[Decomposition and analysis of the natural source SLF spectrum using curvelet transform method]

Area of Science:

• Geophysics
• Electromagnetism
• Signal Processing

Context:

• Natural source super low frequency (SLF) electromagnetic detection technology is crucial for resource exploration, such as coal bed methane.
• Wideband, multi-source signals complicate data interpretation, necessitating effective signal decomposition techniques.
• The Qinshui basin in Shanxi province was surveyed using specialized detection equipment developed by Peking University.

Purpose:

• To investigate the effectiveness of the curvelet transform method for decomposing natural source SLF electromagnetic signals.
• To differentiate between target information and interference signals in SLF electromagnetic data.
• To enhance the interpretation of coal bed methane data by filtering out noise.

Summary:

• The curvelet transform method was applied to decompose SLF electromagnetic data collected during coal bed methane surveys.
• Analysis revealed that high-frequency components primarily represent atmospheric lightning interference, while low-frequency components contain the target information.
• Reconstructed curves based on low-frequency signals proved more effective for target interpretation than original spectrum curves.

Impact:

• The study demonstrates that curvelet transform can significantly improve the quality of SLF electromagnetic data by removing atmospheric interference.
• The findings facilitate more accurate interpretation of natural source SLF electromagnetic data for resource exploration.
• Limitations were identified, as the method could not effectively remove artificial frequency signals.