Surface Wave Data Research Articles

Borehole fibre-optic seismology inside the Northeast Greenland Ice Stream

SUMMARYIce streams are major contributors to ice sheet mass loss and sea level rise. Effects of their dynamic behaviour are imprinted into seismic properties, such as wave speeds and anisotropy. Here, we present results from a distributed acoustic sensing (DAS) experiment in a deep ice-core borehole in the onset region of the Northeast Greenland Ice Stream, with focus on phenomenological and methodological aspects. A series of active seismic surface sources produced clear recordings of the P and S wavefield, including internal reflections, along a 1500 m long fibre-optic cable that was placed into the borehole. The combination of nonlinear traveltime tomography with a firn model constrained by multimode surface wave data, allows us to invert for P and S wave speeds with depth-dependent uncertainties on the order of only 10 m s−1, and vertical resolution of 20–70 m. The wave speed model in conjunction with the regularly spaced DAS data enable a straightforward separation of internal upward reflections followed by a reverse-time migration that provides a detailed reflectivity image of the ice. While the differences between P and S wave speeds hint at anisotropy related to crystal orientation fabric, the reflectivity image seems to carry a pronounced climatic imprint caused by rapid variations in grain size. Further improvements in resolution do not seem to be limited by the DAS channel spacing. Instead, the maximum frequency of body waves below ∼200 Hz, low signal-to-noise ratio caused by poor coupling, and systematic errors produced by the ray approximation, appear to be the leading-order issues. Among these, only the latter has a simple existing solution in the form of full-waveform inversion. Improving signal bandwidth and quality, however, will likely require a significantly larger effort in terms of both sensing equipment and logistics.

Marine Environmental Impact on CFAR Ship Detection as Measured by Wave Age in SAR Images

Satellite synthetic aperture radar (SAR) images are recognized as one of the most efficient tools for day/night, all weather and large area monitoring of ships at sea. However, false alarms discrimination is still one key problem on SAR ship detection. While many discrimination techniques have been proposed for the treatment of false alarms, not enough emphasis has been targeted to explore how obtained false alarms are related to the changing ocean environmental conditions. To this end, we combined a large set of Sentinel-1 SAR images with ocean surface wind and wave data into one dataset. SAR images were separated into three distinct groups according to wave age (WA) conditions present during image acquisition: young wind sea, old wind sea, and swell. A constant false alarm rate (CFAR) ship detection algorithm was implemented based on the generalized gamma distribution (GΓD). Kolmogorov–Smirnov distance was used to analyze the distribution goodness-of-fit among distinct ocean environments. A backscattering analysis of different sizes of ship targets and sea clutter was further performed using the OpenSARShip and automatic identification system (AIS) datasets to assess its separability. We derived a discrimination threshold adjustment based on WA conditions and showed its efficacy to drastically reduce false alarms. To our present knowledge, the use of WA as part of the CFAR and for the adjustment of the threshold of detection is a novelty which could be tested and evaluated for different SAR sensors.

Active and passive seismic methods for site characterization in Nuweiba, Gulf of Aqaba, Egypt

Nuweiba is located on the western coast of the Gulf of Aqaba (GoA), which represents the southern part of the Gulf of Aqaba - Dead Sea transform fault (GoA-DST). The GoA is characterized by high seismic activity and is the source of the November 22, 1995, Nuweiba earthquake, with a moment magnitude of Mw 7.3. This seismic unrest caused extensive damage in the city of Nuweiba due to the strong shaking caused by the earthquake. In addition, soil amplification triggered by the thick soft-sediments of the Wadi Watir delta beneath the city amplified the destructive power of the event. Therefore, an assessment of the effect of the Wadi Watir sediments on the ground motion at Nuweiba is very important for future construction, seismic hazard assessment, and seismic risk mitigation. Accordingly, the goal of the current study is to define the amplification characteristics of the Wadi Watir deposits using microtremor and surface wave data sets. The horizontal-to-vertical spectral ratio (HVSR) technique is applied to the microtremor data and Multichannel Analysis of Surface Waves (MASW) is applied to the surface wave data. The data are processed to obtain the distribution of the fundamental resonance frequency (F0), the depth of the geophysical bedrock using joint inversion of the Rayleigh wave dispersion curve and ellipticity peak, the shear wave velocity structure, the time-averaged shear-wave velocity in the topmost 30 m (Vs30), and the shear wave transfer function. The results of the HVSR show that F0 in the northern part of the city is 4.2–5.2 Hz and 0.5–0.8 Hz in the middle of the city. Bedrock depth is found to be in the range of 27 and 199 m. The one-dimensional velocity structure obtained from the MASW measurements has resolved two sedimentary layers within the upper 15 m. The Vs30 ranges between 207 and 556 m/s, according to the Vs30 the soil in the study area is categorized into soil classes B and C according to Eurocode 8. The obtained shear wave transfer functions show that the highest amplification factors are observed in the middle part of the study area.

