workflow neural network object detection & interpretation

Workflow

Neural network object detection & interpretation

Overall workflow

1. Project set-up

2. “Scanning” the volume, select key lines and slices

3. “Object detections”

4. Integration and interpretation

1 project set-up: steps

• Survey set-up: – fill out known survey ranges & corners– or get these from scanning SEGY file– or get these from Seiswork/Geoframe using Arkcls

data link

• Import data: – volumes (SEGY/Arkcls)– surfaces (ASCII/Arkcls)– wells (track+TD+las/Arkcls)

• Calculate steering cube

1 project set-up: steps

Survey set-up

Import data

1 project set-up: calculate Steering Cube

• What: the Steering Cube contains the dip of the seismic events in inline and crossline direction at every sample point

• Why:– the dip itself is an attribute– the dip is used to implement

other attributes corrected for structure

– the dip is used for Structurally Oriented Filtering (SOF)

– the dip is used to calculate chrono-stratigraphy (SSIS plugin)

Polar Dip: dip is an attribute

Steered Similarity: dip is guiding an other attribute


Concept of dip-steering

Attributes are guided along a three dimensional surface on which the seismic phase is approximately constant

Steered similarity (back-ground on slice) vs none steered similarity (fore-ground slice). Note that the steered similarity has higher contrast, higher resolution and less spurious events

Effect of dip steering


• Quality vs speed:– FFT precise: best result,

very slow– FFT standard: good

result, acceptable speed– BG fast steering:

sensitive to noise, very fast

• Size: try to capture at least ½ T (dominant wave period)

• dGB default: BG fast steering 3x3 followed by median filter 1x1x3

Steering Calculation


• Median filter the Steering Cube:– Do this as a separate step after

calculating the steering cube so you will have both products

– The steering cube: very detailed, follows small scale structural change, noise sensive (spikes and zero-crossings)

– Median filter of steering cube: background structural trends, noise removed

• Set-up for median filter of Steering Cube:– time window: at least ½ T

(dominant wave period)– lateral step: at least 2 times the

lateral step of faults and other small scale structure to be filtered

Median filter of Steering Cube


• When to use the Steering Cube and when to use the Median Filtered Steering Cube:– Do you want to emphasize small scale or large scale structures– Is dip the attribute or used to steer another attribute

Objective Dip is Examples Use

See small scale structures

Attribute Dip, statistics on dip, curvature …

Steering Cube

Steering Similarity, filtering Median Filtered of Steering Cube

See large scale change

Attribute Dip, statistic on dip, curvature …

Median Filtered of Steering Cube

Steering Similarity, filtering Steering Cube

Reduce sensitivity to noise

Attribute or Steering Similarity Median Filtered of Steering Cube

2 scanning the volume

• Identify a number of lines and timeslices:– With typical examples you are looking for– Within areas of knowledge (wells)– Within areas of interest (objectives)– With possible pitfalls (e.g. where the object and non-

object look very much alike)

• Use the key-lines for picking examples and QC (for blind-testing do not pick on every key-line)

• Use the save session and restore session to store useful configurations of lines, section, horizons, etc.

2 scanning the volume

Typical OpendTect session for interpretation and integration. We see a time-slice with similarity attribute, inline with seismic and chimney cube as overlay. Also present is a well with well markers. A random line is connecting the different elements allowing a integrated interpretation.

Sessions can be stored (save) and re-opened (restore). This allows the user to save useful configurations of the data between working sessions.

3 object detection: steps

• Attribute analysis

• Example picking

• Train NN

• Apply to key lines and QC– Iterate previous steps– Tests are satisfactory: apply to volume

processing


all traces

Seismic Data

Apply

Object ‘probability’or

Class + Match

Trained MLP network

Train MLP network

Pick examples Define attribute set

Attributes at example positionsall traces

Seismic Data

Apply

Object ‘probability’or

Class + Match

Trained MLP network

Train MLP network

Pick examples Define attribute set

Attributes at example positions

The basic workflow to create an object detection type neural network. The yellow circles are user actions. The blue boxes are data. Note that user interactions on this basic flow scheme relate to the steps mentioned in the previous slide, except “QC on keylines” and “iteration of previous steps”. These steps can be added to the workflow to refine the final result.


