Next Article in Journal
Morpho-Molecular and Genomic Characterization of Penicillium mexicanum Isolates Retrieved from a Forsaken Gold Mine
Previous Article in Journal
Optimizing Fundamental Frequencies in Axially Compressed Rotating Laminated Cylindrical Shells
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 14
Issue 22
10.3390/app142210596
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Shallow Magmatic System of Arxan Volcano Revealed by Ambient Noise Tomography with Dense Array

by
Lijuan Qu
1,
You Tian
1,2,*,
Cai Liu
1 and
Hongli Li
1
1
College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China
2
Changbai Volcano Geophysical Observatory, Ministry of Education, Jilin University, Changchun 130026, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(22), 10596; https://doi.org/10.3390/app142210596
Submission received: 26 October 2024 / Revised: 13 November 2024 / Accepted: 15 November 2024 / Published: 17 November 2024
(This article belongs to the Section Earth Sciences)
Browse Figures
Versions Notes

Abstract

:
The Arxan Volcanic Field (AVF) is an active volcanic region in Northeast Asia, and its last eruption occurred approximately 2000 years ago. Its eruption mechanism remains unknown. To investigate the shallow magma system beneath the volcanic cones in the AVF, we deployed a dense seismic array consisting of 227 portable seismographs and conducted high-resolution ambient noise tomography (ANT). The results of checkerboard test (CRT) and restoring resolution test (RRT) demonstrate that our imaging results are reliable. These results reveale significant slow-velocity anomalies at depths of 5~9 km below the Tianchi caldera and GD1213 volcano in Arxan, with the highest anomaly reaching up to approximately 15%. These anomalies suggest partial melting in a shallow magma chamber, indicating ongoing volcanic activity in the AVF. The velocity of the magma chamber corresponding to a melt fraction of approximately 7.4~12.9%. Therefore, the presence of the magma chamber poses potential hazards to the Arxan region, including volcanic eruptions and their associated risks.

1. Introduction

Northeast Asia, comprising countries such as China, Japan, and Korea, is influenced by the interactions of multiple tectonic plates, making it one of the most complex geological regions in the world [1]. This complexity primarily arises from the ongoing subduction of the western (Paleo-)Pacific Plate and the consequent formation of back-arc basins, such as the one in the Sea of Japan. These geological processes have contributed to the emergence of intraplate volcanoes, most notably Changbaishan and Arxan (also referred to as the Halaha volcano). Volcanic activity was widespread during both the Mesozoic [2] and the Cenozoic [3,4], resulting in the extensive distribution of volcanic rocks across the region. Previous studies have attributed the formation of intraplate volcanoes in Northeast Asia to processes such as the upwelling of wet and hot materials, dehydration of the big mantle wedge, and movement of asthenosphere materials. However, while the velocity structure beneath the Changbaishan volcano has been extensively documented [5,6,7,8,9,10], information regarding the velocity structures beneath other volcanoes remains scarce.
The AVF is located in the eastern part of the Central Asian Orogenic Belt (CAOB) and the central–west segment of the Great Xing’an Mountain in northeastern China, adjacent to the western side of the North–South Gravity Lineament, which extends westward to Mongolia and eastward to the Songliao Basin (Figure 1a). Geologically, it lies at the southeastern edge of the Siberian Craton and has been influenced by the Mongolia–Okhotsk tectonic belt and the Pacific tectonic domain [2]. The tectonic history of the region involves the closure of the Okhotsk oceanic basin and a late Jurassic to early Cretaceous left-lateral strike-slip and back-arc extension between the Izanagi Plate and the Northeast Asian continent. This tectonic activity led to the development of large-scale north–northeast-trending left-lateral strike-slip fault systems and accompanying volcanic activities across Northeast Asia, with a succession of intracontinental rift-related volcanic depression basins forming during the Mesozoic, gradually shifting from west to east over time. Despite significant research, there remains considerable controversy surrounding our understanding of tectonic evolution, particularly in the western region of the Great Xing’an.
The Great Xing’an volcanic group, located in Northeast China, represents a significant Mesozoic volcanic rock belt [2]. Intense volcanic activity occurred during the Mesozoic era, spanning from the Jurassic to the Early Cretaceous. This period was characterized by the intrusion and eruption of intermediate acid magma [11]. The volcanic rocks predominantly comprise medium-acidic volcanic melt rocks and volcanic detrital rocks, with the latter being the dominant rock type. Geological strata in the region consist of Jurassic volcanic rock formations overlaid by quaternary volcanic deposits, exhibiting a general northeastward distribution. During the Cenozoic era, coinciding with the east–west gradient formation in China, the Great Xing’an experienced significant uplift accompanied by extensive basaltic magma activity. Volcanic activity in the Arxan Volcanic Field (AVF) during the Cenozoic era was multi-phased. While previous geological surveys primarily suggested volcanic activity during the Pliocene and Pleistocene periods, evidence from geological and geomorphological features suggests the possibility of Holocene volcanic eruption activities [12,13]. Bai et al. [14] argue that the active periods of the Yanshan and Gaoshan volcanoes should be classified as Holocene, suggesting their classification as broadly active volcanoes.
Previous studies on the AVF have focused on various disciplines, including geology, volcanology, geothermal studies, seismology, and hydrology [3,4,14,15,16,17,18,19,20,21,22,23,24]. The age of the Tianchi basalts is estimated to be 0.34 ± 0.203 million years, while the age of the Yanshan and Gaoshan charred woods is 2000 years, suggesting that the AVF is relatively young and still has eruptive potential [12,14]. Quaternary lavas from the AVF are characterized by alkaline olivine basalt and olivine–nepheline basanite compositions, typical of island basalt with trace and rare-earth elements and an alkaline, within-plate sodium basalt composition [17]. Studies by Cui et al. [22] on the fluid geochemistry of hot springs in the Arxan region indicated that the overflow gaseous substance primarily originate from the upper mantle or crust such as the He (3–23%) in spring gas bubbles or crater water, and overflow gaseous substance (N2, He, Ar and CO2) in hot spring. Studies have demonstrated that the He level in the AVF is significantly higher than in other dormant volcanoes in China, suggesting ongoing volcanic activity [24]. Additionally, the soil CO2 emission level in the AVF is comparable to that of island volcanoes, indicating significant volcanic degassing activity.
Previous seismic studies have identified a slow velocity (P-wave and S-wave) area in the Arxan upper mantle and crust, yet discrepancies exist in the understanding of its depth and extent, attributed to differences in data coverage and methodology [5,6,19,25,26,27,28,29,30]. Tang et al. [15] employed magnetotelluric methods to identify two magma channels leading to the mantle beneath the volcano, shedding light on subsurface volcanic structures. Han et al. [31] discovered numerous low-resistance anomalies in the lithosphere interior of the eastern section of the CAOB, indicating a possible connection to the upward intrusion of asthenospheric material, suggesting dynamic geodynamic processes. Yan et al. [32] utilized mineral physics and geothermal methods to investigate the Halaha volcanic areas, revealing higher temperatures, a thinner thermal lithosphere, and lower rheological strength, likely tied to the small-scale upwelling of hot asthenospheric material due to lithospheric delamination under the surface of the Songliao Basin. In a recent study, researchers carried out seismic tomography in the AVF, offering insights into the magmatic system and seismicity [23]. However, uncertainties remain regarding the presence of upward-transported magma channels or localized subsidence in the upper mantle of the AVF, highlighting the need for further research to understand the magma system and its associated hazards.
In recent decades, ANT has rapidly progressed, with numerous scholars conducting imaging studies at various scales of velocity structures [33,34,35,36,37,38]. ANT has been particularly successful in imaging the distribution of magma chambers beneath volcanoes, as demonstrated by studies such as those by Masterlark et al. [39], Nagaoks et al. [40], and Seats and Lawrence [41]. Moreover, the continuous development of short-period dense seismic arrays has significantly enhanced the capabilities of ANT by providing a better data source for refined imaging. This method has the advantages of a high resolution and cost-effectiveness, making it increasingly popular for detecting small-scale crustal structures [42,43,44]. Therefore, this study utilized arrival time data from a high-density seismometer array. A detailed 3D S-wave (Vs) model of the upper crust in the AVF was successfully obtained, which provided a high-resolution 3D velocity model to further understand the shallow crustal structure and magma system in the AVF.

