Audiomagnetotelluric Research Articles

Audio-magnetotelluric (AMT) studies over Rajapur hot spring in west coast Maharashtra, India

Audio-magnetotelluric (AMT) studies over Rajapur hot spring in west coast Maharashtra, India

Analysis of subsurface Resistivity Structures in Relation to the Level of Damage Caused by Earthquakes Using the Audio-Magnetotelluric (AMT) Method (Case study: the 2006 Yogyakarta Earthquake)

Abstract The 2006 Yogyakarta Earthquake had caused a disaster in Bantul area. Although the magnitude reached only Mw = 6.4, it left more than 6,000 fatalities and up to 1,000,000 homeless. The earthquakes that occur in this area is shallow earthquakes that originate on land (Opak fault) and triggers quite high surface damage. This damage includes damage to buildings and infrastructure that support community activities. The main objective of this research is to determine the subsurface structure and be able to identify soft layers based on the distribution of resistivity values that can be correlated to the value of the amplification factor. The Audio-Magnetotelluric (AMT) method was conducted in this area using five components of electric and magnetic fields. The Audio-Magnetotelluric (AMT) method utilizes electromagnetic waves with a frequency of 1 Hz to 100KHz. Based on the one-dimensional (1-D) inversion result calculated from 3 sites show that there is a low resistivity value (10 - 70 Ωm) from the surface to the depth of 1000 m. This ticked conductive zone interpreted as a sediment layer that can triggers the high amplification factor values.

Cu–Polymetallic Deposit Exploration under Thick Cover in Gucheng–Yaxi Area Using Audio-Magnetotelluric and Spread-Spectrum-Induced Polarization

Successful geophysical exploration projects in the Gucheng–Yaxi area located in Gaochun District, Jiangsu Province, China, have been limited partly due to the complex geological conditions of the area and high artificial noise in data acquired using electrical and electromagnetic methods. In this study, we deployed the new anti-interference spread-spectrum-induced polarization method (SSIP) and the audio-magnetotelluric (AMT) method to detect a copper–polymetallic deposit in the area. Two-dimensional inversion results in the Gucheng–Yaxi area revealed a high chargeability anomalous zone on the SSIP profile that coincided with a zone of moderate resistivity located between two resistor bodies on the AMT profile. A follow-up 1200 m drill hole was established at this high-chargeability, moderate-resistivity zone which encountered polymetallic (copper, lead, zinc, gold, and silver) mineralization at a depth of ≥400 m. Drill hole data analysis showed that mineralization occurred interspaced in the marble rock mass at varying depths. Furthermore, several low-resistivity, weak-chargeability sections were revealed and attributed to Cretaceous sediments and faults. These faults are thought to have played a critical role in the polymetallic mineralization genesis. In summary, this study demonstrated the successful of application of SSIP and AMT in detecting a metallic deposit in an area with high artificial noise. Hence, the geophysical prospection potential of the Gucheng–Yaxi area is great.

Audio magnetotelluric view of the giant carbonatite-hosted Bayan Obo REE deposit, Inner Mongolia, northern China

As critical minerals in low-carbon economies, rare-earth elements (REEs) are crucial for modern industry, technology, and medicine. The largest known REE resource in the world is the Bayan Obo (BO) deposit in northern China. However, BO features as complicated mineral compositions and has experienced several geological events. As a reuslt, controversy remains about the BO REE genesis and enrichment mechanism, and debates are also ongoing about the controlling structure of this giant deposit. We carried out an multiscale electromagnetism study on BO mineral deposits. The multiscale study consists a microscale on samples, a small scale targeting mineral deposits and a mid scale concerning the mine field. The small and mid scale study were conducted using audio-magnetotelluric (AMT). This study revealed the controlling structure using geoelectrical characteristics. AMT data were acquired from more than 590 stations, and the petro-electromagnetism of several samples was analyzed to understand the geoelectrical features of the ore deposits and their surrounding rocks. The data were interpreted using Bostick transformation and generalized least-squares inversions. We obtained the geoelectrical structure of the BO complex down to a maximum depth of 3500 m, and we present two-dimensional imaging based on the AMT conversion results. The results show that the BO deposit developed at the transition of the high- and low-resistivity geoelectrical structure from south to north. Our AMT results indicate that the tectonic setting of the BO deposit is the transition zone between the rift edge and craton margin slope. The ore geology is presently controlled by the complex slope and the rift remnant, the ore-bearing dolostone in the profiles is Y-shaped. It goes downward from south to north and potentially features a great mineral prospect deep in the BO deposit.

