Advancing understanding of Earth's subsurface
Our work spans exploration geophysics, energy resources, and computational methods
Development and application of magnetotelluric methods for deep subsurface imaging. Our work focuses on 2D and 3D MT inversion, data processing techniques, and joint interpretation with other geophysical datasets. Key applications include geothermal exploration in Northern Thailand and mineral resource assessment.
Characterization of geothermal systems using electromagnetic methods. We work on identifying subsurface fluid pathways, mapping thermal structures, and estimating reservoir properties. Active field sites include Fang, San Kamphaeng, and Mae Chan geothermal areas in Thailand.
Application of electrical resistivity and self-potential methods for dam seepage detection and structural integrity assessment. We develop time-lapse monitoring protocols for earth-fill dams, combining geophysical data with hydrological models to assess dam safety.
Machine learning applications for geophysical data analysis, including automated well-log interpretation, facies classification, and data-driven inversion approaches. We develop Python-based tools that bridge traditional geophysics with modern computational techniques.
Gravity data processing and interpretation for structural geology and basin analysis. Integration of gravity data with MT and seismic data for comprehensive subsurface models.
Development of low-cost geophysical instruments for education and field research, including the EarthExplorer DC resistivity system. Open-source hardware and software for accessible geoscience education.
Current funded research and collaborations
National Research Council of Thailand funded project to characterize deep geothermal reservoirs using broadband magnetotelluric surveys. Includes field campaigns, 3D inversion, and resource assessment.
Collaboration with the Department of Groundwater Resources on electromagnetic surveying and MT data portal development for groundwater resource mapping in Northeast Thailand.
Industry-academia partnership developing computational geoscience curriculum. Python-based workshops covering data analysis, machine learning, and AI-assisted coding for petroleum geoscientists.
Development of a low-cost, open-source DC resistivity instrument using LoRa wireless communication and ESP32 microcontrollers. Designed for geophysics education and field surveys.