Zhang et al.
Progress in Earth and Planetary Science(2024) 11:49
https://doi.org/10.1186/s40645-024-00653-8

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessElectrical conductivity of mantle minerals
beneath East Asia revealed by geomagnetic
observatory data
Yuyan Zhang1,2, Mina Ma1,2, Yujia Hu1,2, Yiliang Han1,2 and Yanhui Zhang1,2*   

Abstract
The electrical conductivity of the earth’s mantle can provide important information about geodynamic phenomena.
East Asia is associated with complex tectonics and geodynamic processes. Hence, it is necessary to better understandthe deep structure beneath East Asia. In this study, geomagnetic data obtained from East Asian observatories are
employed to image the conductivity structure of the mantle at depths ranging from 410 to 900 km. First, the data areprocessed using the modified bounded influence remote reference processing (BIRRP) method and the ratio method
is used to correct for the ocean effect. Thereafter, the stable C-response curves at the 27 observatories are estimated,and 1D electrical conductivity models for these observatories are established using the L-BFGS method. The conductivity-depth profiles reveal a heterogeneous distribution of the electrical conductivity beneath East Asia. The mantle
transition zone (MTZ) beneath East China and Japan is found to be more conductive, whereas the MTZ beneath central and southern regions of China is more resistive. In East China, the dehydration of the stagnant Pacific slab may
lead to an increase in the conductivity of the mantle minerals. There is also the possibility of upwelling of the thermal material from the lower mantle beneath the Japanese Island arc. In Northwest China, there exists a large highconductive body beneath the Tarim area, which could indicate an upwelling of the Tarim mantle plume. Our results
provide insights into the deep structure of the earth at the mantle scale.
Keywords Electrical conductivity, East Asia, 1D inversion, Mantle transition zone, Geomagnetic depth sounding*Correspondence:
Yanhui Zhang
global_em@163.com
1
School of Safety Engineering and Emergency Management,
Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2
Collaborative Innovation Center for Performance and Securityof Large‑Scale Infrastructure, Shijiazhuang Tiedao University,
Shijiazhuang 050043, China© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Zhang et al. Progress in Earth and Planetary Science(2024) 11:49

1 Introduction
A comprehensive understanding of the origin, evolution,and dynamics of the earth requires a thorough investigation of its structure at the mantle scale (Munch et al.
2018). Seismic, gravimetric, and electromagnetic studies
significantly contribute toward inferring the deep structure of the earth (Kelbert et al. 2009).
East Asia is located in the southeast of the Eurasiancontinent, and its geophysical structure and tectonic
activities have been influenced by the powerful interplay
between the Philippine Sea plate, Pacific plate, and Indian
plate. The Pacific and Philippine Sea plates are undergoing subduction beneath Japan and East China (see Fig. 1).
In Southwestern China, the Indian–Asian collision has
resulted in the elevation of the Tibetan plateau (Huang
and Zhao 2006). The deep structure of East Asia is crucial
for understanding the complex tectonics and geodynamic
processes (Yuan et al. 2020).
Multiple seismic images have illustrated the velocity
structure of the mantle transition zone (MTZ) beneathEast Asia (Li and van der Hilst 2010; Zhao et al. 2017).
These images have significantly promoted the development of earth science. However, relying solely on seismic
results leaves many unanswered questions.
Geomagnetic depth sounding (GDS) has emerged asa highly effective technique for obtaining detailed information about the deep structure of the earth (Zhang
et al. 2020). This robust geophysical method can detect
the electrical conductivity at depths ranging from 400 to
1200 km (Olsen 1998).
GDS has been widely employed to investigate the conductivity structure beneath different regions aroundthe world (Olsen 1998; Neal et al. 2000; Shimizu et al.
2010; Koch and Kuvshinov 2015; Munch et al. 2018).
However, there is relativity little research on the electrical conductivity structure deep beneath East Asia. Zhao
et al. (2001) obtained the electrical conductivity structure beneath Northeast China using the Network-MT
method. The MTZ beneath East China was found to
exhibit a high conductivity. Zhang et al. (2020) estimatedthe C-response curves for 31 observatories and inverted
these responses to obtain the electrical conductivity
structure beneath East China. The observations suggested that the MTZ had a higher conductivity in theeast than in the west. Yuan et al. (2020) drew largely thesame conclusions from 45 geomagnetic observatories.
However, due to the lack of data on the MTZ conductivity structure beneath Japanese Islands, research on theoverall morphological structure of the subducting West
Pacific Plate remains insufficient, which limits the studyof the geodynamics deep beneath East Asia.
In this study, the geomagnetic data of 27 observatories were chosen in East Asia, and the C-responses fromPage 2 of 12

these observatories were estimated. A 1D inversion was
performed to obtain the electrical conductivity structureof the mantle. Finally, the cause of the high-conductivity
anomalies could be inferred by combining the geomagnetic and seismic results.