Application of Integrated Geological and Geophysical Surveys on the Exploration of Chalcedony Deposits: A Case Study on Nanhong Agate in Liangshan, China

Nanhong agate, esteemed for its vivid color and natural shine, is experiencing a scarcity in supply despite its high demand. The primary deposits of agate, typically found near the surface, have not been extensively explored due to the predominance of traditional manual excavation methods. This research examined the Nanhong agate deposits in the Zhaojue–Meigu region of Liangshan, China, employing the integration of geological and geophysical surveys. Field geological surveys allowed us to outline the general areas where agate is found. Following this, using magnetic surveys, vertical electrical sounding, and controlled-source audio magnetotellurics, agate deposits were located within the conglomerate layer of the second member of the Feixianguan Formation from the Lower Triassic period at depths of less than 100 m. Our results identify mineralized layers, Xuanwei Formation mudstone, and the underlying bedrock, thus supporting the creation of a mineral prediction map. This research provides essential insights and guidance for agate exploration and the development of associated mineral resources.

Geothermal resources and deep tectonic in Leh Ladakh (NW Himalaya), India: Inference from magnetotelluric studies

The geothermal resources and their connectivity with the deep tectonic are meaningful to understand through their structural geometry in the NW Himalayas. Therefore, over the NW-SE profiles, 23 audio Magnetotelluric (AMT) sites and over the SW-NE profile, 15 broadband Magnetotelluric (BBMT) sites were carefully chosen to provide the detailed structure. The electrical models were produced from a 2-D inversion algorithm based on the non-linear conjugate gradient method. The Chumathang-Mahe and Puga-Sumdo regions offer excellent geothermal potential as heat/fluid passes through the Mahe Fault (MF), Zildat Fault (ZF) and Kiagor Tso Fault (KTF). These faults are well revealed in the electrical models beneath the subsurface. The Puga Valley has an excellent geothermal resource at shallow depth. A ∼1000 m thick sulfide mineral body is defined with high conductivity (∼1 Ω-m). The crustal structure shows a highly conductive mid-crust beneath the Ladakh batholiths with no manifestation roots. The signs of the Indus-Tsangpo Suture Zone (ITSZ) and Shyok Suture Zone (SSZ) are well represented in the crustal model and are associated with the steep-dipping faults. The Chumathang-Mahe and Puga-Sumdo geothermal regions are connected with low resistivity body (C1) at shallow depths in the crustal model along the SW-NE profile and offer a path for deep fluid flow. The frictional heat generated in the process of the Indian plate subducting beneath the Tibetan plateau and the collision zone between India and Asia carried to melt. The high heat surface flow indicates the thermal origin of low resistivity. Thus, a ∼10 sq. km low resistivity reservoir is identified beneath the Tso Morari dome in the crustal model that overlies a resistivity feature. The high conductivity at mid-crust determined at active tectonic fabric may be potential geothermal resources yielding high well-productivity.

Intelligent geological interpretation of AMT data based on machine learning

AMT (Audio Magnetotelluric) is widely used for obtaining geological settings related to sandstone-type Uranium deposits, such as the range of buried sand body and the top boundary of baserock. However, these geological settings are hard to interpret via survey sections without conducting geological interpretation, which highly relies on experience and cognition. On the other hand, with the development of 3D technology, artificial geological interpretation shows low efficiency and reliability. In this paper, a machine learning model constructed using U-net was used for the geological interpretation of AMT data in the Naren-Yihegaole area. To train the model, a training dataset was built based on simulated data from random models. The issue of insufficient data samples has been addressed. In the prediction stage, sand bodies and baserock were delineated from the inversion resistivity images. The comparison between two interpretations, one by machine learning method, showed high consistency with the artificial one, but with better time-saving. It indicates that this technology is more individualized and effective than the traditional way.

Exploring Medium and Low-Temperature Convective Geothermal Resources Through Controlled Source Audio Magnetotelluric Mothod

Medium and low-temperature convective geothermal resources are a crucial component of China's geothermal wealth, primarily concentrated in the eastern coastal regions. To maximize the utilization of these resources, geophysical surveys are essential, with controlled source audio magnetotelluric(CSAMT) being a prominent and effective method employed in geothermal resource assessments. This study focuses on applying CSAMT to survey specific geothermal sites, including the Xiu Yan Gou Tang Hot Spring, Fengcheng Gou Tang Hot Spring, and Hai Cheng Xi Huang di Geothermal Field. The results obtained through this method provide detailed insights into the distribution of both deep and shallow thermal storage spaces within these fields, shedding light on the intricate thermal circulation systems associated with them. These findings serve as a reliable foundation for planning and executing further geothermal resource development projects. CSAMT emerges as a comprehensive tool for understanding the nuances of medium and low-temperature convective geothermal resources.

