Aggregate resources are products of geologic processes like erosion and deposition. In Minnesota, the large continental glaciers and the geologic processes related to glaciation produced the state's sand and gravel deposits. To identify where economic aggregate deposits are located within a county, it is necessary to understand geologic processes that create landforms and the types of sediment within a landform. This type of analysis is known as a landform-sediment association.
For example, glacial landforms, called eskers, are observed throughout the state. Eskers are deposited as meltwater flows in, on top of, or below glacial ice. When the ice melts, meltwater stream sediments are deposited as a long sinuous ridge containing sorted sand and gravel. This landform can then be identified on a topographical map or on an aerial photograph as being a landform with the potential to contain sand and gravel. Unfortunately, not all landforms are this easy to interpret, but eskers serve as a good example to the landform-sediment association used to map aggregate resources.
The Aggregate Resource Mapping Program uses traditional geologic mapping techniques to interpret landforms, such as conducting fieldwork and drilling, integrated with computer software technologies, such as Geographic Information Systems or GIS.
In general, there are three phases of work in aggregate resource mapping: collecting existing information, field work and gathering new geologic data, and identifying and classifying aggregate resources.
Gathering existing data is the first step in the process to classify aggregate resources. Both literature and data searches are conducted to find existing information about construction aggregate resources of the area as well as compiling various digital datasets. The data compilation includes aerial photographs, topographic maps, digital elevation models, shaded relief, subsurface data, gravel pit and quarry data, surficial and bedrock geology, soils, wetlands, lakes, and streams.
Subsurface data used to classify aggregate resources include the County Well Index (CWI) database and the Aggregate Source Information System (ASIS). CWI is an online database maintained by the Minnesota Geologic Survey (MGS) in conjunction with the Department of Health. CWI contains basic information for over 300,000 wells drilled throughout Minnesota. ASIS is maintained by the Minnesota Department of Transportation. ASIS records information related to aggregate quality, textural (i.e. sieve or sediment size analysis) data, pit sheets, and diagrams of test hole locations. Subsurface information is helpful to identify thick aggregate deposits, buried sand and gravel deposits, and identifies the depth to bedrock.
Once all this information is compiled, a computer software program called ArcGIS Desktop is used as a tool to assists with geologic interpretation, and further data compilation summarization. Compiled information is incorporated into the development of a working geologic history and aggregate classification scheme for the county.
Fieldwork is an important step in aggregate mapping. This is where geologists drive every accessible road in the county observing the geology by looking at exposures of sediment and rock, observing the deposits currently being mined at gravel pits and quarries, and talking to landowners and gravel pit operators. Sediments and rock exposed in road cuts , stream exposures, excavations, judicial ditches, construction projects, trenches, and even animal holes are used as observation sites. The distribution of where sand and gravel was directly observed provides a confidence when determining where significant aggregate exists. Where further data is needed, a follow up drilling program is conducted to determine the extent and thickness of the observed deposits. Finally, talking to land owners and aggregate mine operators is another important source of information for understanding the extent and quality an aggregate deposit.
Identifying and Classifying Aggregate
All the existing and newly gathered information described above is used to associate the observed sediments to glacial landforms and landform systems.
Using all available information, landforms are classified by:
- the sediments typically found with in a landform,
- what is the probability, or confidence, that sand and gravel exits in the landform,
- how thick is the sand and gravel,
- how much overburden is on top of the deposit,
- what is the areal extent of the deposit,
- what are the textural characteristics of the deposit, also indicating how much coarse material (gravel) verses fine material (sand), and
- what is the quality of the sand and gravel and does it meet MN/DOT specifications.
By looking at multiple criteria, sand and gravel deposits are then classified into four categories : high, moderate, low, and limited aggregate potential, where potential is defined as-
An assessment of the relative probability that an aggregate deposit exists within a given area, with almost all emphasis placed upon geologic evidence, parameters, and interpretation at a reconnaissance* level.
*1:50,000 scale for sand and gravel
*1:100,000 scale for crushed stone
A reconnaissance-level map at a scale of 1:50,000 is defined by a horizontal accuracy where 90% of well-defined features fall within 25 meters of their true position. A site-specific map at a scale of 1:5,000 should have features within 2.5 meters of the true position.
Sand and gravel may not be found everywhere within a landform, but the designated potential defines the probability that sand and gravel exists somewhere within the landform.
With crushed stone, the evaluation of the aggregate potential depends on the bedrock geology, how much information is available to determine depth to bedrock, and the thickness of glacial sediment over bedrock. Classification of crushed stone is also categorized into high, moderate, low and limited aggregate potential.
The process of determining depth to bedrock uses computer modeling. Data from the county well index indicates if bedrock is encountered within the well. This information is used to model the elevation of the top of the bedrock resource, which is then subtracted from the elevation of the surface. The result is a model of the overburden thickness. A geologist interprets the model and classifies the crush stone potential.