2. Data and Methods

Coupled with the ongoing use of short-period seismometers in seismology, research into the shallow crustal structure is becoming increasingly sophisticated. In September 2020, researchers from Jilin University set up a linear dense seismic array in the study region, consisting of 227 stations that comprised 197 EPS-2-M6Q and 30 Smartsolo seismographs (Figure 1b). In order to detect the magmatic system beneath the AVF, most of the craters in the AVF were covered with seismometers. The TCV and GD1213 craters were situated close to the center of the array. The station interval increased to approximately 500 m as the distance from the TCV volcano cone increases, providing enough tomographic resolution below the TCV crater. This research focused exclusively on vertical component data. According to the modified method of data processing and analysis put forward by researchers [35], the raw data fragments were first merged, cut, band-pass filtered, de-instrumented, and downsampled, and then the daily SAC [45] format files were created for every station. The daily waveform data from all stations underwent preprocessing steps, containing demeaning, detrending, normalization in the time domain, and spectral whitening in the frequency domain.
After preprocessing, cross-correlation calculations were conducted on each station pair to derive daily noise cross-correlation (NCC) functions. Subsequently, all daily NCC functions were linearly overlaid and compute the mean across negative and positive lags. This process aimed to enhance the signal-to-noise ratio to create the last CC function. Notably, Rayleigh waves became evident in the NCC functions following the application of band-pass filters in the 0.08–2 Hz and 0.2–1 Hz ranges (Figure 2). The SNR demonstrates variability dependent on both the station interval and the frequency band of the applied band-pass filter, as depicted in Figure S1. Rayleigh waves exhibit a notably great value of the signal-to-noise ratio in the low-frequency band, particularly evident with greater distances between stations. To enhance the detection and analysis of Rayleigh waves, multiple filtering techniques were executed to the ambient noise CC function, as outlined by Herrmann [46], ultimately facilitating the calculation of the Rayleigh wave group velocity.
Figure 3a presents the Rayleigh wave group velocity dispersion data obtained from the NCC functions using the frequency–time analysis method [35,47,48,49,50], focusing on a time range of 1–6 s. Data were filtered using band-pass filter ranges of 1–20 s and 4–10 s, respectively, to isolate Rayleigh wave signals, and the group velocity was calculated for each frequency band. The resulting velocity data were integrated and concatenated to generate the final dispersion curves. Quality control measures were implemented by selecting dispersion data from NCC functions with an SNR exceeding 3. After screening out dispersion curves with significant velocity fluctuations, we obtained 1894 group velocity curves with a great SNR value from 18,749 station pairs (Figure 3a). Statistical analysis reveals notable peaks in ray path numbers, with one located in periods of approximately 2–5 s (Figure 3a). Additionally, Figure 3b illustrates a relatively even distribution of ambient noise power across the research region.
As a result of intense scattering and attenuation, extracting high-frequency Rayleigh waves (period 0.25–2 s) from station pairs separated by hundreds of kilometers presents considerable challenges [34,36,37]. However, dense station arrays enable the detection of high-frequency Rayleigh wave signals (0.25–2 s) even with small station spacing separated by a few kilometers or even hundreds of meters. This capability is demonstrated to be valuable for imaging shallow, near-surface 3D shear wave velocity structures [42,43].
After measuring Rayleigh wave group velocities, we conducted an inversion using these dispersion data to construct a three-dimensional high-resolution S-wave velocity model of the upper crust (0–9 km). Utilizing the velocity model provided by Li et al. [23] as a reference, we derived a one-dimensional average velocity initial model (Figure S2a).
According to the empirical formula of Brocher [51], the density and pressure wave velocity were coupled with the shear wave velocity inversion. In this study, the surface wave direct inversion method proposed by Fang et al. [43] was used to invert the three-dimensional shear wave velocity structure. Compared with the traditional surface-wave inversion method, this method does not need to invert the group velocity diagram but instead utilizes frequency-dependent ray tracing [52] and direct tomographic inversion based on wavelet sparse constraints. This approach accounts for the effects of the non-great-circle path propagation of surface waves in complex media, addressing the limitations of traditional surface wave imaging methods. We conducted an analysis of depth sensitivity kernels for the 1–6 s period Rayleigh wave group velocity dispersions at S-wave velocity, while the most sensitive depth for waves in the 6 s period range was 9 km (Figure S2b). Hence, the inversion incorporates S-wave velocity data down to 9 km under the surface, although the further resolution of deeper structures remains essential. The line chart depicts the root mean square and standard residual subsequent to each iteration, showing a rapid decreasing trend until it stabilizes at 0.84 (Figure S3a). Figure S3b displays histograms of the RMS time residuals from the reference and last-result models after 13 iterations. The results indicate a significant reduction in travel time residuals after inversion compared to the initial model, with a normal distribution near 0, signifying the reliability of the model after 13 iterations. The derivative weight sum (DWS) provides an average relative measure of the density of seismic rays near a given velocity node. This measure of seismic ray distribution is superior to an unweighted count of the total number of rays influenced by a model parameter, since it is sensitive to the spatial separation of a ray from the nodal location [53]. The DWS serves as an indicator of inversion result stability [54]. In this study, we established a criterion of DWS values over 10 to make sure the stability of the inversion results (Figure S4).

