Joint inversion of long‐offset and central‐loop transient electromagnetic data: Application to a mud volcano exploration in Perekishkul, Azerbaijan

Abstract

Mud volcanism is commonly observed in Azerbaijan and the surrounding South Caspian Basin. This natural phenomenon is very similar to magmatic volcanoes but differs in one considerable aspect: Magmatic volcanoes are generally the result of ascending molten rock within the Earth's crust, whereas mud volcanoes are characterised by expelling mixtures of water, mud, and gas. The majority of mud volcanoes have been observed on ocean floors or in deep sedimentary basins, such as those found in Azerbaijan. Furthermore, their occurrences in Azerbaijan are generally closely associated with hydrocarbon reservoirs and are therefore of immense economic and geological interest. The broadside long‐offset transient electromagnetic method and the central‐loop transient electromagnetic method were applied to study the inner structure of such mud volcanoes and to determine the depth of a resistive geological formation that is predicted to contain the majority of the hydrocarbon reservoirs in the survey area. One‐dimensional joint inversion of central‐loop and long‐offset transient electromagnetic data was performed using the inversion schemes of Occam and Marquardt. By using the joint inversion models, a subsurface resistivity structure ranging from the surface to a depth of approximately 7 km was determined. Along a profile running perpendicular to the assumed strike direction, lateral resistivity variations could only be determined in the shallow depth range using the transient electromagnetic data. An attempt to resolve further two‐dimensional/three‐dimensional resistivity structures, representing possible mud migration paths at large depths using the long‐offset transient electromagnetic data, failed. Moreover, the joint inversion models led to ambiguous results regarding the depth and resistivity of the hydrocarbon target formation due to poor resolution at great depths (>5 km). Thus, 1D/2D modelling studies were subsequently performed to investigate the influence of the resistive terminating half‐space on the measured long‐offset transient electromagnetic data. The 1D joint inversion models were utilised as starting models for both the 1D and 2D modelling studies. The results tend to show that a resistive terminating half‐space, implying the presence of the target formation, is the favourable geological setting. Furthermore, the 2D modelling study aimed to fit all measured long‐offset transient electromagnetic Ex transients along the profile simultaneously. Consequently, 3125 2D forward calculations were necessary to determine the best‐fit resistivity model. The results are consistent with the 1D inversion, indicating that the data are best described by a resistive terminating half‐space, although the resistivity and depth cannot be determined clearly.

Application of Transient Electromagnetic Method for Investigating the Water-Enriched Mined-Out Area

“…The TEM data analysis consisted of many components, as follows: data pro-processing, qualitative analysis, data inversion, and subsequent geological interpretation of the results [34,35]. Before the inversion, preprocessing work must be done to the raw data.…”

Denoising stacked autoencoders for transient electromagnetic signal denoising

“…We also noticed that supervised learning performs well in classification problems such as image recognition and semantic understanding (He et al, 2016;Long et al, 2014). At the same time, unsupervised learning also has a good performance in clustering and association problems (Klampanos et al, 2018), and the goal of unsupervised learning is usually to extract the distribution characteristics of the data in order to understand the deep features of the data (Becker and Plumbley, 1996;Liu et al, 2015).…”

“…Through the analysis of the secondary field signal (SFS) in the transient electromagnetic method (TEM), the information of underground geological composition can be obtained and has been widely used in mineral exploration, oil and gas exploration, and other fields (Danielsen et al, 2003;Haroon et al, 2014). Due to the small amplitude of the late field signal in the secondary field, it may be disturbed by random noise, sensor noise, human noise and other interference (Rasmussen et al, 2017), which leads to data singularities or interference points, and thus the deep geological information can not be reflected well.…”

Integrating Electromagnetic Data with Other Geophysical Observations for Enhanced Imaging of the Earth: A Tutorial and Review

“…Naturally, the discussion applies also to combining electromagnetic and gravity data (e.g., Maier et al 2009), but to a certain degree even when inverting different types of electromagnetic data together (Commer and Newman 2009;Haroon et al 2015) or electromagnetic with DC resistivity data (e.g., Candansayar and Tezkan 2008;Yogeshwar et al 2012;Hoversten et al 2016). Even though in the latter cases the physical parameter under consideration is electrical conductivity, magnetotellurics is sensitive to horizontal conductivity, whereas controlled-source methods are sensitive to horizontal and vertical conductivity which leads to effective anisotropy in finely layered sedimentary environments .…”