Noise Cross-correlation Functions Research Articles

3D shear wave velocity and azimuthal anisotropy structure in the shallow crust of Binchuan Basin in Yunnan, Southwest China, from ambient noise tomography

The Binchuan Basin in northwest Yunnan, southwest China, is a rift basin developed at the intersection of the Red River Fault and Chenghai Fault, where historical earthquakes have occurred. Understanding the fine velocity structure of the shallow crust in this region can help improve earthquake location accuracy and our understanding of the relationship between fault zone structures and fault slip behaviors. Using the continuous waveform data recorded by 381 dense array stations in 2017, we obtained 7 915 Rayleigh-wave phase velocity dispersion curves in the period band of 0.2–6 ​s from ambient noise cross-correlation functions after rigorous data processing and quality control. We determined 3D isotropic and azimuthally anisotropic shear wave velocity models at depths above 6 ​km in the shallow crust based on the direct surface wave azimuthal anisotropic tomography method. The isotropic model reveals a strong correspondence between the S-wave velocity structure at depths of 0–1 ​km and the regional topography and lithology. The Binchuan depocenter, Zhoucheng depocenter, Xiangyun Basin, and Xihai Rift Basin are primarily composed of Quaternary deposits, which show low-velocity anomalies, while the regions with the Paleozoic shale, limestone, and basalt exhibit high-velocity anomalies. The nearly N–S orientation of fast directions from azimuthal anisotropy models are mainly controlled by the active Binchuan Fault with N–S strike as well as the NNW-oriented primary compressive stress.

Improving signal-to-noise ratios of ambient noise cross-correlation functions using local attributes

SUMMARY For seismographic stations with short acquisition duration, the signal-to-noise ratios (SNRs) of ambient noise cross-correlation functions (CCFs) are typically low, preventing us from accurately measuring surface wave dispersion curves or waveform characteristics. In addition, with noisy CCFs, it is difficult to extract relatively weak signals such as body waves. In this study, we propose to use local attributes to improve the SNRs of ambient noise CCFs, which allows us to enhance the quality of CCFs for stations with limited acquisition duration. Two local attributes: local cross-correlation and local similarity, are used in this study. The local cross-correlation allows us to extend the dimensionality of daily CCFs with computational costs similar to global cross-correlation. Taking advantage of this extended dimensionality, the local similarity is then used to measure non-stationary similarity between the extended daily CCFs with a reference trace, which enables us to design better stacking weights to enhance coherent features and attenuate incoherent background noises. Ambient noise recorded by several broad-band stations from the USArray in North Texas and Oklahoma, the Superior Province Rifting EarthScope Experiment in Minnesota and Wisconsin and a high-frequency nodal array deployed in the northern Los Angeles basin are used to demonstrate the performance of the proposed approach for improving the SNR of CCFs.

Noise source localization using deep learning

Summary Ambient noise source localization is of great significance for estimating seismic noise source distribution, understanding source mechanisms and imaging subsurface structures. The commonly used methods for source localization, such as the matched field processing and the full-waveform inversion, are time-consuming and not applicable for time-lapse monitoring of the noise source distribution. We propose an efficient alternative of using deep learning for noise source localization. In the neural network, the input data are noise cross-correlation functions and the output are matrices containing the information of noise source distribution. It is assumed that the subsurface structure is a horizontally layered earth model and the model parameters are known. A wavefield superposition method is employed to efficiently simulate ambient noise data with quantities of local noise sources labelled as training datasets. We use a weighted binary cross-entropy loss function to address the prediction inaccuracy caused by a sparse label matrix during training. The proposed deep learning framework is validated by synthetic tests and two field data examples. The successful applications to locate an anthropogenic noise source and a carbon dioxide (CO2) degassing area demonstrate the accuracy and efficiency of the proposed deep learning method for noise source localization, which has great potential for monitoring the changes of the noise source distribution in a survey area.