WORKFLOW

1) attribute analysis

...

...

0.01080

0.00321

Curvature

.…….....Pick …

211.70.1951134421Pick 3

430.90.230982223Pick 2

…123.50.1121039421Pick 1

Etc.Local DipSimilarityEnergyLocation

2) Select representative train locations

3) Calculate seismic attributes

4) Feed calculated data to neural network and train

5) Apply neural network to data

Output

• In OpendTect there are default sets for detection of many object types (chimneys, salt, faults, …)

• How to use these default sets depends on user-experience, time-constraints and difficulty of the project:– New (or time-constrained)

user: use default sets as they are.

– Advanced user: modify and add attributes

– Expert user/special projects: add and redesign many custom made attributes

3 object detection: attribute analysisOpen default attribute set

3 object detection: attribute analysis• Before starting, think about which attributes you

would assign to the object of interest and what parameter settings will maximize the attribute (sometimes you need the same attribute multiple times, with different parameterization)

• An aid in selecting attributes and options is the attribute redisplay tool

• Neural networks are multi-attribute and non-linear: combined attributes may convey much more information than appears from the single attribute displays

• Individual attributes often have non-uniqueness or completeness issues associated with them; the neural network will solve this, hence individual attributes do not have to be perfect

• To make an attribute as good as possible: use the movie-style attribute evaluation, to optimize the parameter settings

• Output of this step: an optimized set of attributes to be used in the following neural network training

3 object detection: attribute analysis

3 object detection: attribute analysis

A (default) attribute set is a group of (optimized) attributes fit to highlight a particular seismic object. In subsequent training of neural networks all or a sub-selection of attributes may be used

Frequency Similarity

Attributes are similar but not equal. Each individual attribute does not highlight chimney completely or uniquely

Seismic ChimneyCube

Attribute analysis Neural network

The neural network integrates multiple attributes into one meta-attribute and is the transform between seismic and chimney cube

3 object detection: example picking• Pick examples of the object and non-object• Guidelines, try to:

– Pick typical examples– Avoid picking of ambiguous examples (uncertain interpretation)– Do pick difficult examples (certain interpretation, but object and

non-object look alike)– Avoid biased picking (one time level, only in high energy zones

where S/N ratio is high, etc)– Have good spatial sampling (take note of this when selecting

key lines!)– Constrain the solution by picking enough example points (for

neural networks, using 10 to 20 input attributes, use at least 500 example points in training)

• Output of this step: example picksets associated with the different object classes. To be used in the following neural network training.

3 object detection: example pickingOpen/create new pickset Picking examples for a (chimney) object

detection neural network

Interact mode

There are 2 or more picksets: object1, (object2, ..,) background. Highlight the pickset to be manipulated in the data tree. Now you can add or remove pick locations for that set only

chimney background

3 object detection: train neural network

• Train neural network using the attribute sets and example pick sets produced in the previous steps

• Evaluate neural network: which attributes are major inputs, which are insignificant

• Optimize the neural network, by trimming away uneconomical attributes (optional)

• Output of this step: a probability neural network (predicts object probability, one class), or classification neural network (predicts object classes, multi-class)

Feed-forward information


Layer of hidden nodes;computes features from input

Examples (attribute vectors) with user-assigned target values (object-codes) are fed to the untrained neural network. These known examples are used by the neural network to learn –by back-propagation of prediction errors -characteristic features of each object. After finalizing the training the parameterization of the neural network is frozen and the static neural network is applied to the application set

Example sets

Training set

Test set

Application set (input only)

Schematic set-up of supervised neural network

W1

Input Hidden Outputlayer layer layer

Back-propagate errors

Target variables

Non-linear transformations:

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Neural Network

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Conceptual image of an trained neural network

This display shows the multi-dimensional and non-linear character of neural networks. Input variables are input to a multi-dimensional, non-linear transform function for each node in the hidden layer of the neural network. These transform functions are recombined (using a weighted summation) in the output node(s) of the neural network. In effect almost any multi-dimensional, non-linear transform between input and output variables can be modeled.

