Much research on karst geological exploration using geophysical methods has been carried out, and valuable results and conclusions have been obtained. Various geophysical methods have played a critical role in the forecast and early warning of karst disasters from different fields, such as electrical, magnetic and well logging (Chatelier et al. 2011). Given different engineering and geological conditions, the selection, combination and survey lines layout of exploration methods are designed.
3.1 Method selection
The upper stratum of this section is filled with soil, silty sand, pebble and silty clay. The bedrock is mainly dolomitic limestone, sandstone and conglomerate. The karst caves are mainly developed at the cracks of the bedrock surface, and on a large scale, covered by thick Quaternary sediments. It is difficult to accurately detect karst distribution in the thick coverage area. The reason is, firstly, the thick Quaternary sediments have a strong absorption and attenuation effect on the reflection wave, which is not conducive to receiving the effective reflected signal in the stratum; secondly, in the process of Quaternary sedimentation, the sedimentary conditions are complex. There are many geological phenomena such as interlayer and interbedding, and it is easy to interfere with each other in the detection process of different stratum interfaces. GPR is a high-precision method in shallow subsurface detection fields. The geological structure changes are reflected as the opposite and weakening of reflected wave in-phase axis in GPR signal receiving (Cassidy et al. 2011; Liu et al. 2020). When air-filled cavities appear in the stratum, the dielectric constant is small, electromagnetic waves decay fast, and high resistivity will appear. When there is a water-filled cavity, its dielectric constant is generally large, the propagation speed of electromagnetic waves in water is fast, and the relative resistivity is low. It is difficult to determine the deep karst distribution and geological structure under thick overburden by GPR alone. Therefore, in the detection of thick Quaternary sediments area, it is not only required to use high-resolution GPR, but also need to adopt a step by step detection strategy corresponding to the depth to find out the distribution of karst area from shallow to deep.
When the filling in the cavity has good water permeability, there is an obvious difference between the resistivity of the filled cavity and the surrounding rocks. The electrical resistivity is low, especially when the cavity is filled with water (Yao et al. 2012). The resistivity is generally 20–100 Ω·m. Bedrock with good compactness typically has high resistivity value, mostly above 2000 Ω·m. So, the resistivity methods, such as surface ERT and TEM, based on electrical differences has been adopted to detect karst distribution. These methods are sensitive to low resistivity anomalies. Surface ERT has a noticeable detection resolution on mid-deep strata, but it is hard to identify the location of deep karst caves accurately (Gan et al. 2020). TEM has the ability to penetrate thick Quaternary sediments with low resistivity, and has a good detection result on deep strata (Huang et al. 2010). Surface ERT and TEM can obtain the distribution information and characteristics of formation resistivity on subway lines.
According to the analysis of known karst cave characteristics and geological data in the study area, the deepest buried depth of karst cave roof is 45 m. The karst caves are widely distributed. Therefore, the multi-depth and refined detection method for karst geology under thick Quaternary sediments used three methods: GPR, surface ERT and TEM. The three methods are combined and comprehensive interpretation to obtain richer stratum information and anomalous body response characteristics. The relationship and implementation process of each method are shown in Fig. 2.
Figure 2 Schematic of the technology roadmap multi-depth and refined detection method of karst features under thick Quaternary sediments of subway lines
3.2 Method and principle
The principles, application conditions, survey lines layout, detection depth and resolution of surface ERT, TEM and GPR are different. The principles and field implementation of these three methods are introduced. The principles and field implementation of the three methods are described below.
3.2.1 GPR method
GPR is an efficient shallow subsurface geophysical exploration technology, which adopts the difference of electrical parameters of underground media to infer media structure and physical properties by transmitting high-frequency electromagnetic pulse waves with a frequency of 106–109 Hz. With the continuous development of microelectronics and signal processing technology, GPR is widely used in many fields, such as geological engineering survey, building structure survey, highway engineering quality detection, underground pipeline detection, etc. (Demirci et al. 2012; Alani et al. 2013).
The basic principle of GPR is shown in Fig. 3. The transmitting antenna directionally sends the high-frequency short-pulse electromagnetic wave into the ground. When the electromagnetic wave encounters the stratum or target with electrical differences in the propagation process, it will be reflected and transmitted. The receiving antenna receives the reflected wave signal and digitizes it, which is then recorded by a computer as a reflected waveform (Wang 1993). The collected data are processed by direct wave removal, band-pass filtering and background removal. The spatial position, structure and distribution of underground targets can be realized according to the kinematic and dynamic characteristics such as propagation time, amplitude, waveform and frequency of a reflected wave. GPR delineates the underground targets based on the analysis of reflection waveform characteristics. As a result, its detection resolution mainly depends on the electrical difference between the underground target and the surrounding medium, the attenuation degree of electromagnetic wave, the buried depth of the targets, and the strength of external interference.