3. Results

3.1. Checkerboard Resolution Test and Restoring Resolution Test

In tomographic inversion, the DWS is utilized for the preliminary estimation of the three-dimensional velocity model. This parameter is associated with the number of rays intersecting surrounding a grid point [54]. We expect the DWS to show high values, as a higher DWS value at the speed framework mesh point indicates a better resolution. The results suggest that the DWS distribution (Figure S4) is excellent beneath regions with a high concentration of stations, particularly below the vicinity of the GD1213 and TCV calderas, indicating that this part of the model has a higher resolution. However, in the marginal areas of the AVF, relatively low DWS values are observed as a result of the wide spacing between seismic stations. Moreover, the value of the DWS for the model generally diminish below a depth of 9 km due to data limitations, although a sufficient resolution is maintained at the TCV caldera.
We further accomplished a couple types of synthetic tests for the velocity model to evaluate the resolution. One of them was the CRT. In the CRT, negative and positive perturbations of 20% relative to the 1D initial velocity model were alternately allocated to the 3D grid nodes in vertical and lateral directions. We investigated the model resolution with grid intervals of 0.055° × 0.035°, 0.075° × 0.055°, and 0.115° × 0.08° to define the most suitable grid spacing for inversion (Figures S5–S7). A 5% perturbation was applied to the grid nodes in the depth direction, with increased layer thickness to avoid velocity-discontinuity interfaces. The checkerboard resolution results in the depth direction are shown in Figure 4.
At the same time, we introduced a parameter V to represent the recovery degree of CRT [55]. The value of V ranges from 0 to 1, with larger values indicating better recovery, and vice versa. The value of V can be obtained from
V = (Vin − Vref + Vout − Vref)2/2 * [(Vin − Vref)2 + (Vout − Vref)2]
where Vin and Vout represent the input and output velocity models of the CRT, respectively, and Vref denotes the 1D initial velocity model. Figure 5 and Figures S8–S12 effectively illustrate the CRT results by plotting the recovery degree of the CRT values derived from Equation (1). The outcomes illustrate that the model recovers well at depths above 9 km below the TCV and GD1213 calderas from the result.
However, in the peripheral areas of the AVF, the model cannot fully recover due to sparse seismic ray coverage. Generally, all models with varying grid intervals are effective in the vicinity of the volcanic craters of interest, demonstrating a higher resolution compared to when used in marginal areas. Therefore, considering both the resolution results and the checkerboard resolution test results, a grid interval of 0.055° × 0.035° in the horizontal directions was chosen for the inversion.
We also conducted an RRT to estimate the reliability of the result model. To establish the true model, we added velocity anomalies of −10% and 10% into the 1D initial velocity model. In this case, the high- and low-velocity perturbations in the true model are similar to those in the S-wave velocity model (Figure 6 and Figure S13). The input velocity anomalies at a depth of ~9 km were accurately restored in principal components of the S-wave velocity from the RRT results, which were covered by a packed seismic arrangement. However, speed variations at the edges of the packed seismic arrangement could not be adequately resolved. The test results indicate that our model beneath the TCV volcanic region is high-resolution, and the key characteristics, as talked about below, are robust. The resolution around the GD1213 and TCV calderas is sufficient for all cycles, as it is almost at the focal point of the seismic arrangement.

3.2. Three-Dimensional S-Wave Velocity Model

This study acquired a precise spatial shear wave velocity model beneath the AVF at 1–9 km below the surface. The model was clipped dependent on the DWS distribution (DWS > 10) and the recovery degree of the CRT (V > 0.6), as shown in Figure S14. The outcomes of this study indicate significant horizontal variations in the shallow crustal S-wave velocity compositions below the AVF. Low velocities and high velocities are evident in each profile. Figure 7 illustrates, parallel to the horizon, profiles of velocity perturbations at 1~9 km below the surface, which more clearly show lateral velocity than absolute velocity variations. Low S-wave velocities along the northeast direction beneath the AVF align with the distribution of volcanic cones. The 3D S-wave velocity model and velocity perturbations reveal unique features closely associated with volcanic activity. The model displays two different magnitudes of velocity perturbations at shallow and deeper depths. At depths of 1–3 km, there are significant low-velocity perturbations concentrated near the volcanic cones of WSLZ, TCV, YM, GD1308, SHGB, SFS, XDG, GD1153, GD1325, GD1213, GD1201, and DC, which are related to volcanic eruptions. Significant low-speed perturbations can also be seen around Dujuan Lake. At greater depths of 5–9 km beneath the GD1213, more significant low-velocity perturbations become apparent, especially at 7 km below the surface, where the velocity anomaly attains up to nearly 15%. These large low-velocity perturbations are correlated with the residual low-speed bodies left after volcanic eruptions and with magma pipes and chambers that can transport and store partially melted material.
Figure S15 illustrates different vertical profiles of the results traversing the AVF calderas. Additionally, Figure 7 presents three vertical cross-sections of velocity perturbations through the AVF, which highlight more pronounced velocity variations. Furthermore, Figure 8 displays the distribution of seismic earthquakes [23], showing that most seismic events are situated at the transition zones between different perturbation bodies. Predominantly, large negative perturbations under the TCV caldera and GD1213 caldera are observed at depths of approximately 5 km, with the width of velocity perturbations beneath the GD1213 caldera at 7 km being the broadest, at approximately 6–10 km. At depths of 5–9 km, the negative perturbation remains larger than the adjacent component at the equal level. Similarly, notable low-velocity perturbations are present below GD1213 caldera. Additionally, low-velocity anomalies distinct from the surrounding material are identified at 0–3 km depths beneath the volcanic cone of the SHGB and WSLZ, although these anomalies are less pronounced than those under the TCV caldera, DC caldera, and GD1213 caldera. Weaker low-velocity perturbations are also observed at depths of 5 km beneath the GD1286 caldera and WSLZ.