2 Data
2.1 Data selection

To obtain the deep electrical conductivity structure of
East Asia, a large amount of geomagnetic data were collected from East Asia. The data were obtained from the
National Geomagnetic Network of China and the World
Data Center for Geomagnetism. After careful selection,
27 observatories were selected for the C-response estimation, with a minimum record length of at least 10 years.

3 C‑response estimation
In a 1D layered model, P10 can be used to represent theapproximated magnetospheric source. The C-response
can be defined in terms of the GDS data (Banks 1969):
C(ω) = −α0 tan θHr (ω)/2Hθ (ω)(1)

where ω is the angular frequency; α0 is the mean radiusof the Earth; θ denotes the geomagnetic colatitude; Hrand ­Hθ are the vertical and northward components of thegeomagnetic fields, respectively. Under 1D conditions,tanθ in Eq. (1) is often considered a compensation term
for the source space structure.
The data error of the C-response can be evaluated asfollows:(2)
δC(ω) = |C| (1 − coh2 /coh2 )(1/β)1/(L−1)
Based on the study by Schmucker (1999), the qualityof the estimated C-responses can be evaluated using thesquared coherence (­coh2) expressed in Eq. (2). To obtain­coh2 over sections for each frequency, block averaging
was applied, which was derived from the bounded influence remote reference processing (BIRRP) software package (Chave et al. 2004). Notably, 1−β represents the levelof confidence, which is typically set to 0.9; L denotes thenumber of sections for a specific frequency.
When processing the geomagnetic data, the outliers in
the original time series should be removed, and the secular variations that were obtained from the International
Association of Geomagnetism and Aeronomy were also
removed. Since the forward calculation of the GDS is
performed in the geomagnetic system, the observed data
should be rotated from the geographic coordinate system
to the geomagnetic coordinate system to obtain the corresponding magnetic field components. Finally, based on
the BIRRP data processing software (Welch 1967; see Percival and Walden 1993), the self-reference method wasZhang et al. Progress in Earth and Planetary Science(2024) 11:49

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Fig. 1 Topographic map and tectonic background distribution of East Asia. The colors represent the surface topography. The red circles indicate
geomagnetic observatories employed in this study. The red triangles indicate the volcanoes in China. The red dashed line is the North–Southgravity lineament (NSGL). The white lines indicate the plate boundaries

used instead of the remote reference method (Semenov
and Kuvshinov 2012) to calculate the C-responses with aperiod of 1.3–113 days.
GG, geodetic geographic coordinates system; GM, geomagnetic coordinates system.
Figure 2 shows a comparison of the C-responses estimatedusing the BIRRP software and those obtained from the study
by Semenov and Kuvshinov (2012). Evidently, the BIRRPestimated curves are smoother and more stable, which canbe characterized by the lower error bars. This method caneffectively suppress the outliers in the horizontal and vertical components. Therefore, this method performs well whenprocessing geomagnetic data with significant disturbances.
After careful data processing and considering the data distribution, 27 observatories could produce stable C-response
curves and were employed in this study. Figure 1 shows thespatial distribution of these observatories, while Table 1 presents more detailed information.

4 Inversion method
4.1 Ocean effect correction

Oceans are high-conductivity bodies, and when the distance between the measuring point and the coastline

is less than the skin depth of the low-frequency electromagnetic signal, the presence of an ocean will affectthe distribution of the electromagnetic field. Therefore,
for C-responses with short periods, the east–west current in highly conductive seawater will undergo severe
distortion on encountering a high-resistance continent.
These perturbations in the C-responses need to be particularly considered for coastal observatories.
For 1D inversion, the ratio method can be used tocorrect for the ocean effect (Everett et al. 2003; Kuvshinov and Olsen 2006; Püthe et al. 2015a). The specificcorrection equation is as follows:

C obs,corr = C obs k = C obs (C 1D /C 1D+3Dsurface )(3)

where the corrected C-response is denoted by C obs,corr ,while the observed response is denoted by C obs, and k is
the correction coefficient. C 1D is the C-response derived
from the 1D conductivity model (Püthe et al. 2015b),
while the predicted response C 1D+3D surface incorporates
the variability in the ocean conductivity considering theheterogeneity described by Everett et al. (2003) and Kelbert et al. (2014).