1D Audio Magnetotelluric Modelling for Deep Aquifer Identification in the Lava Fan Area of Haruman Peak, Malabar Mountains

The Malabar Mountains area acts as a catchment and infiltration zone for rainwater. Haruman Peak is the location of one of these areas. Information on the well-preserved depth of the shallow aquifer at 40 meters reinforces this. This research reviews the results of subsurface 1D resistivity structures from AMT data to obtain information on the depth of deep aquifers on the western slope of the Haruman Peak Lava Fan, Haruman Mountains. 1D modelling shows an aquifer at depth of 140.56-2080.07 meters with resistivity ranging from 5.25-68.08 Ωm. At depths of 453.32 m (HR02), 530.8 m (HR03), 1464.97 m (HR01), and 2080.07 m (HR02), interbedded tuff-andesite with minor pumice identifies the deep aquifer. In addition, a depth of 140.56 m (HR02) with a resistivity value of 68.08 Ωm indicates a shallow aquifer. Looking at the elevation of the Bandung Basin, water from aquifers located at elevations > 700 meters above sea level will flow into the Bandung Basin.

Deep resistivity geophysics of the San Juan–Silverton caldera complex, San Juan County, Colorado (USA)

Magnetotelluric (MT) and audiomagnetotelluric (AMT) data are used to better understand the subsurface geology and mineral resources in the San Juan–Silverton caldera complex located near Silverton, Colorado, western United States, as part of the extensive southern Rocky Mountains volcanic field that covers much of southwestern Colorado and northern New Mexico. Seven MT and AMT profiles of varying lengths image resistivity structure to depths of ~5 km. The AMT inversion models characterize geophysical responses of near-surface lithologies, structures, and mineralized systems and also help corroborate airborne electromagnetic data at shallow levels. The MT inversion models extend our depth of investigation from near the surface to great depths (~5 km) and help to form hypotheses about roots of the hydrothermal plumbing that fed shallower mineralized systems. Subsurface high resistivities occur beneath intermediate-composition lava flows and Proterozoic units. Subsurface moderate- to low-resistivity values may reflect hydrothermal plumbing that served as flow paths for mineralizing fluids and metallic ore formation. The model interpreta­tions presented in this study could be utilized in remediation planning or mineral resource applications. The methods used could be applied to other watersheds with similar volcanic environments containing acid-generating historical mines or hydrothermally altered and mineralized source rocks.

Atom-profile updating dictionary learning with nucleus sampling attention mechanism sparse coding for audio magnetotelluric denoising

Audio magnetotellurics (AMT), as a commonly used passive geophysical technique, provides outstanding metal ore exploration capabilities based on the resistivity structure of the earth. However, the accuracy of AMT in translating geoelectrical structures decreases when the data collected in mining areas are of poor quality and contain complex anthropogenic noise, leading to distorted apparent resistivity-phase curves and posing significant challenges for mineral exploration. To effectively denoise AMT data, we develop a new denoising method that combines atom-profile updating dictionary learning (APrU) with the nucleus sampling attention mechanism (NSAM) sparse coding. First, we use APrU to accurately learn the characteristics of the noise in the AMT data; then, we apply the updated dictionary to perform sparse coding on the AMT data by NSAM to obtain the noise; finally, we subtract the noise from the original AMT data to obtain the denoised data. Our experimental results suggest that the proposed method can learn an overcomplete dictionary via the to-be-processed AMT data, thereby enabling the sparse representation of the noise within the learned dictionary. We also demonstrate the efficacy of this method with a set of field data collected from the Lu-Zong mining area, and the attained denoised data faithfully restore the geoelectrical structures with heightened accuracy. The findings confirm that the proposed method realizes the unsupervised learning of the AMT data and allows us to achieve precise denoising performance.

