Paper No. 8
Presentation Time: 8:00 AM-6:00 PM
A FRAMEWORK FOR BUILDING QUANTITATIVE SKILLS AND FIELD EXPERIENCE IN NEAR-SURFACE GEOPHYSICS BY INCORPORATING MULTIPLE TECHNIQUES AND INSTRUCTIONAL METHODS
The need for geoscience curricula that emphasizes both quantitative skills and knowledge of how to carry out field work effectively has become increasingly apparent in today’s job market. There has been a paradigm shift within engineering and geologic firms towards recognizing the importance for all their employees to become integrated in the process of planning and executing data collection and processing stages of each project. It is the responsibility of the college/university to evolve with the demands of industry to ensure their graduates are not only knowledgeable of the subject matter, but proficient in the skills demanded of them to be more marketable. This paper presents the framework of The University of Tennessee’s contribution towards meeting this need. The Tennessee Intensive Near-surface Geophysics Study (TINGS) program is a three week Monday-thru-Friday (9am-5pm) course that introduces multiple near-surface geophysical techniques and allows the student to become familiar with the theory behind each technique, gain experience operating the geophysical equipment, and be trained in the software packages specific to each technique by processing their own data. Individual students, as well as the overall effectiveness of the program, are assessed by means of a comprehensive final project where all associated data sets are correlated together to discriminate types of subsurface features and targets that are present at the experimental field site. Emphasis is placed on proper survey design and working in a team environment to implement the plans successfully. Additionally, the types of errors associated with geophysical surveys are discussed, leading to an understanding of the importance in the development of a quantitative data integration methodology for improving subsurface imaging and reducing uncertainty in discrete anomaly detection.