Zhang et al. Progress in Earth and Planetary Science(2024) 11:49

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Fig. 2 Comparison of the C-responses estimated using the BIRRP method and those obtained from the study by Semenov and Kuvshinov(2012). The blue circles and lines indicate the results obtained by Semenov and Kuvshinov (2012), while the red circle and lines indicate
the BIRRP-estimated C-responses

Table 1 Geomagnetic observatories used for inversion, along with their acronyms
CodeStationGG-lat (°)

GG-long (°)

GM-lat (°)

GM-long (°)

Data length

BJI

Beijing

40.0

116.2

30.0

187.3

1957–2015

CDP

Chengdu

31.0

103.7

20.9

176.1

1995–2017

CHL

Changli

39.7

119.2

29.8

189.9

1985–2007

CHM

Chongming

31.6

121.4

21.8

192.3

1995–2015

CHQ

Chongqing

29.6

106.6

19.5

178.7

1995–2015

CNH

Changchun

43.8

125.3

34.1

195.1

1980–2015

GEM

Ge’ermu

36.4

94.9

26.6

168.3

1995–2015

GUY​

Guiyang

26.7

106.6

16.6

178.8

1980–2015

JAI

Jaipur

26.9

78.9

18.0

152.9

1980–2015

JYG

Jaiyuguan

39.8

98.3

29.9

171.5

1995–2015

KAK

Kakioka

36.2

140.2

27.5

209.0

1990–2015

LAS

Lasa

29.7

91.1

20.0

164.5

1990–2015

LZH

Lanzhou

36.1

103.8

26.0

176.3

1980–2012

MIZ

Mizusawa

39.1

141.2

30.5

209.6

1980–2017

MZL

Manzhouli

49.6

117.4

39.6

187.9

1995–2015

NAJ

Nanjing

32.1

118.8

22.1

189.9

1995–2015

QIX

Qianling

34.6

108.2

24.5

180.2

1995–2015

SHY

Shaoyang

27.3

111.5

17.2

183.3

2000–2015

SSO

Simosato

33.6

135.9

24.6

205.5

1958–2020

TAY​

Taiyuan

37.9

112.6

27.8

184.1

2000–2015

THJ

Tonghai

24.0

102.7

14.0

175.1

1995–2015

WHN

Wuhan

30.5

114.6

20.5

186.1

1980–2015

YSS

YuzhnoSakhalinask

47.0

142.7

38.4

209.8

1958–2015

WUS

WuShi

41.201

79.2

32.2

154.7

2007–2017

WEQ

WenQuan

44.961

81.0

35.8

156.7

2007–2017

HOQ

HongQian

45.359

84.8

35.9

160.1

2007–2017

WMQ

Urumqi

44.36

86.9

34.8

161.8

2007–2017

Zhang et al. Progress in Earth and Planetary Science(2024) 11:49

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Fig. 3 Comparison of the C-responses with and without ocean effect at Changli (CHL) and Chongming (CHM) stations. The C-responsesof the model incorporated with the variability in the ocean conductivity are indicated by red lines, while the C-responses of the 1D conductivity
model with no surface conductivity layer are indicated by the blue lines

For the predicted C 1D+3D surface , the staggered-gridfinite difference method is used for the forward estimation (Uyeshima and Schultz 2000). A thin-shelllayer of 12.6 km is used with a resolution of 1° × 1°
(Munch et al. 2018) to simulate the surface conductance of the earth.
Figure 3 shows the ocean effect on the C-responses at
Changli (CHL) and Chongming (CHM) stations along thecoastal region. A gap can be observed between the primary
C-responses and the corrected ones at small periods particularly when the period is less than 10 days, while theocean effect disappears at larger periods.