4. Discussion

Following the latest volcanic eruption in the AVF, the magma system still exhibits partial melting and high temperatures in the crust around the volcanic cones. In the late 20th century, researchers inferred the upper limit of the Quaternary magma chamber’s depth using pressure values derived from the crystallization of the first crystalline phase olivine in the most recently erupted lava flow at WDLC [56]. In response to the distinct features of the velocity perturbation anomaly structure in the AVF, various explanations have been proposed for different velocity anomalies at various depths beneath the AVF volcanic cone (Figure 8).
In this study, the S-wave model is highly consistent with surface features. There are many intersecting faults distributed in the study area (Figure 1b), predominantly trending in the northeast and north–northeast directions, with the majority situated near the volcano cone (although no exposed faults have been observed on the surface [24]). The region features several volcanogenic lakes, including TCV Lake, WSLZ Lake, and DC Lake. TCV Lake, located at the summit of the volcano, formed from the accumulation of water within the caldera. Researchers detected He from the mantle in bubbling gasses and proposed that deep fluids transport heat upward through magma pipes [24]. WSLZ is a volcano created by phreatomagmatic activity that re-erupted after caldera breaks in the Mesozoic. DC Lake may have origins similar to those of the Mauna Ulu lava lake [57] and Aloi lava pond [57,58] in the Kilauea volcano, Hawaii, and was provisionally attributed to a collapsed lava lake.
In contrast to the Yellowstone volcanic group [59], the low-velocity zone observed at shallower depths (approximately 3 km) may result from fluid accumulation within fractures in the area, rather than from the migration of magmatic fluids (such as gasses and hydrothermal fluids) and melts from the magma chamber to shallower depths. In the Yellowstone volcanic area, shallow (0–3km), low-velocity bodies are linked with the main magma chamber located at a depth of 5–14 km under the Yellowstone volcano, aiding the transport of lava flow to shallow depths [59,60]. Otherwise, no significant hydrothermal variations were observed around the volcanic cones (XDG, GD1286, GD1202, SHGB, and GD1201) in the AVF. Consequently, we inferred that the slow velocity perturbation at shallower depths (1 km) under GD1201, as well as the slow velocity regions at ~3 km under the surface below calderas GD1213, GD1153, and SHGB, can be caused by the presence of fluids (such as surface water circulating through cracks or atmospheric precipitation) instead of lava flow [44,60,61].
The surrounding AVF area is characterized by high-frequency seismic activity. According to statistics, from 1991 to 1999, a total of 1946 earthquakes occurred, with 70% registering below a magnitude of 2.0. This low-magnitude, high-frequency seismicity is similar to volcanic seismicity [14]. The upward migration of magma is accompanied by a decrease in pressure and a change in volume, leading to a phase transition between liquid and gas [44], which may also coincide with seismic activity [62]. The depth of volcanic earthquakes often reflects the proximity of magma to the surface [62,63]. Figure 8 shows the focal depth [23], indicating conditions conducive to the existence of a magma chamber in the shallow crust. Therefore, the low-velocity body at a 5–9 km depth below some craters (TCV, GD1213, and GD1153) (Figure 8) can be classed as a magma chamber with partial melting and magmatic fluids at high temperatures. These features also correspond to the anomalous great value of the wave velocity ratio in this area shown by researchers [23]. Bodies resembling slow-velocity bodies have been observed beneath Mt Asama volcano in Japan, interpreted as a magma chamber [40], as well as beneath Yellowstone volcano [41,59,60,64], Toba in Indonesia [65], Lastarria volcano in Chile [61], and Laoheishan volcano in Wudalianchi. XDG is a Holocene volcano, and multiple volcanic earthquakes occur around the low-velocity body beneath the XDG volcanic cone. We consider these low-velocity bodies as pipes that can transport magma fluid and partially melted material at high temperatures. Similarly, they may be described as pipes beneath WSLZ volcanic cones that can transport magmatic fluid and partially melted material at high temperatures. In this study, no magma chamber or pipe was found beneath GD1201. Additionally, the weaker low-velocity zones beneath the SHGB volcanic cones can be interpreted as cooling magma pipes. Regarding whether there is a magma chamber beneath GD1286 and how it is spatially distributed (Figure 8), further geophysical surveys are needed to boost the resolution and verify our results due to the relatively low resolution in this area.
Based on our model, we assesed that there is a magma chamber with a bulk of no less than 200 cubic kilometers (~8 × 7 × 4 km) beneath the GD1213 and TCV near the crater. Although many volcanoes have magma chambers directly below the caldera [39], a horizontally offset magma chamber from the volcano’s summit is a common feature [40]. The volcanic area contains a total of 20 craters within approximately 40 × 40 square kilometers. It is possible that there are several magma chambers under the AVF. Based on our present tomographic results and previous research, the magma chamber beneath GD1213 and TCV in Arxan may have pipes connecting it to the lower crust. This may be the primary cause of volcanic eruptions near craters (TCV, GD1213, DC, XDG, GD153, and GD1325) in the AVF. If this hypothesis is confirmed, it would suggest that although the migration channel of the magma reservoir is blocked by high-speed material at 3–5 km (Figure 8), this obstruction could significantly impact the physical mechanisms of magma migration. Consequently, the risk of volcanic eruption remains due to the magma reservoir’s connection to the lower crust.
Due to the limitations of tomographic imaging using short-period Rayleigh wavelengths (<6 s), it is not possible to obtain magma distributions at greater depths using these imaging results. However, previous geophysical and geochemical studies can still provide valuable information regarding the structure of the lower and middle crust, as well as the upper mantle.
The origin of Cenozoic volcanism in Northeast China is closely linked to the subduction of the western Pacific plate rather than to deep mantle plum-type hotspot volcanos [66]. Body wave tomography has revealed significant slow-velocity bodies extending 200–400 km under the surface beneath Northeast China. This suggests that Cenozoic volcanic activity is closely associated with deep processes such as the subduction and dehydration of the Pacific Plate beneath the continental mantle transition zone in Northeast Asia, as well as convection circulation within the BMW, leading to the partial melting and upwelling of asthenospheric material [67]. Geochemical studies indicate that the magma system of the AVF originates from the mantle. Basaltic melts can ascend from the upper mantle to the Moho and subsequently rise to the lower crust, where they provide partially melted siliceous fuel to the magma chamber in the upper and middle crust [17,22,24]. Zhang et al. [18,19,20] found a low-velocity body extending to the vicinity of the mantle transition zone beneath Arxan, which is connected to the low-velocity bodies in the southern Songliao Basin and related to volcanic origins. Furthermore, the imaging results reveal prominent low-velocity bodies in the crust [19]. The southern and northern thicknesses, as well as the degree of partial melting of the asthenosphere in the eastern segment of the CAOB, exhibit heterogeneity [31]. Meanwhile, tomographic imaging results indicate east-thick, west-thin characteristics in the asthenosphere of the CAOB. Receiver function results indicate significant lithosphere thinning in the eastern part of the north–south gravity gradient zone [68]. Arxan has an ascending mantle flow that partially melts, exhibiting a lower degree of partial melting compared to the Changbaishan volcano [69]. This study’s results on the lithosphere’s temperature structure suggest that the temperature beneath the volcanic area is high, possibly due to the small-scale upwelling of thermal material brought about by lithospheric layering beneath the Songliao Basin [32]. The high-speed subsiding flow beneath the Songliao Basin triggers secondary convection in the western part, causing local upwelling of the asthenosphere and resulting in a slow velocity in the mantle and lower crust below the Arxan volcano [28].
Studies have carried out a magnetotelluric survey around Arxan, identifying varying degrees of low-resistance bodies in the middle-lower crust near XDG and GD1213 [15]. They have suggested that these anomalies are likely remnants of crustal magma pockets and cooling magma channels associated with the most recent eruption, revealing two magma channels leading to the mantle below the two volcanic belts. Li et al. [23] proposed the existence of crustal pipes for transporting magma fluids and partially molten magma systems at high temperatures beneath the AVF, indicating a crust-scale magma system. These findings suggest that basaltic magma can ascend from the upper mantle to the Moho and subsequently rise to the lower crust, providing fuel for the partial melting of the continental crust in the middle to shallow crust magma chamber.
Utilizing the method proposed by Chu et al. [70], we determined the correlation between the melt fraction and shear velocity based on the velocity in the magma reservoir. By substituting rhyolite with basalt, we estimated the melt fraction for the AVF. The velocity of the magma chamber lies between 2.894 and 3.136 km/s, corresponding to a melt fraction of approximately 7.4~12.9% (Figure 9). Given that the low-velocity anomaly is attributed to partial melting and the limitations of seismological methods, we calculated the melt fraction to be between 7.4% and 12.9%. Although the melt fraction has not yet reached the threshold for volcanic eruption [71], it is imperative to closely monitor the eruption risk.

