SURVEY 1

1. Data Processing in Surveying

Data processing is the first step after field data collection. The purpose of this stage is to organize and convert raw survey data into a usable format for further analysis, design, and reporting.

1.1. Organizing Raw Survey Data

Raw survey data typically includes distances, angles, and elevations measured during fieldwork, along with geographic coordinates (latitude, longitude) and control points. The first step is to organize this data systematically.

• Field Logs: Surveyors record measurements and observations on field notebooks or digital logbooks. This includes the date, time, instrument readings, environmental conditions (e.g., temperature, pressure), and any other relevant information.
• Data Transfer: The raw data collected from instruments like total stations, GPS units, or levels is transferred to a computer for further processing, usually using specialized software such as AutoCAD, Civil 3D, or GIS software.
• Data Entry: The surveyor enters the raw measurements into a digital format, which includes creating data tables or importing files into survey software.

1.2. Conversion to Coordinate Systems

Survey data needs to be converted into a defined coordinate system for integration with design software and other project datasets.

• Coordinate Systems: The raw data may include local or site-specific coordinate systems (e.g., grid coordinates). For compatibility with regional or global maps, the data may need to be transformed into standard coordinate systems like Universal Transverse Mercator (UTM) or geographic coordinate systems (latitude/longitude).
• Georeferencing: The survey data can be referenced to known geodetic control points or benchmarks using the Global Positioning System (GPS) or traverse methods.

1.3. Computation of Coordinates

Survey data such as angles and distances must be processed mathematically to compute the positions of points in a coordinate system.

• Linear Measurements: For survey lines, distances are used to determine the position of points along the line by applying trigonometric formulas (e.g., sine, cosine) based on known reference points.
• Angular Measurements: Using the measured angles, surveyors calculate the direction of the surveyed line or point. In most cases, trigonometric relationships (e.g., triangulation, traversing) are used to compute the coordinates of points.

For example, if two points are measured, and an angle is formed at a known position (from a control point), trigonometric relationships (such as the Law of Sines or Cosines) are used to calculate the unknown point’s coordinates.

1.4. Data Filtering and Smoothing

Sometimes raw field data contains extraneous or noisy values due to instrument errors, human mistakes, or environmental influences. This can be mitigated by:

• Filtering: Smoothing techniques are applied to reduce any abrupt deviations in the data, especially for large datasets.
• Statistical Analysis: Statistical methods are often used to identify outliers or invalid readings and adjust them accordingly.
• Interpolation: For points that are missing or data that is insufficient, interpolation techniques can be applied to estimate values based on neighboring data.

2. Error Correction in Survey Data

Survey measurements are subject to various errors due to factors like instrument limitations, human mistakes, environmental conditions, and systematic biases. Therefore, error correction is an important part of the data processing phase. There are two primary categories of errors:

• Systematic Errors: These errors follow a predictable pattern (e.g., instrument calibration issues or measurement biases) and can be corrected through known formulas or by recalibrating instruments.
• Random Errors: These are errors that occur unpredictably and usually arise from external factors like wind, temperature fluctuations, or human error. They can be minimized but not entirely eliminated.

2.1. Sources of Errors

1. Instrumental Errors: These occur due to inaccuracies in the surveying equipment, such as miscalibrated total stations or defective GPS receivers.

   • Correction: Calibration of instruments before and during fieldwork, as well as accounting for instrument-specific corrections (e.g., temperature or pressure adjustments), helps minimize these errors.

2. Human Errors: These can include mistakes in recording measurements, misinterpretation of instrument readings, or errors in data entry.

   • Correction: Cross-checking field measurements, using multiple team members to verify results, and conducting independent checks of calculations helps mitigate human error.

3. Environmental Errors: Factors like temperature, humidity, and atmospheric pressure can affect measurement accuracy, especially for instruments that rely on electromagnetic signals, such as total stations and GPS systems.

   • Correction: Surveyors adjust for environmental conditions using established correction tables or applying atmospheric corrections in the survey software.

2.2. Error Correction Techniques

1. Instrumental Corrections:

   • Temperature and Barometric Adjustments: For measurements affected by temperature and atmospheric pressure, survey software often applies corrections based on the field conditions (e.g., using standard atmospheric tables).
   • Distance and Angle Adjustments: For total stations and EDM measurements, software automatically compensates for instrument-specific factors (e.g., EDM scale, collimation errors, or the curvature of the Earth).

2. Field Observation Adjustments:

   • Redundant Observations: Surveyors often make multiple independent measurements (e.g., measuring angles or distances from different points) to help identify discrepancies. These can then be averaged or weighted to reduce error.
   • Repetition of Measurements: Repeated measurements are taken for key points to minimize human and environmental errors. If measurements differ significantly, they are discarded or corrected using the appropriate methodology.

3. Adjustment of Survey Data

Data adjustment is a statistical method used to correct measurement discrepancies and minimize cumulative errors. After collecting data, it’s essential to adjust the survey results for the overall consistency and accuracy.

3.1. Types of Adjustments

1. Least Squares Adjustment (LSA):

   • Purpose: The most common method of data adjustment is the least squares adjustment, which minimizes the sum of the squared differences between observed values and calculated values. This method adjusts the measured data to fit the desired model (e.g., the positions of points in a control network or along a survey line).
   • Process: For each observation, a residual error is calculated, and the least squares method finds the solution that minimizes these residuals. This technique can be applied to both angles and distances to adjust the results.
   • Application: This is used in more complex surveys like control networks, triangulation, and large-scale traverses where multiple observations are made.

2. Differential Correction:

   • Purpose: This method is used primarily in GPS surveys to correct for systematic errors in satellite positioning.
   • Process: Differential corrections are applied by comparing data from a base station (known position) with data from a rover station (mobile measurement point). The difference is used to correct the rover’s GPS readings.
   • Application: This is commonly used in high-precision applications like geodetic surveys or projects requiring sub-centimeter accuracy.

3. Closing the Traverse:

   • Purpose: In traverse surveys, errors accumulate from one station to the next. The final station position should match the starting point for a closed loop.
   • Process: The difference between the calculated and observed closing points is the "closing error." This error is distributed across the traverse network to adjust the positions of all stations proportionally.
   • Application: Used in boundary surveys, route surveys, and control networks.

4. Adjustments for Curvature and Refraction:

   • Purpose: Adjustments are made for the Earth's curvature and atmospheric refraction when performing large-scale or geodetic surveys.
   • Process: These adjustments correct for errors caused by the bending of light and the Earth's curvature. For long-distance surveys, specific correction formulas are applied based on the observed distance and the height of the instrument.