5 One‑dimensional inversion method
The L-BFGS inversion method can efficiently solve nonlinear optimization problems (Nocedal 1980; Liu et al.
1989). It has been widely used in electromagnetic inversions owing to its lower memory requirement and higher
convergence stability (Avdeev and Avdeeva 2009; Liu et al.
2013; Weng et al. 2015).
The basic iteration format of L-BFGS is as follows:
mk+1 = mk + αk pk(4)pk = −H−1
k ∇k

(5)



∇�k = (∂�/∂m1 , ∂�/∂m2 , · · · , ∂�/∂mN )T 

(6)

In the above equations, the number of iterations isrepresented by k. αk denotes the search step, and T
is the transposition of the matrix. The search direction is denoted by pk . H−1
k is the inverse of the Hessian matrix. By utilizing the second derivative of
the objective function to adjust the search direction, as expressed in Eq. (5), the convergence can be
accelerated.

6 Results
The accuracy of the inversion results significantly
depends on the initial model. Several initial models were
tested to find a suitable model for the inversions at thedifferent observatories. Figure 4 shows the corresponding inversion results. During the tests, two types of earthmodels were designed: a complex model, as shown in
Fig. 4a, and a simplified model comprising only five layers, as shown in Fig. 4b.
The forward models represent the models that were
designed to simulate the earth’s structure. The corresponding C-responses were calculated, and 3% Gaussian
noise was added to constitute the observed responses.
Figure 4a and b shows the inversion results of the two
designed forward models under five types of initial models, respectively. For all these initial models, the earth was
divided into different numbers of layers, and every model
was a half-space model of 1 Ω⋅m. When the change inobjective function less than ­10–5 or the regularization
factor is less than ­10–4, the inversion terminates.
Figure 4 shows that most of the initial models could
effectively restore the forward model in the depth rangeof 600–1200 km, whereas the 54-layer model was muchcloser to the designed model regardless of the complexity of the earth’s structure. Therefore, in the inversion of
the real data, the 54-layer model was chosen as the initialmodel.
Using the estimated C-response mentioned above,
the Ocean effect was corrected, and then a 1D L-BFGS
inversion was performed, and the initial model for the
inversion comprised a half-space with a conductivity of
1 S⋅m−1. The earth was divided into 54 layers, with thefirst layer having a thickness of 10 km and the subsequent layers increasing in thickness by a factor of 1.05.
The regularization parameter initially took a value of
2 and decreased by a coefficient of 0.5. Figure 5 shows
the typical corresponding fitting curves. Evidently, theZhang et al. Progress in Earth and Planetary Science(2024) 11:49

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Fig. 4 Initial model tests with two different forward models. The different colored lines indicate the results of different initial models used in the 1D
inversion; 38, 41 and 54 layers represent the initial models, which divide the earth into various layers, and “Kuvshinov and Olsen 2006” represents
the model developed by Kuvshinov and Olsen (2006), which divides the earth into more than 130 layers. a 41-layer model tests; the modelis the global mean conductivity model developed by Püthe et al. (2015a, b); b 5-layer model tests

Fig. 5 Fitting curves of the inversion result at typical stations. The blue circles indicate the observed C-responses after ocean effect correction,and the red lines represent the C-responses of the inversed modelZhang et al. Progress in Earth and Planetary Science(2024) 11:49

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Fig. 6 RMS distribution of the 1D inversion at different observatories.
The red color represents a poor fitting, whereas the blue color
represents a good fitting

inversion curves exhibit an excellent fit with the observed
C-responses at small periods, while the fitting gaps
become significant toward the large periods.
To better display the fitting effect of the response
curves at these observatories, a map of the root meansquare (RMS) of the 1D inversion was drawn. As
depicted in Fig. 6, most observatories have a low RMSof less than 0.4, indicating a better fitting between the
response curves of the inverted model and the observed
data. Some of the observatories in the south and west of
China have higher RMS, but the values are still less than0.5, implying that the inversion results are convincing.

