Fast structural interpretation with structure‐oriented filtering

Abstract

We present a new approach to structural interpretation of 3D seismic data with the objectives of simplifying the task and reducing the interpretation time. The essential element is the stepwise removal of noise, and eventually of small‐scale stratigraphic and structural features, to derive more and more simple representations of structural shape. Without noise and small‐scale structure, both man and machine (autotrackers) can arrive at a structural interpretation faster. If the interpreters so wish, they can refine such an initial crude structural interpretation in selected target areas. We discuss a class of filters that removes noise and, if desired, simplifies structural information in 3D seismic data. The gist of these filters is a smoothing operation parallel to the seismic reflections that does not operate beyond reflection terminations (faults). These filters therefore have three ingredients: (1) orientation analysis, (2) edge detection, and (3) edge‐preserving oriented smoothing. We discuss one particular implementation of this principle in some detail: a simulated anisotropic diffusion process (low‐pass filter) that diffuses the seismic amplitude while the diffusion tensor is computed from the local image structure (so that the diffusion is parallel to the reflections). Examples show the remarkable effects of this operation.

Image-guided inversion in steady-state hydraulic tomography

“…The diffusion tensor field D(x) defined by Eq. (19) is here determined at six pixels x i (red filled circles). The tensor components are shown as ellipses determined from the intensity of the grey scales using the image-gradient at these points.…”

“…Such type of smoothing is performed using the eigenvectors of the structure tensor field determined in Section 2.3.1. Weickert [56] and Fehmers and Höcker [19] proposed to parameterize a structureoriented smoothing filter by solving an anisotropic diffusion equation defined as @gðx;sÞ=@s ¼ r Á ½DðxÞrgðx;sÞ; ð17Þ subject to the following initial conditions gðx;s ¼ 0Þ ¼ fðxÞ;…”

“…It also gives the coherence of these directions. The structure tensor is evaluated at every pixel and is defined as the smooth outer product of the local gradients image [19,53,56]. In our case, the guiding image will be given in terms of shades of grey (see for instance in Fig.…”

Simultaneous multiple well-seismic ties using flattened synthetic and real seismograms

“…The metric tensor field DðxÞ represents the coherence and orientation of seismic structures and therefore often provides anisotropic and spatially variant coefficients for the eikonal equation. As discussed by Hale (2010a), we construct the metric tensor field using structure tensors (Van Vliet and Verbeek, 1995;Fehmers and Höcker, 2003) computed from the 3D seismic image. With such a metric tensor field, the computed nearest neighbor interpolant conforms to structures apparent in the seismic image.…”

Suavização De Dados De Amplitude Através De Difusão Anisotrópica Com Preservação De Feições Sísmicas

“…Neste sentido, o trabalho desenvolvido por Dave Hale (21) descreve uma forma modificada do método de filtragem por difusão anisotrópica desenvolvido por Höcker e Fehmers (22), (23). O tensor de estrutura do gradiente da amplitude é utilizado com objetivo de orientar o método de filtragem.…”