Next Article in Journal
Sustainable Adaptation Plan in Response to Climate Change and Population Growth in the Iraqi Part of Tigris River Basin
Previous Article in Journal
Developing a Project-Expectancy Inventory for the Construction Industry from the Owner’s Perspective
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 7
10.3390/su16072677
sustainability-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

Methods for the Geophysical Exploration and Sustainable Utilisation of Coalbed Methane Resources in Abandoned Mines of Shanxi, China

by
Chunlin Liu
1,
Guoxun Li
2 and
Chuantao Yu
1,*
1
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
Shanxi Huarun Liansheng Energy Investment Co., Ltd., Lvliang 033000, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(7), 2677; https://doi.org/10.3390/su16072677
Submission received: 2 February 2024 / Revised: 6 March 2024 / Accepted: 8 March 2024 / Published: 25 March 2024
(This article belongs to the Section Energy Sustainability)
Browse Figures
Review Reports Versions Notes

Abstract

:
Underground coal mining results in large goafs and numerous abandoned mines that contain substantial amounts of coalbed methane. If this methane is not used and controlled, it will escape into the atmosphere through geological fractures and can result in serious greenhouse gas effects and environmental damage. Exploring and developing the coalbed methane resources of abandoned mines can not only improve coal mine safety and protect the ecological environment but also reuse waste and mitigate energy shortages. Geophysical methods have made some progress in detecting abandoned coal mines, but there are still some challenges and difficulties. The resolution of seismic exploration may not be enough to accurately describe the details of coal seams and CBM rich areas, and the effect of resistivity method in deep CBM exploration is limited. In addition, the geological structure of abandoned coal mines is usually more complex, such as faults, folds, etc., which makes the application of exploration methods more difficult and increases the difficulty of data interpretation. Therefore, it is necessary to develop and perfect exploration technology continuously including the application of geophysical big data, deep learning, and artificial intelligence inversion to realize the accurate detection and evaluation of CBM resources in abandoned coal mines.

1. Introduction

In China, socioeconomic development fed by coal resources has led to some coal mines reaching the end of their lifecycle, while others face closure or abandonment owing to outdated and unsafe production, high mining costs and serious losses. Over the long-term, coal mining not only wastes substantial resources but also poses a hazard to safety, the environment, and society owing to the formation of extensive voids in the abandoned mines. Because of current mining techniques and geological factors, substantial untapped resources persist in abandoned mines such as coal, gas, water, and geothermal energy [1]. In particular, these abandoned mines hold substantial reservoirs of coalbed methane [2]. From 2011 to the present, more than 7800 coal mines have been closed in China, and approximately 15,000 mines are projected to close by 2030. These abandoned mines are estimated to hold approximately 500 billion m3 of coalbed methane, which not only threatens the safety of subsequent coal mining operations but may also migrate along fractures in geological formations to result in increased greenhouse gas emissions further harm to the ecological environment. However, Chinese coal enterprises generally demonstrate weak awareness of how to properly close or reuse abandoned mines, and most mines are abandoned without redevelopment of resources with them. The exploration and development of coalbed methane resources in abandoned mines can improve safety, protect the ecological environment, turn waste into valuable resources, and mitigate energy shortages, which can help China meet its ‘dual carbon’ goals of reaching peak carbon emissions by 2030 and carbon neutrality by 2060.
Detailed geological investigations and geophysical exploration are necessary to understanding the geological and hydrological conditions of coalbed methane resources in abandoned mines. The production of coalbed methane is influenced by various factors including the geological conditions of coal seams, the goaf distribution, the surrounding rock permeability, the migration and storage of coalbed methane, geological structures, the hydrogeological conditions, post-mining rock movement, and damage conditions. The distribution characteristics and enrichment patterns of coalbed methane have been found to vary greatly for different types of geology and coal mining processes. Geophysical exploration methods are attractive for application in abandoned coal mines owing to their low cost and non-destructive detection of deep layers, and they are advantageous for characterising the geological structure, goaf distribution, and coalbed methane reservoirs of coal mines. Techniques such as the seismic method [3,4], electromagnetic method [5,6,7], high-density electrical method [8,9], induced polarization method [10], micromotion method [11,12], and radioactive detection method [13,14] have been used to accurately survey mined-out voids, water accumulation, and old workings. These methods can contribute not only to safe and efficient coal mining but also to the restoration and management of the mining environment, and they can be used to establish a geological foundation for the exploration and development of coalbed methane resources in abandoned mines [15,16].
Although geophysical methods have made some progress in detecting abandoned coal mines, there are still shortcomings and challenges. Some geophysical methods, such as ground electromagnetic method and resistivity method, have limited effectiveness in deep coalbed methane exploration, which limits their application in abandoned coal mine deep coalbed methane exploration. Although seismic exploration can provide deeper detection depths, its resolution may not be sufficient to accurately depict the details of coal seams and coalbed methane enrichment areas under complex geological conditions [17]. The geological structure of abandoned coal mine areas is usually complex, such as faults, folds, etc. [18]. The existence of these structures makes the application of geophysical methods more difficult, and also increases the difficulty of data interpretation. Although geophysical technology is constantly evolving, it remains a challenge to effectively utilize these technologies for precise detection and evaluation of coalbed methane, a special resource [19].
This paper presents a topical review on the current state of research and application of geophysical exploration methods to coalfield goafs. To lend context, the coalbed methane resources of the numerous abandoned coal mines in Shanxi Province are described. A geological model is presented for coalbed methane enrichment in abandoned mines, and the geophysical properties of goafs are described. Based on this foundation, the advantages and limitations of different geophysical exploration methods for application to the exploration and development of coalbed methane resources in abandoned mines are discussed. Finally, future prospects for the development direction of new geophysical technologies are considered.

2. Overview of Coalbed Methane Resources in Shanxi Province

As shown in Figure 1, Shanxi Province has a well-developed stratigraphy with the main coal-bearing formations being the Carboniferous and Permian. The north also contains coal-bearing formations of the Jurassic. The coal resources are mainly in the form of medium to thick coal seams abundant in quantity and high in quality. They contain anthracite, bituminous coal, and brown coal. The burial depth of the coal seams range from around 100 to approximately 2000 m, which contributes to the complexity of goafs. After years of large-scale mining, Shanxi has developed extensive goafs storing substantial coalbed methane resources in abandoned mines.
In 2018, Shanxi Province conducted a comprehensive survey to quantify the coalbed methane resources of its abandoned mines, and some of the main findings are listed in Table 1. The total area of goafs has reached 4027 km2, and it continues to increase. Over half of these goafs contain coalbed methane resources that are exploitable. The abandoned mines are predicted to hold more than 70 billion m3 of coalbed methane. In particular, seven mining areas covering an area of approximately 870 km2 are predicted to hold more than 30 billion m3 of coalbed methane. Shanxi Province has constructed over 100 wells that have extracted more than 100 million m3 of coalbed methane from its abandoned mines. However because of incomplete historical data, unclear geological conditions and varying conditions for the occurrence and sealing of coalbed methane, the success rate and daily production of these wells differ, even across different sections within the same mine. Moreover, the production of coalbed methane from goafs is unstable and rapidly declines, which poses a major challenge to their extraction from abandoned mines.