7 Discussion
7.1 Heterogeneity in the conductivity

Figure 7 presents the typical conductivity-depth profiles
from six geomagnetic observatories. As shown by the
inverted models, it is apparent that the electrical conductivity varies significantly beneath the different regionsin East Asia. For the CNH (Changchun) observatory,
the conductivity significantly increased at the bottomof the MTZ, which has also been identified through themagnetotelluric network (Ichiki et al. 2001). The MZL(Manzhouli) and WMQ (Wulumuqi) observatories also
showed enhanced conductivities in the MTZ and theupper lower mantle. The four observatories in Northwest China all showed a high conductivity in the depth
range of 410–900 km, forming a large high-conductivity
area. In comparison, the JYG (Jiayuguan) observatory
maintained a low conductivity for depths less than 670
km, though the conductivity significantly increased at
the lower mantle. For the CDP (Chengdu) observatory,
the conductivity was significantly lower than the global
mean for depths shallower than 900 km, consistent with

Fig. 7 Typical conductivity-depth curves of the observatories
corresponding to different regions in East Asia. The thick black line
indicates the results of the global mean model which is modified
from Kelbert et al. (2009)

the conclusions drawn by Yuan et al. (2020). The MIZ(Mizusawa) observatory, which is located in the Japanese Islands and near the ocean, exhibited a considerably
higher conductivity than the global average for depths
greater than 670 km. These variations in the electricalconductivity in the depth range of 410–900 km indicatethat the mantle structure of East Asia has heterogeneity.

8 Map view of the conductivity
To more effectively illustrate the conductivity variationsin the MTZ and in the upper lower mantle, electricalconductivity maps were constructed based on the 1D
inversion results obtained from 27 observatories (Fig. 8).
These maps display the conductivity at depth intervals of
410–520, 520–670, and 670–900 km to highlight the heterogeneity in the conductivity more vividly, where every
slice has its own color bar. The global-scale conductivity model suggested the existence of a high-conductivity
anomaly in the MTZ of Eastern China (Karato 2011).
Moreover, 3D inversions have confirmed this phenomenon in East China (Kelbert et al. 2009; Semenov et al.
2012).
Evidently, the conductivities in the MTZ of East China
and Japan are considerably higher than that in the MTZ
of Western China (as shown in Fig. 8a). Notably, in thedepth range of 520–670 km, the high and low conductivity characteristic is consistent with the undulation ofChina’s surface topography, and many scholars believethat the dividing line (NSGL) can be considered the frontZhang et al. Progress in Earth and Planetary Science(2024) 11:49

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Fig. 8 Map view of the electrical structure of East Asia at different layers and corresponding seismic images: a Conductivity distribution at different
depths; b Maps of the logarithm of the conductivity; c Maps of the seismic images at different depths (Huang and Zhao 2006). The pink dashed line
indicates the NSGL, and the pink lines indicate the plate boundaries

edge of the subducting Pacific plate (Huang et al. 2006;
Yuan et al. 2020; Zhang et al. 2020). Furthermore, thefour observatories in Northwestern China distinctly indicate the presence of a high-conductivity anomaly in thedepth range of 410–900 km, which supports the findings
reported by Guo et al. (2021).
Figure 8b illustrates the variation in the conductivity
with the depth in the form of a unified logarithmic conductivity color bar. With the increase in the depth, theconductivity of the study area showed an upward trend.
The conductivity ranged from 0.01 to 1 S/m. In the MTZ,
the conductivity of the north and east of the study regionis higher than other regions.

9 Distribution of the water contents
The main minerals in the MTZ are wadsleyite and ringwoodite, which are the Earth’s main reservoirs of water,
and the existence of water has a significant impact on
the conductivity of the mantle (Karato 1990). Moreover,physical experiments on rocks under high-temperatureand high-pressure conditions have shown that the electrical conductivity of wadsleyite and ringwoodite in the
MTZ is closely related to the temperature and water content (Xu and Shankland 1998; Huang et al. 2005).
Huang et al. (2006) determined the effects of water andtemperature on the conductivity of minerals wadsleyite
and ringwoodite to infer the water content of the MTZ

Zhang et al. Progress in Earth and Planetary Science(2024) 11:49

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Fig. 9 The distribution of temperature and water content on electrical conductivity modified from Huang et al (2005): a Wadsleyite; b ringwoodite.
The numbers on the curves represent electrical conductivity (in S/m); the orange lines represent the temperature used in the estimation