Illuminating Urban Near-Surface with Distributed Acoustic Sensing Multimodal Noise Surface-Wave Imaging

Abstract Urban subsurface exploration requires high spatial and temporal resolution, cost-effective operation, and minimal interference with urban activities. Distributed acoustic sensing (DAS)—an innovative seismic observation tool—emerges as a promising solution for urban surveys. In this study, we repurposed a 7.9 km telecommunication cable traversing Hefei into a seismic observation array with 3850 channels spaced at 2 m intervals. Noise cross-correlation functions (NCFs) were constructed from recordings by iDAS2 and ZD-DAS interrogators along the entire cable. Spatial variation in the NCFs was observed and attributed to different traffic conditions. Employing the recently developed modified frequency–Bessel transform method to NCFs from the 2 km southern subsection of the optic cable, we extracted broadband, high-resolution multimodal dispersion curves. The inverted near-surface structure beneath the cable unveiled a sediment thinning trend from the center to the periphery of the Hefei basin, consistent with borehole inspections. The three-station interferometry (C3) method and beamforming with the Bessel kernel function are applied to mitigate challenges arising from the weak coupling between the cable and the Earth, as well as persistent localized noise sources. These techniques facilitated the acquisition of broadband surface waves. Distinct secondary scatters are observed in NCFs near channels 2090 and 2287, accompanied by a substantial velocity contrast of 30%–40%, suggesting the existence of a blind fault. The study reaffirms the significant potential of DAS arrays for high-resolution imaging of subsurface structures in challenging urban environments, emphasizing the importance of advanced processing techniques to enhance imaging accuracy and robustness.

Distributed Acoustic Sensing for Crowd Motion and Firecracker Explosions in the Fireworks Show

Abstract Urban seismology has recently emerged as a vibrant scientific field, driven by the growing interest in seismic signals generated by major public events, sports gatherings, and transportation services. However, deploying dense traditional seismometers in economically active, densely populated urban areas with heavy traffic poses significant challenges. In this study, we conducted a field experiment utilizing distributed acoustic sensing (DAS) technology during a fireworks display in Guangzhou on 5 February 2023. About 572 m of optical fiber was turned into 286 seismic sensors and deployed on LingShan Island to monitor various vibration signals generated during the fireworks show. Our analysis revealed substantial correlations between crowd motions during different phases of the event and ambient noise features recorded by DAS. Moreover, the cross-correlation functions of the ambient noise with its dispersion characteristics pointed to near-field pedestrian activity as the primary noise source. Real-time heat maps of human crowd motions were reconstructed from DAS recording, offering significant insights into the variations of activity intensity across different locations. Discerning fireworks events on the DAS array is more effective than on a scattered seismometer array, because it is easier to ensure that the same event is picked for all the sites in the DAS dense linear configuration. The DAS data inspection allowed us to pick up a total of 549 firecracker explosions in comparison to the seismometer data that only allowed us to detect 116 firecracker events. The heights of fireworks were located by the grid-search method and predominantly distributed at 75–300 m, closely aligning with actual fireworks explosion locations. Our findings underscore that the DAS technology can monitor crowd motion and detect vibration signals in the air, bridging the gap between fundamental earth science research and human social activities.

Calculation of Noise Cross-Correlation Functions Between Different Types of Sensors without Removing Instrument Responses

The noise cross-correlation function between two stations provides an efficient way for understanding the subsurface structure. Removing instrument response is a necessary step for the calculation of the noise cross-correlation function (NCF) between different types of sensors, which requires the knowledge of the instrument response. However, the instrument response files may be missing or not provided, restricting us to only the calculation of NCF between the same type of instruments. We propose to calculate the NCF between different sensors deployed at the same site and obtain the phase differences between different instrument responses. The phase differences can then be removed resulting in the unbiased NCF between different sensors deployed at different locations. The effectiveness of our method is verified through a real data example.

Sequencing Seismic Noise Correlations for Improving Surface Wave Retrieval and Characterizing Noise Sources

Abstract Cross-correlating continuous seismic data is a commonly employed technique to extract coherent signals to image and monitor the subsurface. However, due largely to site effects and poorly characterized noise sources in oceanic environments, its application to ocean-bottom seismometer (OBS) recordings often requires additional processing. In this contribution, we propose a method to improve the quality of the retrieved surface waves from OBS data and characterize the noise sources. We first cluster the pre-stack noise cross-correlation functions (NCFs) based on a sequencing algorithm, followed by selectively stacking those consisting of coherent and stable signals that are consistent with predicted surface-wave arrival times. Synthetic tests show that the sequenced NCFs can be used to recover the spatial and temporal distribution of noise sources. Applying the method to an OBS array offshore California increases the signal-to-noise ratios of the obtained Rayleigh waves. In addition, we find that the annual temporal distribution of selected NCFs with frequencies ranging from 0.04 to 0.1 Hz is nearly homogeneous during the recording period. In contrast, many NCFs excluded for stacking are temporally clustered. This method has the potential to be applied to other OBS recordings or possibly onland deployments, thus helping to obtain high-quality surface waves and to analyze temporal noise source characteristics.

