OpendTect dGB Plugins User Documentation version 4.2
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Chapter 2. Steering

Table of Contents

2.1. Background
2.2. Create Steering Data
2.3. Attributes with steering
2.4. Benchmark Steering Cube Creation

2.1. Background

Directivity is a concept in which dip and azimuth information is used to improve attribute accuracy and object detection power:
For example, consider the calculation of a similarity attribute. This attribute compares two or more trace segments by measuring their distance in a normalized Euclidean space. Two identical trace segments will yield an output value of one, while two completely dissimilar trace segments will return the value zero. In case layering is horizontal, this will work well but in a dipping environment the results will deteriorate. Thus, instead of comparing two horizontally extracted trace segments the local dip should be followed to find the trace segments for comparison. The process of following the dip from trace to trace is called steering and requires a SteeringCube as input. The steering-plugin for OpendTect supports two different modes of data-driven steering: Central Steering and Full Steering. In Central steering the dip / azimuth at the evaluation point is followed to find all trace segments needed to compute the attribute response. In Full steering the dip/azimuth is updated at every trace position. The difference between 'no steering', 'central steering' and 'full steering' is shown in the following figures. Note that these figures show the 2D equivalent of steering, which in fact is a 3D operation.


A SteeringCube is computed using a sliding 3D Fourier analysis technique in OpendTect. A small (typically 7x7x7) cube is transformed to the Fourier domain where its maximum is determined. The maximum value corresponds to the dip, which is stored in the SteeringCube in two components: inline dip and crossline dip.

Directivity also plays a role in defining attribute sets that are tuned to a particular seismic object. For example, in Chimney detection the knowledge that chimneys are vertical bodies with a certain dimension is utilized by selecting attributes in three vertically oriented attribute windows. Open the default chimney attribute set and notice that all attributes occur three times with different time windows (-120,-40 / -40,+40 / +40,+120). When these attributes are fed to a neural network, the network learns that the responses in the three dimensions around the evaluation point are similar when a chimney is present and different when the location is not close to a chimney.

For chimneys the windows must obviously be arranged vertically. Similarly you can argue that for flat spot detection one should use horizontal windows. Can this concept also be used for e.g. fault detection? The answer is yes. You should steer the attributes along the fault plane directions. The problem of course is that the fault plane direction is seldom known, neither can it be calculated directly from seismic data. However, we have successfully calculated fault plane directions from a predicted FaultCube and used these directions to improve TheFaultCubeŽ. The process is called iteration and can be performed in OpendTect as follows:

  1. Create a FaultCube similarly as when creating a ChimneyCube.

  2. Filter the first generation FaultCube (FC-1) with a velocity fan filter (this is a 3D-kf filter that must be defined in the attribute definition window).

  3. Create a SteeringCube from the dip-filtered FC-1.

  4. Extract new attributes from the original data, steered along the faultplanes where needed and optionally extract attributes from FC-1 to create FC-2 using the same process as in step 1.

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Introduction   Create Steering Data