Fig. 10 The distribution of water content in the MTZ: a 410–520 km; b 520–670 km(as shown in Fig. 9). It also reveals that conductivity ofthese minerals depends strongly on water content but
weakly on temperature. The presence of water can elevatetheir conductivity by up to three orders of magnitude(Koyama et al. 2006; Yoshino et al. 2008).
To further reveal the cause of the anomalies beneathdifferent regions, the inverted conductivity values of different stations are put in Fig. 9 to obtain the water content at each station. Since the temperature has relativesmall influence on conductivity, we set the temperatureof MTZ in the study area uniformly to 1850 K.
The estimated water content of MTZ at each geomagnetic station is shown in Fig. 10. At the depth of 410–
520 km, the northeast region of China (Such as WMQ,
WUS and HOQ stations) has relatively high water content of about 0.05–0.1 wt%. In the Bohai Sea and eastern
coastal areas of China, the average water content is about

0.08 wt%. In addition, the water content of the Japaneseisland arc is also relatively high, especially for the stations
of YSS and SSO where the water contents are close to
0.15 wt%.
At the depth of 520–670 km, the water content is lower.
In the northeast China and the eastern coastal areas ofChina, the water content is about 0.04 wt%; and in theJapanese island arc, the water content is about 0.02 wt%;
in addition, CHL and YSS stations have high water content more than 0.2 wt%.
9.1 Properties of anomaly bodies beneath Eastern China

In the MTZ of Eastern China, a stripe of high-conductivity and high-velocity anomaly bodies can be observed, as
shown in Fig. 8. As mentioned above, both temperature
and water content can increase the conductivity of theminerals in MTZ. However, based on the temperatureZhang et al. Progress in Earth and Planetary Science(2024) 11:49

alone, a high velocity should correspond to a relatively
low conductivity in the MTZ (Huang et al. 2005). Therefore, the water content of the minerals in the MTZ
beneath Eastern China should be considered. In addition,
Research on rock physics has revealed that the seismic
wave velocities are only weakly sensitive to water content (Karato 2011). Therefore, the high-conductivity andhigh-velocity anomaly bodies may represent the material with high water content and density. Moreover, many
global and regional tomographic images indicate that
the Pacific slab has reached the MTZ of China and that
it is horizontally distributed over a range of over 1000km on a discontinuous interface at a depth of 660 km in
Eastern China (Fukao et al. 2001; Zhao et al. 2009; Chenet al. 2017). It has been estimated that the water content
in this region is about 0.08 wt%, according to Guo andYoshino (2013), the stagnant Pacific slab has the capacity to convey an immense volume of water into the MTZ.
Consequently, this high-conductivity body in the MTZ of
Eastern China could be attributed to this water-enrichedslab.
9.2 Properties of anomaly bodies beneath Japaneseislands arc

It is worth noting that, due to the use of geomagnetic
data of Japanese islands, we also obtained the conductivity structure beneath the Japanese islands arc. Beneath
the Japanese Islands, the MTZ and upper lower mantle
can still be characterized by a high conductivity. However, in view of the seismic images, this high-conductivity body corresponds to a low velocity. Evidently, theproperty of this body differs from that of the body foundbeneath Eastern China, and the main factor contributing
to the high conductivity in the MTZ beneath the Japanese Islands may not solely be the water content.
Meanwhile, evident low-wave-velocity anomaly zones
could be seen at or near the front edge of most stagnant
slabs (Hayes et al. 2018; Goes et al. 2017), and these zones
showed high-conductivity features (Li et al. 2020; Kelbert
et al. 2009; Semenov and Kuvshinov 2012).
Without considering the temperature changes, the estimated water content in this area is about 0.15 wt%, which
exceeds the estimated critical water content in the upper
mantle (Bell and Rossman 1992), suggesting that partial
melting may occur. Considering the above factors, it canbe inferred that the high-conductivity and low-velocity
body in the MTZ beneath the Japanese Island could be
attributed to the combined effect of the temperature andwater content. The water content can lower the solidus of
the minerals, and the high temperature can likely cause
partial melting.
Nakajima and Hasegawa (2007) revealed the presenceof a distinct low-velocity anomaly in the upper mantle