3. Geological Model of an Abandoned Mine

Generally, gases migrate from areas of high pressure and concentration to areas of low pressure and concentration. In an abandoned mine, coalbed methane desorbs from unmined coal seams, coal pillars and the surrounding rock to migrate into a goaf under pressure. Because methane is less dense than air, it tends to migrate upward within the goaf. Under the influence of gravity, the overlying strata above the goaf undergo displacement, collapse, and other effects. As shown in Figure 2, three zones can be identified based on the degree of damage: the caving, fracture, and sagging zones. The caving zone is identified by a decrease in the overlying rock pressure and increase in fractures, and it serves as a crucial pathway for the upward migration of coalbed methane. The fracture zone is influenced by both concentration and pressure fields, and it is identified by the upward movement of coalbed methane through fractures in the goaf [20]. If the top of the fracture zone is sealed, coalbed methane may accumulate there beneath the sagging zone. If the fractures extend to the surface, then the coalbed methane can migrate to the surface and does not accumulate [21]. If groundwater is abundant in the mine, it can increase the pressure within the goaf. Some coalbed methane may dissolve in the water or re-adsorb onto the surrounding rocks, which can further destabilise the coalbed methane production of extraction wells in abandoned mines [22]. Thus, determining suitable locations to drill wells for extracting coalbed methane is very difficult. For instance, experimental wells drilled in the Jincheng mining area frequently encountered coal pillars, mined-out water accumulation zones, coal–rock tunnels, and even in situ coal, which lowered the success rate. Geophysical exploration methods can help fully understand the goaf distribution, cover layers, fractures, and groundwater conditions within abandoned mines and provide essential data for the assessment and development of coalbed methane resources.

4. Geophysical Properties of a Goaf

4.1. Seismic Wave Field Characteristics

Coal deposits are generally stable, but there are substantial differences in the physical properties of the roof and floor strata. When seismic waves propagate through the strata, they can form continuous, high-amplitude, and easily comparable reflection waves at different times. After the coal is mined, the continuity between strata is disrupted. When seismic waves pass through a goaf, scattering occurs, and energy is absorbed. This weakens the reflection wave energy, lowers the frequency and amplitude, and increases the discontinuity, which allows the extent of the goaf to be delineated. When seismic waves pass through a coal seam, the differences in impedance between the coal seam and surrounding rocks lead to distinct response characteristics in the seismic profile. When seismic waves pass through a goaf, features such as discontinuities, misalignment, or chaotic patterns for the same phase may occur [23]. These characteristics provide a basis for using the reflection wave method to detect a goaf for the exploration and evaluation of coal resources.

4.2. Electrical Characteristics

Coal seams are formed by sedimentation to reach a stable equilibrium with surrounding rocks, and they generally demonstrate relatively uniform electrical characteristics with a stratified nature. After the coal is mined, the original continuity of the coal seam is disrupted, which results in the strata reaching a new equilibrium and alters the electrical characteristics. In particular, goafs are characterised by localised high-resistivity anomalies. When goafs are filled with rocks, soil, or water, they may exhibit localised low-resistivity anomalies. Furthermore, longitudinal low-resistivity zones can form around goafs due to the collapse or subsidence of overlying strata, which can lead to fragmentation or fracture of the surrounding rocks and reduce their resistance [20].

4.3. Induced Polarization Characteristics

Most rocks generally have a weak polarization effect, and the polarizability does not exceed 2.0%. However, the presence of clay minerals, metallic sulphide ores or carbonaceous minerals can greatly enhance the polarization effect and cause the polarizability to exceed 4%. Aquifers and water-rich layers typically have polarisability of 2.0–4.0%, but polarisability does not exceed 5.0%.

4.4. Abnormal Characteristics of Radioactive Radon

The creation of a goaf after mining of an underground coal seam causes the overlying rock strata to experience fragmentation and subsidence, which forms a fracture zone. Radon gas can migrate upward through these fractures due to pressure and concentration gradients, air convection, and the cluster effect, which forms areas with high radon concentrations and major variations in concentration. In contrast, normal geological formations exhibit lower radon concentrations with minimal fluctuations. Radon anomalies generally exhibit characteristic morphologies such as strips or rings that can be used to infer the distribution patterns and extent of underground geological bodies, including goafs and faults.

4.5. Impact of Goaf Parameters

The parameters of goaf include size, shape, depth, type of filling material, and properties of surrounding rocks. As shown in Figure 3, different geophysical methods have different sensitivities to these parameters, so some methods may be more suitable for specific goaf detection than others. Usually, two or more methods are used to comprehensively interpret the results better.

5. Geophysical Exploration Methods

5.1. Seismic Exploration

Seismic exploration is a crucial geophysical method for inferring underground geological structures and locating gas reservoirs. It involves observing and analysing the response of seismic waves as they pass through targets and surrounding rocks that differ in density and elasticity. The reflection wave method is the primary technique used to detect a goaf. It involves analysing the amplitude and phase characteristics of seismic waves artificially generated and reflected back to the surface upon encountering interfaces with different impedances underground, which can be used to infer information about subsurface geological structures [24,25]. Reflected seismic waves are commonly employed to detect large-scale anomalies, but diffraction or Rayleigh waves can be employed if the target is smaller than the lateral resolution of the reflected wave [26]. As shown in Figure 4, diffraction waves are effective for locating and exploring small goafs with a limited area and shallow burial depth. They represent the seismic response at scales smaller than the Fresnel zone, and they are generated by substantial changes in geological conditions along the seismic wave propagation path, such as faults, sharp extinction points, small subsidence columns and karst caves. In three-dimensional seismic data volumes, these conditions manifest as distorted and split same-phase axes, weakened energy and diminished continuity [27].
Rayleigh waves are surface waves, and they are generally used to detect small-scale targets. Yang and Cheng [28] used a coal seam model to analyse the propagation characteristics of body and surface waves in horizontally layered coal seams and demonstrated that Rayleigh waves have higher energy than reflection waves when used to detect small-scale targets, which makes them easier to extract and identify. Because of the concentration of energy within the range of a wavelength near a free interface, Rayleigh waves exhibit unique dispersion characteristics corresponding to their phase velocity and spatial distribution in layered media such as strata, which can be utilised for detection. For a goaf that has not collapsed, the dispersion curve of Rayleigh waves at the roof of the goaf shows a Z-shaped inflection point with a rapidly decreasing velocity, which allows the extent of the goaf in the vertical direction to be determined [29]. Many scholars have focused on improving the accuracy and application of Rayleigh wave detection. Hu et al. [30] proposed a full-waveform inversion method for wave field separation that uses the sensitivity kernel of P-waves to improve the accuracy of the reconstructed P-wave velocity model and thus the precision of Rayleigh wave detection. Wang and Lv [31] used the finite difference method to study the characteristics of the Rayleigh wave response in stratified formations with complex cave distributions. Their results provided effective guidance for solving complex cave problems and detecting coal mine goafs using surface waves. Because seismic exploration techniques obtain results in three dimensions, they offer a major advantage in accuracy compared with other exploration methods that utilise electromagnetic properties. However, the presence of multiple goafs or water can make it difficult for seismic waves to penetrate layers, which limits their applicability to exploring multilayer mined-out and water-rich areas. In such cases, electromagnetic methods are the preferred choice. Current research on seismic exploration has centred around acquiring high-precision 3D seismic data, seismic attribute analysis and inversion to identify surrounding rock structures, lithology, sedimentary facies, and reservoir constituents [32,33]. Such techniques can facilitate the precise detection of sealed coalbed methane resources in abandoned mines for exploration and development.
Based on conventional 3D seismic methods, Scholars conduct in-depth research on wide-angle seismic exploration. By designing a wide-angle observation system, high-quality seismic data volumes were effectively collected. After processing with techniques such as offset vector slices (OVT), a five-dimensional pre-stack seismic trace set in the OVT data domain was obtained, laying the data foundation for the interpretation of five-dimensional seismic data [34,35]. This method has rich azimuth information and comprehensively utilizes multiple attributes to detect changes in underground fractures and lithology with changes in azimuth, which significantly increases seismic exploration accuracy and improves seismic imaging effects. With the increase in wide-angle seismic data, the research and application of fracture prediction methods based on longitudinal wave attribute azimuthal anisotropy have also flourished [36]. Wide azimuth seismic exploration has obvious advantages in improving seismic imaging and reservoir description accuracy, but there are also many problems that need to be solved urgently. How to effectively collect, process, and utilize wide-angle seismic data is the key to fully tapping into the advantages of wide-angle seismic exploration.