Trans-dimensional inversion of multimode seismic surface wave data from a trenched distributed acoustic sensing survey

SUMMARYSeismic data acquired from surface-deployed distributed acoustic sensing (DAS) fibre are broad band and typically dense spatially sampled. Corresponding to these features, compared with geophone data, the low-frequency components in DAS data show higher signal-to-noise ratio and multimode dispersion curves by broad-band DAS data exhibit a higher resolution, which increases the investigation depth of near-surface structures and enhances identification and picking of dispersion curves, respectively. Therefore, DAS data are ideal for the estimates of reliable and highly resolved near-surface velocity profiles. As surface-wave dispersion inversion (SWD) is a natural scheme for near-surface investigation, in this study we have formulated a DAS-SWD inversion in which multiple SWD modes are extracted from the DAS data, and are used as input to a trans-dimensional (TD) inversion procedure, in which the number of subsurface layers is treated as an unknown. Vibroseis data with a minimum frequency of 1 Hz were sensed along a horizontal surface trench as part of a baseline seismic survey carried out by the University of Calgary at the Containment and Monitoring Institute Field Research Station in Newell County, Alberta, Canada. These surface DAS data readily permit the picking of multimode dispersion curves, which are observed to enhance velocity profile resolution in both shallow and deep regions of the near-surface simultaneously, with the TD algorithm adapting the model to reflect this improved resolution. To avoid collecting abnormal model samples with thin-interleaved high- and low-velocity layers based on the known geological information of the field site, we employed constraints that preclude the structures that have velocity drops over 100 m s−1 along depth. Data errors are estimated via a non-parametric iterative process in terms of covariance matrices that include off-diagonal elements. Synthetic examples show that SWD with higher-order modes provides additional constraints on the structure and accurate noise estimation. Inversion of the field data resulted in high-resolution estimates of shear wave velocity as a function of depth throughout the top 120 m of the subsurface. The inferred structure is consistent with existing estimates of the regional lithology but resolves additional layers between 1- and 50-m depth.

Effectiveness of stacking in filtering noise from surface wave data

Effectiveness of stacking in filtering noise from surface wave data

A Joint Seismic and Space‐Based Investigation of the 2016 Lamplugh Glacier and 2017 Wrangell Mountains (Alaska) Landslides

AbstractLandslides commonly occur in areas with steep topography and abundant precipitation and pose a significant hazard to local communities. Some of the largest known landslides occur in Alaska, including several that caused local tsunamis. Many landslides may have gone undetected in remote areas due to lack of observations. Here, we develop a semiautomated workflow using both seismic and geodetic observations to detect, locate, validate, and characterize landslides in Alaska. Seismic observations have shown promise in continuously monitoring landslide occurrence, while remote sensing techniques are well suited for verification and high‐resolution imaging of landslides. We validate our procedure using the 28 June 2016, Lamplugh Glacier landslide. We also present observations of a previously unknown landslide occurred on 22 September 2017 in the Wrangell Mountains region. The Wrangell Mountains landslide generated a coherent surface wavefield recorded across Alaska and the contiguous United States. We used Sentinel‐1 Synthetic Aperture Radar and Sentinel‐2 optical imagery to map the respective mass deposit. To investigate the landslide dynamics, we inverted regional seismic surface wave data for a centroid single force failure model. Our model suggests that the Wrangell Mountains landslide lasted for about 140 s and had two subevents involving at least five distinct stages. We estimate that the landslide had displaced 3.1–13.4 million tons of rocks over a distance of ∼2 km. Our results suggest that combining seismic and geodetic observations can vastly improve the detection and characterization of landslides in remote areas in Alaska and elsewhere, providing new insights into the landslide dynamics.

