing Probability Distribution Prediction From Multicomponent Seismic Data

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Yang Jiuqiang¹,Lin Niantian²^ORCID,Zhang Kai¹,Fu Chao³,Cui Yan¹,Li Guihua¹

Affiliation:

1. College of Earth Sciences and Engineering, College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China

2. College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao, China

3. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, China

Funder

China Postdoctoral Science Foundation

Natural Science Foundation of Shandong Province

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10034981/10064327.pdf?arnumber=10064327

Reference15 articles.

1. Reservoir characterisation method with multi-component seismic data by unsupervised learning and colour feature blending

2. An Effective PSO-LSSVM-Based Approach for Surface Roughness Prediction in High-Speed Precision Milling

3. Predicting distribution of hydrocarbon reservoirs with seismic data based on learning of the small-sample convolution neural network;lin;Chin J Geophys,2018

4. kNN-based gas-bearing prediction using local waveform similarity gas-indication attribute — An application to a tight sandstone reservoir

5. Decoupling Control of Outer Rotor Coreless Bearingless Permanent Magnet Synchronous Motor Based on Least Square Support Vector Machine Generalized Inverse Optimised by Improved Genetic Algorithm

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