5.2. Microseismic Exploration

The interior and surface of Earth constantly experience weak vibrations, which is commonly referred to as ‘microseismicity’ [12]. Microseismic exploration involves capturing and analysing natural microseismic signals to detect underground geological structures. This method is primarily based on the theory of stationary stochastic processes, and it involves extracting the dispersion curve of Rayleigh surface waves from the vertical microseismic signals recorded by a seismic array, as shown in Figure 5. By inverting the dispersion curve, the shear wave (S-wave) velocity of the subsurface medium is obtained [37]. This S-wave velocity can then be used to analyse goafs based on their low-velocity anomalies [4], as shown in Figure 6. Depending on the objective, microseismic exploration can be used to measure single points (e.g., depth measurements) or obtain microseismic profiles.
In recent years, microseismic exploration has seen rapid development and increased application. Yu et al. [11] used microseismic exploration to detect the goaf of a coal mine. In experiments at known mining areas, they obtained data consistent with actual conditions, which demonstrated the applicability of microseismic exploration to goaf detection. Du et al. [38] applied the elliptical polarization imaging method to unidirectional and three-component microseismic signals to extract high signal-to-noise Rayleigh surface waves. Tian et al. [39] used microseismic exploration to search for geothermal resources in an environmentally friendly and reliable manner. Wang et al. [40] used microseismic exploration during the construction of an underground tunnel to predict potential changes in the local geology.
Because of factors such as surface topography, three-dimensional seismic exploration detects structures such as subsidence columns and tunnels with an accuracy of less than 50%. Meanwhile, the electrical differences between water-free subsidence columns and surrounding rocks are small, which makes their detection based on electromagnetic properties a challenge. In contrast, microseismic exploration is advantageous for detecting the goaf of a coal mine during construction projects. The detection instruments are lightweight and convenient, and their requirements for the terrain and surrounding environment are easy to satisfy. Arrays can be arranged differently to adapt to various terrain conditions. Moreover, no artificial seismic sources are required, so this method does not affect the environment and saves both labour and resources. Microseismic exploration can also be combined with other geophysical exploration methods for comprehensive data analysis to greatly improve the detection accuracy [12].

5.3. Direct Current Method

The direct current (DC) method is based on exploiting the electrical differences between rocks and minerals, and it is primarily used for geological mapping, coal exploration, detection of concealed geological structures (e.g., faults, subsidence columns, karst), mapping of mine water inflow channels, and delineation of coal seams. Different types of equipment can be used according to the tasks and electrical conditions, which allow for its flexible application in diverse construction projects. The DC method enables the accurate inference of the occurrence and influence of geological anomalies within the exploration area [41,42].
China began researching high-density electrical detection methods and their application in the late 20th century, and it has been continuously exploring and refining their theory and practical application since then. For a considerable period, the DC method has been a mainstay of coal exploration owing to its foundational theory being the earliest and most mature. As shown in Figure 7, the DC method primarily uses the Schlumberger device for electrical depth measurements, and it is still indispensable for preventing water egress in coal mines [9]. Various researchers have used the DC method to detect geological structures such as goafs and subsidence columns. Wang et al. [43] used the DC method to detecting groundwater sources for coal seam disasters under the influence of large machinery. In simulations, the results of their method aligned with the actual conditions. Wei et al. [44] used high-density electrical resistivity measurements to clarify the geological characteristics, distribution patterns and controlling factors for the collapse of karst foundations. Based on their results, they predicted development trends and proposed countermeasures. Yang et al. [45] studied the influence of electrode polarization on the DC method and improving the detection accuracy. Bharti et al. [8] studied the detection of underground concealed voids/cavities/galleries in Chinchuria railway station, Raniganjh coalfield, in India, for ground stabilisation using electrical resistivity tomography (ERT) technique. Many other studies have validated the feasibility of applying the DC method to detect a goaf in a coal mine [46].
The DC method is greatly influenced by the topography. It is difficult to apply when bedrock is exposed at the surface and no power is available. It generally cannot explore depths exceeding 300 m. Moreover, with increasing depth, the cost and labour increase, while the efficiency and resolution decrease. The DC method is often used in areas with relatively flat terrain and a readily available power supply at the surface. High-density electrical resistivity can only be detected at shallow depths, and the fixed electrode spacing can artificially create false anomalies. The mine DC method cannot accurately delineate anomalies, and its inversion methods require further research. Further research is also needed on well–ground and interhole resistivity imaging techniques, as well as 3D resistivity imaging and other emerging technologies [47,48].

5.4. Transient Electromagnetic Method

As shown in Figure 8, the transient electromagnetic method (TEM) uses the decay pattern and characteristics of an induced magnetic field to investigate underground geological conditions [49,50]. The length of the transient process increases for a geological body with higher conductivity. TEM offers the advantages of high efficiency, sensitivity to low-resistance anomalies and easy construction. It is particularly suitable for detecting the accumulation of water in the goaf of a coal mine. A single excitation can simultaneously obtain underground information of multiple components at different depths for highly efficient detection [51]. Xue’s group [5,6] used TEM to detect groundwater-rich zones in East China and Shanxi. Chang et al. [52] used TEM with a full-space model to detect groundwater in the goaf of a coal mine. Guo et al. [53] used full-space transmission electric mirrors to detect water collapse columns in coal mines in advance. As shown in Figure 9, the elevation higher than 890 m with the resistivity lower than 70 Ω·m represents unconsolidated sediments in the Quaternary formation, the thickness of this layer changed at different area, the thickness at the ridge is thicker than valley. The elevation of No. 10 coalbed is decreased slowly from west to east, and the average elevation is 770 m, the resistivity range from 200 to 270 Ω·m in normal coalbed, and there are two main parts of abnormal areas with the resistivity lower than 150 Ω·m indicate the mined-out area with water, due to the different volume of water filled in the areas, the resistivity varies [54].
Although current research on TEM is very active, the basic theory remains weak, which has led to difficulties with interpreting the data. TEM is also inaccurate at distinguishing anomalies and determining the spatial positions of geological bodies. Further research is needed on calculating the detection depth, correcting for topographic effects, suppressing interference and extracting effective signals. Research on forward and inversion methods of TEM in mines has been lagging, and the influences of roadways and full-space effects have not been effectively resolved because of the lack of a theoretical basis for spatial directionality. The calculation of the apparent resistivity of small-loop devices with multiple overlapping coils still presents problems. Furthermore, as mining activities take place at greater depths, goafs tend to become multilayer with multiple goafs and water accumulation areas often existing at the same position on different planes. The transient electromagnetic fields caused by these goafs overlap, which makes them impossible to distinguish. Further research on the electromagnetic response of multilayer goafs is required, along with the development of data acquisition hardware and software based on big data and artificial intelligence.