Uncertainty quantification of multimodal surface wave inversion using artificial neural networks

An inversion of surface wave dispersion curves (DCs) is a nonunique and ill-conditioned problem. The inversion result has a probabilistic nature, which becomes apparent when simultaneously restoring the S-wave velocity and layer thickness. Therefore, the problem of uncertainty quantification is relevant. Existing methods through deterministic or global optimization approaches of uncertainty quantification via posterior probability density (PPD) of the model parameters are not computationally efficient because they demand multiple solutions to the inverse problem. We have developed an alternative method based on a multilayer fully connected artificial neural network (ANN). We improve the current unimodal approach, which is known from publications, to multimodal inversion. We use Cox’s and Teague’s algorithm to determine optimal parameterization (number of layers) and the ranges of possible model parameters. We uniformly draw training data sets within estimated ranges and train the ANN. Saved ANN weights map the phase velocity DCs to values of the S-wave velocity and layers thickness. To estimate the uncertainties, we adapt the Monte Carlo simulation strategy, according to which the frequency dependent data noise and the inverse operator errors are projected onto the resulting velocity model. The inverse operator errors are evaluated by the prediction of the training data set. The proposed combination of surface wave data processing methods, configured with each other, provides a novel surface wave multimodal dispersion data inversion and uncertainty quantification approach. We first test our approach on synthetic experiments for various velocity models: a positive gradient velocity, a low-velocity layer, and a high-velocity layer. This is done considering unimodal inversion at first and then compared with the multimodal inversion. Afterward, we apply our approach to field data and compare the resulting models with the body S-wave processing by the generalized reciprocal method. The experiments indicate high-potential results — using ANN yields the possibility to accurately estimate the PPD of restored model parameters without a significant computational effort. The PPD-based comparison demonstrates the advantages of a multimodal inversion over a unimodal inversion. The trained ANN provides reasonable model parameters predictions and related uncertainties in real time.

Joint estimation ofS-wave velocity and damping ratio of the near-surface from active Rayleigh wave surveys processed with a wavefield decomposition approach

SUMMARYThe use of surface wave measurements to derive an S-wave velocity profile of the subsurface has become a widely applied procedure; however, their potential use to reconstruct the S-wave material damping properties of the subsoil is generally overlooked, due to the difficulties in obtaining consistent surface wave amplitude information from field data and translating them into robust estimates of the dissipative properties of the near-surface. In this work, we adapt a wavefield decomposition technique for the processing of elastic surface wave data to the extraction of the complete set of properties of Rayleigh waves generated by a controlled source and propagating in dissipative geomaterials. Retrieved information includes multimodal phase velocity and ellipticity as well as the frequency-dependent attenuation coefficient. We exploit the key advantages of wavefield decomposition processing (joint interpretation of multicomponent recordings, coupled estimation of wave propagation parameters, modelling of multiple superimposing modes) to maximize the robustness of the retrieval of Rayleigh wave properties, especially of the dissipative ones. For the subsequent interpretation of Rayleigh wave dispersion, ellipticity and attenuation data we implement a joint Monte Carlo inversion yielding a coupled estimate of S-wave velocity and damping ratio profile for the subsurface; we incorporate a series of geophysical constraints to narrow down the searched parameter space to realistic soil models. We apply this processing and inversion scheme to a bespoke synthetic data set and to a field survey for the characterization of a strong motion station; in both cases, we succeed in retrieving Rayleigh wave multimodal dispersion, ellipticity and attenuation curves. From the inversion of data from the simulated seismogram we are able to reconstruct the properties of the synthetic model. As for the real case, we determine an S-wave velocity and damping ratio model for the soil column below the station, through which we are able to model the inelastic earthquake local response observed at the site. Basing on the results obtained for the real case, we argue that one of the advantages brought by our processing method—the possibility to process active Rayleigh wave data acquired by a 2-D array illuminated by different source positions—may play a key role in allowing to retrieve dissipative properties of the near-surface closer to the material damping of the soil materials, and less influenced by scattering determined by possible discontinuities in the subsurface.