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beneath Southwestern Japan through seismic tomography. It has been speculated that this anomalous region
was caused by temperature variations exceeding 200 K
and that it is associated with a melt fraction of less than
1% (Drewes 2009). This mushroom-shaped low-velocity
body may be the remnant of an upwelling mantle plume(He 2019). Owing to the proximity of the hot mantle
upwelling to the subducting plate, its elevated temperature may induce partial melting at the base of the West
Pacific subducting plate (Yang and Faccenda, 2020).
Therefore, it is speculated that there is a possible partial melting layer underneath the subducting slab. Thispartial melting layer may facilitate the deflection andflattening of the plate at the base of the MTZ, potentiallyaccounting for slab stagnation (Mao and Zhong 2018).
9.3 Properties of anomaly bodies beneath northwest
China

The inversion results also indicate notable high-conductivity anomalies in the MTZ and the upper lower mantle
beneath the northwestern region of China, particularlyin the upper lower mantle at Urumqi station. Seismic
imaging results have shown a large-scale lateral anomaly
characterized by a low velocity beneath the Tarim Basin(Lei et al. 2007; Wei et al. 2020), and these two types ofanomalies almost overlap.
The high conductivity in the MTZ is typically related
to increased temperature or water content (Huang et al.
2005). The water content in this area is estimated about
0.15 wt%, and such a high water content is typicallyrelated to the dehydration of the stagnant slab. Since
there is no evident stagnant slab in the MTZ beneath
Tarim Basin, and for the lower mantle, the water content is extremely low (generally not exceeding 20 ppm(Hirschmann et al. 2009)), the high conductivity beneath
Tarim Basin could be due to the high temperature.
The conductivity at different observatories near Tarim
Basin could be fitted by a rise in temperature, and thisrise in temperature may have caused changes in the mineral morphology, which correspond to the low velocity
inferred from the seismic images.
In summary, it can be speculated that there is a hotmaterial upwelling beneath the Tarim basin (Lei et al.
2007), originating from the lower mantle and penetrating
the discontinuous interface of 670 km into the MTZ. This
also provides electrical evidence for the possible existence of mantle plumes beneath the Tarim basin.
9.4 Properties of anomaly bodies beneath north China

For North China (around MZL observation), it can be seenfrom Fig. 8 that at greater depths, within the depth slice of
670–900 km, the high-conductivity body enlarges, whichcorresponds to a low velocity. Since the volcanic rock isZhang et al. Progress in Earth and Planetary Science(2024) 11:49

widely distributed in Great Xing’an Mountains (Xu et al.
2013), this high-conductivity and low-velocity anomaly may
related to the deep material sources of the volcanic activities. Therefore, this high-conductivity and low-wave-velocity anomaly zone may be the hot melten phase material.

10 Conclusions
The following are the main results and inferences
obtained by combining electrical conductivity modelingand seismic imaging of the earth’s mantle structure:
The subducting Pacific slab can convey a large amountof water into the MTZ beneath East China, and the dehydration of the stagnant slab may result in a high conductivity of the MTZ.
A low-resistance and low-velocity body, different fromthe cold subduction slab, was discovered beneath theJapanese Islands. It could be partially melted material andmay have played a certain role in the stagnation of the
Pacific slab.
In the WMQ and surrounding regions, a large lowresistance and low-velocity body was discovered in the
MTZ. It could be a hot upwelling material and may be
associated with the Tarim mantle plume.
In future, all these C-responses processed in this study
will be used for inverting a finer 3-D electrical structureof the MTZ and the uppermost lower mantle beneath
China, which could provide a more reliable model for the
deep structure of earth.
Acknowledgements
The authors thank A.D. Chave for kindly providing the BIRRP software package
and the National Geomagnetic Network Center of China for providing thegeomagnetic data. Some figures were prepared using GMT software (Wessel
and Smith 1998).
Author contributions
Yanhui Zhang finished the data processing, 1D inversion and drafted themanuscript. Yuyan Zhang designed and helped improved the work. Mina Ma
designed and managed the work. Yujia Hu and Yiliang Han have helped in
improving the inversion code and the interpretation.
Funding
This research was funded by the National Natural Science Foundation ofChina, grant number 42104079, the Natural Science Foundation of Hebei
Province of China, and grant number D2021210007 and E2022210060, theHebei Province Higher Education Science and Technology Research Young
Top Talent Project (No. BJK2024037) and the technology development projectof Shuohuang Railway Development Co., Ltd. (GJNY-20-230).
Availability of data and materials
The data and materials will be provided if needed.

Declarations
Competing interests
The authors declare that they have no competing interests.
Received: 11 April 2024 Accepted: 21 August 2024

Page 11 of 12

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