Peak Finder

Peak Finder has been designed for the detection and grouping of time-domain electromagnetic (TEM) anomalies measured along flight lines. Anomaly markers can be exported to Geoscience ANALYST, along with various metrics for characterization and targeting.

The peak-finder application can operate in two modes:

• Interactive (shown above)

• Standalone

While initially designed for TEM data, the same application can be used for characterization of anomalies of mixed data types (eg. magnetics, gravity, topography, etc..).

Methodology

This section provides technical details regarding the algorithm used for the detection of anomalies along profile.

Anomalies are identified from the detection of maximum, minimum and inflection points calculated from the first and second order derivatives of individual data channels. Detection parameters are available for filtering and grouping co-located anomalies. The selection process is done in the following order:

Primary detection

Loop over the selected data channels:

1. Apply the Minimum Data Value threshold.

2. For every maximum (peak) found on a profile, look on either side for inflection and minimum points. This forms an anomaly.

3. Keep all anomalies larger than the Minimum Amplitude

Grouping

Anomalies found along individual data channels are grouped based on spatial proximity:

1. Find all peaks within the Maximum Peak Migration distance. The nearest peak is used if multiple peaks are found on a single channel.

2. Create an anomaly group that satisfies the following criteria:

   • The data channels must be part of a continuous series (maximum of one channel skip is allowed)

   • A group must satisfy the Minimum number of channels

Detection Parameters

Masking Data

property PeakFinderParams.masking_data: Data | None

Mask object to focus peak finding within an area of interest.

Masking data is used to filter out data points that are not considered by the algorithm. This is useful for focusing on specific regions of interest.

Smoothing

property PeakFinderParams.smoothing: int

Number of neighbors used in running mean smoothing.

The running mean replaces each data by the average of it’s N neighbours:

\[d_i = \frac{1}{N}\sum_{j=-\frac{N}{2}}^{\frac{N}{2}}d_{i+j}\]

where averaging becomes one sided at both ends of the profile. The result is a smoothed data set where the degree of smoothing scales with the number of neighbours used in the mean.

Fig. 1 The residual between the original and smoothed data.

Minimum Amplitude

property PeakFinderParams.min_amplitude: int

Threshold on the minimum amplitude of the anomaly, expressed as a percent of the height scaled by the minimum value.

Threshold value (\(\delta A\)) for filtering small anomalies based on the anomaly minimum (\(d_{min}\)) and maximum (\(d_{max}\)).

\[\delta A = \left|\left|\frac{d_{max} - d_{min}}{d_{min}}\right|\right| \cdot 100\]

See figure for a visual example of the anomaly amplitude.

Minimum Data Value

property PeakFinderParams.min_value: float

Minimum absolute data value to be considered for anomaly detection.

The minimum data threshold (\(\delta_d\)) (see Figure) can be defined by:

\[\begin{split}\begin{equation} d_i = \begin{cases} d_i & \;\text{for } d_i > \delta_d \\ nan & \;\text{for } d_i \leq \delta_d\\ \end{cases} \end{equation}\end{split}\]

Minimum Width

property PeakFinderParams.min_width: float

Minimum anomaly width (m) measured between start and end of bounding minima.

The minimum distance (m) between the start and the end of an anomaly group to be considered.

Maximum Peak Migration

property PeakFinderParams.max_migration: float

Threshold on the lateral shift (m) of peaks within a grouping of anomalies.

The maximum distance (m) between the peaks within a group of anomalies. This parameter depends on the Minimum number of channels.

See figure for a visual example of migration within a group of anomalies.

Minimum number of channels

property PeakFinderParams.min_channels: int

Minimum number of data channels required to form a group.

The minimum number of data channels required to form a group of anomalies.

See figure for a visual example of channels making up a group of anomalies.

Merge N Peaks

property PeakFinderParams.n_groups: int

Number of consecutive peaks to merge into a single anomaly.

Post-grouping of anomalies based on the number of consicutive peaks. The parameter depends on the Max Group Separation.

Max Group Separation

property PeakFinderParams.max_separation: float

Maximum separation between peaks to merge into single anomaly.

The maximum distance (m) between the start and the end of a neighboring groups.