5.5. Airborne Transient Electromagnetic Method

The airborne transient electromagnetic method (ATEM) uses flying platforms such as helicopters, fixed-wing aircraft, airships, and hot air balloons as flying platforms to carry the transmission and reception systems for TEM. As shown in Figure 10a, a half-sine pulse current with a certain frequency is injected into the transmitting coil, and the receiving coil is used to obtain the secondary field generated by the interrupted current. The measured electromagnetic data can be analysed to determine the distribution of underground geological bodies. ATEM effectively overcomes the influence of terrain and topography and can penetrate harsh environments such as forests and mountains to provide electromagnetic data on geological bodies efficiently. Thus, it is suitable for large-scale mineral surveys. Wu et al. [55] trained wavelet neural networks to remove noise caused by helicopter motion, which they applied to ATEM in Inner Mongolia, China. Wu et al. [56] used convolutional neural networks to solve the inversion problem for ATEM and demonstrated the general applicability of convolutional neural networks to three-dimensional synthetic electromagnetic responses. Ji et al. [57] compared the shutdown time responses of ATEM when detecting goaf areas and studied the influence of the slope time on anomaly detection. They demonstrated that the shutdown time response could be used to effectively reveal shallow targets. Thus, the full-waveform ATEM is effective for detecting shallow targets. As shown in Figure 10b, the semi-airborne transient electromagnetic method (semi-ATEM) is an extension of ATEM that uses ground-based electromagnetic sources for transmission and airborne platforms for reception. Single or multiple transmission sources can be used and are easy to deploy, which make ATEM highly adaptable to complex terrain. The receiver can be on a manned or unmanned aircraft, so ATEM retains the high efficiency of TEM while requiring a much smaller payload for improved safety. ATEM has been successfully applied in surveys of geothermal areas, volcanic structures, underground tunnels, groundwater salinity, goafs, and ancient riverbeds.
As shown in Figure 11, the semi-ATEM results of a certain area in Shanxi Province. The mining area is an integrated mine with abandoned mines. Honda is located on the west side of the Taihang Mountain Uplift, at the northern end of the Qinshui Coalfield. It belongs to the uplift zone of the Yuxian Depression in the Qinshui Depression. The minable coal seams in the mine are Shanxi Formation No. 3 and No. 6, and Taiyuan Formation No. 12 and No. 15 coal seams. In addition, there are rivers passing through the mine field, and there is a high possibility of water accumulation in the goaf. The apparent resistivity value is less than 40 Ω m, which is inferred to be a reaction of goaf water accumulation in the coal seam, and the results are consistent with existing data. Semi-ATEM not only overcomes the constraint of terrain conditions on ground-based TEM but also solves the low signal-to-noise ratio and shallow exploration depth problems of ATEM, which has led to its wide application [58]. Various researchers have applied semi-ATEM to detecting water accumulation in goafs with good results [7,59].

5.6. Controllable-Source Audio Magnetotellurics

Controllable-source audio magnetotellurics (CSAMT) uses an artificial source for frequency-domain sounding. When electromagnetic waves propagate through a rock formation, the propagation depth is related to the frequency of the injected current and the resistivity of Earth. Differences in resistivity can be calculated by measuring the orthogonal components of the electric field ( E x , E y ) and magnetic field ( H x , H y ) [60]. By sequentially changing the supply and measurement frequencies within the audio frequency range (n × 10−1 to n × 103 Hz), variations in the resistivity ρ and impedance phase angle φ z with frequency can be observed. The orthogonal components of the electric and magnetic fields can be calculated as follows:
E x = I A B ¯ ρ 2 π r 3 ( 3 c o s 2 ⁡ φ − 2 )
E y = 3 I A B ¯ ρ 2 π r 3 s i n ⁡ φ c o s ⁡ φ
H x = − 3 I A B ¯ 2 π r 3 k sin ⁡ φ cos ⁡ φ = − 1 + i 3 I A B ¯ 4 π r 3 2 ρ ω μ sin ⁡ φ cos ⁡ φ
H y = I A B ¯ 2 π r 3 k ( 3 c o s 2 ⁡ φ − 2 ) = ( 1 + i ) I A B ¯ 4 π r 3 2 ρ ω μ ( 3 c o s 2 ⁡ φ − 2 )
where r is the transmitter–receiver distance, A B ¯ is the length of the power supply dipole, φ is the angle between the directions of r and A B ¯ , I is the current intensity of the power supply and ρ is the resistivity of Earth. Then, the apparent resistivity ρ s and impedance phase φ z can be obtained as follows:
ρ s = 1 5 f E x H y 2
φ z = φ E x − φ H y
H ⁡ = 256 ρ f
where H is the exploration depth and f is the frequency.
CSAMT has high detection efficiency, and a single launch can detect multiple physical points with a detection depth of 1–2 km. Generally, A B ¯ is between 1 and 3 km, and the receiving lines are arranged parallel to A B ¯ on one or both sides within a sector with an angle of 30° [61]. The resistivity of undisturbed coal seams does not fluctuate much in the lateral direction. As shown in Figure 12, a goaf with accumulated water is detected as an anomaly with a low apparent resistivity. CSAMT can be used to detect the distribution of groundwater and identify the geological structures in coal seams and gas-rich areas [62]. It has a moderate detection depth, and it is suitable for exploring water and mineral resources owing to the strong and high-quality observation signals. Chen and Yan [63] used physics to explain the recording rules, shadows, and source interference effects of CSAMT. Wang [64] conducted experiments in the northern Qinshui Basin and demonstrated that CSAMT could effectively obtain the electrical characteristics of geological structures and identify water-rich zones. Zhou et al. [65] derived a formula for determining the optimal transmitter–receiver distance and provided steps for the practical application of CSAMT in the field. However, CSAMT is relatively expensive compared with other electromagnetic methods, and it is not suitable for large-scale surveys. Thus, it is commonly used as a validation tool in key areas [66,67].

5.7. Induced Polarization Method

The induced polarization method uses an artificial source to explore differences in the electrochemical properties (i.e., polarizability) of geological bodies. The DC power supply is applied to the ground by the electrodes A and B, and a potential difference that increases with time is generated between the measurement electrodes M and N. The potential difference then stabilises at the time T. After the power supply is disconnected, a slowly changing potential difference still exists between M and N, which is referred to as the secondary potential difference. The difference in polarizability between different rocks and minerals can be represented as follows:
η ( T , t ) = Δ U 2 ( T , t ) Δ U ( T ) × 100 %
where η is the polarizability, Δ U 2 ( T , t ) is the secondary potential difference, Δ U ( T ) is the total potential difference, T is the time when the power supply is on, and t is the time when the power supply is turned off.
In a goaf, the apparent resistivity is generally low, while the apparent polarization is relatively high. The exploration depth of the induced polarization method is directly proportional to the transmitter–receiver separation because a sufficient current density needs to be ensured. By changing the distance between the electrodes A and B, information can be obtained at different depths. The induced polarization method can accurately determine the depth of anomalies, but it is inefficient and costly for large-scale construction projects [68]. It is most suitable as a verification tool for key areas. Kozhevnikov and Antonov [10] compared the effects of current and voltage sources on transient-induced polarization and analysed their advantages for different conditions. Yang et al. [69] proposed a set of induced polarization methods using metal ore areas as an example and applied them to exploring mineral resources.

