• Geophysics and Geophysical Exploration
• /
• v.18 no.4
• /
• pp.223-231
• /
• 2015

Self-potential (SP) is sensitive to groundwater flow and there are many causes to generate SP. Among many mechanisms of SP, pore-fluid flow in porous media can generate potential without any external current source, which is referred to as electrokinetic potential or streaming potential. When calculating SP responses on the surface due to geothermal fluid within an engineered geothermal system (EGS) reservoir, SP anomaly is usually considered to be generated by fluid injection or production within the reservoir. However, SP anomaly can also result from geothermal water fluid within EGS reservoirs experiencing temperature changes between injection and production wells. For more precise simulation of SP responses, we developed an algorithm being able to take account of SP anomalies produced by not only water injection and production but also the fluid of geothermal water, based on three-dimensional finite-element-method employing tetrahedron elements; the developed algorithm can simulate electrical potential responses by both point source and volume source. After verifying the developed algorithm, we assumed a simple geothermal reservoir model and analyzed SP responses caused by geothermal water injection and production. We are going to further analyze SP responses for geothermal water in the presence of water production and injection, considering temperature distribution and geothermal water flow in the following research.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.933-948
• /
• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.