What Extra Information Can Be Provided by Multi-Component Seismic Data: A Case Study of 2D3C Prospecting of a Copper–Molybdenum Mine in Inner Mongolia, China

Abstract

With the decrease in shallow mineral reserves, deep mineral resources have become the focus of exploration. Seismic exploration, renowned for its deep penetration and high spatial resolution and precision, stands as a primary technique in geophysical exploration. In comparison to traditional P-wave seismic exploration, multi-component seismic techniques offer the advantage of simultaneously acquiring P-wave and S-wave data, overcoming the limitations of single P-wave impedance in predicting lithology and enabling high-precision imaging of subsurface structures. Constrained by field survey costs, the reflection seismic illumination is lower and results in a poor signal-to-noise ratio of multi-component seismic data in metallic ore exploration, which poses great challenges in imaging converted S-waves. Based on the seismic and geological characteristics of metallic ores, this study conducts imaging research on metallic ore models through synthetic data and field multi-component seismic data from a copper–molybdenum mine in Inner Mongolia, China. The emphasis is given to PS-wave pre-stack time migration based on precisely sorting the commonly converted point so as to explore the feasibility and technical advantages of multi-component seismic exploration in metal mines. Synthetic data and field data testing demonstrate that PS-wave imaging contains more abundant structural and lithological information compared to PP-waves, indicating promising prospects for the application of multi-component seismic data in metallic ore exploration.

1. Introduction

Metallic minerals are critical industrial strategic resources, and the reserves of shallow mineral deposits in China have been decreasing annually [1]. Therefore, the exploration and exploitation of deep-seated mineral resources have become a concern [2], especially the mineral resources in the second-depth space (500–2000 m), which will be a focal point for China’s current and future development [3,4].

In the exploration of metallic minerals, in addition to geological, drilling, and geochemical techniques, geophysical methods, such as gravity, magnetic, and electrical methods, also play important roles. These methods offer notable advantages such as low cost, lightweight equipment, high flexibility, and environmental friendliness. They can provide information on underground anomalies related to magnetism, electrical conductivity, and density, and metallic mineral exploration can be achieved by integrating geological, drilling, and geochemical information. As exploration depth increases, it becomes necessary to reduce the number of boreholes and enhance geophysical surveys. However, the limitations in the precision and resolution of gravity, magnetic, and electrical methods become more pronounced with increasing depth. Compared to these methods, seismic exploration, due to its superior detection depth and resolution, has become a focal point in the exploration of metallic mineral deposits [5,6,7,8,9]. As early as 1959, few countries began researching the application of seismic techniques in metallic ore exploration [10]. The United States, Canada [11,12,13,14], Australia [15,16], South Africa [17], and Europe [18,19] have all conducted experimental and applied seismic exploration in metallic ore deposits. Their results have demonstrated that the exploration precision for complex geological structures of metallic ores can be significantly improved via the seismic method. However, it is worth noting that the current applications of the seismic method are mainly focused on two-dimensional (2D) PP-wave seismic exploration. Regarding the complex geological conditions of metallic ore deposits, three-dimensional (3D) seismic imaging is necessary to improve the visualization of underground structures and ore bodies [11,12,13].

The development of the reflection imaging seismic technique in metallic mineral exploration has faced challenges due to the irregular and non-horizontal nature of geological anomalies. In contrast, the scattering wave imaging technique has attracted increasing attention in the field of metallic mineral seismic exploration due to its higher fold and greater illumination [20,21,22,23,24]. For instance, Li et al. (2009) [25] simulated the scattering wave seismic records based on a metallic mineral model and performed imaging using scattering waves. Malcolm et al. (2011) [26] incorporated the secondary scattered wave field into migration and achieved favorable imaging results. Furthermore, Hu et al. (2020) [27] conducted scattering wave migration imaging based on the scattering wave equation, which exhibited higher resolution compared to the conventional reverse time migration. Moreover, Xu et al. (2024) [28] achieved good imaging of multiple scattered waves based on inverse scattering theory. To enhance the imaging effect of local velocity anomalies or structures, many scholars [29,30,31,32] have separated scattering and reflection signals and then performed imaging of the scattering signals.