5.8. Radiometric Exploration

Radiometric exploration monitors the radioactive intensity generated during the decay of natural underground radioactive substances to explore geological structures. The most widely used technique is the radon measurement method, which uses activated carbon [14] The mining of an underground coal seam forms a goaf, which fractures the overlying rock layers to create a fracture zone. These conditions often prompt the transfer of moisture and gas from high-stress areas to low-stress areas within the rock mass, which alters the underground migration and accumulation of gas and water. These effects cause radon gas to accumulate in the goaf and decrease in nearby areas [70]. Radon migrates through the goaf and fracture zones to the surface, which causes a noticeable anomaly on the surface that corresponds to the shape of the goaf below. Therefore, the location and extent of the goaf can be accurately determined by measuring the radon concentration at the surface. Radon anomalies usually exhibit two forms on a plane: linear and circular. Sukanya et al. [13] explained the distribution patterns of radon dissolved in water and the latest measurement techniques. They highlight the potential of radon as a tracer and precursor in various hydrogeological and geological applications. Lu et al. [70] studied the principles of radon gas transport in goafs and changes in the migration mode under coal self-ignition conditions. Their results can be used as a basis for locating goafs, subsidence columns, uranium ore bodies, tectonic fracture zones and groundwater resources. The radon measurement method is simple to implement, low in cost and high in efficiency, which makes it suitable for large-scale surveys. It can effectively distinguish the planar position of goafs, but it cannot provide depth information and thus needs to be complemented with other methods [13].

6. Future Prospects and Conclusions

Although major advances have been made in the development of principles, methods, and instruments for geophysical exploration, meeting requirements for detailed characterisation of geological structures remains a challenge. Future research on applying geophysical exploration methods to exploiting coalbed methane resources in abandoned mines should focus on the following aspects:
  • High-precision electromagnetic imaging technology: Wide-frequency electromagnetic waves, efficient techniques for the excitation and reception of electromagnetic waves and forward and inverse simulation techniques are needed for the precise detection and assessment of coalbed methane reservoirs in abandoned mines.
  • Application of big data: Big data can be used to gain deeper insights into the characteristics and variation patterns of coalbed methane reservoirs. The development of efficient data processing and cleaning techniques for different sources and types of geophysical data will improve accuracy and reliability as well as provide a foundation for subsequent data analyses and applications.
  • Deep learning and artificial intelligence inversion: Machine learning and deep learning methods can be applied to the pattern recognition, classification, clustering analysis and feature extraction of geophysical data for improved detection and detailed characterisation of coalbed methane reservoirs.
The abandoned mines in China hold enormous reserves of coalbed methane that can potentially be developed and used. However, the extraction of such methane resources is influenced by various factors such as the goaf distribution, overlying strata and surrounding rock conditions, development of fracture zones, conditions of the coal seam roof and floor, mining method, and presence of groundwater. The interconnected effects of these factors make it difficult to use a single geophysical exploration method to assess the coalbed methane resources of abandoned mines. To address this issue, an integrated approach combining multiple geophysical exploration methods is essential. New methods need to be developed and researched, and a comprehensive goaf detection system should be established that combines multiple wavefields, methods, dimensions, and spatial aspects. By fully utilising geophysical big data and multiscale artificial intelligence joint inversion technology, such an integrated approach can greatly improve the resolution and accuracy of goaf detection to support the development of coalbed methane resources in abandoned mines while reducing exploration costs.

Author Contributions

Writing—original draft preparation, C.L.; writing—review and editing, G.L.; supervision, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research is funded by the Natural Science Foundation of Shanxi Province (Grant number: 201901D111045, 202303021221050) and Shanxi Scholarship Council of China (2022-076).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

Author Guoxun Li was employed by the Shanxi Huarun Liansheng Energy Investment Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

