Abstract:
Discovering changes in the earth's environment can require tedious and time-consuming chemical, sedimentological and paleoentological analyses. This is especially true in modern paleoclimatic work, where the quest for understanding climate change requires closely spaced samples from high sedimentation rate areas. In the classroom, this problem is often handled by providing completed data sets. Nevertheless, the thought of years of time spent in labor-intensive data generation can be discouraging to students, especially those with a mathematical orientation to doing science; an orientation which needs to be encouraged in the earth sciences. In the research environment, one approach to the need for high sample density is the use of remotely sensed sedimentological data. Examples of these types of approaches include use of borehole logs and closely spaced geophysical measurements on cores. Today, it is rare for marine cores to be taken without some form of whole or split core measurements, such are magnetic susceptibility or reflectance. In fact, all Ocean Drilling Program Cores (ODP) are now logged using both whole and split core analysis tracks. Unfortunately, students usually do not have an intuitive feel for these measurements and their. Examining cores and core photos along with remotely sensed data helps to make this transition. We have developed a classroom exercise that helps students to make connections between cores, discrete core analyses and geophysical core measurements and to use these measurements to interpret paleoceanographic changes. The exercise also provides an opportunity for students to master quantitative skills in an accessible environment. This is done with grayscale analysis of ODP core photographs. The analysis is accomplished using NIH Image (http://rsb.info.nih.gov/nih-image) and Microsoft Excel. The data, once generated is compared and contrasted with ODP shipboard data (whole-core and discrete samples, http://www.oceandrilling.org). The data sets can be used in several exercises including, interpreting depositional environments, time-series analysis, proxy measurements for sedimentological and statistical measurements.