Fast imaging of subsurface conductors using very low‐frequency electromagnetic data

Abstract

The study presents a fast imaging technique for the very low‐frequency data interpretation. First, an analytical expression was derived to compute the vertical component of the magnetic field at any point on the Earth's surface for a given current density distribution in a rectangular block on the subsurface. Current density is considered as exponentially decreasing with depth, according to the skin depth rule in a particular block. Subsequently, the vertical component of the magnetic field due to the entire subsurface was computed as the sum of the vertical component of the magnetic field due to an individual block. Since the vertical component of the magnetic field is proportional to the real part of very low‐frequency anomaly, an inversion program was developed for imaging of the subsurface conductors using the real very low‐frequency anomaly in terms of apparent current density distribution in the subsurface. Imaging results from the presented formulation were compared with other imaging techniques in terms of apparent current density and resistivity distribution using a standard numerical forward modelling and inversion technique. Efficacy of the developed approach was demonstrated for the interpretation of synthetic and field very low‐frequency data. The presented imaging technique shows improvement with respect to the filtering approaches in depicting subsurface conductors. Further, results obtained using the presented approach are closer to the results of rigorous resistivity inversion. Since the presented approach uses only the real anomaly, which is not sensitive to very small isolated near‐surface conducting features, it depicts prominent conducting features in the subsurface.

Elevated Equilibrium Geotherm in Stable Continental Shield: Evidence From Integrated Structural, Hydrological, and Electromagnetic Studies on Nonvolcanic Hot Springs in the Eastern Ghats Belt, India

“…However, the main limitation of the above filtering approach is that it is difficult to estimate the actual depth as well as shape of the causative body; even vertical structures appear to be dipping in apparent current density cross sections. Singh and Sharma () compared the results of different qualitative and quantitative VLF interpretation techniques (e.g., Karous & Hjelt, ; Pedersen & Becken, ), and proposed a new resistivity inversion technique that yielded more reasonable results. Thus, in the present work, the Singh and Sharma () approach has been used to interpret the measured VLF data.…”

“…Raw (blue star with solid line) and processed (blue circle with solid line) VLF data are shown from Profile 3 (see Figure ). Red dashed line is the computed VLF data obtaining from inverted model using the inversion approach of Singh and Sharma ().…”

“…The location of one of the hot springs at Tarbalo was within a swampy area at the center point of Profile 1 as shown in Figure . VLF data from all the profiles P1–P20 were inverted using the inversion approach developed by Singh and Sharma (). The RMS error between field and computed VLF data for all profiles ranged between 0.08 (Profile 18) and 4.3 (Profile 3), as shown in Figure S7.…”

“…The resulting model of the resistivity distribution is shown in Figure . It is emphasized that the inversion approach developed by Singh and Sharma () is two dimensional. We have performed two‐dimensional inversion on each individual VLF profile and obtained a resistivity distribution that was stacked together to obtain a 3‐D pseudo resistivity section.…”

“…This contrasts with the results obtained by Mandal et al (), who used the digital linear filtering approach of Karous and Hjelt () to interpret their VLF data. As mentioned earlier, this interpretation approach has some limitations, especially for prediction at depth and identification of vertical fractures (Singh & Sharma, ). Thus, our results are able to depict both isolated and continuous fracture zones near the two hot springs, unlike Mandal et al ().…”