Multi-component seismic exploration, due to its ability to simultaneously acquire PP- and PS-waves, provides comprehensive underground elastic wave field information, overcoming the limitation of poor accuracy in lithological prediction associated with PP-wave imaging, and has found application in the field of metallic mineral seismic exploration. For instance, Bellefleur et al. (2004) [33] utilized the vertical seismic profiling (VSP) technique at Half Mile Lake in New Brunswick, Canada, to image sulfide deposits, where they found that PP- and SS-waves, as well as PS- and SP-converted waves, could find applicability in the imaging of different parts of the deep ore body. Snyder et al. (2009) [34] performed imaging using two-dimensional three-component seismic (3C) data in the Abitibi greenstone belt in southern Canada. Malinowski et al. (2011) [35] demonstrated the effectiveness of converted PS-waves as supplementary information for PP-wave seismic imaging in the Flin Flon mining area in Canada using high-resolution 3C seismic data. Malehmir et al. (2015) [36] discovered that horizontal components could provide important shallow structural information, enhancing the understanding of ore-controlling structures in multi-component seismic exploration in Laisvall, Sweden. However, these multi-component seismic experiments have used an approximate combination of asymptotic conversion point (ACP) gather and dip-move out (DMO) for PS-wave imaging, with only pre-stack migration applied to PP-waves. This mismatch in processing procedures between PP- and PS-waves fails to give full play to the multi-component seismic wavefield advantages, leaving room for further improvement in the characterization accuracy of metallic ore structures. This ambiguity also hinders a clear understanding of the role of multi-component seismic techniques in metal ore exploration.

This study focuses on the seismic, geological characteristics of metallic ore deposits and analyzes the seismic wavefield characteristics in metallic mineral exploration through typical metallic seismic geological modeling and wave field simulation. Furthermore, the feasibility and technical advantages of applying multi-component seismic methods to metallic mineral exploration are investigated through the processing of theoretical synthetic data and field data.

2. Seismic Geological Characteristics and Imaging Methods of Metallic Ores

Metallic ore deposits are commonly found in structurally complex mountainous areas outside sedimentary basins, regardless of whether they are at the margin, within the plate, or in the continental interior. These deposits can be roughly categorized into four types based on mineralization processes: magmatic deposits, hydrothermal deposits, volcanic deposits, and contact metamorphic deposits [37]. Therefore, the formation of metallic ores tends to occur in areas with crystalline rocks. Furthermore, over the course of geological evolution, already-formed metallic ore deposits are influenced by multiple periods of mineralization and tectonic activity, further increasing the deposit complexity [38]. This complexity is manifested by the frequent development of fault zones, magmatic intrusions, and steep structures, which leads to significant variations in both geological conditions and in lithology within metallic ore districts, further resulting in few stable and nearly horizontal lithological interfaces [39].

Owing to the geological characteristics of metallic ores [40], seismic data of metallic ore deposits typically exhibit the following features: (1) Metallic ore deposits are often located in areas with crystalline rocks, where the impedance contrast between different rock is relatively small, resulting in weak reflection signals [41]. (2) The complex structural features of metallic ores generally generate a seismic wavefield with complex and varied wave-forms that interfere with each other, making it difficult to form continuous and stable seismic reflection signals [42], thus increasing the challenges in processing and imaging seismic reflection signals. (3) Metallic ore deposits are often located in mountainous regions with large variations in surface elevation, leading to complex surface conditions and subsurface structures that are unfavorable for conducting drilling. Moreover, fieldwork conditions are constrained, resulting in seismic data with low coverage and low signal-to-noise ratio, posing great difficulties for seismic data processing and interpretation. (4) The small scale and complex morphology of metallic ore-bearing units lead to the development of scattered wavefields [43]. Even in the case of P-wave excitation, the seismic wavefield simultaneously includes both P- and S-waves, and the velocity difference in P- and S-waves leads to the generation of different P-wave and S-wave seismic responses. Multi-component seismic techniques that record both P-waves and S-waves are advantageous for maximizing the exploration of underground media and structural characteristics. For multi-component seismic pre-stack time-domain imaging, the 3D Kirchhoff integral equation with continuous source and receiver points can be represented as follows [44]:

$$u_n(\stackrel{\rightharpoonup }{x},t)=\frac{1}{4\pi }{\displaystyle \underset{-\infty }{\overset{\infty }{\int }}{\displaystyle \underset{-\infty }{\overset{\infty }{\int }}\left\{\begin{array}{l}\left[\left(1-2{\nu }^2\right){\delta }_{m3}{\widehat{r}}_n+2{\nu }^2{\widehat{r}}_m{\widehat{r}}_n{\widehat{r}}_3\right]\times {\dot{u}}_m({\stackrel{\rightharpoonup }{x}}^{\prime },t+t_P)/\left(r{V}_P\right)\\ +\left({\delta }_{mn}{\widehat{r}}_3+{\delta }_{n3}{\widehat{r}}_m-2{\widehat{r}}_m{\widehat{r}}_n{\widehat{r}}_3\right)\times {\dot{u}}_m({\stackrel{\rightharpoonup }{x}}^{\prime },t+t_S)/\left(r{V}_S\right)\end{array}\right\}}}d{x^{\prime }}_{1}d{x^{\prime }}_2$$

(1)

where u_n($\overrightarrow{x}$, t) represents the particle displacement; $\dot{u}$(${\overrightarrow{x}}^{\prime },t$) represents the particle velocity; m, n = 1, 2, 3 represent the X, Y, Z coordinates; $\widehat{r}$_m$,\widehat{r}$_n represent the projection of the unit vector on the m, n coordinates; V_P, V_S represent the longitudinal and transverse wave velocities; t_P, t_S represent the travel times of P-waves and S-waves from the diffraction point to the receiver point; r represents the distance from the diffraction point to the receiver point; ν = V_P/V_S represents the ratio of P- to S-wave velocities; and δ_mn is the Kronecker delta function.

Compared to the assumption of the common mid-point (CMP) for PP-wave with P-wave incidence and reflection, the trajectory of the conversion point (reflection point) for PS-waves varies with depth due to the inconsistent up-going and down-going waves, gradually approaching the receiver as the depth becomes shallower, as shown in Figure 1. Therefore, when calculating the travel time t in Equation (1), PS-waves are more complex than PP-waves. Furthermore, since metallic ore deposits mostly have steep geological structures, the movement of the reflection point towards the inclined interface direction under the inclined interface does not satisfy the horizontal interface assumption of traditional reflection seismic exploration. Consequently, this study focuses on improving the method for calculating PS-wave conversion points on inclined interfaces.

Figure 2 illustrates the ray paths of PS-waves under both inclined and horizontal interfaces. In the figure, the dashed black lines represent the ray paths of the PS-wave under the horizontal interface, while the solid black lines represent the ray paths of the PS-wave under the inclined interface. It can be observed from the figure that for the seismic wavefield generated by source S when its conversion point projected onto the ground is at O, there is a significant distance difference between the conversion point C₁ on the horizontal interface and the conversion point C₂ on the inclined interface. The converted S-waves generated are, respectively, received by detectors R₁ and R₂. Furthermore, as the inclination angle δ increases, the distance between the conversion points C₁ and C₂ will gradually increase [45].

Therefore, when imaging PS-waves in metal deposits within highly steep formations, it is crucial to consider inclination angles. In this study, we calculate the conversion points based on the 3D conversion point equation proposed for inclined interfaces by Yao et al. (2005) [46] to perform PS-wave imaging:

$$r_R=\frac{h}{\sqrt{g^2(h/r_0-\mathit{sin}\stackrel{-}{\delta }{)}^2(g^2-1){\mathit{cos}}^2\stackrel{-}{\delta }}-\mathit{sin}\stackrel{-}{\delta }}$$

(2)

where $\stackrel{-}{\delta }$ represents the dip angle of the interface. g is the ratio of P-wave velocity to S-wave velocity; h is the normal depth of the interface; r₀ is the horizontal distance between the source and the conversion point; r_R is the horizontal distance between the receiver and the conversion point.