5. Conclusions

Tomography based on a dense array offers novel insights into the distribution of the magma system in the AVF. Volcano eruptions have significantly advanced our comprehension of the magma system compared to previous studies.
This study identified low-velocity bodies at a depth of 5–9 km below the AVF, coinciding with a series of volcanic earthquakes near these anomalies, indicating the potential existence of a magma chamber in the crust with a volume of at least 200 km3. Extensive volcanic activity during the Quaternary period erupted along the deep rift system in the AVF, possibly related to the stagnant subduction of the Pacific Plate beneath northeastern China, leading to the continuous accumulation and upward thickening of the lithosphere. Moreover, the presence of magma channels and chambers in the middle-lower crust and upper mantle suggests ongoing magma replenishment, potentially contributing to the eruption potential near the TCV crater and GD1213. However, due to limited detailed information, a reliable resolution is primarily observed in the central AVF area and some surrounding volcanoes. Therefore, further seismic observations spanning larger spatial and longer temporal scales are necessary to comprehensively delineate the entire magma system in the lower and middle crust and upper mantle, thereby facilitating the analysis of its relationship with shallow magma chambers. The melt fraction of the magma chamber was found to be between 7.4% and 12.9%. Although the threshold for a volcanic eruption has not yet been reached, it is still essential to closely monitor the potential eruption risk.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/app142210596/s1: Figure S1: Multiple filtered waveforms. The filtering range is located in the upper right corner of each waveform. (a) The distance between the station pair is 14.2128 km. (b) The distance between the station pair is 18.8153 km. (c) The distance between the station pair is 32.8185 km. Figure S2: (a) The initial 1D S wave model utilized in this research. (b) Normalized depth sensitivity kernels for S-wave velocity regarding the Rayleigh wave group velocity ranging from 1 to 6 s. Figure S3: (a) The standard residual and root mean square residual after each iteration. (b) Comparison of double-difference travel time residuals. The blue histograms represent the travel-time residuals before iterations, while the orange histograms represent the travel time residuals after thirteen iterations. Figure S4: (a–f) The DWS distributions with a grid size of 0.055° × 0.035° for the vs. inversion. The value of the DWS for each block can be seen in the color bar. All subplots are marked with different depths in the bottom left corner. Figure S5: (a–f) Graphs displaying the recovery results of the CRT. Each block’s size was set to 0.055° × 0.035° for tomography result comparison, and the value of the recovery degree for each block can be seen in the right lower corner. All subplots are marked with different depths at the bottom left corner. Figure S6: (a–f) Graphs displaying the recovery results of the CRT. Each block’s size was set to 0.075° × 0.055°. Figure S7: (a–f) Graphs displaying the recovery results of the CRT. Each block’s size was set to 0.115° × 0.08°. Figure S8: (a–f) Graphs displaying the recovery degree of the CRT. Each block’s size was set to 0.055° × 0.035° for tomography result comparison. The value of the recovery degree for each block can be seen in the right lower corner. All subplots are marked with different depths in the bottom left corner. Figure S9: (a–f) Graphs displaying the recovery degree of the CRT. Set each block’s size to 0.075° × 0.055°. Figure S10: (a–f) Graphs displaying the recovery degree of the CRT. Each block’s size was set to 0.115° × 0.08°. Figure S11: (a–c) Vertical profiles show the extent of CRT recovery along the three profiles (L1L1′, HH’, L2L2′) indicated in Figure 8. The value of the recovery degree for each block can be seen in the color bar. Each block’s size was set to 0.075° × 0.055°. Figure S12: (a–c) Vertical profiles show the extent of CRT recovery along the three profiles (L1L1′, HH’, L2L2′) indicated in Figure 8. The value of the recovery degree for each block can be seen in the color bar. Each block’s size was set to 0.115° × 0.08°. Figure S13: Graphs displaying the recovery results of the RRT. The red color blocks illustrate low-velocity perturbations; the blue color blocks are the same as for the red color blocks but for high-velocity perturbations. The magnitude of the perturbation value for each block can be seen in the color bar. Each block’s size was set to 0.055° × 0.035°. (a) The input model of RRT. (b) Restoration results of RRT. Figure S14: (a–f) Tomographic images of shear wave velocity at 1–9 km below the surface. All subplots are marked with different depths in the bottom left corner. The other labels are identical to those in Figure 7. Figure S15: (a–c) Vertical profiles show a 3D comparison. The velocity of each block can be seen on the right.

Author Contributions

Data curation, formal analysis, and writing—original draft preparation, L.Q.; data curation, formal analysis, writing—review and editing, and funding acquisition, Y.T.; supervision and formal analysis, C.L.; data curation and formal analysis, H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (grant No. 42274065), the National Key R&D Program of China (Grant No. 2022YFF0801003), and Fundamental Research Funds for the Central Universities in China.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data will be made available on request.

Acknowledgments

We truly appreciate the editor and two anonymous reviewers for their constructive review comments and suggestions, which have improved this paper. We thank Hongjian Fang for developing the direct surface-wave inversion method. The Generic Mapping Tools (GMT Version 6.4.0) software package was used to plot most of the figures [72].