Figure 3 Schematic diagram of GPR method
3.2.2 Surface ERT method
Surface ERT is a resistivity exploration technology proposed in the 1980s (Dong and Wang 2003). Based on the difference of electrical parameters between the surrounding rock and water-bearing geological structure, surface ERT infers geological conditions and resistivity distribution in the detection area according to the distribution law of conduction current field in surrounding rock under the action of an applied electric field. Surface ERT has the advantages of sufficient data, low expense, little field interference, high work efficiency and convenient interpretation corresponding to the resistivity cross-sections. It is mainly used to detect the water content of the geological body, bedrock buried depth, concealed structure, fault, fracture zone, etc., and has achieved apparent geological effect and remarkable socioeconomic performance (Chen et al. 2017). However, it is difficult to carry out quantitative interpretation, which is greatly affected by topographic relief, and it is not convenient to employ in the exposed bedrock area.
The implementation of surface ERT is to arrange a certain number of electrodes in the detection area. The apparatus automatically supply direct current (A and B electrodes) according to a certain sequence, and measure the potential difference between the two electrodes (M and N electrodes), to calculate the apparent resistivity profiles (Dong and Wang 2003). Through the inversion of the apparent resistivity profile, the resistivity distribution of surrounding rock is obtained. The basic principle of surface ERT is shown in Fig. 4. In the field observation, all electrodes are arranged at one time. The intelligent electrode converter and measuring instrument are used to realize the full-automatic data acquisition. The collected data are processed by computer, generating the resistivity section.
Figure 4 Schematic diagram of the surface ERT method, electrode arrangement and data processing flowchart
3.2.3 TEM method
TEM method is a time-domain electromagnetic method that employs the ungrounded loop to emit a pulsed magnetic field in front of the working face. When the current in the transmitter loop is suddenly switched off, the medium will induce the secondary magnetic field to maintain the primary field generated when the current is connected. The magnitude and attenuation characteristics of the secondary magnetic field are related to the electrical distribution of the surrounding medium. The variation characteristics of the secondary field with time are observed intermittently in the primary field. After processing, the electrical properties, scale, and occurrence of the underground medium can be understood to delineate the targets, as shown in Fig. 5 (Xue et al. 2007). TEM is employed to directly observe the secondary magnetic field, which greatly reduces the detection difficulty of the abnormal body, and its detection effect and ability have been greatly improved. It has the advantages of small effect, high vertical and horizontal resolution, sensitive response to low resistivity targets and fast arrangement. It is widely used in various engineering geophysical exploration. It is an ideal detection method to realize the detection of rich-water areas and engineering hydrogeology (Lin et al. 2016).
Figure 5 Schematic diagram of the TEM method
3.2.4 PCA data fusion
Data fusion combines data information from different sources (spatially or temporally redundant or complementary) to obtain a consistent interpretation or description of the detected target. In geosciences, many researchers favour PCA due to its advantages in extracting the main components of overlapping information. The PCA method was pioneered in 1901 and is widely used in image enhancement, image fusion, signal compression, and noise removal (Bajorski 2011; Hansen et al. 2014; Chen et al. 2020). Subsequently, PCA was applied to geophysical data fusion. Yin et al. (2008) used a nonlinear kernel function for the dimension reduction optimization of seismic data, achieving good data extraction performance. Lu (2018) denoised the airborne electromagnetic data through PCA, which enhanced the resolution of airborne electromagnetic exploration of deep underground anomalies. Therefore, the usage of PCA is based on the idea of dimension reduction. The purpose of resistivity data fusion is to combine the advantages of different methods in exploring different depths of the formation. On this basis, unified imaging results containing comprehensive information can be obtained through PCA fusion processing, thereby improving the utilization of geophysical data.