Noise correlation analysis: the role of the inflection points in the formation of zero-time waves

SUMMARY The cross-correlation function (CCF) of seismic noise recorded at two stations often exhibits a prominent burst of energy near zero time. However, the excitation mechanism of such unexpected signals remains unclear. In this study, we demonstrate that this phenomenon arises from constructive interference of body waves radiating from sources near the mid-vertical plane between two receivers. This interference is driven by the occurrence of an inflection point effect in the traveltime difference between waves reaching the two receivers. When noise source distribution is asymmetric, the cumulative effect of such sources results in the generation of zero-time waves. It is noteworthy that a noise source distribution that is either uniform or follows a cosϕ pattern, with ϕ denoting the azimuth from the centre of the receiver pair, can counteract the formation of these waves. Additionally, our investigation reveals that this constructive interference does not occur in the correlation of 2-D surface waves. We used numerical simulation to support our findings. In conclusion, this study offers a theoretical explanation for the emergence of zero-time waves on the CCF and provides efficient means of investigating the extraction of inter-receiver waves with uneven noise source distribution.

Magma and hydrothermal sources below the northern part of Paramushir Island (Kuril Arc) inferred from ambient noise tomography

Paramushir is the northernmost island of the Kuril Arc comprising several Holocene volcanoes, of which two are presently active: Ebeko and Chikurachki. We present the first crustal-scale three-dimensional seismic shear-wave velocity model of the northern part of Paramushir derived with the use of data of 20 temporary stations operated in 2021–2022 and one permanent station. The continuous seismic data were used to obtain the Rayleigh wave dispersion curves for periods ranging from 0.5 s to 12 s through computing ambient seismic noise cross-correlation functions. Then the 3D shear wave velocity distribution was constructed by seismic tomography. The synthetic tests demonstrate that the model has sufficient resolution down to 7–10 km depth. We observe a series of low-velocity anomalies at the depths of 4–6 km below the volcano-origin Vernadsky Ridge. They may likely represent magma reservoirs that are responsible for Holocene eruptions along the ridge. These anomalies are overlain by the high-velocity anomalies associated with the rigid cover consisting of an upper-crustal granite-granodiorite body and consolidated magma intrusions. Along the eastern flank of Ebeko we reveal a thin low-velocity anomaly at a depth of ∼1 km that can be interpreted as a water-saturated layer. The contact of this layer with hot magma intrusions leads to the formation of a large amount of steam that is ejected during the frequent phreatic eruptions of Ebeko and from numerous fumaroles in the summit area. On the other hand, this layer, which is following down to the Pacific Coast, might be promising for the geothermal energy exploitation.

Soundscapes from deep-water moored receivers in the vicinity of the New England Seamounts

Acoustic noise interferometry in the ocean relies on synchronized measurements of ambient sound at spatially separated points to passively measure the acoustic travel times between receiver locations. A network of four autonomous, moored, near-bottom acoustic receivers were deployed in the vicinity of the New England Seamounts for 52 days as part of the larger New England Seamount Acoustics (NESMA) Pilot experiment. Receiver depths ranged from 2500–4475 m. The moored receivers provided stable observation platforms in a region with highly variable and occasionally strong currents. The noise interferometry network aimed to investigate the feasibility of utilizing passive acoustic remote sensing methods to study a highly dynamic ocean region with complex bathymetry. Strong ocean variability at the experimental site was induced by its proximity to the Gulf Stream. This paper presents the initial analysis of water-depth dependence and intermittency of ambient sound spectra and spectrograms on near-bottom receivers. The relation between the spectral features of ambient sound and ocean dynamics is explored. Additionally, the feasibility of using the onboard Chip Scale Atomic Clocks to synchronize the experimental data and calculate broadband noise cross-correlation functions for each of the receiver pairs is discussed.