Due to the fact that the projection of the PS-wave conversion point on the horizontal surface gradually approaches the receiver point as the depth decreases, the conversion points at different depths will have different spatial coordinates. Seismic data are typically processed using a gridding method, and thus, we have also employed a gridding method for conversion point calculations. As shown in Figure 3, the projection of the conversion points at various depths falls within different imaging grids. Thus, for a given seismic trace, the imaging grid CCP_i corresponding to the conversion point at depth z can be expressed as [47]:

$$CC{P}_i=(x(\mathit{r},\mathit{s},g,{v_P}^{RMS},z),y(\mathit{r},\mathit{s},g,{v_P}^{RMS},z))$$

(3)

here, v_P^RMS represents the root-mean-square P-wave velocity; x represents the horizontal coordinate of the conversion point, and y represents the vertical coordinate of the conversion point.

Calculating the conversion points using Equation (3) and then utilizing ray theory to calculate the corresponding travel time t for the seismic trace and complete data sorting, we have the following equation:

$$t=\frac{\sqrt{r_s^2+z^2}}{{v_P}^{RMS}}+\frac{g\sqrt{r_g^2+z^2}}{{v_P}^{RMS}}$$

(4)

here, r_s and r_g, respectively, represent the horizontal distances between the conversion point at depth z and the source and the receiver.

3. Numerical Examples

Based on the structural–geological characteristics of metal ore deposits, a simple seismic geological model for a molybdenum deposit is established, as shown in Figure 4. Considering significant physical property differences between the ore body and surrounding rocks and potentially smaller differences between the mineralized alteration zone and host rocks [30], the physical parameters for this 2D molybdenum deposit are defined in

Table 1. Model parameters.

Rock Type	P-Wave Velocity (m/s)	S-Wave Velocity (m/s)	Density (g/cm3)
Metamorphic Sandstone	5580	3250	2.8
Granodiorite	5270	2970	2.74
Molybdenum Ore Body	4879	2600	3.77

. The model extends 1200 m in length and is buried at a depth of 1000 m, in which the ore body is characterized by a narrow and inclined distribution within a granodiorite body. The seismic acquisition scheme includes a source interval of 30 m, with a receiver spacing of 5 m, utilizing a dual-source shooting method, with the smallest offset at 0 m and the largest offset at 1450 m, involving a total of 47 shots. To mitigate the influence of surface waves, absorbing boundary conditions are employed during forward modeling, using a 50 Hz Ricker wavelet for elastic wave finite difference simulation, as illustrated by a typical shot gather recordin Figure 5. From the figure, it can be observed that the steep ore body and irregular geological interfaces lead to complex interactions among direct waves, reflected waves, and scattered waves, resulting in a highly intricate seismic wavefield on the X– and Z– components, making it challenging to identify hyperbolic reflection waves in the shot gather. Furthermore, the wave-form leakage phenomena can also be observed, as indicated by red arrows, by comparing Figure 5a,b.

After applying the wavefield separation method based on the affine coordinate system to the synthetic data [48], pre-stack time migration imaging was performed separately for the X and Z components, resulting in the imaging shown in Figure 6. It is apparent that both PP-waves and PS-waves have achieved good imaging of inclined interfaces and ore bodies. However, it should be noted that at the location indicated by the red arrow in Figure 6, because of the extremely steep interface, reflection signals from this lithological interface are only obtained from the source–receiver pairs in the x: 0~200 m, resulting in poor illumination and, consequently, causing discontinuities in the imaging. Furthermore, in the comparison between the PP-wave and PS-wave migrated sections, it is observed that due to its longer travel time, the PS-wave events have a steeper dip compared to the PP-wave, making the PS-wave more effective in describing fine structures, as indicated by the green arrow. Further analysis of the stacked sections revealed that when imaging the multi-layer inclined ore body on the leftmost side (the red box), the seismic resolution of the 1/4 wavelength prevents accurate identification of the internal features for thin ore layers, only delineating the ore deposit. However, when the spacing between ore bodies exceeds 1/4 wavelength, the internal features of the ore bodies are clearly depicted, as observed in the double-layer inclined ore body in the middle of the model (green box). Additionally, due to the incomplete separation of the P- and S-waves, and in order to accurately depict steep structures during migration, we utilized a large image dip, thus introducing imaging noise, as illustrated by the blue arrow in Figure 6.

