A Joint Inversion-Segmentation approach to Assisted Seismic Interpretation

Abstract

Structural seismic interpretation and quantitative characterisation are intertwined processes, which benefit from each others’ intermediate results. In this work, we redefine them as an inverse problem that tries to jointly estimate subsurface properties (e.g., acoustic impedance) and a piece-wise segmented representation of the subsurface based on user-defined macro-classes. By inverting for these quantities simultaneously, the inversion is primed with prior knowledge about the regions of interest, whilst at the same time it constrains this belief with the actual seismic measurements. As the proposed functional is separable in the two quantities, these are optimized in an alternating fashion, and each sub-problem is solved using a Primal-Dual algorithm. Subsequently, an ad-hoc workflow is proposed to extract the perimeters of the detected shapes in the different segmentation classes and combine them into unique seismic horizons. The effectiveness of the proposed methodology is illustrated through numerical examples on both synthetic and field datasets.