A graph approach for fast dense array inter-station phase delay interpretation

SUMMARY We present a phase-delay detection procedure adapted for seismic arrays composed of a large number of stations. We use graph formalism to introduce a direct method to compute station phase delays starting from inter-station phase differences deduced from noise cross-correlation functions. We focus this study on surface wave main arrival as phase-difference evaluation at each station requires high coherence level. Then, we perform numerical experiments with synthetic data computed as a realistic and dense network of 79 stations in a 400 by $400\, \mathrm{km^2}$ square box. For one of the 79 stations, we simulate a phase difference of 15 per cent of the signal period. Further, we evaluate the accuracy and precision of phase-delay estimation at each station with regard to the quality of the cross-correlation functions (i.e. the signal-to-noise ratio). When the inter-station coherence levels are larger than 0.6 (i.e. a high-quality signal), we show good agreement between the phase-delay estimation and its expected value of 15 per cent of the signal period. We introduce a coherence-weighted estimate of phase delay and show that applying this weighting allows us to be less vulnerable to phase-delay underestimation for intermediate-quality signals. Then, the method is applied to experimental data recorded by a high density nodal array with 923 vertical geophones with 19 d of continuous records, centred on the 600 by $600\, \mathrm{m^2}$ damage zone of the Clark branch of the San Jacinto Fault Zone, Southern California (USA). We verify that the San Jacinto network is well synchronized, as most of the estimated phase delays are less than 2 per cent of the central period of the signal, and they are associated with high levels of inter-station coherence. More surprisingly, the spatial features of the estimated phase delays show deterministic geographical patterns that are related to topography and that exhibit similarities with phase velocity maps at $4.5\, \mathrm{Hz}$ from previous 3-D velocity inversions. This suggests that the topographic effect may be accounted for to estimate accurate phase delays. Also, we note that the temporal variability of the estimated phase delays in the case of the San Jacinto data set are related to atmospheric forcing. Our direct method for estimating phase delays is applicable to structure-oriented monitoring studies, and it opens perspectives in the monitoring of seismic velocity variations.

A Local Seismic Project near the Dalk Glacier Area, Larsemann Hills, East Antarctica: Toward Subice Imaging and Icequake Monitoring

Abstract During the 2019–2020 field season of the 36th Chinese National Antarctic Research Expedition, two seismic arrays were deployed in the Dalk Glacier area of Larsemann Hills, East Antarctica. The arrays consisted of 100 short-period nodal stations and were intended to investigate the physical properties and seismic events in the region, with the goal of enhancing our understanding of the glacier’s structure and dynamics. With these data, we use horizontal-to-vertical spectral ratio (HVSR) analysis to estimate the ice thickness. Noise cross-correlation functions and multimode dispersion curves of Rayleigh waves were extracted from the vertical-component ambient noise data to illuminate near-surface glacial structures. Teleseismic events with Mw >5.5 and two typical kinds of icequakes were observed via visual inspection. These initial results improve the understanding of the physical properties of the ice sheet as well as the glacial seismicity in Dalk Glacier.

Observation of Higher-Mode Rayleigh Waves from Ambient Noise in the Tarim Basin, China

Abstract Higher mode surface waves, which can provide additional constraints on subsurface structures in addition to fundamental modes in surface-wave tomography, have been observed from ambient noise cross-correlation functions (CCFs) in sedimentary basins in oceans or near coastlines. However, few studies show that higher mode surface waves can be observed and extracted directly from ambient noise CCFs in inland basins. In this study, we report observations of high signal-to-noise ratio fundamental and the first higher mode Rayleigh waves at a period range of 0.2–1.90 s and 0.2–1.35 s, respectively, from ambient noise CCFs in the southeastern margin of the Tarim basin, the biggest inland basin in China. We confirm the credibility of the first higher mode surface waves by showing that the observed first higher mode dispersion curves are matched with predicted ones calculated from S velocity models solely constrained by fundamental-mode dispersion curves. After the verification of the credibility of the first higher mode surface waves, we demonstrate that the inclusion of the first higher mode dispersion curves helps image deeper structures with an increase of average depths from ∼0.73 to ∼1.24 km, which will be beneficial to future explorations of deep oil and gas resources in the Tarim basin.