Identification and Suppression of Multicomponent Noise in Audio Magnetotelluric Data Based on Convolutional Block Attention Module

Identification and Suppression of Multicomponent Noise in Audio Magnetotelluric Data Based on Convolutional Block Attention Module

Imaging freshwater and saline aquifers beneath Bradford County, Pennsylvania, USA, using Audio-Magnetotelluric (AMT) data

We investigate the utility of audio-magnetotelluric (AMT) data for detecting high electrical conductivity zones indicative of fresh and saline groundwater lenses at depths of < ∼1000 m at seven survey sites in the southwestern corner of Bradford County, PA. Dimensionality analysis indicates the AMT data can be interpreted using two-dimensional (2-D) models. 2-D electrical resistivity models obtained from inverting the AMT data reveal conductive zones at depths of <30 m at all sites, between depths of 50–150 m at six sites, and between depths of 400–600 m at one site. The models show slightly lower resistivities (∼15–30 Ω m) in the deeper zones compared to the shallower zones (>30 Ω m). Consistent with well log data from two boreholes in the study area and salinity estimates obtained from the imaged resistivities, we interpret the <30 m deep conductive zone as the freshwater aquifer, and the two deeper conductive zones as salty/briny groundwater lenses. Our salinity estimates for the deeper zones vary from 1 to 380 (unitless Practical Salinity Scale), which agrees well with previous estimates of Appalachian brine salinity of 10–343. We attribute the high-salinity groundwater lenses to Appalachian Basin brine migrating upwards from deeper in the basin via bedrock fracture and fault systems. Our findings indicate AMT surveying can be useful for imaging fresh and saline aquifers at shallow depths (< ∼1000 m), at least within the northeastern section of the Appalachian Basin in Pennsylvania.

Audio Magnetotelluric Imaging of a Concealed Mineral Deposit: A Case Study of Carbonate Hosted Zhugongtang lead-zinc Deposit, NW Guizhou Province, China

Non-intrusive geophysical exploration methods play a major role in ore deposit detection. This study aimed to characterize the subsurface electrical resistivity structure across the recently discovered super-large Zhugongtang lead-zinc deposit in north-western Guizhou province of China, using audio magnetotellurics (AMT) method. The results of this procedure were compared with available geological information. The estimated geoelectrical strike by impedance tensor invariants ranged between N285°E and N315°E. This range is consistent with known NW-SE trending structures in the Zhugongtang mining area. The obtained 2D inversion models revealed the presence of formations with an exceptionally low resistivity (< 15 Ωm), which corresponded to the known location of lead-zinc bearing formations. Moderate to low resistivity anomalies (< 63 Ωm) were observed at both shallow and greater depths. These anomalies were interpreted as indicative of geological formations composed of shale, sandstone, claystone, and silty mudstone. In addition, the resistivity models also revealed widespread high resistivity anomalies (> 1000 Ωm). These anomalies were thought to represent dolomite and limestone lithologies. In summary, the results presented in this study make a valuable contribution to the existing body of literature on the Zhugongtang deposit.

Magnetotelluric Time Series Denoising Using Encoder-Decoder Consisted of LSTM Cells

Electromagnetic signals in geophysics are frequently disturbed by various interference in field data acquisition. Denoising for passive electromagnetic methods such as magnetotelluric (MT) or audio magnetotelluric (AMT) data is significant to improve data quality and finally imaging to the geoelectrical structure. Conventionally, most denoising methods are employed in frequency domain and few of them are applied in time domain. However, a great number of irregular noise in the electromagnetic time series prove difficulty to be removed. We propose a denoising method, using Encoder-Decoder consisted of Long Short-Term Memory cells (ED-LSTM), to reduce the effect of the step noise and the random-impulsive noise. Supervised learning and transductive learning are used for the denoising of the step noise and the random-impulsive noise, respectively. Our results indicate that the step and random-impulsive noise could be successfully removed from the raw time series. The result indicate that ED-LSTM could potentially to be wildly used in the electromagnetic time series denoising and then improve data quality.

Audio‐Magnetotelluric Survey for Groundwater Investigation in the Al‐Jaww Plain in Eastern Abu Dhabi, Al‐Ain, United Arab Emirates

AbstractThe United Arab Emirates (UAE), located in an arid climate zone with low rainfall, relies on shallow aquifers for freshwater. Understanding the depth and extent of such aquifers is crucial for meeting water supply needs. The UAE's hydrogeology is influenced by neighboring mountains in Oman. The Al‐Jaww Plain in southeast of Al‐Ain city is an essential groundwater source, characterized by a large, flat area of gravel and sand deposits from the Oman Mountains. This study aims to map groundwater aquifers in the Al‐Jaww Plain by integrating the audio‐magnetotelluric (AMT) method, seismic reflection profiling, and borehole data. AMT data were collected along an 11‐km ENE–WSW profile and a 2D resistivity model was generated. The resulting model delineates three distinct geo‐electrical zones from the surface to a depth of 5 km. First, a shallow layer with low resistivity (0–15 Ωm) represents the Quaternary and Pliocene aquifers, in addition to the Upper Cretaceous Simsima and Tertiary groundwater aquifer zone, extending to a depth of 1.5 km. Second, a moderately resistive layer (15–250 Ωm) is recorded beneath the first layer, corresponding to the Upper Cretaceous Aruma foreland basin sequence. Finally, a high‐resistivity region (&gt;250 Ωm) at depths exceeding 3 km is attributed to the allochthonous Hawasina thrust sheet, which is associated with Late Cretaceous obduction of the Semail ophiolite. These findings have practical implications for managing groundwater resources in Al‐Ain.

