Controlled-Source Audio Frequency Magnetotellurics (CSAMT) is an artificial-source electromagnetic technique that partially mitigates the limitations of weak natural field signals. However, practical field surveys inevitably encounter strong interference, severely affecting signal quality. Traditional methods like Fourier transformation, which directly computes apparent resistivity from frequency-domain information, are inadequate for this context, so we need alternative denoising approaches. However, research on CSAMT denoising is currently limited. Given the excellent performance of Long Short-Term Memory (LSTM) neural networks in the processing of Magnetotelluric (MT) data, as demonstrated by previous studies, this paper proposes the use of LSTM to denoise CSAMT signals in the time domain. Unlike traditional denoising methods, we aim to directly extract the target frequency signal from the time series data for denoising. For MT data, target frequency signals and noise are all mixed together, so noise suppression can only be achieved by identifying noise characteristics in the time series. However, unlike MT data, CSAMT data has an artificial transmitting source, and the frequency of the valid signal is fixed within a time interval. This allows for the direct extraction of target frequency signals without considering the complex characteristics of noise. In this study, we developed a neural network based on bidirectional LSTM to accomplish the task of noise suppression. After conducting both simulated and measured data tests, this method was able to, on average, improve the signal-to-noise ratio (SNR) of CSAMT data by approximately 20dB and partially address the challenge of denoising when the data's SNR falls below 0dB.

Electromagnetic (EM) telemetry is one of the key research subjects that has increased application in transmitting real-time data between the borehole and surface during drilling. There are several methods that can be used to model an EM telemetry system, including the method of moments (MoM) and the electric field integral equation method. We have developed a pulse basis function for the MoM, which assists in the analytical estimation of thin wire kernel integrations and avoids singular problems when simulating current distributions. The scattering field is solved using the current distribution. We examine the method using a simple example of vertical drilling. In the model, a vertically placed drill string is driven through a layered earth model. The drill string represents a long, thin wire antenna with a source located near the drill bit. The validity of the method is confirmed by comparing our results to the analytical solution in a half-space model as well as the current distributions along the drill string derived from the proposed method with those derived from a commercial finite-element solver, COMSOL-Multiphysics. Furthermore, the efficiency of this method is demonstrated by comparing its computational costs with those of COMSOL. The simulated EM telemetry in a known geologic setting is then compared with a field data example. These comparisons show that the proposed method is accurate and efficient. Moreover, we develop insights into how the parameters needed for calculations alter current distributions along the drill string and the signal received at the surface. The proposed method is therefore useful for obtaining the optimal design parameters for EM telemetry systems.

Taohemu deposit is a superlarge silver‐polymetallic deposit discovered in the Great Xing'an Range metallogenic belt of China in recent years. The deposit occurs in the terrestrial volcanic rock sequences of Late Jurassic age. The ore bodies consist mainly of rhyolitic tuffs, brecciated tuffs, and volcanic breccia bearing Ag‐Pb‐Zn mineralization, and are largely conformable with the surrounding layered volcanic and volcaniclastic rocks with a gradual transitional relationship manifesting as strata‐bounded features in general. The mineralization system appears to be generally controlled by a paleo‐volcanic chamber. It is suggested that the late volcanic hydrothermal fluids flowed within the permeable layers of rhyolitic pyroclastics consisting mainly of volcanic breccias and breccia‐bearing tuffs. This resulted in the formation of superlarge Ag‐Pb‐Zn polymetallic ore deposit metasomatism and fracture filling. The majority of the deposit occurs as shallowly dipping concealed ore bodies, as defined by sparsely distributed drill holes. Because of its short discovery history, new mineralization style and low degree of comprehensive research, the boundaries of most ore bodies have not been effectively delineated along vertical and lateral extensions. At present, it is crucial to strengthen the comprehensive studies on the deposit, conduct more detailed geological exploration, and establish the metallogenic and prospecting models. For this reason, we have designed and implemented long sections of magnetotelluric (MT) and controlled‐source audio magnetotelluric (CSAMT) sounding across the Taohemu ore field and obtained a large number of geological structures and valuable underground exploration results that provided reliable geophysical support for promoting deep resource exploration.

The Central Asian Orogenic Belt (CAOB) is a Phanerozoic accretionary orogen with a complex formation process. This study imaged the three‐dimensional electrical structure of the lithosphere using the magnetotelluric data collected covering the southern margin of the CAOB. The resistivity model shows alternating high and low resistivities along the southern margin of the CAOB in the north, with the low resistivities in the middle and lower crust interpreted as remnants of the subducted crust of the Paleo‐Asian Ocean (PAO). The belt‐like low‐resistivity body encased by high‐resistivity bodies on both sides is interpreted as residual back‐arc oceanic crust. The Alxa Block (AB) in the south exhibits overall high‐resistivity characteristics, but a large low‐resistivity area exists near the southeastern connection to the Ordos block. Based on the characteristics of the resistivity model, we propose that the final closure position of the PAO in the southern margin of the CAOB is the Enger Us fault zone, with a north‐south bidirectional subduction mode. The widespread low‐resistivity anomaly near the Badain Jaran fault zone is interpreted as residual subduction of the back‐arc oceanic crust. The subduction of the PAO is a typical “trench‐arc‐basin” model. The variation in the subduction angle of the PAO along the strike may indicate that the later stage of the southern margin of the CAOB was subjected to the convergence effect of northeast‐directed stress from the northeast margin of the Tibetan Plateau and the associated tectonic activities.

Controlled-source audio-frequency Magnetotellurics method (CSAMT) partially overcomes the drawbacks of weak natural field signals. However, substantial interference is an inevitable part of field surveys in practice, which negatively impacts signal quality. We require new denoising techniques since traditional techniques, such as Fourier transformation, which compute apparent resistivity directly from frequency-domain data, are insufficient in our situation. CSAMT denoising research is currently lacking, nevertheless. This research proposes the use of long short-term memory (LSTM) neural networks to denoise CSAMT signals in the time domain, given their good performance in processing Magnetotelluric (MT) data as shown by prior studies. We seek to directly extract the desired frequency signal for denoising from the time series data, in contrast to conventional denoising techniques. Since noise and target frequency signals are mixed together in MT data, the only way to suppress noise is to find the characteristics of the noise in the time series. CSAMT, on the other hand, differs from MT in that it uses an artificial transmitting source and fixes the valid signal frequency within a temporal window. This makes it possible to extract target frequency signals directly without taking into account the intricate properties of noise. In order to complete the noise suppression job, we created a neural network in this study that is based on bidirectional LSTM. This method was able to partially handle the difficulty of denoising when the data's SNR falls below 0 dB and, on average, enhance the signal-to-noise ratio (SNR) of CSAMT data by roughly 20 dB after executing both simulated and measured data testing.