A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

To avoid the blindness of the overall de-noising method and retain useful low frequency signals that are not over processed, we proposed a novel audio magnetotelluric (AMT) signal-noise identification and separation method based on multifractal spectrum and matching pursuit. We extracted two sets of multifractal spectrum characteristic from AMT time-series data to analyze the singularity. We used a support vector machine approach to learn the multifractal spectrum characteristics in a sample’s library and generate a model of support vector machine to distinguish between sections with and without interference in the measured AMT data. The matching pursuit algorithm was used to separate only those sections identified as having interference. Experimental results showed that the proposed method can effectively identify interference in the EMTF mathematical model and measured AMT data. Sections without interference were accurately preserved and reconstructed AMT signals were close to the natural electromagnetic field. The resulting apparent resistivity-phase curve is more continuous and smooth, and effectively improves the quality of AMT data. Moreover, the proposed method provides more reliable AMT data for subsequent electromagnetic inversion.

Natural magnetotelluric signals are extremely weak and susceptible to various types of noise pollution. To obtain more useful magnetotelluric data for further analysis and research, effective signal-noise identification and separation is critical. To this end, we propose a novel method of magnetotelluric signal-noise identification and separation based on ApEn-MSE and Stagewise orthogonal matching pursuit (StOMP). Parameters with good irregularity metrics are introduced: Approximate entropy (ApEn) and multiscale entropy (MSE), in combination with k-means clustering, can be used to accurately identify the data segments that are disturbed by noise. Stagewise orthogonal matching pursuit (StOMP) is used for noise suppression only in data segments identified as containing strong interference. Finally, we reconstructed the signal. The results show that the proposed method can better preserve the low-frequency slow-change information of the magnetotelluric signal compared with just using StOMP, thus avoiding the loss of useful information due to over-processing, while producing a smoother and more continuous apparent resistivity curve. Moreover, the results more accurately reflect the inherent electrical structure information of the measured site itself.

Aeromagnetic gradient data have higher horizontal resolution on shallow sources, a magnetic anomaly that can better reflect the features of deeper sources. Therefore, we used a joint survey of aeromagnetic and gradient data to obtain the distribution of sources with different depths. In this paper, we propose an intersection constraint weighting (ICW) method based on aeromagnetic and gradient data for original and gradient anomalies with inconsistent high-low variation characteristics. The ICW method can effectively improve the resolution of inversion results and can more accurately obtain the distribution of magnetic bodies via cross-gradient by gradually adding a gradient component and applying a normalized property weighting function. Our theoretical model tests indicate that the distribution of the recovered magnetic susceptibility model of the ICW method was similar to that of the true model. In addition, the anomaly containing noise with different signal-to-noise ratios verified that the ICW method had a stronger anti-noise ability than other methods. We also inverted real data in the Zhurihe area of the Inner Mongolia Autonomous Region in northern China. The inversion result showed that the main trend of high magnetic bodies was in the northeast direction, that the shallowest depth of high magnetic bodies was 100 m, and that the greatest depth was 960 m.

A critical challenge in magnetotelluric (MT) studies is the effective suppression of noise in collected data prior to investigating deep geological structures and detecting deep-seated blind ore bodies. To address this issue, this study proposes a novel method for magnetotelluric signal-noise separation based on stagewise orthogonal matching pursuit. The approach involves constructing a redundant dictionary comprising wavelet packets, cosine atoms, and other basis atoms to sparsely represent segmented MT data. A Gaussian matrix is then employed to observe and sample the sparsely represented data. Subsequently, the stagewise orthogonal matching pursuit algorithm is applied to suppress strong interference and accurately reconstruct useful MT signals. The method is validated using both simulated typical strong interference and measured MT data from an ore concentration area. Comparisons with matching pursuit, orthogonal matching pursuit, and the remote reference method demonstrate that the proposed technique offers higher computational efficiency, fewer iterations, better convergence, and more effective noise suppression. Furthermore, the calculated apparent resistivity-phase curves exhibit improved continuity and smoothness, and the quality of low-frequency MT data is significantly enhanced.