Identification of re-liquefaction potential based SPT and MASW data in Mpanau, Sigi after the earthquake 2018

In September 2018, there was an earthquake followed by liquefaction which caused damage to thousands of houses and infrastructure in Central Sulawesi Province, Indonesia. Liquefaction in Central Sulawesi has the potential to recur, so identifying potential liquefaction in areas that have occurred is essential. The objective of this research is to compare the results of determining liquefaction potential based on geotechnical and geophysical data. Based on the peak ground acceleration earthquake value, it shows that the liquefaction potential obtained in Mpanau is resemblant. The simulation will be conducted using Settle3 software based on methods, then the values of Cyclic Resistance Ratio (CRR) and Cyclic Stress Ratio (CSR) will be compared based on existing Multi-Channel Analysis of Surface Waves (MASW) data. MASW needs to be considered in the way of potential identification because it is advantageous. This Research uses MASW data where liquefaction flow and damage do not occur in Mpanau area even though the potential is quite significant. The result of this research shows that at distinct points of the SPT, MASW and the 7.5 Mw earthquake resulted in potential liquefaction still present in several soil layers, especially in the Gumbasa canal. It is necessary to repair and countermeasure to prevent liquefaction from disrupting the irrigation system's performance.

Field Measurements and Modelling of Vessel-Generated Waves and Caused Bank Erosion—A Case Study at the Sabine–Neches Waterway, Texas, USA

The Sabine–Neches Waterway (SNWW) is home to the largest commercial port of the United States military and of the refineries that produce 60% of the nation’s commercial jet fuel. The deposited sediments from bank erosion due to wake wash result in frequent dredging to keep the waterway operational. This study investigates vessel-generated waves and their impacts on bank erosion. Surface wave data at Golden Pass and the City of Port Arthur Park dock were measured using a 1 MHz Aquadopp Profiler. Bank properties such as soil strengths were measured and soil samples were collected. Acceptable predictive models for estimating the maximum wave heights caused by vessels sailing through the SNWW were developed and validated with recorded data. Vessel-generated waves are found to produce enough shear forces to mobilize bed sediments and cause bank erosion. The bed erosion rate increases with an increase in wave height or a decrease in water depth. Bank and bank toe erosion occurs at both monitoring locations. Bank stability and toe erosion model (BSTEM) results suggest that potential bank protection options are large woody debris and riprap at Port Arthur. However, other stronger stabilization methods are required at Golden Pass.

3‐D S Wave Imaging via Robust Neural Network Interpolation of 2‐D Profiles From Wave‐Equation Dispersion Inversion of Seismic Ambient Noise

AbstractAmbient noise seismic data are widely used by geophysicists to explore subsurface properties at crustal and exploration scales. Two‐step dispersion inversion schema is the dominant method used to invert the surface wave data generated by the cross‐correlation of ambient noise signals. However, the two‐step methods have a 1‐D layered model assumption, which does not account for the complex wave propagation. To overcome this limitation, we employ a 2‐D wave‐equation dispersion (WD) inversion method which reconstructs the subsurface shear (S) velocity model in one step, and elastic wave‐equation modeling is used to simulate the subsurface wave propagation. In the WD method, the optimal S velocity model is obtained by minimizing the dispersion curve differences between the observed and predicted surface wave data, which makes WD method less prone to getting stuck to local minima. In our study, the observed Scholte waves are generated by cross‐correlating continuous ambient noise signals recorded by each ocean bottom node (OBN) in the 3‐D Gorgon OBN survey, Western Australia. For every two OBN lines, the WD method is used to retrieve the 2‐D S velocity structure beneath the first line. We then use a robust neural network (NN)‐based method to interpolate the inverted 2‐D velocity slices to a continuous 3‐D velocity model and also generate a corresponding uncertainty model. Moreover, we compared the predicted dispersion curves and waveforms to the observed data, and a robust waveform and dispersion match are observed across all of the Gorgon OBN lines.