• Neural network training guidelines:– Train until the test set error has reached it’s

minimum or the train set error levels out – it is often educational to train past the point of

overtraining, but be sure to “clear” the neural network training and retrain to avoid applying a overtrained neural network

– A normalized rms error of 0.7 and a misclassification of 0.25 can be considered good, but this also depends on how difficult the examples were picked

– If you trim away uneconomical attributes, realize neural network prediction is non-linear and attribute may have unexpected contributions to the result. So compare error levels before and after trimming the neural network



Train a neural network


Any subselection of attributes out of the active attribute set may be chosen to be input to the neural network. These attributes will be calculated “on the fly” both in training and application phase of the neural network. In addition any number of stored volumes can be input to the neural network.

At least two target picksets must be defined. Typically one trains a neural network on an object and a background pickset. But a neural network can be trained to discriminate

between three or more objects/picksets

To highlight seismic objects: use the supervised method

Define neural network model for training

Part of the example are set apart as test set to monitor overtraining. This is done automatically by the software according to a user defined ratio

3 object detection: train neural networkNeural network training display

Left windows: training performanceRight window: attribute input nodesColor scale: hotter color means more important input node

Input nodes with minor impact may be removed. To do this one should redo the neural network training Further

optimization after initial overtraining

Err

or

Iterations

Train setTest set

Err

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Iterations

Train setTest set

Err

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Iterations

Train setTest set

Stopping points for neural network training

Leveling

Overtraining

STOP

STOP

STOP

STOP?

3 object detection: apply to keylines/QC

• Apply the neural network to key lines• Evaluate the result and note points that

are not predicted satisfactory• Decide between applying or rejecting the

neural network – note that especially in more difficult problems the result will never be 100% flawless

• Reject: iterate previous object detection steps and fine tune attributes, pickset and neural network – see next two slides

• Accept: apply to volume – see third next slide

Training locations in the picksets that could not be correctly predicted by the neural network are stored in a separate pickset named “misclassified” (red dot). Analyzing these location may lead to better/additional attributes for increased resolutions

3 object detection: apply to keylines/QC

Evaluation of chimneycube on key line

Noise in chimney cube due to low S/N ratio of seismic input

Really chimneys or mud slide resulting in hummocky seismic???

For many seismic object a time-slice or horizon is best for QC and interpretation. It allows to better discriminate signal from artifact

True chimney

3 object detection: iterate object detection steps

• Update attributes: – design attributes that provide better discrimination in areas

where the neural network does not yet perform satisfactory– fine tune parameterizations, add attributes to set, design

specific purpose attributes using math, logic and volume statistics, or attributes on attributes.

• Update pick-sets: overlay results over the key lines and add extra picks in the problem areas to emphasize these during neural network training. Also study mis-classified picks (red dots) and remove picks where needed.

• Neural network training: – experiment with different sets of input attributes– train longer & consider applying a slightly over-trained network:

if, after a short period of overtraining, the test set error starts decreasing again, the benefit of the extra optimization of the neural network might outweigh the error introduced by overtraining

3 object detection : apply to volume

OpendTect supports multi-platform distributed computing. The system needs to be setup properly to use this scheme. See Administrator’s manual.

4 integration of results

• OpendTect– Saltcube– Chimney (fluid migration

cube)– Fault Cube– Fracture detection using

curvature, anisotropy or other attributes

– Reservoir-Waste-Seal analysis (lithofacies plus fluid analysis)

– Frequency (high freq attenuation, low freq shadows)

– AVO/AVA fluid indicators– Well data– …

• Outside information– Geological interpretation

and studies– Regional knowledge

(kitchens, charging mechanisms)

– Pressure data– Basin modeling– Horizons and interpretation

(e.g. pockmarks and paleo-mud volcano’s)

– Gas sniffing surveys– …

workflow neural network object detection & interpretation

Documents

steering cube median

steering cube concept

median filtered steering

steering attributes

filteringsteering cube

steering cube quality

project setup

attribute slide