• Seismic data in structured land areas are characterised by low data density, low signal-to-noise ratios, and high structural complexity
• Automated methods for velocity model building break down under these conditions
• Structural-geology constraints are key to seismic imaging success in these areas, as illustrated graphically with this fault-geometry scenario test

• Built convolutional neural network (CNN) to estimate TTI PSDM velocity models from shot gathers
• Traditional automated methods for PSDM velocity estimation in complex-structure land areas are unstable, so we rely on the human understanding of the geology to build geologic models
• The goal is to use machine learning to supplement human learning
• Field-data example shows imaging improvements on certain shallow reflectors, which shows the potential of the method

This presentation develops the themes presented in Chapter 2 of the AAPG publication, Andean Structural Styles: A Seismic Atlas. Seismic data in areas like the Andes have unique challenges that break traditional seismic-imaging methods designed for offshore exploration. Reducing exploration risk in these basins requires a workflow tailored to the geologic setting. The under-constrained nature of the seismic data requires tight integration with the structural geologist.

Seismic imaging is a vital tool for mapping the complex geologic structures of the Andes. The method of imaging the Earth’s subsurface with seismic waves is powerful, and it has certain limitations—especially when deployed in complex-structure land areas like the mountain ranges and high plains of the Andes. Understanding the technologies involved and how they are applied to this specific geologic setting will improve our understanding of the risks and uncertainties involved in the interpretation of structures on seismic images.

Seismic data in thrust-belt environments are typically low data density and have low signal-to-noise ratios, all while attempting to image complex geologic structures. The data are acquired over rough topography with laterally varying velocities from the surface down. If the near surface is the lens through which we image the subsurface, our lens is bumpy and distorted. These are the challenges of seismic processing in fold thrust belts, and decades of technology development has gone into facing those challenges, from weathering corrections for the near-surface, to advance migration algorithms that can image below major thrust faults.

• Seismic data in structured land areas have severe limitations
• Geologic interpretation and human collaboration can overcome these limitations
• Examples from Colombia and Peru show how we resolve these issues through geoscience collaboration
• Increased accuracy of an imaging algorithm also means increased sensitivity: PSTM → PSDM → RTM

• 2D seismic imaging along the foothills of the Indo-Burma range
• Interpretive geologic constraints required for model building in noisy seismic data
• Resulting seismic images showed structural details that time processing could not fully image.

• FD acoustic anisotropic modelling quantifies the imaging problem resulting from tight folds in the near surface
• TTI PSDM is sensitive to the model dip
• Surface-geology measurements helped constrain model dip

Vestrum, R.W., and Lawton, D.C., 2010, Reflection point sideslip and smear in imaging below dipping anisotropic media: Geophysical Prospecting, 58, 541–548.

• In folded and faulted geologic settings, dipping anisotropic strata above exploration targets blur and misposition imaged seismic reflectors. Correcting for these anisotropic effects can significantly reduce exploration risk.
• Raytracing and numerical seismic models illustrate and quantify anisotropic imaging problems to show the importance of correcting for anisotropy in seismic imaging.

Vestrum, R.W., Florez, I.C., and Gittins, J.G., 2009, Anisotropic depth migration in the Colombian Llanos Foothills as a key to understanding the structure in depth: 2009 AAPG ACE, Denver, USA.

• This case history shows the unpredictable nature of imaging below for lateral-velocity heterogeneity and seismic anisotropy. The surprise ending shows how the imaged structures move as we correct for each wave-propagation effect.

Vestrum, R.W., 2006, Seismic imaging and interpretive model building in the Colombian Andes: IX Simposio Bolivariano, Cartagena, Colombia.

• This manuscript describes the problems of sideslip and smear before going into the interpretive model-building methodology.