Comprehensive survey methodology study of the hidden level of yield

Peizhao Chen; Yu Wei

doi:10.47297/taposatWSP2633-456910.20250604

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Comprehensive survey methodology study of the hidden level of yield

6卷第4期

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Comprehensive survey methodology study of the hidden level of yield
Comprehensive survey methodology study of the hidden level of yield
Guangxi Water & PowerDesign Institute Co.,Ltd 530023
2025年6卷第4期页码：60-64
纸质出版日期：2025-04-22
DOI：10.47297/taposatWSP2633-456910.20250604
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(2025). Comprehensive survey methodology study of the hidden level of yield. 科技理论与实践（英文版）, 6(4), 60-64.

Chen Peizhao,Wei Yu.Comprehensive survey methodology study of the hidden level of yield[J].Theory and Practice of Science and Technology,2025,06(04):60-64.
(2025). Comprehensive survey methodology study of the hidden level of yield. 科技理论与实践（英文版）, 6(4), 60-64. DOI： 10.47297/taposatWSP2633-456910.20250604.

Chen Peizhao,Wei Yu.Comprehensive survey methodology study of the hidden level of yield[J].Theory and Practice of Science and Technology,2025,06(04):60-64. DOI： 10.47297/taposatWSP2633-456910.20250604.

摘要

Abstract

This study proposes a comprehensive methodology for hidden layer exploration that integrates neural networks

remote sensing technologies

and engineering evaluation techniques. The method aims to enhance the accuracy

efficiency

and cost-effectiveness of geological surveys by combining large-scale remote sensing data with precise ground-based survey information. Neural networks

optimized with an appropriate number of hidden layers and nodes

effectively process complex

non-linear geological data to predict the characteristics of subsurface layers. The approach was tested against traditional exploration methods

such as drilling and seismic surveys

and showed superior performance in terms of time efficiency

cost reduction

and scalability. Furthermore

the method reduces the reliance on invasive techniques

improving safety during the exploration process. The study also discusses the integration of the Analytic Hierarchy Process (AHP) to evaluate different exploration techniques. Despite its promising results

the research highlights areas for future development

including enhancing remote sensing data resolution

refining neural network models

and incorporating additional data sources for more robust analysis. Validation of the method across diverse geological environments is also recommended to assess its broader applicability and practical effectiveness.

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Keywords

references

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