References

  1. Li, Q.; Wang, F.; Wang, Y.; Forson, K.; Cao, L.; Zhang, C.; Zhou, C.; Zhao, B.; Chen, J. Experimental investigation on the high-pressure sand suspension and adsorption capacity of guar gum fracturing fluid in low-permeability shale reservoirs: Factor analysis and mechanism disclosure. Environ. Sci. Pollut. Res. 2022, 29, 53050–53062. [Google Scholar] [CrossRef]
  2. Li, Q.; Liu, J.; Wang, S.; Guo, Y.; Han, X.; Li, Q.; Cheng, Y.; Dong, Z.; Li, X.; Zhang, X. Numerical insights into factors affecting collapse behavior of horizontal wellbore in clayey silt hydrate-bearing sediments and the accompanying control strategy. Ocean Eng. 2024, 297, 117029. [Google Scholar] [CrossRef]
  3. Schmelzbach, C.; Greenhalgh, S.; Reiser, F.; Girard, J.-F.; Bretaudeau, F.; Capar, L.; Bitri, A. Advanced seismic processing/imaging techniques and their potential for geothermal exploration. Interpret.-J. Subsurf. Charact. 2016, 4, SR1–SR18. [Google Scholar] [CrossRef]
  4. Zagorskii, L.S.; Shkuratnik, V.L. Method of determining the vertical seismic profile of a rock massif using Rayleigh-type waves. Acoust. Phys. 2013, 59, 197–206. [Google Scholar] [CrossRef]
  5. Xue, G.Q.; Cheng, J.L.; Zhou, N.N.; Chen, W.Y.; Li, H. Detection and monitoring of water-filled voids using transient electromagnetic method: A case study in Shanxi, China. Environ. Earth Sci. 2013, 70, 2263–2270. [Google Scholar] [CrossRef]
  6. Chen, W.Y.; Xue, G.Q.; Muhammad, Y.K.; Gelius, L.J.; Zhou, N.-n.; Li, H.; Zhong, H.S. Application of Short-Offset TEM (SOTEM) Technique in Mapping Water-Enriched Zones of Coal Stratum, an Example from East China. Pure Appl. Geophys. 2015, 172, 1643–1651. [Google Scholar] [CrossRef]
  7. Yu, C.T.; Zhang, X.; Chen, W.Y.; Zhang, F.M.; Tian, Z.B. Detection of water-enriched goaf via the semi-airborne transient electromagnetic method. J. Appl. Geophys. 2023, 212, 105011. [Google Scholar] [CrossRef]
  8. Bharti, A.K.; Singh, K.K.K.; Ghosh, C.N.; Mishra, K. Detection of subsurface cavity due to old mine workings using electrical resistivity tomography: A case study. J. Earth Syst. Sci. 2022, 131, 39. [Google Scholar] [CrossRef]
  9. Torrese, P. Investigating karst aquifers: Using pseudo 3-D electrical resistivity tomography to identify major karst features. J. Hydrol. 2020, 580, 124257. [Google Scholar] [CrossRef]
  10. Kozhevnikov, N.O.; Antonov, E.Y. Current and voltage source induced polarization transients: A comparative consideration. Geophys. Prospect. 2018, 66, 422–431. [Google Scholar] [CrossRef]
  11. Yu, C.T.; Wang, Z.; Tang, M.Y. Application of Microtremor Survey Technology in a Coal Mine Goaf. Appl. Sci. 2023, 13, 466. [Google Scholar] [CrossRef]
  12. Li, H.; Chang, X. A review of the microseismic focal mechanism research. Sci. China-Earth Sci. 2021, 64, 351–363. [Google Scholar] [CrossRef]
  13. Sukanya, S.; Noble, J.; Joseph, S. Application of radon (222Rn) as an environmental tracer in hydrogeological and geological investigations: An overview. Chemosphere 2022, 303, 135–141. [Google Scholar] [CrossRef]
  14. Salazar, S.; Castillo, L.; Montes, L.; Martinez, F. Utilizing the radiometric and seismic methods for hydrocarbons prospecting in the Rancheria sub-basin in Colombia. Appl. Radiat. Isot. 2018, 140, 238–246. [Google Scholar] [CrossRef] [PubMed]
  15. Ao, W.H.; Huang, W.H.; Weng, C.M.; Xiao, X.L.; Liu, D.M.; Tang, X.Y.; Chen, P.; Zhao, Z.G.; Wan, H.; Finkelman, R.B. Coal petrology and genesis of Jurassic coal in the Ordos Basin, China. Geosci. Front. 2012, 3, 85–95. [Google Scholar] [CrossRef]
  16. Xue, G.Q.; Hou, D.Y.; Qiu, W.Z. Identification of Double-layered Water filled Zones Using TEM: A Case Study in China. J. Environ. Eng. Geophys. 2018, 23, 297–304. [Google Scholar] [CrossRef]
  17. Wei, Y.; Zhao, L.; Liu, W.; Zhang, X.; Guo, Z.; Wu, Z.; Yuan, S. Coalbed Methane Reservoir Parameter Prediction and Sweet-Spot Comprehensive Evaluation Based on 3D Seismic Exploration: A Case Study in Western Guizhou Province, China. Energies 2023, 16, 367. [Google Scholar] [CrossRef]
  18. Salmi, E.F.; Malinowska, A.; Hejmanowski, R. Investigating the post-mining subsidence and the long-term stability of old mining excavations: Case of Cow Pasture Limestone Mine, West Midlands, UK. Bull. Eng. Geol. Environ. 2020, 79, 225–242. [Google Scholar] [CrossRef]
  19. Huang, Y.; Zha, H.; Gao, J.; Linghu, J.; Xuan, J.; Zhou, J.; Dong, R.; Huo, J.; Zhang, H. Predicting the distribution of coalbed methane by ambient noise tomography with a dense seismic array. Chin. J. Geophys.-Chin. Ed. 2021, 64, 3997–4011. [Google Scholar] [CrossRef]
  20. Xue, G.Q.; Chen, W.Y.; Cheng, J.L.; Liu, S.C.; Yu, J.C.; Lei, K.X.; Guo, W.B.; Feng, X.H. A Review of Electrical and Electromagnetic Methods for Coal Mine Exploration in China. IEEE Access 2019, 7, 177332–177341. [Google Scholar] [CrossRef]
  21. Shuck, E. Multicomponent, Three-Dimensional Seismic Characterization of a Fractured Coalbed Methane Reservoir: Cedar Hill Field, San Juan County, New Mexico; ProQuest Dissertations Publishing: Denver, CO, USA, 1993. [Google Scholar]
  22. Heo, W.; Lee, W.-S.; Lee, D.S. Hydraulic fracturing design for coalbed methane in Barito Basin, Indonesia. Geosyst. Eng. 2015, 18, 151–162. [Google Scholar] [CrossRef]
  23. Liu, Y.S.; Teng, J.W.; Xu, T.; Wang, Y.H.; Liu, Q.Y.; Badal, J. Robust time-domain full waveform inversion with normalized zero-lag cross-correlation objective function. Geophys. J. Int. 2017, 209, 106–122. [Google Scholar] [CrossRef]
  24. Di Stefano, R.; Chiarabba, C.; Chiaraluce, L.; Cocco, M.; De Gori, P.; Piccinini, D.; Valoroso, L. Fault zone properties affecting the rupture evolution of the 2009 (Mw 6.1) L’Aquila earthquake (central Italy): Insights from seismic tomography. Geophys. Res. Lett. 2011, 38, 310. [Google Scholar] [CrossRef]
  25. Youldash, M.; Al-Dossary, S.; AlDaej, L.; AlOtaibi, F.; AlDubaikil, A.; AlBinali, N.; AlGhamdi, M. Applying Non-Local Means Filter on Seismic Exploration. Comput. Syst. Sci. Eng. 2022, 40, 619–628. [Google Scholar] [CrossRef]
  26. Zeng, C.; Xia, J.H.; Miller, R.D.; Tsoflias, G.P. An improved vacuum formulation for 2D finite-difference modeling of Rayleigh waves including surface topography and internal discontinuities. Geophysics 2012, 77, T1–T9. [Google Scholar] [CrossRef]
  27. Carlson, G.; Shirzaei, M.; Werth, S.; Zhai, G.; Ojha, C. Seasonal and Long-Term Groundwater Unloading in the Central Valley Modifies Crustal Stress. J. Geophys. Res.-Solid Earth 2020, 125, e2019JB018490. [Google Scholar] [CrossRef]
  28. Yang, S.T.; Cheng, J.L. The method of small structure prediction ahead with Rayleigh channel wave in coal roadway and seismic wave field numerical simulation. Chin. J. Geophys.-Chin. Ed. 2012, 55, 655–662. [Google Scholar] [CrossRef]