One-dimensional magnetotelluric parallel inversion using a ResNet1D-8 residual neural network

Deep Learning is an effective tool to invert the underground electrical conductivity structure. In this study, we build a new 8-layer residual neural network (ResNet1D-8) for audio magnetotelluric (AMT) data inversion based on the deep learning theory. In terms of the network structure, the degradation of the model with the increase of depth is effectively avoided by adding shortcut connections. Meanwhile, adding a batch normalization layer greatly improves the model training speed and network generalization ability. This study uses the parallel technology to quickly generate millions of samples, which effectively reduces the computational time and provides a large number of high-quality samples for deep learning model training. Compared with the simulated annealing algorithm, the neural network model in this paper has the advantages of high reliability, short inversion time, and strong model generalization ability. Moreover, we add Gaussian noise to the data of testing samples, and inversion results show that the model has good robustness. The inversion test is carried out on the field measured AMT data set collected in the Dachaidan area of Qinghai Province, China. The results show that the ResNet1D-8 residual neural network model established in this paper can effectively invert the underground electrical structure.

Simulation and Observations of Audio Magnetotelluric Measurements over Water-Covered Areas

Electromagnetic (EM) surveys play a significant role in mineral exploration. However, the EM method often faces limitations when investigating minerals in areas covered by rivers, lakes, or other water bodies. This paper introduces audio magnetotelluric (AMT) observation technology that utilizes separated electric and magnetic channels to deal with this challenge over water-covered areas. The study analyzes and discusses the characteristics of the relative error of the magnetic field through forward simulation. The observation and profile experiments were conducted at the estuary of a river in Liaoning Province, China, and high-quality data in the river and the pseudo-geoelectric section of the underwater space were successfully obtained. The results demonstrate the feasibility and effectiveness of the AMT observation technology over water-covered areas, emphasizing the importance of locating the magnetic channel in a quiet zone at a certain distance from the shore. This configuration helps reduce the influence of resistivity differences between water and shore, ultimately improving data quality and accuracy. The research suggests that the AMT observation technology, utilizing separated electric and magnetic channels, has the potential for further improvement and can serve as a valuable guide for mineral exploration over water-covered areas.

Deep-learning-based sferics recognition for audio magnetotelluric data processing in the dead band

In audio magnetotelluric (AMT) sounding data processing, the absence of sferic signals in some time ranges results in a lack of energy in the AMT dead band, causing unreliable resistivity estimations. To address this issue, we develop a deep convolutional neural network (CNN) to automatically recognize sferic signals from redundantly recorded data over a long time range and use these sferic signals to accurately estimate resistivity. The CNN is trained using field time-series data with different signal-to-noise ratios (S/Ns) acquired from different regions of mainland China. To solve the potential overfitting due to the limited number of sferic labels, we develop a training strategy that randomly generates training samples with random data augmentations while optimizing the CNN model parameters. The training process and data generation are stopped when the training loss converges. In addition, we use a weighted binary cross-entropy loss function to solve the sample imbalance problem to optimize the network better, use multiple reasonable metrics to evaluate the network performance, and perform ablation experiments to optimize the model hyperparameters. Extensive field data applications indicate that our trained CNN can robustly recognize sferic signals from noisy time series for subsequent impedance estimation. The results indicate that our method can significantly improve the S/Ns and effectively solve the lack of energy in the dead band. Compared with the traditional processing method, our method can generate smoother and more reasonable apparent resistivity-phase curves and depolarized phase tensors, correct the estimation error of the sudden drop in high-frequency apparent resistivity and abnormal behavior of phase reversal, and better estimate the real shallow resistivity structure.

Capacitively coupled effect and capacitive decoupling of multichannel controlled-source audio magnetotellurics observations

Capacitively coupled effect and capacitive decoupling of multichannel controlled-source audio magnetotellurics observations