The imaging of the synthetic data has shown that the multi-component seismic technique exhibits good performance in describing the distribution and internal structures of steep geological formations and ore bodies. Moreover, the longer travel time of PS-waves results in a clearer depiction of small-scale structures compared to PP-waves, highlighting the advantages of multi-component seismic technology in the exploration of metal ores.

4. A Case Study of 2D3C Seismic Exploration for Copper–Molybdenum Deposits in Inner Mongolia

4.1. Geological Overview

The 2D3C seismic experimental area of the Inner Mongolia copper–molybdenum mine is located in the Dalaimiao-Wurinitu area north of Erenhot-Sunit Zuoqi in Inner Mongolia, China. The regional geological background is shown in Figure 7a [49,50]. The structural position belongs to the early Paleozoic continental margin accretionary belt on the southeastern continental margin of the Siberian Plate. The southern wing of the Dalaimiao anticline is the main structural feature in the mining area with non-obvious fold structures, and the predominant structural trend is northeast-trending faulting, with northwest-trending faults as secondary structures [51], as shown in Figure 7b.

The exposed stratigraphic sequence within the area includes the Middle Ordovician (O₂b), Upper Carboniferous (C₃h), Upper Jurassic (J₃b), and Quaternary (Q) formations. Among them, the Upper Carboniferous Hong’aobao Formation clastic rock section (C₃h) is the most widely exposed formation in the area. The upper part is mainly composed of metamorphic arkose, tuffaceous arkose, tuffaceous feldspathic sandstone, and andesite, while the lower part is dominated by metamorphic arkose. Quaternary gravel layers (Q) are primarily distributed in the northern part of the working area. In addition, magmatic rocks are widely distributed within the experimental area, including exposed Permian granodiorite (γδ₄³) and Jurassic biotite granite (γ₅²) [51].

The mineralized rock mass in this area is the Permian granodiorite, in which quartz veins (q) are well developed and often contain molybdenum mineralization. Therefore, the molybdenum-bearing quartz veins constitute the ore body. The ore body is mainly located within a nearly north-west oriented extensional fault zone, crossing the diorite veins, indicating a relatively late mineralization process. The surrounding rock of the ore body is predominantly composed of granodiorite, with local occurrences of metamorphic arkose [52]. Density and velocity tests were conducted on the collected rock and ore samples to obtain the average P-wave and S-wave velocity and density data for different rock and ore types, with specific data listed in

Table 2. Density and velocity of rock and mineral samples in the study area.

Rock Types	P-Wave Velocity (m/s)	S-Wave Velocity (m/s)	Density (g/cm3)
Granodiorite	5271.4	2973.7	2.7402
Diorite (Vein)	5566.0	3189.0	2.9094
Mineralized granite diorite	5576.4	3251.5	2.8003
Copper–molybdenum mineralized Granite diorite (ore)	5297.5	2769.3	2.8467
Granodiorite	4879.0	2600.0	3.7670

[53].

4.2. Data Analysis and Processing

The data acquisition in this study utilized a multi-fold coverage observation system that combined single-sided and central excitation. The parameters included a source spacing of 30 m, receiver spacing of 15 m, minimum offset of 30 m, maximum offset of 3615 m, 60-fold coverage, and 240 channels for receivers. The acquisition duration was 7 s with a sampling interval of 0.5 ms. The excitation method employed an explosive source well with a depth of 10 m and a charge of 5 kg. The elevation within the working area ranged from 1200 to 1250 m, and the variation in elevation along the survey lines is depicted in Figure 8. From the figure, it can be observed that the elevation fluctuation along the survey lines is relatively gentle, and the maximum elevation difference is approximately 30 m.