  29. Liu, G.F.; Liu, Y.; Meng, X.H.; Liu, L.B.; Su, W.J.; Wang, Y.Z.; Zhang, Z.F. Surface wave and body wave imaging of passive seismic exploration in shallow coverage area application of Inner Mongolia. Chin. J. Geophys.-Chin. Ed. 2021, 64, 937–948. [Google Scholar] [CrossRef]
  30. Hu, Y.; Pan, Y.D.; Xia, J.H. Wavefield-Separated Full-Waveform Inversion of Shallow-Seismic Rayleigh Waves. Pure Appl. Geophys. 2022, 179, 1583–1596. [Google Scholar] [CrossRef]
  31. Wang, D.; Lv, L.J. Analysis of Rayleigh wave response characteristics in complicated cave system and detection methods. Mech. Syst. Signal Process. 2023, 185, 109806. [Google Scholar] [CrossRef]
  32. Brodic, B.; Malehmir, A.; Pacheco, N.; Juhlin, C.; Carvalho, J.; Dynesius, L.; van den Berg, J.; de Kunder, R.; Donoso, G.; Sjolund, T.; et al. Innovative seismic imaging of volcanogenic massive sulfide deposits, Neves-Corvo, Portugal—Part 1: In-mine array. Geophysics 2021, 86, B165–B179. [Google Scholar] [CrossRef]
  33. Bruno, P.P.G. Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review. Appl. Sci. 2023, 13, 9473. [Google Scholar] [CrossRef]
  34. Zhan, S.; Chen, M.; Li, L.; Tao, C.; Wan, Z.; Zhao, X. Offset vector tile domain wide-azimuth prestack seismic interpretation methods and applications. Interpret.-J. Subsurf. Charact. 2018, 6, T337–T347. [Google Scholar] [CrossRef]
  35. Wu, C.; Feng, B.; Wang, H.; Wang, T. Three-dimensional angle-domain double-square-root migration in VTI media for the large-scale wide-azimuth seismic data. Acta Geophys. 2020, 68, 1021–1037. [Google Scholar] [CrossRef]
  36. Zhang, L.; Li, A.; Yang, J. Data processing of a wide-azimuth, broadband, high-density 3D seismic survey using a low-frequency vibroseis: A case study from Northeast China. Explor. Geophys. 2020, 51, 652–666. [Google Scholar] [CrossRef]
  37. Tsivadze, A.Y.; Sirotinskii, Y.V.; Klyuev, A.L.; Abaturov, M.A.; Grafov, B.M.; Arutyunov, S.L. Stochastic Electrochemical Approach to Detecting Microseismic Noise Fields. Russ. J. Electrochem. 2018, 54, 1274–1277. [Google Scholar] [CrossRef]
  38. Du, Q.L.; Pan, Y.Y.; Xin, Y.Y. Research and application of Rayleigh wave extraction method based on microtremors signal analysis. Front. Phys. 2023, 11, 1158049. [Google Scholar] [CrossRef]
  39. Baoqing, T.; Zhifeng, D.; Liming, Y.; Yifan, F.; Bo, Z. Microtremor Survey Method: A New Approach for Geothermal Exploration. Front. Earth Sci. 2022, 10, 817411. [Google Scholar] [CrossRef]
  40. Wang, Z.; Sheng, J.C.; Wang, R.; Li, X.B.; Xiao, Y.J.; Yi, Z. Analysis of Microtremor Exploration Application and Construction Monitoring in a Large-Diameter Shield Tunnel. Appl. Sci. 2023, 13, 263. [Google Scholar] [CrossRef]
  41. Lubang, J.; Liu, H.F.; Chen, R.J. Combined Application of Hydrogeological and Geoelectrical Study in Groundwater Exploration in Karst-Granite Areas, Jiangxi Province. Water 2023, 15, 865. [Google Scholar] [CrossRef]
  42. Seher, T.; Tezkan, B. Radiomagnetotelluric and Direct Current Resistivity measurements for the characterization of conducting soils. J. Appl. Geophys. 2007, 63, 35–45. [Google Scholar] [CrossRef]
  43. Wang, Y.Z.; Yu, J.C.; Feng, X.H.; Ma, L.; Gao, Q.H.; Su, B.Y.; Xing, X.J. A Case Study of Direct Current Resistivity Method for Disaster Water Source Detection in Coal Mining. Elektron. Ir Elektrotechnika 2021, 27, 41–48. [Google Scholar] [CrossRef]
  44. Wei, X.; Liu, Y.; Li, X.; Lu, Y. Application of High Density Resistivity Method in Karst Exploration: A Case Study. Teh. Vjesn.-Tech. Gaz. 2023, 30, 1283–1291. [Google Scholar] [CrossRef]
  45. Yang, C.; Liu, S.D.; Feng, Y.; Yang, H.P. Influence of electrode polarization on the potential of DC electrical exploration. J. Appl. Geophys. 2018, 149, 63–76. [Google Scholar] [CrossRef]
  46. Srivastava, S.; Pal, S.K.; Kumar, R. A time-lapse study using self-potential and electrical resistivity tomography methods for mapping of old mine working across railway-tracks in a part of Raniganj coalfield, India. Environ. Earth Sci. 2020, 79, 332. [Google Scholar] [CrossRef]
  47. Braham, M.; Boufekane, A.; Bourenane, H.; Amara, B.N.; Bensalem, R.; Oubaiche, E.H.; Bouhadad, Y. Identification of groundwater potential zones using remote sensing, GIS, machine learning and electrical resistivity tomography techniques in Guelma basin, northeastern Algeria. Geocarto Int. 2022, 37, 12042–12072. [Google Scholar] [CrossRef]
  48. Azffri, S.L.; Ibrahim, M.F.; Godeke, S.H. Electrical resistivity tomography and induced polarization study for groundwater exploration in the agricultural development areas of Brunei Darussalam. Environ. Earth Sci. 2022, 81, 233. [Google Scholar] [CrossRef]
  49. Kaminskyj, R.; Shakhovska, N.; Michal, G.; Ladanivskyy, B.; Savkiv, L. An Express Algorithm for Transient Electromagnetic Data Interpretation. Electronics 2020, 9, 354. [Google Scholar] [CrossRef]
  50. He, J.S.; Xue, G.Q. Review of the key techniques on short-offset electromagnetic detection. Chin. J. Geophys.-Chin. Ed. 2018, 61, 1–8. [Google Scholar] [CrossRef]
  51. Geng, Q.Q.; Ye, Y.; Wang, X.L. Identifying void defects behind Tunnel composite lining based on transient electromagnetic radar method. Ndt E Int. 2022, 125, 102562. [Google Scholar] [CrossRef]
  52. Chang, J.; Su, B.; Malekian, R.; Xing, X. Detection of Water-Filled Mining Goaf Using Mining Transient Electromagnetic Method. Ieee Trans. Ind. Inform. 2020, 16, 2977–2984. [Google Scholar] [CrossRef]
  53. Guo, C.G.; Tan, T.J.; Ma, L.Z.; Chang, S.; Chen, Y.D.; Zhao, K. Response and Application of Full-Space Numerical Simulation Based on Finite Element Method for Transient Electromagnetic Advanced Detection of Mine Water. Sustainability 2022, 14, 15024. [Google Scholar] [CrossRef]
  54. Yu, C.T.; Liu, X.Y.; Liu, J.S.; Li, E.G.; Yue, P.; Yan, S.B. Application of Transient Electromagnetic Method for Investigating the Water-Enriched Mined-Out Area. Appl. Sci. 2018, 8, 1800. [Google Scholar] [CrossRef]
  55. Wu, X.; Xue, G.Q.; Xiao, P.; Li, J.T.; Liu, L.H.; Fang, G.Y. The removal of the high-frequency motion-induced noise in helicopter-borne transient electromagnetic data based on wavelet neural network. Geophysics 2019, 84, K1–K9. [Google Scholar] [CrossRef]
  56. Wu, S.H.; Huang, Q.H.; Zhao, L. Convolutional neural network inversion of airborne transient electromagnetic data. Geophys. Prospect. 2021, 69, 1761–1772. [Google Scholar] [CrossRef]
  57. Ji, Y.J.; Zhu, Y.; Yu, M.M.; Li, D.M.; Guan, S.S. Calculation and application of full-wave airborne transient electromagnetic data in electromagnetic detection. J. Cent. South Univ. 2019, 26, 1011–1020. [Google Scholar] [CrossRef]
  58. Yu, C.T.; Zhang, X.; Wu, X.; Tang, M.Y.; Wang, Z. The effects of relative position of anomalous body and topography on the semi-airborne transient electromagnetic survey. Explor. Geophys. 2023, 1–13. [Google Scholar] [CrossRef]
