A Preliminary Investigation on Electric Structure of the Crust and Upper Mantle in Arxan Volcanic Area

Tang, Ji; Wang, Ji‐Jun; Chen, Xiaobin; Zhao, Guosong; Zhan, Yan

doi:10.1002/cjg2.642

Cited by 13 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The electrical structure in the depth range 25∼40km is a conductive layer with about one hundred Ωm resistivity, and the resistivity of the structure beneath 40km increases but is smaller than one kilo-Ωm. With this electrical structure as the initial model, a MT-2D inversion [37] is carried out by the NLCG algorithm with both TE and TM mode's resistivity and impedance phase data, which fits the observational data very well and is very similar to the model shown in Fig. 4, but the two good conductive chunnels are more clear.…”

Section: Application To Observational Datamentioning

confidence: 87%

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

Chen

Zhao

Tang

et al. 2005

Chinese J. Geophys.

Self Cite

View full text Add to dashboard Cite

In this paper, a new inversion method, Adaptive Regularized Inversion Algorithm (ARIA), is presented to overcome the difficulty of the determination for the regularized factor. Firstly, a new data variance disposing method, data variance normalization method, is put forward. This method uses a new way to calculate the influence matrix of data variance in inversion. Thus, the data variance only influences data fitting, and has no influence on the weight between data object function and the model constraint object function. So the influencing factors of the determination of the regularized factor are reduced. Secondly, the definition of the roughness kernel matrix is presented in the course of constructing model constraint object function, and a concise equation of it is derived. Thus the construction of the model object function becomes very simple and direct. Thirdly, two adaptive methods of the regularized factor are put forward based on the relations of data object function, the model constraint object function, and the regularized factor. Finally, ARIA is used to solve an magnetotelluric (MT) one‐dimensional inversion problem by the constraint of the flattest model. Several examples are illustrated to exemplify the effect of ARIA.

show abstract

Section: Application To Observational Datamentioning

confidence: 87%

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

Chen

Zhao

Tang

et al. 2005

Chinese J. Geophys.

Self Cite

View full text Add to dashboard Cite

show abstract

“…500 km. The deep-cut faults provide two channels for Quaternary magma migration from the mantle: one displays a high temperature and fluidenriched body approaching the depth of 10-12 km, and another has become cold above a depth of 30 km (Tang et al, 2005). The series of NNW-trending basement rifts deeply cut the crust, favoring the deep cycle of groundwater and the formation of hot springs.…”

Section: Geological and Hydrothermal Settingmentioning

confidence: 99%

Contribution of deep-earth fluids to the geothermal system: A case study in the Arxan volcanic region, northeastern China

et al. 2023

View full text Add to dashboard Cite

Investigations of the hot spring water and gas in the volcanic region are involved in assessing geothermal resources and understanding groundwater circulation, volcano, and earthquake activities. The origins of water and gas of the hot springs, lakes, rivers, and rain in the Arxan volcanic region (AVR), northeastern (NE) China, were investigated by conducting a field survey and geochemical analysis. The low electrical conductivity (40–835 μS/cm) and low total dissolved solids (TDS, 23.83–540.00 mg/L) of the water samples indicate that they are fresh water. δ18O and δD values of the water samples range from −4.1% to −16.0% and from −61.3% to −119.9%, respectively. Enrichment of heavy isotopes in the rainwater and the crater lake waters was caused by evaporation. The component H2O of the water samples predominantly originated from the meteoric water, with less than 1 vol% contributed by deep-earth fluids. Ions in the rain sample were predominantly derived from sea salt and continental aerosol. Ions in the surface water samples had multiple origins (mineral dissolution, atmospheric, and anthropogenic sources). While the ions in the hot spring water were predominantly derived from both the dissolution of rocks and deep-earth fluids, the latter contributed 73%–87% of Cl− and 86%–99% of Na+ to the hot spring waters. Gases from the hot springs were composed of more than 95% N2 and less than 5% O2 and Ar, with 3He/4He ratios of 0.14–1.17 RA (RA=1.4×10−6). Excess N2, Ar, He, and CO2 of the hot springs were mainly derived from both the crust and upper mantle. About 3%–23% of the total He in the bubbling gases from the crater lake waters and hot springs is derived from the mantle, implying a supplement of heat energy from the mantle to the geothermal systems. Significantly, about 12% of the He dissolved in the Budonghe water is derived from the mantle, indicating that plenty of mantle-derived heat transported by deep-earth fluids keeps the river water from freezing. Our results indicate that Cl and Na ions and 3He/4He ratio are the feasible geochemical indicators for source partitioning of geothermal fluids.

show abstract

“…Magnetotelluric (MT) use of natural alternating electromagnetic field study the earth electrical structure of a geophysical prospecting method, because of its low cost, convenient work, from the high resistance layer shielding, high resolution, the low resistance layer and vary with the frequency of the electromagnetic field exploration depth, light can be dozens of meters, the depth can reach hundreds of kilometers, Widely used in oil and gas exploration, structural detection and seismic monitoring, etc. [1,2]. Due to the complex structure and weak signal of the natural electromagnetic field, it is easy to be interfered with by noise.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of Deep Learning Selection Method for Magnetotelluric Power Spectrum Based on Rhoplus Correction

Xi,

Xie,

Zhou

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Magnetotelluric (MT) is an electromagnetic exploration method using natural field sources, which are unstable, irregular, and vulnerable to interference from electromagnetic signals. The total power spectrum of the magnetotelluric signal is superimposed by a sub-power spectrum of segmented time series. To reduce the influence of electromagnetic interference, the pre-processing of magnetotelluric data will delete the severely interfered sub-power spectrum. The selection of power spectrum often requires human-computer interaction, which is time-consuming and laborious, heavily dependent on personal experience, and has great randomness. This article innovation is put forward based on the classification of Rhoplus correction and deep learning automatic power spectrum method, first of all, the MT power spectrum impedance estimation results are Rhoplus one dimensional inversion and obtain fitting, using neural network learning fitting differential characteristics and classification, and then for the power spectrum of MT again after low-quality quantum power spectrum to eliminate overlay, Impedance estimation is performed, and the above process is repeated to find the optimal power spectrum combination. Through the comparison and verification of measured MT data, the automatic power spectrum selection method based on Rhoplus correction and deep learning classification achieve similar results to manual editing.

show abstract

A Preliminary Investigation on Electric Structure of the Crust and Upper Mantle in Arxan Volcanic Area

Cited by 13 publications

References 9 publications

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

Contribution of deep-earth fluids to the geothermal system: A case study in the Arxan volcanic region, northeastern China

Research and Application of Deep Learning Selection Method for Magnetotelluric Power Spectrum Based on Rhoplus Correction

Contact Info

Product

Resources

About