The 2D3C shot gathers and frequency spectrums are shown in Figure 9. From the figure, it can be observed that due to the influence of complex seismic geological conditions, the shot gathers mainly exhibit significant direct waves, refracted waves, surface waves, and scattered waves. Therefore, data processing presents a challenge due to the low signal-to-noise ratio, which makes it difficult to observe effective reflection signals. Moreover, the frequency spectra show that the dominant frequency for the X- and Y-components is 33 Hz, while the dominant frequency for the Z-component is 37 Hz, with minimal differences in dominant frequencies. Additionally, despite the majority of the surface in the area being covered by Quaternary deposits, there are still exposures of granite and metamorphic rocks, leading to complex near-surface seismic conditions and a relatively high near-surface velocity. The near-surface P-wave velocity is approximately 5500 m/s, and the S-wave velocity is approximately 3200 m/s.

Based on the data characteristics of the study area, a technical workflow for the processing of multi-component seismic was constructed, as shown in Figure 10. Our focus was reflected on attenuating linear noise, surface waves, and random noise to improve the signal-to-noise ratio of the data. Moreover, amplitude compensation was employed to equalize the energy at different spatial positions. The preprocessed seismic records are depicted in Figure 11. From the figure, it can be observed that after denoising and amplitude compensation, previously unobservable reflection and scattering signals are highlighted, and the bandwidth is significantly increased. Furthermore, in terms of static correction, we applied the refraction static correction method for the PP-wave and performed the structural constraint method for the PS-wave.

4.3. Imaging

Through pre-stack time migration, we obtained migrated sections of PP- and PS-waves, as well as the corresponding root-mean-square velocity field, as shown in Figure 12 and Figure 13. From the figures, it is evident that when faced with low signal-to-noise ratio and complex seismic geological conditions, we have achieved good imaging results, effectively characterizing the subsurface structures. Observing the migrated sections of PP- and PS-waves, we can see that the underground structures are predominantly characterized by high-angle steep formations, and the migrated sections display numerous small-scale discontinuous events. Furthermore, the main wave characteristics of the PS-wave migrated section correspond well with the PP-wave. In Figure 13, lateral variations are observed in the root-mean-square velocity field, reflecting the complexity of the structure in this area.

To facilitate the interpretation, we transformed the time-domain migrated sections into the depth domain through the time-to-depth conversion. Furthermore, we converted the root-mean-square velocity from the time domain to the depth domain. The results are illustrated in Figure 14 and Figure 15. From the depth-domain migrated sections in Figure 14, we can observe that the main wave characteristics between PP- and PS-waves become more pronounced, as indicated by the red arrows. Affected by the low signal-to-noise ratio, the PS-wave imaging quality is not as high as the PP-wave. However, the structural information provided by PS-waves is more comprehensive, complementing the areas that cannot be imaged by PP-waves, as indicated by the red box in the figure. From the depth-domain interval velocity in Figure 15, it can be observed that PP- and PS-waves exhibit similar structures. Furthermore, based on previous studies [51,52], we conducted a comparison between our imaging results of PP and PS-waves and the corresponding geological cross-section (Figure 16). Our findings demonstrate that our imaging sections effectively portray the structural characteristics of the mineral deposit, confirming their accuracy and high imaging quality. Additionally, comparing the velocity field depicted in Figure 15 with the geological cross-section, we observed that the overall morphological features of the velocity field are consistent with the background structures. Unfortunately, the geological cross-section covers a relatively short span, approximately corresponding to common depth point (CDP) positions 1–350, limiting further comparison beyond this range. Nevertheless, taking into account the directional features of the mineral deposit and the geological structural background, the imaging results from CDP positions 350–700 remain accurate and exhibit commendable imaging quality.

