Search for: All records

Title: Efficient Access and Manipulation of Big Seismic Data from Disparate Sources Seismological data recordings have been growing exponentially in the last three decades. For instance, the data archive at the IRIS Data Management Center (DMC) grew from less than 10 Tebibytes in 1992 to greater than 750 Tebibytes today (in 2022). In addition to such big data archives, retrieving and merging data from various disparate seismic sources also creates big data which will enable obtaining higher-resolution seismic images and understanding phenomena such as earthquake cycles. Moreover, recent progress in geosciences with the application of AI/ML using big data has shown the potential of discovering patterns that were not previously recognized. However, aggregating large seismic datasets introduces its own challenges. Some of these challenges arise from the fact that many data centers have their own way of distributing data, and the format of the data and metadata are different in many cases. The objective of this investigation is the development of data access and manipulation tools for retrieval, merging, processing, and the management of big seismic data from disparate seismic data sources. We develop a free, open-source, direct data accessing, gathering, and processing software toolbox for disparate sources using Python. Aggregating data from different data centers will enable us to investigate the seismic structure beneath a region of interest at a higher resolution by merging the seismic databases. Such a merged dataset can be applied on studies around the boundaries between countries if those countries have different networks. One boundary region of geologic interest to exemplify the benefits of aggregated seismic datasets from different networks in two countries is the southern side of the Rio Grande Rift including the bordering areas between the US and Mexico. Previous more detailed seismic studies on Rio Grande were conducted mostly on the US side of the Rift Valley. 

The COVID-19 pandemic forced many colleges and universities to remain on a completely online or remote educational learning for more than a year; however, due to distraction, lack of motivation or engagement, and other internal/external pandemic contributing factors, learners could not pay attention 100% to the learning process. Additionally, given that transportation classes are very hands-on, students could not do the experiment from home due to limited resources available, thereby hampering all three phases of learner interactions. The limitation of the implementation of physical, hands-on laboratory exercises during the pandemic further exacerbated students’ actualization of the critical Accreditation Board for Engineering and Technology (ABET) outcomes in transportation: An ability to develop and conduct experiments or test hypotheses, analyze and interpret data and use scientific judgment to draw conclusions. Subsequently, this paper highlights the development and implementation of experiment centric pedagogy (ECP) home-based active learning experiments in three transportation courses: Introduction to Transportation Systems, Traffic Engineering, and Highway Engineering during the pandemic. Quantitative and qualitative student success key constructs data was collected in conjunction with the execution of classroom observation protocols that measure active learning in these transportation courses. The results reveal a significant difference between the pre, and post- tests of key constructs associated with student success, such as motivation, critical thinking, curiosity, collaboration, and metacognition. The results of the Classroom Observation Protocol for Undergraduate STEM (COPUS) show more active student engagement when ECP is implemented.