Assistant Researcher at Chengdu Center, China Geological Survey, China
Chen Hao is an Assistant Researcher at the Chengdu Center, China Geological Survey (Geoscience Innovation Center of Southwest China), specializing in electromagnetic geophysics with a focus on magnetotelluric (MT) data processing. His work addresses the development of high-precision impedance estimation methods, noise suppression strategies, and data quality evaluation frameworks for subsurface conductivity mapping. He has made significant contributions to advancing MT methodology, particularly in refining preprocessing techniques and formulating objective criteria for data quality assessment. His research is widely cited in the field and continues to shape practices in geophysical exploration and electromagnetic data interpretation.
Profile
Orcid
Education
Chen Hao holds a doctoral-level education in geophysics, with specialization in magnetotelluric methods and electromagnetic induction theory. His academic training focused on applying physical principles to analyze natural-source electromagnetic field data, enabling the development of innovative processing techniques. His doctoral thesis introduced a new MT data quality assessment framework that integrates phase differences and linearity metrics to categorize data types, forming the foundation of his future research trajectory. This educational background provides the theoretical rigor and analytical depth that underpin his contributions to geophysical signal analysis and inversion.
Experience
Professionally, Chen Hao has extensive experience working on both theoretical and field-based geophysical research. At the China Geological Survey, he has applied advanced MT methodologies to large-scale surveys, focusing on improving the quality and interpretability of electromagnetic data in complex geological environments. His completed project on “Magnetotelluric Data Noise Suppression and Quality Assessment” contributed a novel preprocessing framework that minimizes the need for high-quality datasets by introducing quantitative evaluation metrics. He is currently investigating MT data variability in response to geomagnetic storms, aiming to build real-time monitoring tools for space weather using geophysical measurements. His hands-on experience with time-series analysis, noise diagnostics, and impedance estimation techniques positions him as a methodological innovator in the domain of electromagnetic surveys.
Research Interest
Chen Hao’s primary research interests lie in magnetotelluric signal processing, time-series noise suppression, and the development of quality-driven inversion techniques. His work emphasizes understanding non-stationary noise in MT data and applying statistical and physical diagnostics to improve reliability. He is particularly interested in integrating linearity, phase differences, polarization direction, prediction errors, and hat matrix elements to create a multi-parameter MT data evaluation framework. His current research explores the relationship between MT signal integrity and geomagnetic activity, linking geophysics with space weather monitoring. His innovations continue to enable more consistent and objective MT processing workflows, especially in data-limited or noise-prone environments.
Award
Although he has not yet received formal awards, Chen Hao is a deserving nominee for the Best Researcher Award due to his impactful scientific contributions, rigorous methodology, and peer-reviewed publications. His quality assessment framework and its application in MT signal preprocessing have already influenced data processing practices in geophysics. His growing recognition within the scientific community is evidenced by the citation of his work in prominent journals. This nomination reflects his commitment to scientific advancement and his potential as a leader in electromagnetic geophysical research.
Publications
Chen Hao has authored several high-quality, peer-reviewed articles in SCI-indexed journals, each contributing to the development of MT processing techniques:
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Chen, H., Mizunaga, H., Tanaka, T. (2022). Influence of geomagnetic storms on the quality of magnetotelluric impedance. Earth Planets Space, 74, 1–17. (Cited by 10 articles)
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Chen, H., Zhang, L., Ren, Z., Cao, H., Wang, G. (2023). An Automatic Preselection Strategy for Magnetotelluric Single-Site Data Processing Based on Linearity and the Polarization Direction. Frontiers in Earth Science, 11, 1230071. (Cited by 7 articles)
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Chen, H., Zhang, L. (2025). Assessing Magnetotelluric Data Quality Based on Linearity and Phase Differences. Geophysics, 90: E79-E90. (Cited by 3 articles)
These works provide robust methodologies for MT data assessment and preprocessing, combining theoretical modeling with empirical validation, and have been cited in related geophysical literature.
Conclusion
Chen Hao exemplifies excellence in geophysical research through his integration of electromagnetic theory, statistical analysis, and computational methods. His innovations in MT data processing have improved signal reliability, optimized impedance estimation, and set new standards for data quality evaluation. His research has already influenced academic practices and offers substantial potential for future applications in resource exploration and environmental monitoring. With a growing body of influential publications, a clear research focus, and strong methodological contributions, Chen Hao stands out as a promising early-career researcher in geophysics. His nomination for the Best Researcher Award is a recognition of both his current impact and his potential for continued scientific leadership.