De-noising magnetotelluric data based on machine learning

Abstract

The magnetotelluric (MT) sounding is a common geophysical exploration technique, but it is highly polluted by various types of cultural noise. In the realm of MT data processing, traditional techniques often rely on the quality of the measured MT data. Conventional MT time domain denoising methods tend to eliminate valuable signals, potentially leading to unreliable resistivity estimates. To address this concern, we propose employing machine learning to effectively suppress strong noise interference in MT data, thereby preventing the loss of valuable signals. We augment this approach with mathematical morphological filtering (MMF) to capture low-frequency signals, preserving their integrity. We constructed a signal sample library based on a substantial volume of signal samples. Through consistent training, we establish a support vector machine (SVM) classification model that distinguishes high-quality signal fragments from noisy signals. Subsequently, we use adaptive K-singular value decomposition (K-SVD) dictionary learning to extract noise profiles and suppress noisy signals. To validate the feasibility of our method, we apply machine learning to measured data from two distinct observation areas. The measured data were analyzed and processed, and the results were compared with the robust results. This method can effectively eliminate large-scale strong interference in time domain sequences and preserve more low-frequency slow change information and high-quality signals in the reconstructed signals. The apparent resistivity phase curve of synthetic data is smoother and more continuous, and the data quality in the low-frequency range is significantly improved. The results can more accurately and reliably reflect underground electrical structure information.