  59. Tian, Z.B.; Ma, Y.L.; Li, X.; Ji, Y.J.; Li, D.S. A method for detecting coalbed methane gas storage structure in coal goaf—A case in Qinshui coal field in Shanxi province. Chin. J. Geophys.-Chin. Ed. 2022, 65, 4495–4503. [Google Scholar] [CrossRef]
  60. Kouadio, K.L.; Liu, R.; Mi, B.; Liu, C.-m. pyCSAMT: An alternative Python toolbox for groundwater exploration using controlled source audio-frequency magnetotelluric. J. Appl. Geophys. 2022, 201, 104647. [Google Scholar] [CrossRef]
  61. Yu, C.; Chang, S.; Han, Y.; Liu, J.; Li, E. Characterization of Geological Structures under Thick Quaternary Formations with CSAMT Method in Taiyuan City, Northern China. J. Environ. Eng. Geophys. 2019, 24, 621–628. [Google Scholar] [CrossRef]
  62. Sumanovac, F.; Oreskovic, J. Exploration of buried carbonate aquifers by the inverse and forward modelling of the Controlled Source Audio-Magnetotelluric data. J. Appl. Geophys. 2018, 153, 47–63. [Google Scholar] [CrossRef]
  63. Chen, M.S.; Yan, S. Analytical study on field zones, record rules, shadow and source overprint effects in CSAMT exploration. Chin. J. Geophys. 2005, 48, 951–958. [Google Scholar] [CrossRef]
  64. Wang, X.-B.; Chen, J.-C.; Guo, Q.-S.; Liu, Y.; Yu, J.-B.; Zhang, B. Research of the CSAMT exploration mode and experiment for the coalbed methane enrichment region in the north Qinshui basin. Chin. J. Geophys.-Chin. Ed. 2013, 56, 4310–4323. [Google Scholar] [CrossRef]
  65. Zhou, L.; Xie, X.B.; Wang, X.Y.; Yan, L.J. Optimal Transceiver Distance of Controlled-source Audio- frequency Magnetotelluric Sounding Method. J. Environ. Eng. Geophys. 2021, 26, 111–116. [Google Scholar] [CrossRef]
  66. Kumar, I.; Kumar, S.V.; Babu, V.R.; Kumar, B.V.L.; Dash, J.K.; Chaturvedi, A.K. Controlled Source Audio Magneto Telluric (CSAMT) Studies for Uranium Exploration in Durgi Area, Palnad Sub-basin, Cuddapah Basin, India. J. Geol. Soc. India 2017, 89, 12–20. [Google Scholar] [CrossRef]
  67. Asch, T.H.; Sweetkind, D.S. Audiomagnetotelluric characterization of range-front faults, Snake Range, Nevada. Geophysics 2011, 76, B1–B7. [Google Scholar] [CrossRef]
  68. Mostafaei, K.; Ramazi, H. Investigating the applicability of induced polarization method in ore modelling and drilling optimization: A case study from Abassabad, Iran. Near Surf. Geophys. 2019, 17, 637–652. [Google Scholar] [CrossRef]
  69. Yang, Y.B.; Zhang, G.B.; Yao, C.L.; Deng, Z.J.; Ren, Z.M.; Li, C.F. Application of Induced Polarization Method in Mineral Resource Exploration. Sustainability 2023, 15, 3840. [Google Scholar] [CrossRef]
  70. Lu, Z.F.; Zhou, B.; Wang, J.F.; Chan, Z.K.; Yang, Q.F.; Dong, Z.Y.; Dong, K.L. Study on the formation mechanism of a radon source during coal spontaneous combustion in a goaf. Fuel 2023, 336, 127–135. [Google Scholar] [CrossRef]
Sustainability 16 02677 g001
Figure 1. Distribution map of coalfield distribution in Shanxi Province.
Figure 1. Distribution map of coalfield distribution in Shanxi Province.
Sustainability 16 02677 g001
Sustainability 16 02677 g002
Figure 2. Geological model for the occurrence and distribution of coalbed methane in the goaf of an abandoned mine.
Figure 2. Geological model for the occurrence and distribution of coalbed methane in the goaf of an abandoned mine.
Sustainability 16 02677 g002
Sustainability 16 02677 g003
Figure 3. The influence of different goaf parameters on method selection.
Figure 3. The influence of different goaf parameters on method selection.
Sustainability 16 02677 g003
Sustainability 16 02677 g004
Figure 4. Typical time profile characteristics of a diffraction wave used for seismic exploration of a goaf.
Figure 4. Typical time profile characteristics of a diffraction wave used for seismic exploration of a goaf.
Sustainability 16 02677 g004
Sustainability 16 02677 g005
Figure 5. Layouts of arrays for observation of irregular microseismic signals.
Figure 5. Layouts of arrays for observation of irregular microseismic signals.
Sustainability 16 02677 g005
Sustainability 16 02677 g006
Figure 6. Shear wave velocity structure in the goaf of a coal mine.
Figure 6. Shear wave velocity structure in the goaf of a coal mine.
Sustainability 16 02677 g006
Sustainability 16 02677 g007
Figure 7. High-density electrical detection results for a water-rich goaf of an abandoned mine.
Figure 7. High-density electrical detection results for a water-rich goaf of an abandoned mine.
Sustainability 16 02677 g007
Sustainability 16 02677 g008
Figure 8. Basic principle of TEM.
Figure 8. Basic principle of TEM.
Sustainability 16 02677 g008
Sustainability 16 02677 g009
Figure 9. Typical cross-section of a goaf obtained by TEM.
Figure 9. Typical cross-section of a goaf obtained by TEM.
Sustainability 16 02677 g009
Sustainability 16 02677 g010
Figure 10. Schematics of (a) ATEM and (b) semi-ATEM.
Figure 10. Schematics of (a) ATEM and (b) semi-ATEM.
Sustainability 16 02677 g010
Sustainability 16 02677 g011
Figure 11. Detection results of semi-ATEM in Shanxi.
Figure 11. Detection results of semi-ATEM in Shanxi.
Sustainability 16 02677 g011
Sustainability 16 02677 g012
Figure 12. Detection by CSAMT of a goaf in an abandoned mine with water accumulation.
Figure 12. Detection by CSAMT of a goaf in an abandoned mine with water accumulation.
Sustainability 16 02677 g012
Table 1. Coalbed methane resources in the main mining areas of Shanxi Province.
Table 1. Coalbed methane resources in the main mining areas of Shanxi Province.
MineCoal GroupMine Area (km2)Goaf Area (km2)Coalbed Methane in Goaf (CBM)Resource Abundance (CBM.km2)
XishanUpper497.40114.6130.040.26
Lower523.2839.0517.360.44
WuxiaUpper142.3737.298.810.24
Lower115.455.342.010.38
LuanUpper466.7593.9315.500.17
Lower390.780.530.120.23
LiliuUpper390.9152.0928.930.56
Lower382.724.251.310.31
HuodongUpper198.9031.553.940.12
Lower111.090.560.200.36
YangquanUpper489.28101.8619.240.19
Lower429.7336.1339.640.10
JinchengUpper681.81129.0648.430.38
Lower523.6000——
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

Liu, C.; Li, G.; Yu, C. Methods for the Geophysical Exploration and Sustainable Utilisation of Coalbed Methane Resources in Abandoned Mines of Shanxi, China. Sustainability 2024, 16, 2677. https://doi.org/10.3390/su16072677

AMA Style

Liu C, Li G, Yu C. Methods for the Geophysical Exploration and Sustainable Utilisation of Coalbed Methane Resources in Abandoned Mines of Shanxi, China. Sustainability. 2024; 16(7):2677. https://doi.org/10.3390/su16072677

Chicago/Turabian Style

Liu, Chunlin, Guoxun Li, and Chuantao Yu. 2024. "Methods for the Geophysical Exploration and Sustainable Utilisation of Coalbed Methane Resources in Abandoned Mines of Shanxi, China" Sustainability 16, no. 7: 2677. https://doi.org/10.3390/su16072677

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