Based on the PCA transformation with an orthogonal linear relationship of information content, the data are projected into a new coordinate system using the linear projection method. The new component vector is distributed according to the information content, and the first principal component contains the most extensive information. The basic idea is to synthesize p observation variables into p new variables. For the p-dimensional sample observation data x, the PCA transformation process mainly includes standardization processing, calculating the sample correlation coefficient matrix, calculating the eigenvalues and eigenvectors of the correlation coefficient matrix by Jacobian method, and selecting important components. The corresponding eigenvector is the new coordinate base. In an actual analysis, not all p principal components are selected; instead, the first k principal components are selected according to the cumulative contribution rate (CCR) of each principal component. This contribution rate is defined by the eigenvalues λ in Eq. 1:
A large contribution rate indicates that the corresponding principal component contains critical information regarding the original variable. Generally, the CCR of more than 85% is the threshold for selecting the principal component to ensure that the comprehensive variable includes the most information about the original variable. Finally, the p-dimensional vector is mapped to the selected k-dimensional coordinate space.
3.3 Numerical simulation analysis
In order to verify the response of GPR, surface ERT and TEM to stratigraphic boundary and anomalies, the corresponding geoelectric models were established for numerical simulation according to the drilling information. The area of the survey line (0–560 m) corresponds to the surface ERT model. The area enclosed by red the dashed box (265–294 m) in the survey line was used as the forward model of the GPR and TEM (Fig. 5). The parameters of the stratigraphic type, boundary depth, relative dielectric constant , and resistivity of each layer are shown in Table 1 according to the different layers from top to bottom.
Table 1 Numerical simulation model parameters
Table 1 Numerical simulation model parameters
Figure 6 Model of numerical simulation
In the GPR forward model, the excitation source was the Ricker wavelet with a dominant frequency of 100 MHz, and the sampling time window was 1100 ns. The interface depth can be calculated using the wave velocity and two-way travel time data of the reflected wave in the medium (Schultz 1982; Liu et al. 2018). The corresponding exploration depth is about 15 m. The grid spacing used was Δx = 0.05 m and Δy = 0.05 m. The transceiver antenna spacing was 0.5 m. The common offset was 0.5 m, and 56 records were generated. The waveform results after the direct wave removal, band-pass filtering, background removal and other processing is shown in Fig. 7. The figure shows that the shallow stratum (0–15 m) interfaces were well-reflected and high detection resolution. The deep stratum (15–65 m) reflected wave was weakened, and poor identification ability. Due to the little difference in the relative dielectric constant in the three stratums of 16–40 m, increasing depth, and the attenuation of electromagnetic waves, the deep stratum and karst cave's resolution is inaccurate. Therefore, GPR can detect cavities and fractures in near-surface anomalies.
As shown in Fig. 6, surface ERT forward modelling was conducted. The Wenner-Schlumberger array was used for the forward calculation. The total number of electrodes was 141, the unit electrode spacing was 4 m, and the horizontal survey line length was 560 m. The apparent resistivity data obtained by forward modelling were inverted. The inversion profile of the surface ERT was obtained through conventional processing (see Fig. 8). The figure exhibits that surface ERT has four interfaces at a depth of 10, 16, 35, 47 meters. As the first three layers resistivity is relatively smaller than that of other stratums, the inversion result of these three stratums shows low resistivity and no interface response. However, it can be seen that the distribution of formation resistivity changed from low-high-low-high. The position of the formation interface could be roughly identified, as it was affected by gradual changes in the resistivity contour.
TEM method adopts a central loop. The equivalent radius of the transmitter loop was 30 m, the receiving loop was 1 m, and the number of coil turns was 10. The simulation result is shown in Fig. 9. The TEM response to the low resistivity anomalies is obvious in the figure. There is an apparent low-resistivity anomaly at 41–46m, and the change of resistivity is consistent with the model parameters. Due to the delay effect of transient electromagnetic, the shallow resolution is low, and it isn't easy to distinguish the specific horizon within a certain depth range (0–20m). Due to the delay effect of TEM, the near-surface resolution is low, and it is difficult to distinguish the specific layers within 0–20m.
Figure 7 Numerical simulation of eight stratums using GPR
Figure 8 Numerical simulation of eight stratums using surface ERT
Figure 9 Numerical simulation of eight stratums using TEM
According to the detection results, PCA is performed on the inversion resistivity data of the overlapped part of the survey line, and the fusion result is shown in Fig. 10. Compared with the TEM inversion result, the near-surface resolution of the fused image is improved, and the detection resolution of the deep karst cave is retained. Two low resistivity areas shown in the depths of 4–8 m and 41–46 m.
Figure 10 Cross-section after TEM and surface ERT inversion resistivity data fusion