Reduction of data

For most geophysical methods it is necessary to apply a variety of corrections to the data obtained‘raw’ from the field acquisition system in order to ‘reduce’, or prepare, the

Line spacing Overlap between survey line & detectable target response S Target Station spacing a) L W 0.2 0.4 0.6 0.8 1.0 b) S=20 m 0.62 0.31 L/W=1.0 L/W=2.0 L/W=4.0 L/W=1.0 L/W=2.0 L/W=4.0 L/W=1.0 L/W=2.0 L/W=4.0 L/W=1.0 L/W=2.0 L/W=4.0 0.0 0 200 400 600 0 200 400 600 Line spacing (m) L/W=1.0 L/W=2.0 L/W=4.0 L/W=1.0 L/W=2.0 L/W=4.0 L/W=1.0 L/W=2.0 L/W=4.0 L=500 L=1000 L=250 0.2 0.4 0.6 0.0 0.8 1.0 c) L=125 0.38 Line spacing (m) S=200 m Probability Probability

Figure 2.13 Probability of detecting an elliptical anomaly. (a) Parameters used to determine the probability of a line-based survey crossing an elliptical anomaly, with major axis length L and minor

axis length W, with an overlap of at least the specified length S. b) Probability versus line spacing for an overlap of 20 m. c) Probability of detection for an overlap of 200 m. Redrawn, with permission,

data for enhancement and display. It is essential that, as far as is possible, all acquisition-related errors, and errors and noise emanating from external sources, be corrected or compensated as afirst stage of data processing. Otherwise, the errors will propagate and increase through the various stages of data processing. The situation is stated succinctly by the maxim:‘Errors don’t go away, they just get bigger’. Reduction compensates for various sources of error and noise, sometimes using secondary data acquired during the survey (see Section 2.6). Some reductions may be carried out automatically by the data acquisition instrumentation, provided that the instrument records the necessary second- ary data and that the magnitude of the correction is easily determined. More complicated reduction processes are applied post-survey.

In its simplest form, reduction involves a manual assess- ment of the data to remove readings that are obviously unsuitable, for example those dominated by noise. Correc- tions applied during reduction include compensation for the geophysical response of the survey platform; variable sensor orientation/alignment; and various temperature, pressure and instrumental effects. In addition, corrections are applied to suppress environmental noise (seeTable 2.1), which may require measurements obtained from a second- ary sensor specifically deployed to monitor the noise. The corrections applied during reduction of the data are only as good as the secondary data on which they are based. For example, information about the local topography may be insufficient to remove its effects completely.

Generally, reduction processes are parameter specific and survey type specific, and are described in detail later in our descriptions of each geophysical method. Here we describe some more generally applicable operations.

2.7.1.1 De-spiking

Spikes, or impulses, are abrupt changes in the data which have short spatial or temporal extent, and usually occur as a single data point. These may be caused by instrumental problems or they may originate in the natural environ- ment. Spikes can be removed using various numerical data processing techniques (see Smoothing inSection 2.7.4.4) or by manual editing. Erroneous data are then replaced by interpolating from adjacent readings. It is important to de-spike early in the processing sequence since some pro- cessing operations will produce artefacts if spikes are not removed, notably those that involve a Fourier transform

(seeAppendix 2).

2.7.1.2 Levelling

The success of the data reduction in eliminating errors can be assessed by comparing corrected repeat measurements made at the same location. Ideally the reduced (repeat) values should be identical. Any remaining differences, known as residual errors, can be used to further reduce the data to remove these errors. This process is not based on measurements from the natural environment; instead it is a pragmatic attempt, based on statistical methods, to lessen the influence of remaining errors by redistributing them across the entire dataset. The process is known as levelling because the adjustment made to the amplitude of each measurement changes the overall amplitude level of the measurements.

Tie lines provide the necessary repeat readings where they intersect the survey lines (see Section 2.6.3.3). The basic idea is to use the tie-line data to‘tie’ adjacent survey lines together at regular intervals, with the residual errors at the line intersections used to adjust the data. For air- borne surveys this is an imperfect process because of the inevitable errors due to differences in survey heights at the line intersections, i.e. the two readings are not from exactly the same location. Tie lines may also be part of a ground survey; but if measurements have been made at one or more base stations, then these provide the necessary repeat readings. It is common practice to make repeat readings at selected stations for this specific reason.

There are various means of redistributing the residual errors; see Luyendyk (1997), Mauring et al. (2002) and Saul and Pearson (1998) for detailed descriptions regarding airborne datasets. One simple approach when tie lines are available is based on using the residual values at the inter- sections to interpolate a 2D error function across the survey area. By subtracting the relevant error function values from the readings along the survey lines, the residual errors are reduced.

A quick and easy way to assess the quality of the data reduction is to view it with shaded relief (see Section

2.8.2.3), with the illumination perpendicular to the survey

line direction, optimum for highlighting across-line level- ling errors. Residual errors in the levelled data are revealed as corrugations or ripples between the survey lines (see Fig. 3.24). Also, derivative images, being sensi- tive to gradients, are usually effective in revealing residual errors in a levelled dataset. Any remaining errors indicate either that the various corrections applied are imperfect or that other sources of survey error have not been accounted for.

2.7.1.3 Microlevelling

Small residual errors can be dealt with through a process known as microlevelling. These algorithms operate on the gridded data (see Section 2.7.2); a method based on directional filtering (see Trend in Section 2.7.4.4) is described by Minty (1991). Microlevelling aims to remove residual errors remaining after the levelling process and new errors introduced by the gridding process. The com- puted corrections can be applied to the levelled line data to produce‘microlevelled’ line data.

The key aspect of microlevelling is that it is a cosmetic process designed to make the data look good, i.e. to make it look as it is expected to look! If not applied carefully, these methods can remove significant amounts of signal, especially higher-frequency components, and may even introduce unreal features. An example of microlevelled magnetic data is shown inFig. 3.24d.