Electromagnetic (EM) noise induced by high-voltage power grids remains a significant impediment to seismic data acquisition, particularly in large-scale hydrocarbon exploration projects. While conventional frequency-based filtering methods are commonly used to suppress EM noise, they often fall short in handling the transient and spatially variable nature of such interference. Moreover, these techniques risk attenuating critical portions of the seismic signal when the frequency spectra of signal and noise overlap.
This study was focused on evaluating the performance of a novel approach to removing EM noise (herein referred to as “pure EM noise removal”), where the complex synthetic Marmousi II model was used as the testbed. In the evaluated denoising method, the EM noise was supposed to be recorded concurrently with seismic data via dedicated EM receivers, allowing direct application of amplitude and phase corrections prior to subtraction from the raw seismic traces. The Marmousi II model, with its high-resolution representation of geologically realistic subsurface structures, provides a rigorous and controlled environment to examine the behavior of EM noise suppression under varied conditions.
By simulating diverse acquisition scenarios within the Marmousi II framework, effects of critical parameters were systematically explored, including the phase correction window size (in number of samples), amplitude correction factor, signal-to-noise ratio (SNR), and the spatial positioning of the EM receivers relative to the source of the EM noise (i.e., power lines). Optimal performance was achieved with a 500-sample phase correction window, a negative amplitude correction factor, an SNR of 3, and positioning the EM receiver at an identified optimal location (EM5), resulting in denoising errors as low as 0.009%. The spatial positioning of EM receivers emerged as a key factor in the effectiveness of the denoising. Incorrect placement led to reduced quality of recorded noise and, hence, increased denoising errors or waveform distortion after processing. The controlled layout of the Marmousi II model enabled fine-grained assessment of receiver geometry, ensuring that optimal locations could be reliably identified and replicated across different acquisition trials.
Furthermore, the analysis highlighted the importance of synchronized acquisition of the EM noise together with the primary seismic acquisition, as the inherently dynamic and non-stationary behavior of the EM noise makes the real-time concurrent recording essential. Using the Marmousi II model made it possible to systematically follow a one-factor-at-a-time approach, offering insights that are often obscured by environmental noise, equipment variability, and logistical limitations in field data acquisition.
Time-domain and frequency-domain evaluations confirmed that the pure EM noise removal method effectively preserved the integrity of the seismic signal, even in cases of spectral overlap with the EM noise. The results suggest that the proposed method not only offers enhanced denoising capabilities but also reduces the risk of signal loss compared to traditional filtering techniques.
The findings underscore the potentials of the proposed method as a robust alternative to frequency-based filters, particularly in environments with strong power grid interference. Its adoption could significantly improve seismic data quality and interpretation reliability in both synthetic and real-world scenarios.