Weighted stacking of ambient noise correlations based on the coherency of 3C data

Seismic interferometry (SI) is widely used in imaging subsurface structures at different scales by extracting empirical Green's functions (EGFs) of surface wave propagation from ambient noise. The EGFs may be misestimated, however, because the anisotropic distribution of noise sources violates the SI's basic assumption of energy equipartitioning. A common solution is to stack all short-duration noise cross-correlation functions (NCFs) over a long period of time to attenuate the effect of anisotropic noise sources. The effectiveness of the common method is limited by the time of observation, and it may even be counterproductive. The influence of anisotropic noise sources may be reinforced if these sources possess strong energy. To obtain reliable EGFs, we develop a new weighted stack method named the components-coherency-weighted stack (CCWS). We calculate the coherency of different components of noise recorded by a single station to scan the azimuth of the main source in one segment. We select the segments according to the azimuth for NCF computation and then stack the NCFs with a weight relevant to the coherency. We first test the method on simulated data containing strong azimuthal noise sources and show that our method yields high quality EGFs. The method is then used for two real data examples at a regional scale of hundreds of kilometers and an engineering scale of tens of meters. Two examples confirm the ability of our approach to improve the EGFs and its applicability at different scales.

A technology for monitoring the onset of malfunctions in oil field reservoir pressure maintenance equipment

Existing control and management systems of oil field reservoir pressure maintenance equipment do not warn of the beginning of the latent period of accidents. At the same time, accidents are preceded by the emergence of certain defects, followed by their latent period, after which they develop. Only after that they begin to affect the readings of measuring instruments of control and management systems. The duration of the latent period depends on the dynamics of defect development. Because of the above, control systems detect the beginning of an emergency when it becomes explicit. This leads to unreasonable costs, since elimination of the defect at the moment of its origin requires much less funds and time than after-accident pump repairs. It is shown in the paper that as a result of continuous rotational motion under high pressure, a vibration process is inevitably formed in this equipment, and the beginning of the latent period of malfunctions makes a clear impact on their noisy vibration signals, with a correlation emerging between the noise and the useful component. To control the beginning of malfunctions in reservoir pressure maintenance equipment it is proposed to use the estimates of noise variance and cross-correlation functions between the useful signal and the noise, which allow forming informative attributes for warning and control of the beginning of the latent period of malfunctions. The proposed technologies can also be used to improve accident-free operation at compressor stations of main oil and gas pipelines, at drilling rigs, at artesian wells, in transport, etc.

Optimal Multichannel Stretch Factors for Estimating Changes in Seismic Velocity: Application to the 2012 Mw 7.8 Haida Gwaii Earthquake

ABSTRACT We propose new methods for assessing temporal changes in seismic velocity using the S-wave coda for repeating earthquakes and cross-correlation functions of ambient noise. For a pair of seismic waveforms representing a common source–receiver path, the relative change in path-averaged velocity over the corresponding time interval is directly proportional to the factor by which one waveform needs to be stretched or compressed with respect to the other to achieve maximum coherence. For an arbitrary number of waveforms, initial pair-wise stretch factors determined through standard approaches can be improved through solution of an overdetermined system and further refined through an iterative approach exploiting the singular value decomposition to minimize rank of the stretched waveform section. We apply this combined approach to both repeating earthquakes and ambient noise correlations for Haida Gwaii in western Canada, the site of a Mw 7.8 thrust earthquake in 2012. Optimal stretch factors for repeating earthquake families indicate that path-averaged S velocities dropped by up to 0.16% after the earthquake. Ambient noise correlations indicate that velocities dropped by between 0.26% and 0.39%, which we interpret to be more pronounced in the uppermost levels of the crust. We explore these results in terms of changes in crustal porosity and hydrogeologic conditions by considering the observation that hot spring activity on Haida Gwaii ceased following the 2012 mainshock and recovered over the next several years.

Defining the Yangtze–Cathaysia suture zone in South China using ambient noise tomography

SUMMARYThe South China Craton formed after collision between the Yangtze and Cathaysia Blocks. The suture zone between these two blocks is the crux of understanding the geological evolution of South China. Due to the relative paucity of high-resolution subsurface data, the location of the suture zone in the southwestern region remains uncertain. To find the suture zone, we created a high-resolution model of the crust and upper mantle structure using 27 months of continuous waveform data acquired from 457 stations in South China. The model uses extracted phase velocity dispersion curves of Rayleigh waves at 4–40 s from ambient noise cross-correlation functions, to perform a direct inversion for an azimuthal anisotropic VSV model. In the crust a prominent belt of strong azimuthal anisotropy exists between the Yangtze and Cathaysia Blocks. The fast polarization axes in this belt correspond to the NE-trending orogen between the two blocks. In the deeper crust of the Cathaysia Block we image multiple areas of diffuse low-velocity anomalies. As defined by the margins of the strongly anisotropic belt, the Yangtze–Cathaysia suture zone is delineated by lines that traverse: Shaoxing–Jiangshan–Pingxiang–Longsheng–Hechi and Shitai–Jiujiang–Yueyang–Dayong–Jishou. Further analysis of the seismic data links deep low velocities with widespread Late Mesozoic magmatism in the Cathaysia Block. The deep low velocities are likely a result of dehydration of the subducting Pacific Plate causing mantle upwelling and underplating.

