<p>High-precision geophysical observation data is the basis of inversion interpretation. Field geophysical instruments (such as seismic geophones, electromagnetic sensors, logging probes) are affected by temperature, humidity, drift and calibration process, which will produce systematic errors and affect data quality and geological interpretation reliability. In this paper, an error correction method based on multi-model comparison strategy is proposed to correct the indication of single observation element. The adaptive multimodel fusion strategy is systematically introduced into the quality assurance of geophysical instrument data for the first time, which provides a new tool for improving the reliability of original data. By establishing linear regression model, polynomial model, random forest model and adaptive modified linear regression model, the performance of each data set in the model is tested, and the optimal model is selected for data correction. Additionally, this research innovatively proposes an error correction method based on an adaptive correction strategy for humidity observation elements. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²) are used as quantitative evaluation metrics. The experimental results show that the data quality is significantly improved after correction, effectively addressing the data deviation between observed values and true values at the provincial metrological verification level during equipment operation. After the correction, errors are reduced and the data quality is improved, which is of great significance for weather forecasting, research and applications. At the same time, this method can also be applied to the calibration of geophysical instruments or the error correction of observation data, which has certain reference value for improving the original quality of geophysical data and reducing the multi-solution of inversion.</p>