Selective Trace Mix: A New Processing Tool to Enhance Seismic Imaging of Complex Subsurface Structures

Abstract

In seismic imaging, the trace mixing process involves merging neighboring traces in seismic data to enhance the signal-to-noise ratio and improve the continuity and spatial coherence of seismic data. In regions with complex subsurface structures, current trace mix filters are often ineffective as they introduce artifacts that reduce interpretability and obscure the signatures of important structures, such as faults and folds. We introduce the selective trace mix as a novel, data-dependent filter. This filter enhances amplitude consistency, spatial coherence, and the definition of reflections, while it preserves complex structures and maintains their clarity. Selective trace mix uses sequential steps of evaluation, referencing, exclusion, weighting, and normalization of all samples within the filter operator. As a result, selective trace mix is a temporally and spatially variable, data-dependent filter. The filter's effectiveness is validated using both synthetic and real field seismic data. Synthetic data is a portion of the Marmousi seismic model, while real data include land and marine seismic datasets imaging complex subsurface fault/fold structures. When compared to three of the commonly used conventional filters, the selective trace mix yields far better results in terms of horizon integrity and fault clarity.