Furthermore, in the comparison of migrated sections between PP- and PS-waves, we found that although PS-wave imaging is generally not as good as PP-wave imaging, the imaging of PS-waves within the red box in Figure 14 is superior to that of PP-waves, and the distribution of energy in the migrated section is relatively homogeneous. As migrated sections correspond to wave impedance interfaces, the greater the difference in wave impedance, the stronger the reflection energy. Therefore, based on the physical properties of rocks in Table 2, we calculated the wave impedances of P-waves and S-waves, as shown in

Table 3. Absolute values of wave impedance contrast for various rock types (P-wave).

Rock Types	Granodiorite	Diorite (Vein)	Metamorphic Sandstone	Mineralized Granite Diorite	Copper–Molybdenum Mineralized Granite Diorite (Ore)
Granodiorite	—	1749	1171	636	3935
Diorite (vein)	—	—	578	1113	3934
Mineralized granite diorite	—	—	—	535	2764
Copper–molybdenum mineralized granite diorite (ore)	—	—	—	—	3299

and

Table 4. Absolute values of wave impedance contrast for various rock types (S-wave).

Rock Types	Granodiorite	Diorite (Vein)	Metamorphic Sandstone	Mineralized Granite Diorite	Copper–Molybdenum Mineralized Granite Diorite (Ore)
Granodiorite	—	1130	957	265	1646
Diorite (vein)	—	—	172	1395	516
Mineralized granite diorite	—	—	—	1222	682
Copper–molybdenum mineralized granite diorite (ore)	—	—	—	—	1911

. From the tables, it is evident that there is significant fluctuation in the wave impedance contrast of P-waves, with a notable difference in wave impedance between the ore body and surrounding rock, which is advantageous for identifying mineralization locations. However, it may also lead to weaker reflection signals between certain interfaces, resulting in poor imaging. In contrast, the fluctuation in wave impedance contrast between different rocks for PS-waves is relatively small, leading to minor differences in the energy of the reflection signals, which is beneficial for accurately delineating various geological structures. This overcomes the disadvantage of weak wave impedance contrast in PP-waves and holds significant implications for interpreting the overall structure of metallic deposits.

Therefore, through the study of the multi-component seismic data of the copper–molybdenum ore, we conclude that the imaging results of PP- and PS-waves complement each other, thus validating the effectiveness of multi-component seismic techniques in metallic mineral exploration. In comparison to traditional P-wave processing methods, multi-component seismic techniques offer more comprehensive information, compensating for the weak impedance contrast of crystalline rocks.

5. Discussion

Through synthetic data and field data testing, the study demonstrates that multi-component seismic techniques can better characterize subsurface structures when faced with complex surface environments and geological formations. Due to the different impedance contrasts of P- and S-waves, the resulting seismic responses are various. Therefore, we utilize PS-wave imaging results to complement areas where PP-waves cannot image, addressing the problem of weak impedance contrast for crystalline rock regions. Therefore, in mineral exploration, multi-component seismic techniques exhibit significant advantages in structural description compared to traditional P-waves.

High-quality imaging relies on the cooperation of various processing steps. Metal deposits, limited in scale, present challenges such as complex structures, low signal-to-noise ratio, discontinuous reflections, and strong scattering. Therefore, it is necessary to fully utilize noise suppression techniques to highlight effective signals. Since the seismic wavefield is a vector field, it is essential for multi-component seismic exploration to maintain the polarization characteristics of waves and to construct a rational processing workflow by utilizing vector noise suppression techniques as much as possible. Additionally, steep and small-scale metal deposits result in developed scattered waves, leading to interference between reflected waves and scattered waves. Numerous algorithms for separating reflected waves from scattered waves have been developed in the oil and gas exploration field. Consequently, we may consider first separating reflected waves from scattered waves and then conducting imaging with multi-component seismic data.

The study primarily focuses on the effective role of multi-component seismic techniques in characterizing structures for mineral exploration. However, it is important to note that multi-component seismic data contains abundant information about the subsurface. Therefore, future research can be directed towards the joint inversion of PP and PS-waves to further obtain the elastic parameters of the subsurface, providing additional assistance to mineral exploration.