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bird, P. An updated digital model of plate boundaries. Geochem. Geophys. Geosyst. 2003, 4, 1–52. [Google Scholar] [CrossRef]
  2. Xu, W.-L.; Pei, F.-P.; Wang, F.; Meng, E.; Ji, W.-Q.; Yang, D.-B.; Wang, W. Spatial–temporal relationships of Mesozoic volcanic rocks in NE China: Constraints on tectonic overprinting and transformations between multiple tectonic regimes. J. Asian Earth Sci. 2013, 74, 167–193. [Google Scholar] [CrossRef]
  3. Liu, J.; Han, J.; Fyfe, W.S. Cenozoic episodic volcanism and continental rifting in northeast China and possible link to Japan Sea development as revealed from K–Ar geochronology. Tectonophysics 2001, 339, 385–401. [Google Scholar] [CrossRef]
  4. Pan, B.; Liu, G.; Cheng, T.; Zhang, J.; Sun, Z.; Ma, B.; Wu, H.; Liang, G.; Guo, M.; Kong, Q.; et al. Development and status of active volcano monitoring in China. Act. Volcanoes China 2021, 510, 227–252. [Google Scholar] [CrossRef]
  5. Zhao, D.; Lei, J.; Tang, R. Origin of the Changbai intraplate volcanism in Northeast China: Evidence from seismic tomography. Chin. Sci. Bull. 2004, 49, 1401–1408. [Google Scholar] [CrossRef]
  6. Lei, J.; Zhao, D. P-wave tomography and origin of the Changbai intraplate volcano in Northeast Asia. Tectonophysics 2005, 397, 281–295. [Google Scholar] [CrossRef]
  7. Zhao, D.; Tian, Y. Changbai intraplate volcanism and deep earthquakes in East Asia: A possible link? Geophys. J. Int. 2013, 195, 706–724. [Google Scholar] [CrossRef]
  8. Zhang, H.; Tian, Y.; Zhao, P. Dispersion Curve Interpolation Based on Kriging Method. Appl. Sci. 2023, 13, 2557. [Google Scholar] [CrossRef]
  9. Tian, Y.; Zhu, H.; Zhao, D.; Liu, C.; Feng, X.; Liu, T.; Ma, J. Mantle transition zone structure beneath the Changbai volcano: Insight into deep slab dehydration and hot upwelling near the 410 km discontinuity. J. Geophys. Res. Solid Earth 2016, 121, 5794–5808. [Google Scholar] [CrossRef]
  10. Zhu, H.; Tian, Y.; Zhao, D.; Li, H.; Liu, C. Seismic Structure of the Changbai Intraplate Volcano in NE China From Joint Inversion of Ambient Noise and Receiver Functions. J. Geophys. Res. Solid Earth 2019, 124, 4984–5002. [Google Scholar] [CrossRef]
  11. Shao, J.A.; Zhang, L.Q.; Xiao, Q.H.; Li, X.B. Rising of Da Hinggan Mts in Mesozonic: A possible mechanism of intracontinental orogeny. Acta Petrol. Sin. 2005, 23, 789–794. [Google Scholar]
  12. Liu, J. Study on geochronology of the Cenozoic volcanic rocks in northeast China. Acta Petrol. Sin. 1987, 3, 21–31. [Google Scholar]
  13. Liu, J.; Guo, Z.; Liu, Q. Volcanic hazards and monitoring. Quat. Sci. 1999, 19, 414–422. [Google Scholar]
  14. Bai, Z.D.; Tian, M.Z.; Wu, F.D.; Xu, D.B.; Li, T.J. Yanshan, Gaoshan-Two Active Volcanoes of the Volcanic Cluster in Arshan, Inner Mongolia. Earthq. Res. China 2005, 21, 113–117. [Google Scholar]
  15. Tang, J.; Wang, J.; Chen, X.; Zhao, G.; Zhan, Y. Preliminary investigation for electric conductivity structure of the crust and upper mantle beneath the Aershan volcano area. Chin. J. Geophys. 2005, 48, 196–202. [Google Scholar] [CrossRef]
  16. Zhao, Y.; Fan, Q. Yanshan and Gao Shan Volcanoes in the Daxingan mounta in range—A new eruption style. Seismol. Geol. 2010, 32, 28–37. [Google Scholar]
  17. Ho, K.-S.; Ge, W.-C.; Chen, J.-C.; You, C.-F.; Yang, H.-J.; Zhang, Y.-L. Late Cenozoic magmatic transitions in the central Great Xing’an Range, Northeast China: Geochemical and isotopic constraints on petrogenesis. Chem. Geol. 2013, 352, 1–18. [Google Scholar] [CrossRef]
  18. Zhang, F.; Wu, Q. Velocity structure in upper mantle and its implications for the volcanism near by the north edge of Songliao Basin. Chin. J. Geophys. 2019, 62, 2918–2929. [Google Scholar]
  19. Zhang, F.; Wu, Q.; Li, Y. The traveltime tomography study by teleseismic P wave data in the Northeast Chinese area. Chin. J. Geophys. 2013, 56, 2690–2700. [Google Scholar]
  20. Zhang, F.; Wu, Q.; Li, Y.; Zhang, R. The deep seismic velocity structures beneath volcanoes in GreatXing’an Range and volcanichanism. Chin. J. Geophys. 2022, 65, 1271–1287. [Google Scholar]
  21. Gu, X. Geochemical Characteristics and Evolution Mechanism of Thermal and Mineral Springs in Arxan. Ph.D. Thesis, China University of Geosciences, Beijing, China, 2018. [Google Scholar]
  22. Cui, Y.; Sun, F.; Liu, L.; Xie, C.; Li, J.; Chen, Z.; Li, Y.; Du, J. Contribution of deep-earth fluids to the geothermal system: A case study in the Arxan volcanic region, northeastern China. Front. Earth Sci. 2023, 10, 996583. [Google Scholar] [CrossRef]
  23. Li, J.; Tian, Y.; Zhao, D.; Yan, D.; Li, Z.; Li, H. Magmatic System and Seismicity of the Arxan Volcanic Group in Northeast China. Geophys. Res. Lett. 2023, 50, e2022GL101105. [Google Scholar] [CrossRef]
  24. Pan, X.; Gu, G.; Han, D.; Bao, B.; Guan, S.; Song, Y. Investigation of hot spring gas components and soil gas fluxes in Arxan Holocene volcanic field, Inner Mongolia, NE China. Front. Earth Sci. 2023, 11, 1174315. [Google Scholar] [CrossRef]
  25. Zhao, D.; Maruyama, S.; Omori, S. Mantle dynamics of Western Pacific and East Asia: Insight from seismic tomography and mineral physics. Gondwana Res. 2007, 11, 120–131. [Google Scholar] [CrossRef]
  26. Lei, J.; Xie, F.; Fan, Q.; Santosh, M. Seismic imaging of the deep structure under the Chinese volcanoes: An overview. Phys. Earth Planet. Inter. 2013, 224, 104–123. [Google Scholar] [CrossRef]
  27. Guo, Z.; Chen, Y.J.; Ning, J.; Feng, Y.; Grand, S.P.; Niu, F.; Kawakatsu, H.; Tanaka, S.; Obayashi, M.; Ni, J. High resolution 3-D crustal structure beneath NE China from joint inversion of ambient noise and receiver functions using NECESSArray data. Earth Planet. Sci. Lett. 2015, 416, 1–11. [Google Scholar] [CrossRef]
  28. Guo, Z.; Chen, Y.J.; Ning, J.; Yang, Y.; Afonso, J.C.; Tang, Y. Seismic evidence of on-going sublithosphere upper mantle convection for intra-plate volcanism in Northeast China. Earth Planet. Sci. Lett. 2016, 433, 31–43. [Google Scholar] [CrossRef]
  29. Liu, Y.; Niu, F.; Chen, M.; Yang, W. 3-D crustal and uppermost mantle structure beneath NE China revealed by ambient noise adjoint tomography. Earth Planet. Sci. Lett. 2017, 461, 20–29. [Google Scholar] [CrossRef]
  30. Zhang, F.; Wu, Q.; Li, Y. The traveltime tomography study by teleseismic S wave data in the Northeast Chinese area. Chin. J. Geophys. 2014, 57, 88–101. [Google Scholar]
  31. Han, J.; Kang, J.; Liu, C.; Liu, W.; Zhang, Y.; Wang, T.; Guo, Z.; Yuan, T.; Liu, L. Characteristics of the asthenosphere structure beneath the eastern segment of the Central Asia orogenic belt inferred from a long-period magnetotelluric survey. Chin. J. Geophys. 2019, 62, 1148–1158. [Google Scholar]
  32. Yan, D.; Tian, Y.; Zhao, D.; Li, H. Thermal and rheological structure of lithosphere beneath Northeast China. Tectonophysics 2022, 840, 229560. [Google Scholar] [CrossRef]
  33. Shapiro, N.M.; Campillo, M.; Stehly, L.; Ritzwoller, M.H. High-Resolution Surface-Wave Tomography from Ambient Seismic Noise. Science 2005, 307, 1615–1618. [Google Scholar] [CrossRef] [PubMed]
  34. Yao, H.; Van Der Hilst, R.D.; De Hoop, M.V. Surface-wave array tomography in SE Tibet from ambient seismic noise and two-station analysis—I. Phase velocity maps. Geophys. J. Int. 2006, 166, 732–744. [Google Scholar] [CrossRef]
  35. Bensen, G.D.; Ritzwoller, M.H.; Barmin, M.P.; Levshin, A.L.; Lin, F.; Moschetti, M.P.; Shapiro, N.M.; Yang, Y. Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements. Geophys. J. Int. 2007, 169, 1239–1260. [Google Scholar] [CrossRef]
