Nonlinear Seismic Signal Denoising Using Template Matching with Time Difference Detection Method
Year: 2025
Type: article
Abstract: As seismic exploration shifts towards areas with more complex surface and subsurface structures, the complexity of the geological conditions often results in seismic data with low signal-to-noise ratio. It is therefore essential to implement denoising in order to enhance the signal-to-noise ratio of the seismic data. At present, the prevailing denoising techniques are based on the assumption that the signal adheres to linear model. However, this assumption is frequently invalid in complex geological conditions. The main challenge lies in the fact that linear models, which are foundational to traditional signal processing, fail to capture the nonlinear components of seismic signals. The objective of this paper is to present a methodology for the detection of nonlinear signal structures, with a particular focus on nonlinear time differences. We propose a method for detecting nonlinear time differences based on template matching, wherein the seismic wavelet is treated as the template. Template matching, a fundamental pattern recognition technique, plays a key role in identifying nonlinear structures within signals. By employing a local signal as a template, the template matching technique can identify all the structure of the signal, thereby enabling the detection of nonlinear features. By employing template matching, the nonlinear time differences in the signal are identified and corrected, thus enabling the signal to align with the assumption of linearity. Subsequently, linear denoising methods are employed to effectively remove noise and enhance the signal-to-noise ratio. The results of numerical experiments demonstrate that the proposed template matching method is highly accurate in detecting nonlinear time differences. Furthermore, the method’s efficacy in removing random noise from real seismic data is evident, underscoring its superiority.
Source: Remote Sensing
DOI: 10.3390/rs17040674
Institutions: Tongji University, State Key Laboratory of Marine Geology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4407625809