Regions: Worldwide

Integrating Machine Learning Models for Predictive Analysis in Water Quality Assessment

10 June 20246:00 AM (CDT) The water quality project utilized the application of Python to analyze water quality datasets to assess potability. The datasets were obtained from the Kaggle data repository. The data obtained were various physical and chemical parameters associated with water quality such as pH, hardness, sulfate, chloramine, etc. The objective is to predict water potability, make informed decisions about water treatment processes, and ensure compliance with water quality standards. Machine learning techniques

Geophysical Monitoring of Permafrost

12 June 202410:00 AM (CDT) As the climate warms rapidly in the North, permafrost thaw is affecting infrastructure, landscapes, ecosystems, and carbon fluxes. Geophysical methods like electrical resistivity tomography (ERT) can be used to characterize permafrost conditions at the site scale and evaluate changes over time. This presentation will discuss recent advances in geophysical monitoring of permafrost, including time-lapse ERT datasets collected in northern Canada, the implementation of automated ERT systems, and the recent initiation

SEG Hydrogeophysics Webinar: So You Want to be a Practicing Near-Surface Geophysicist?

15 May 202410:00 AM (CDT) If you are studying and either love near-surface geophysics or think you could learn to love it and be paid for the privilege and want to pursue it as a career, you have two main choices. Both are admirable and rewarding choices, but this talk is about what it is like to be a practicing geophysicist, providing geophysical services, in government and particularly in a consulting environment. The world needs

Advancing Subsurface Exploration: Precision Fault Detection in Vertical Electrical Sounding Data Through Machine Learning Innovations

13 May 20247:00 AM (CDT) In near-surface geophysics, accurate fault detection in Vertical Electrical Sounding (VES) data is crucial for subsurface exploration and characterization. Traditional manual methods are time-consuming and subjective, necessitating automated solutions for enhanced efficiency and precision. This project aims to develop a novel approach leveraging machine learning techniques to automate fault detection in VES data. The project’s purpose is to streamline the fault detection process, enabling rapid and objective identification of subsurface

Landslide Hazards Assessment, Monitoring, and Modeling in Joshimath

15 April 20246:00 AM (CDT) The Uttarakhand region, nestled within the western Himalayas, grapples with the heightened risk of landslide hazards attributed to its seismically active nature. This study incorporates the assessment, monitoring, and modeling of landslide hazards in the Joshimath region, emphasizing its vulnerability due to seismic activity and geological fragility. With over 1100 landslide incidents reported in 2023 alone, and an annual occurrence of hundreds of landslides in the Joshimath area, the need