  36. Yang, Y.; Ritzwoller, M.H.; Jones, C.H. Crustal structure determined from ambient noise tomography near the magmatic centers of the Coso region, southeastern California. Geochem. Geophys. Geosystems. 2011, 12, Q02009. [Google Scholar] [CrossRef]
  37. Yang, Y.; Ritzwoller, M.H.; Levshin, A.L.; Shapiro, N.M. Ambient noise Rayleigh wave tomography across Europe. Geophys. J. Int. 2007, 168, 259–274. [Google Scholar] [CrossRef]
  38. Shen, W.; Ritzwoller, M.H.; Kang, D.; Kim, Y.; Lin, F.-C.; Ning, J.; Wang, W.; Zheng, Y.; Zhou, L. A seismic reference model for the crust and uppermost mantle beneath China from surface wave dispersion. Geophys. J. Int. 2016, 206, 954–979. [Google Scholar] [CrossRef]
  39. Masterlark, T.; Haney, M.; Dickinson, H.; Fournier, T.; Searcy, C. Rheologic and structural controls on the deformation of Okmok volcano, Alaska: FEMs, InSAR, and ambient noise tomography. J. Geophys. Res. Solid Earth 2010, 115, B02409. [Google Scholar] [CrossRef]
  40. Nagaoka, Y.; Nishida, K.; Aoki, Y.; Takeo, M.; Ohminato, T. Seismic imaging of magma chamber beneath an active volcano. Earth Planet. Sci. Lett. 2012, 333–334, 1–8. [Google Scholar] [CrossRef]
  41. Seats, K.J.; Lawrence, J.F. The seismic structure beneath the Yellowstone Volcano Field from ambient seismic noise. Geophys. Res. Lett. 2014, 41, 8277–8282. [Google Scholar] [CrossRef]
  42. Lin, F.-C.; Li, D.; Clayton, R.W.; Hollis, D. High-resolution 3D shallow crustal structure in Long Beach, California: Application of ambient noise tomography on a dense seismic array. Geophysics 2013, 78, Q45–Q56. [Google Scholar] [CrossRef]
  43. Fang, H.; Yao, H.; Zhang, H.; Huang, Y.-C.; van der Hilst, R.D. Direct inversion of surface wave dispersion for three-dimensional shallow crustal structure based on ray tracing: Methodology and application. Geophys. J. Int. 2015, 201, 1251–1263. [Google Scholar] [CrossRef]
  44. Li, Z.; Ni, S.; Zhang, B.; Bao, F.; Zhang, S.; Deng, Y.; Yuen, D.A. Shallow magma chamber under the Wudalianchi Volcanic Field unveiled by seismic imaging with dense array. Geophys. Res. Lett. 2016, 43, 4954–4961. [Google Scholar] [CrossRef]
  45. Goldstein, P.; Snoke, A. SAC Availability for the IRIS Community. Inc. Inst. Seismol. Data Manag. Cent. Electron. Newsl. 2005, 7, 875360. [Google Scholar]
  46. Herrmann, R.B. Computer Programs in Seismology: An Evolving Tool for Instruction and Research. Seismol. Res. Lett. 2013, 84, 1081–1088. [Google Scholar] [CrossRef]
  47. Zheng, S.; Sun, X.; Song, X.; Yang, Y.; Ritzwoller, M.H. Surface wave tomography of China from ambient seismic noise correlation. Geochem. Geophys. Geosyst. 2008, 9, 1–8. [Google Scholar] [CrossRef]
  48. Sun, X.; Song, X.; Zheng, S.; Yang, Y.; Ritzwoller, M.H. Three dimensional shear wave velocity structure of the crust and upper mantle beneath China from ambient noise surface wave tomography. Earthq. Sci. 2010, 23, 449–463. [Google Scholar] [CrossRef]
  49. Xu, Z.J.; Song, X.; Zheng, S. Shear velocity structure of crust and uppermost mantle in China from surface wave tomography using ambient noise and earthquake data. Earthq. Sci. 2013, 26, 267–281. [Google Scholar] [CrossRef]
  50. Bao, X.; Song, X.; Li, J. High-resolution lithospheric structure beneath Mainland China from ambient noise and earthquake surface-wave tomography. Earth Planet. Sci. Lett. 2015, 417, 132–141. [Google Scholar] [CrossRef]
  51. Brocher, T.M. Empirical Relations between Elastic Wavespeeds and Density in the Earth’s Crust. Bull. Seismol. Soc. Am. 2005, 95, 2081–2092. [Google Scholar] [CrossRef]
  52. Rawlinson, N.; Sambridge, M. Wave front evolution in strongly heterogeneous layered media using the fast marching method. Geophys. J. Int. 2004, 156, 631–647. [Google Scholar] [CrossRef]
  53. Toomey, D.R.; Foulger, G.R. Tomographic inversion of local earthquake data from the Hengill-Grensdalur Central Volcano Complex, Iceland. J. Geophys. Res. Solid Earth 1989, 94, 17497–17510. [Google Scholar] [CrossRef]
  54. Miyano, K.; Aizawa, K.; Matsushima, T.; Shito, A.; Shimizu, H. Seismic velocity structure of Unzen Volcano, Japan, and relationship to the magma ascent route during eruptions in 1990–1995. Sci. Rep. 2021, 11, 22407. [Google Scholar] [CrossRef]
  55. Yan, D.; Tian, Y.; Zhao, D.; Li, H. Seismicity and Magmatic System of the Changbaishan Intraplate Volcano in East Asia. J. Geophys. Res. Solid Earth 2023, 128, e2023JB026853. [Google Scholar] [CrossRef]
  56. Linqi, X. On the evolution of volcanic magma from Wudalianchi. Acta Petrol. Sin. 1990, 6, 13–29+97. [Google Scholar]
  57. Schmincke, H.U. Volcanism; Springer: Berlin/Heidelberg, Germany, 2004; pp. 35–41, 209–228. [Google Scholar]
  58. Bardintzeff, J.M.; McBirney, A.R. Volcanology; Jones and Bartlett Publishers: Sudbury, MA, USA, 2000; pp. 76–81. [Google Scholar]
  59. Farrell, J.; Smith, R.B.; Husen, S.; Diehl, T. Tomography from 26 years of seismicity revealing that the spatial extent of the Yellowstone crustal magma reservoir extends well beyond the Yellowstone caldera. Geophys. Res. Lett. 2014, 41, 3068–3073. [Google Scholar] [CrossRef]
  60. Huang, H.-H.; Lin, F.-C.; Schmandt, B.; Farrell, J.; Smith, R.B.; Tsai, V.C. The Yellowstone magmatic system from the mantle plume to the upper crust. Science 2015, 348, 773–776. [Google Scholar] [CrossRef]
  61. Díaz, D.; Heise, W.; Zamudio, F. Three-dimensional resistivity image of the magmatic system beneath Lastarria volcano and evidence for magmatic intrusion in the back arc (northern Chile). Geophys. Res. Lett. 2015, 42, 5212–5218. [Google Scholar] [CrossRef]
  62. Liu, B.; Tong, W.; Zhang, B.; Zhang, Z. Microseismic obvervation in tengchong volcano-geothermal region. Chin. J. Geophys. 1986, 29, 547–556. [Google Scholar]
  63. Gilpin, B.; Lee, T.-C. A microearthquake study in the Salton Sea geothermal area, California. Bull. Seismol. Soc. Am. 1978, 68, 441–450. [Google Scholar] [CrossRef]
  64. Husen, S.; Smith, R.B.; Waite, G.P. Evidence for gas and magmatic sources beneath the Yellowstone volcanic field from seismic tomographic imaging. J. Volcanol. Geotherm. Res. 2004, 131, 397–410. [Google Scholar] [CrossRef]
  65. Jaxybulatov, K.; Shapiro, N.M.; Koulakov, I.; Mordret, A.; Landès, M.; Sens-Schönfelder, C. A large magmatic sill complex beneath the Toba caldera. Science 2014, 346, 617–619. [Google Scholar] [CrossRef] [PubMed]
  66. Tian, Y.; Ma, J.; Liu, C.; Feng, X.; Liu, T.; Zhu, H.; Yan, D.; Li, H. Effects of subduction of the western Pacific plate on tectonic evolution of Northeast China and geodynamic implications. Chin. J. Geophys. 2019, 62, 1071–1082. [Google Scholar]
  67. Ma, J.; Tian, Y.; Liu, C.; Zhao, D.; Feng, X.; Zhu, H. P-wave tomography of Northeast Asia: Constraints on the western Pacific plate subduction and mantle dynamics. Phys. Earth Planet. Inter. 2018, 274, 105–126. [Google Scholar] [CrossRef]
  68. He, Y.; Chen, Q.-F.; Chen, L.; Wang, X.; Guo, G.; Li, T.; Zhang, K.; Li, J.; Chen, Y. Distinct Lithospheric Structure in the Xing’an-Mongolian Orogenic Belt. Geophys. Res. Lett. 2022, 49, e2021GL097283. [Google Scholar] [CrossRef]
  69. Fan, X.; Chen, Q.-F.; Ai, Y.; Chen, L.; Jiang, M.; Wu, Q.; Guo, Z. Quaternary sodic and potassic intraplate volcanism in northeast China controlled by the underlying heterogeneous lithospheric structures. Geology 2021, 49, 1260–1264. [Google Scholar] [CrossRef]
  70. Chu, R.; Helmberger, D.V.; Sun, D.; Jackson, J.M.; Zhu, L. Mushy magma beneath Yellowstone. Geophys. Res. Lett. 2010, 37, L01306. [Google Scholar] [CrossRef]
  71. Cashman, K.V.; Sparks, R.S.J.; Blundy, J.D. Vertically extensive and unstable magmatic systems: A unified view of igneous processes. Science 2017, 355, eaag3055. [Google Scholar] [CrossRef]
