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To achieve comprehensive analyses, the presentation of comprehensive geophysical results usually involves the use of separate imaging and the combination of various results. At present, few studies have considered the correlation degree and unified imaging of different types of geophysical data. We establish a set of data fusion imaging methods for multiple geophysical data based on their reflection coefficients. As geophysical exploration results are primarily provided through waveform and resistivity sections, waveform and resistivity data were selected for fusion and were converted into reflection coefficients, and ground-penetrating radar (GPR) and surface electrical resistivity tomography (ERT) were taken as examples. Re-sampling and feature reconstruction were performed to unify the data in space and resolution. Finally, principal component analysis was used to calculate the correlation of the reconstructed reflection coefficient and to perform data fusion; this led to unified imaging based on the reflection coefficient of the considered geophysical data. Numerical simulation analyses and field experiments proved the efficacy of this method for producing unified imaging of multiple geophysical data. In summary, we provide a novel method for the unified interpretation of multiple geophysical data and enhance the identification ability of geological interfaces and anomaly distribution. [ABSTRACT FROM AUTHOR]

Electrical resistivity tomography (ERT) is a widely used method in geophysical prospecting. In practical applications, the smoothness-constrained least squares method is usually used to invert the apparent data of ERT. Although the resistivity distribution in the detection area can be obtained according to the inversion result, it is difficult to resolve the specific boundary of anomalies directly, which will affect the accuracy of the interpretation results of the detection data. This paper proposes a high-resolution imaging and interpretation method for ERT based on reflection coefficient analysis. This method can amplify the data features at the boundary of anomalies by performing the secondary processing and analysis on inversion results, thereby identifying the specific boundary of anomalies. This research improves the accuracy of interpretation results and realizes high-resolution imaging. Compared with the multi-scale inversion and other methods that enhance the resolution by optimizing the inversion algorithm, this method can more intuitively determine the specific boundary of anomalies in the inversion cross-section. [ABSTRACT FROM AUTHOR]

During tunnel construction, large groundwater influx poses serious safety hazards. To ensure construction safety, effective measures such as early warning, grouting, and framework support are needed to counteract potential water hazards. It is also critical to clarify the spatial location and size of the water-bearing geological structures before construction to avoid catastrophe. Electrical resistivity tomography (ERT) operates at a high resolution and is sensitive to low-resistivity targets such as water. Thus, it has strong application potential for the detection of karst geomorphology around tunnel construction. However, the use of a single-ERT method may not be sufficient to accurately locate the spatial distribution of karst features. Therefore, it is necessary to carry out multiple-ERT data fusion to generate a comprehensive interpretation of karst geohazards. According to the spatial distribution of different karst caves, we established four geoelectric models for numerical simulation based on the method of principal component analysis. Studies have shown that data fusion is superior to conventional comparative analysis of geophysical data results and creates a more accurate comprehensive description of the same target. For example, inversion data fusion improves poor imaging of the cross-hole ERT near the borehole and reduces the distribution of pseudo-anomalies near the electrode. In this study, the vertical resolution of the surface ERT, and the horizontal resolution of the cross-hole ERT were both improved, while the imaging ability of the borehole-surface ERT in the peripheral low-sensitivity area was enhanced. Moreover, a three-dimensional (3D) conversion method of two-dimensional (2D) detection data in the tunnel environment was employed. An interpolation 3D-imaging of the 2D detection data was carried out, rendering the interpretation of the results more intuitive and comprehensive. Finally, we applied this method in the Daba Tunnel Water Governance Project in Hunan Province, China, and demonstrated that it could overcome the limitations of the single-ERT survey. We were able to accurately delineate the distribution range of the main water-rich bodies around the tunnel. Our findings provide key guidance for the successful implementation of tunnel water-inrush treatment. • Fusion of multi-resistivity methods improves karst feature detection. • 3D imaging conversion is realized for tunnel environment interpretation. • The proposed method overcame the defects of single-ERT imaging. • Distribution range of water-rich bodies was more accurately delineated. • The method is useful for effectively determining and treating groundwater influx. [ABSTRACT FROM AUTHOR]