Evaluating and correcting short-term clock drift in data from temporary seismic deployments

Temporary seismic network deployments often suffer from incorrect timing records and thus pose a challenge to fully utilize the valuable data. To inspect and fix such time problems, the ambient noise cross-correlation function (NCCF) has been widely adopted by using daily waveforms. However, it is still challenging to detect the short-term clock drift and overcome the influence of local noise on NCCF. To address these challenges, we conduct a study on two temporary datasets, including an ocean-bottom-seismometer (OBS) dataset from the southern Mariana subduction zone and a dataset from a temporary dense network from the Weiyuan shale gas field, Sichuan, China. We first inspect the teleseismic and local event waveforms to evaluate the overall clock drift and data quality for both datasets. For the OBS dataset, NCCF using different time segments (3, 6, and 12-h) beside daily waveforms data is computed to select the data length with optimal detection capability. Eventually, the 6-h segment is the preferred choice with high detection efficiency and low noise level. For the land dataset, higher drift detection is achieved by NCCF using the daily long waveforms. Meanwhile, we find that NCCF symmetry on the dense array is highly influenced by localized intense noise for large interstation distances (>1 ​km) but is well preserved for short interstation distances. The results have shown that the use of different segments of daily waveform data in the OBS dataset, and the careful selection of interstation distances in the land dataset substantially improved the NCCF results. All the clock drifts in both datasets are successfully corrected and verified with waveforms and NCCF. The newly developed strategies using short-segment NCCF help to overcome the existing issues to correct the clock drift of seismic data.

Detection of clock errors in seismic records and estimation of time shifts for a seismic network

In the records of autonomous seismological observations occurrence of quartz watch clock drift often leads to incorrect time records. This paper presents a method for detecting such errors in a seismic station network based on the analysis of ambient seismic noise cross-correlation functions using Monte-Carlo Markov chain (MCMC) approach without using a reference signal. The proposed method was tested on the seismic data of a temporary seismic network installed on Paramushir Island in 2021-2022 for which time shifts were successfully estimated and corrections to the cross-correlation functions were made.

Enhancing noise sources in stationary-phase zones for accurate phase-velocity estimation of high-frequency surface waves

Passive surface-wave methods are appealing tools for their ability to noninvasively obtain shear-wave velocity with high accuracy and low costs. A linear array of stations is widely adopted in urban areas for its convenient deployments and efficient utilization of ambient noise energy from stationary-phase zones (SPZs), which contributes the most to surface-wave retrieval. However, noise sources in nonstationary-phase zones may threaten the reliability of velocity estimation when they possess strong energy. To obtain reliable phase-velocity estimation of high-frequency surface waves (>1 Hz), we enhance the contribution of noise sources in SPZs by using the multichannel-coherency-weighted stack (MCWS) method for the stacking of noise crosscorrelation functions and dispersion spectra. We model a nonuniform source distribution with strong offline sources. The seismic interferometry (SI) and passive multichannel analysis of surface waves (PMASW) methods overestimate velocities at lower frequencies (<5 Hz), whereas phase velocities of short wavelengths are relatively accurate. The true velocities of short wavelengths are then set as the velocity scanning range for MCWS, and the waves whose apparent velocities lie within the scanning range would be emphasized by MCWS. Noise sources in SPZs are enhanced because the apparent velocities of waves from other noise sources are higher and not in the scanning range. After using MCWS, the phase-velocity estimations of SI and PMASW are consistent with the theoretical dispersion curve. A field data example at a crossroads demonstrates that uneven source distributions might cause serious artifacts and the feasibility of our method is again confirmed. In addition, the enhancement of noise sources in SPZs is verified through noise source distribution imaging by a matched field processing technique.