  72. Wessel, P.; Smith, W. New, improved version of generic mapping tools released. Eos. Trans. AGU 1998, 79, 579. [Google Scholar] [CrossRef]
Applsci 14 10596 g001
Figure 1. (a) Regional structural diagram (AVF, Arxan Volcano Field; WDLC, Wudalianchi Volcano; CAOB, Central Asian Orogenic Belt; CBS, Changbaishan volcano; JPH, Jingpohu Volcano). The red triangles represent volcanic areas. The red oblong located in the bottom left corner of Figure 1a shows the region of the subfigure. The blue dotted line represents the North–South Gravity Lineament. The blue rectangular in Figure 1a around the AVF shows the location of Figure 1b. (b) AVF location map. Black and red triangles represent the stations and volcanoes, respectively.
Figure 1. (a) Regional structural diagram (AVF, Arxan Volcano Field; WDLC, Wudalianchi Volcano; CAOB, Central Asian Orogenic Belt; CBS, Changbaishan volcano; JPH, Jingpohu Volcano). The red triangles represent volcanic areas. The red oblong located in the bottom left corner of Figure 1a shows the region of the subfigure. The blue dotted line represents the North–South Gravity Lineament. The blue rectangular in Figure 1a around the AVF shows the location of Figure 1b. (b) AVF location map. Black and red triangles represent the stations and volcanoes, respectively.
Applsci 14 10596 g001
Applsci 14 10596 g002
Figure 2. Ambient noise seismic waveforms after cross-correlation (CC). The reference velocities of 2.0 km/s and 4.0 km/s are represented by blue and red lines, respectively. (a) The filter range is a period between 0.5 and 20 s. (b) The band-pass filter range is 1–5 s periods.
Figure 2. Ambient noise seismic waveforms after cross-correlation (CC). The reference velocities of 2.0 km/s and 4.0 km/s are represented by blue and red lines, respectively. (a) The filter range is a period between 0.5 and 20 s. (b) The band-pass filter range is 1–5 s periods.
Applsci 14 10596 g002
Applsci 14 10596 g003
Figure 3. (a) Dispersion data of Rayleigh wave group velocity. The gray square represents the velocity along the separate periods at dissimilar pathways. The number of ray paths at each period is shown in the blue histogram. The mean group velocity of Rayleigh wave is displayed as a pink line. (b) The map of the ambient noise source distribution.
Figure 3. (a) Dispersion data of Rayleigh wave group velocity. The gray square represents the velocity along the separate periods at dissimilar pathways. The number of ray paths at each period is shown in the blue histogram. The mean group velocity of Rayleigh wave is displayed as a pink line. (b) The map of the ambient noise source distribution.
Applsci 14 10596 g003
Applsci 14 10596 g004
Figure 4. (a–h) Vertical profiles of the CRT for S-wave velocity model alongside four cross-sections (AA’–DD’): on the left is the input model, and on the right is the restoration result. The profiles are shown in Figure 1b.
Figure 4. (a–h) Vertical profiles of the CRT for S-wave velocity model alongside four cross-sections (AA’–DD’): on the left is the input model, and on the right is the restoration result. The profiles are shown in Figure 1b.
Applsci 14 10596 g004
Applsci 14 10596 g005
Figure 5. (a–c) Vertical profiles show the recovery degree of the CRT for velocity model along the three profiles (L1L1′, HH’, L2L2′). The value of the recovery degree for each block can be seen in the color bar. Each block’s size was set to 0.055° × 0.035°.
Figure 5. (a–c) Vertical profiles show the recovery degree of the CRT for velocity model along the three profiles (L1L1′, HH’, L2L2′). The value of the recovery degree for each block can be seen in the color bar. Each block’s size was set to 0.055° × 0.035°.
Applsci 14 10596 g005
Applsci 14 10596 g006
Figure 6. (a–h) Vertical profiles show the outcomes of the RRT for the S-wave velocity model following four cross-sections (AA’, BB’, CC’, and DD’). The red color blocks illustrate low-velocity perturbations; the blue color blocks are the same as the red color blocks but for high-velocity perturbations. The magnitude of the perturbation value for each block can be seen in the color bar. Each block’s size was set to 0.055° × 0.035°.
Figure 6. (a–h) Vertical profiles show the outcomes of the RRT for the S-wave velocity model following four cross-sections (AA’, BB’, CC’, and DD’). The red color blocks illustrate low-velocity perturbations; the blue color blocks are the same as the red color blocks but for high-velocity perturbations. The magnitude of the perturbation value for each block can be seen in the color bar. Each block’s size was set to 0.055° × 0.035°.
Applsci 14 10596 g006
Applsci 14 10596 g007
Figure 7. (a–f) Tomographic images of velocity perturbations at different distances below the surface. According to the dispersion curve path distribution, the areas where the ray paths are sparser are covered with an approximate gray color. All subplots are marked with different depths at the bottom left corner. The black cruciform in the figure represents the position of the portable seismometers. The black triangles represent the position of each crater in the AVF. The black dotted lines represent faults shown in (a).
Figure 7. (a–f) Tomographic images of velocity perturbations at different distances below the surface. According to the dispersion curve path distribution, the areas where the ray paths are sparser are covered with an approximate gray color. All subplots are marked with different depths at the bottom left corner. The black cruciform in the figure represents the position of the portable seismometers. The black triangles represent the position of each crater in the AVF. The black dotted lines represent faults shown in (a).
Applsci 14 10596 g007
Applsci 14 10596 g008
Figure 8. (a–c) S-wave velocity perturbation profiles below TCV, WSLZ Lake, GD1325, GD1286, GD1201, SHG Basin, GD1213, and GD1153. Red triangles indicate volcanic cones along the profile. Black asterisks indicate earthquakes of different magnitudes at different depths. (d) Profile location map. Red triangles indicate volcanic cones in the AVF.
Figure 8. (a–c) S-wave velocity perturbation profiles below TCV, WSLZ Lake, GD1325, GD1286, GD1201, SHG Basin, GD1213, and GD1153. Red triangles indicate volcanic cones along the profile. Black asterisks indicate earthquakes of different magnitudes at different depths. (d) Profile location map. Red triangles indicate volcanic cones in the AVF.
Applsci 14 10596 g008
Applsci 14 10596 g009
Figure 9. Relationship between S-wave velocity and melt fraction.
Figure 9. Relationship between S-wave velocity and melt fraction.
Applsci 14 10596 g009
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Qu, L.; Tian, Y.; Liu, C.; Li, H. Shallow Magmatic System of Arxan Volcano Revealed by Ambient Noise Tomography with Dense Array. Appl. Sci. 2024, 14, 10596. https://doi.org/10.3390/app142210596

AMA Style

Qu L, Tian Y, Liu C, Li H. Shallow Magmatic System of Arxan Volcano Revealed by Ambient Noise Tomography with Dense Array. Applied Sciences. 2024; 14(22):10596. https://doi.org/10.3390/app142210596

Chicago/Turabian Style

Qu, Lijuan, You Tian, Cai Liu, and Hongli Li. 2024. "Shallow Magmatic System of Arxan Volcano Revealed by Ambient Noise Tomography with Dense Array" Applied Sciences 14, no. 22: 10596. https://doi.org/10.3390/app142210596

APA Style

Qu, L., Tian, Y., Liu, C., & Li, H. (2024). Shallow Magmatic System of Arxan Volcano Revealed by Ambient Noise Tomography with Dense Array. Applied Sciences, 14(22), 10596. https://doi.org/10.3390/app142210596

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop