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An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China
Research article (Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 2022) · cited 12× · AI/ML
An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China
Summary
An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China is a scholarly article[1].
Key Facts
An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China. Retrieved May 24, 2026, from https://4ort.xyz/entity/an-approach-for-predicting-geothermal-reservoirs-distribution-using-wavelet-transform-and-self-organizing-neural-network
MLA“An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/an-approach-for-predicting-geothermal-reservoirs-distribution-using-wavelet-transform-and-self-organizing-neural-network.
BibTeX@misc{4ortxyz_an-approach-for-predicting-geothermal-reservoirs-distribution-using-wavelet-transform-and-self-organizing-neural-network_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China}}, year = {2026}, url = {https://4ort.xyz/entity/an-approach-for-predicting-geothermal-reservoirs-distribution-using-wavelet-transform-and-self-organizing-neural-network}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China — https://4ort.xyz/entity/an-approach-for-predicting-geothermal-reservoirs-distribution-using-wavelet-transform-and-self-organizing-neural-network (retrieved 2026-05-24)
