Electrical resistivity tomography (ERT) is one of the most widely used geophysical methods in geological, hydrogeological, and geo-environmental investigations. Although 3D ERT is now available, 2D ERT remains state-of-the-practice due to its simplicity in fieldwork and lower space requirements. 2D ERT assumes that the ground condition is perpendicular to the survey line and outside the survey line is homogeneous. This assumption can often be violated in conditions such as geologic strikes not perpendicular to the survey line and topographic changes or buried objects near the survey line. Possible errors or artifacts in the 2D resistivity tomogram arising from violating the 2D assumption are often overlooked. This study aimed to numerically investigate the boundary effects on 2D ERT under various simplified conditions. Potential factors including resistivity contrast, depth and size of buried objects, and electrode spacing were considered for the parametric studies. The results revealed that offline geologic features may project onto the 2D tomogram to some extent, depending on the aforementioned factors. The mechanism and implications of boundary effects can be drawn from these parametric studies.

In the past decade, the 2D electrical resistivity tomography (ERT) has been extensively used in the investigation and monitoring of geotechnical engineering and environment engineering, but there are many uncertainties hidden behind its vivid color earth-resistivity profiles. In order to use the 2D ERT in the scale of geotechnical engineering effectively, the accuracy and spatial resolution capability of measurements must be enhanced, or at least these uncertainties should be mastered to avoid overreading the measurement results. There were seven common geological models built in this study to discuss the variance in spatial analysis capability of 2D electrical resistivity profiles under different geologic conditions. The findings show that the resolution capability of 2D electrical resistivity profiles was influenced by depth, and in different strata, it may be influenced by the resistivity ratio, layer depth, covering depth, interlayer thickness, tilt angle, medium size, and noise intensity. Generally speaking, the relatively low resistance stratum had better resolution capability; if the relatively high resistance stratum was located under the relatively low resistance stratum, its resolution capability declined. In different strata, the resolution capability may be degraded under the effect of different factors. In addition, any noise in the course of measurement resulted in a random jump of the electrical resistivity profile, which worsened as the noise increased. These circumstances should be paid special attention to avoid misrecognition of electrical resistivity profile images.

Seawater intrusion is a major issue in coastal areas, and the traditional technology of investigation mainly makes use of drilling for water level observation and sample analysis of groundwater; as drilling data is an information of points, it will cost more if it is extended to be information on the surface. Non-destructive geophysical exploration technology has been used in the investigation of the underground environments at all kinds of contaminated sites in recent years. Seawater intrusion is also a groundwater pollution situation, and “Surface and Space” information can be obtained with a small amount of drilling data. This research will use Electric Resistivity Tomography as a geophysical exploration technology to assess the situation at the interface of seawater intrusion and to explore the dynamic changes of seawater intrusion through long-term monitoring. The test site of this research is situated in the seawater intrusion area along Kinmen Island. The research results showed that the front edge of the seawater intrusion could be detected with Electric Resistivity Tomography, and a delay effect existing between seawater intrusion and tidal action was observed by means of Time-lapse. If a long-term observation system is established in future, it could contribute to the confrontation of seawater intrusion and salinization phenomena.

Leakage is the most serious problem in reservoir operation, because heavy leakage influences reservoir impoundment efficiency and even leads to a complete loss of reservoir functions. Since its completion in 1982, Qionglin Reservoir has never been fully filled with water except in the spring of 1983 when there was heavy rain. The reservoir management unit suspected that its side slopes, bottom or dam might leak and carried out a number of leakage prevention and improvement works, but all of them failed to fulfill the impoundment function of the reservoir. Hence, this study attempts to find out the reasons why the reservoir cannot impound water. A series of tests and investigations are carried out in this study, including electrical resistivity tomography of dam, tracer test, geological drilling test, reservoir water level observation, investigation of reservoir catchment area and field investigation of dam. The test results and investigation results show that no leakage path and leakage are found. According to the analysis, there is no serious leakage of this reservoir. The main reason for the failure of impoundment is that massive improper development in the catchment area influences the runoff into the reservoir.

Kinmen Island was in a state of combat readiness during the 1950s–1980s. It opened for tourism in 1992, when all troops withdrew from the island. Most military installations, such as bunkers, anti airborne piles, and underground tunnels, became deserted and disordered. The entries to numerous underground bunkers are closed or covered with weeds, creating dangerous spaces on the island. This study evaluates the feasibility of using Electrical Resistivity Tomography (ERT) to detect and discuss the location, size, and depth of underground tunnels. In order to discuss the reliability of the 2D-ERT result, this study built a numerical model to validate the correctness of in situ measured data. In addition, this study employed the artificial intelligence deep learning technique for reprocessing and predicting the ERT image and discussed using an artificial intelligence deep learning algorithm to enhance the image resolution and interpretation. A total of three 2D-ERT survey lines were implemented in this study. The results indicate that the three survey lines clearly show the tunnel location and shape. The numerical simulation results also indicate that using 2D-ERT to survey underground tunnels is highly feasible. Moreover, according to a series of studies in Multilayer Perceptron of deep learning, using deep learning can clearly show the tunnel location and path and effectively enhance the interpretation ability and resolution for 2D-ERT measurement results.

In parameter estimation, geotechnical engineers are accustomed to provide their best estimates with engineering judgments. But the bounds or indication of the significance of their estimates are seldom given. It is difficult to routinely implement reliability-based design if gauging uncertainties of engineers' estimates is not mandatory or does not become a usual practice. The objective of this paper is to advocate the importance of gauging uncertainty in the course of parameter estimation. A framework for parameter estimation and uncertainty analysis incorporating engineering judgment is illustrated based on Bayesian inversion. The data uncertainty is considered by additive noise model and the degree of confidence of engineering judgment is accounted for by the prior information in the Bayesian paradigm. The information contained in the posterior distribution resulting from the Bayes theorem is interpreted by optimal estimator, uncertainty of estimation, and resolution operator. The advantages of Bayesian inversion over commonly-used least square method are illustrated by some simple examples.