A Guide on Data Analysis and Presentation Using Inclinometer
Inclinometer data represents the deflection / lateral movement and plots the shape/profile of the inclinometer casing.
Data monitored at set frequencies/ schedule are compared to identify whether the changes in the profile have occurred, which represents ground deformation or lateral movement.
Data assessment of installed IPIs would depend on the no. of sensors installed at different levels of the borehole.
Checking and evaluation data measurements
There is a set of rules for checking and evaluating data assessment. The precision and bias issues can arise, but there are no standards assigned for assessment as there is no approved practice.
System field accuracy of ± 2 mm per 30 m, which consists of a combination of random and systematic errors, as shown in figure 2.
Random errors occurs within sensors, limiting the precision of the probe.
Systematic errors occur due to human actions which affect the condition of the inclinometer probe, cable reel and the data monitoring procedure. The rules/procedures for these assessment are described here.
Checksum- The first assessment of the data is a review of the checksums. Procedure for checksum is the sum of “0” and “180” of the inclinometer probe to check the quality of data. Magnitude should be approximately same with an opp. sign on one of the axis. This method is just to check the quality of the data and is carried out on site after completion of monitoring.
Theoretically, the checksums are almost zero as the readings have opposite signs and it cancel out the values. In practice, the checksums produce a constant value, where a low standard deviation would confirm data quality. Checksums are used to evaluate the data for possible errors. The checksums ideally are constant for all depth intervals in a data set.
Factors which influence the checksum are as follows:
- Condition of casing groove and geological variations at that particular region
- Performance of Instrument (zero-offset of the probe),
- Probe positioning accuracy, and operator inconsistencies–errors.
Slight variations do not indicate a problem since slight variations are nearly impossible to eliminate. If the checksum exceeds above 10 units for A axis and above 20 for B axis then it would be a concern. If large checksum differences are localized to one depth, the data can be corrected based on the mean of the other checksums. However, if large checksums and variations occur in a dataset, the readings should be repeated until satisfactory checksums are achieved.
Analysis includes the preparation of graphs showing the profile of the inclinometer casing, the changes at each depth which is useful to identify the disturbing/faulty zones of movement. Commonly cumulative lateral deformation with depth, starting from bottom of the casing and summation of increments of displacements for each measured interval till the top of casing.
The use of expanded horizontal scales is avoided because errors are magnified and could cause misinterpretations.
Incremental/ cumulative plots plotted shows the deformation at each depth. If assumed, no systematic errors then the profile would be assumed as a straight line. If the ground deformation is observed at a discrete depth, then the profile would show a lateral deformation at that particular depth. Where movements are detected, calculations of rates of ground movement can be performed. Only the deformation occurring at particular depth(s) are analysed rather than plotting the apparent cumulative displacement at the ground surface. The latter can include the cumulative systematic errors and can magnify r understate the actual ground information.
Diagnostics for Systematic Errors
Inclinometer profiles: displacement against each depth and change in plots can be affected by systematic errors. Tracking and random errors show a small portion of potential errors and cannot be readily corrected.
Systematic errors include:
• Bias shift
• Rotation error
• Sensitive drift
• Depth position error
Evaluation and Interpretation
Once the instrumentation data is checked, monitored for accuracy and reliability, the next step is to identify whether ground movement is indicated by the data and the type of ground movement that is inferred. It is important to understand:
- the cause of the apparent deformation,
- probable trends
- potential concerns.
- to be familiar with common deformation trends for various types of ground movement.
Systematic and random errors can create the appearance of deformation where there is none.