Development and Application of a Portable Vibroseis Source for Acquisition and Analysis of Seismic Surface Wave Data

Development and Application of a Portable Vibroseis Source for Acquisition and Analysis of Seismic Surface Wave Data

Analysis of Seismic Refraction and Surface Wave Data for the Evaluation of Layers and Saturation of Solid Waste from a Landfill in Brasília, Brazil

The present work discusses the characterization of landfilled solid waste and saturated zones considering the response of P and S-wave velocities (Vp and Vs), Poisson ratio (υ), Young’s modulus (E) and shear modulus (G0), obtained from velocity models in an area located in the former Jockey Clube Controlled Landfill. The obtained Vp values ranged from 231 to 1,160 m/s, while Vs values range from 124 to 449 m/s. The calculated υ ranged from 0.11 to 0.4, while G0 and E ranged from 15 to 319 kPa and from 42 to 901 kPa, respectively. The values of G0 and E indicate that the landfilled material is poorly competent. The combined interpretation of Vp, Vs and elastic parameters allowed the definition of three main layers in the surveyed area and their respective distance from soil surface, defined as: 1) Civil construction residual material, of around 10 meters thick; 2) A solid waste layer, of around 18 meters thick, marked as a lower Vs and higher υ interval, possibly associated with saturated material; and 3) the estimated natural landfill terrain, below the depth of 28 meters, composed by the oxisol.

Characterizing the Vs Profile from Surface Wave Data Using a Customized Artificial Jellyfish Search Algorithm

Characterizing the Vs Profile from Surface Wave Data Using a Customized Artificial Jellyfish Search Algorithm

Investigation of Road Pavement Failures Using Active and Passive Surface Wave Methods in Parts of Calabar Municipality, Cross River State, Nigeria

The active and the passive techniques of surface wave were employed to investigate some major road pavement failures within the Calabar metropolis, Cross River State. The dispersion curves from MASW (active) and ReMi (passive) were inverted to give a 1D shear wave velocity model. The aim of this study was to comparatively investigate the major causes of the incessant road pavement failures especially few months after its construction and repairs within the study area using the shear wave velocity and blow count (N values). These roads are all located within the same geologic setting of the Calabar Coastal Plan Sand and showed evidence of road failures ranging from pot holes, alligator cracking, rutting and shear failure cracking etc. For each site, two sets of data were collected; the Passive and the Active seismic surface wave data. The survey was able to gather the following data from each location; shear wave velocity, primary wave velocity, N-values for both the active and the passive data which were both derived by the Seisimager surface wave software. The software was used to delineate low velocity layers within the depth of 4m – 6m at the failed portions of the survey sites exception of location 1 (the control site) which showed an ideal situation of increasing velocity with depth. These low velocity layers were inferred to be the major cause of the incessant failure of the pavements in the survey areas. These velocities and the N- values (blow count) were used to classify each location into sites C, D and E, according to the 1997 National Earthquake Hazards Reduction Program (NEHRP).

Eikonal Tomography With Physics‐Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau

AbstractWe present a novel eikonal tomography approach using physics‐informed neural networks (PINNs) for Rayleigh wave phase velocities based on the eikonal equation. The PINN eikonal tomography (pinnET) neural network utilizes deep neural networks as universal function approximators and extracts traveltimes and velocities of the medium during the optimization process. Whereas classical eikonal tomography uses a generic non‐physics based interpolation and regularization step to reconstruct traveltime surfaces, optimizing the network parameters in pinnET means solving a physics constrained traveltime surface reconstruction inversion tackling measurement noise and satisfying physics. We demonstrate this approach by applying it to 25 s surface wave data from ChinArray II sampling the northeastern Tibetan plateau. We validate our results by comparing them to results from conventional eikonal tomography in the same area and find good agreement.

Shallow shear wave velocity structure of the Dongshan sag area using surface wave data in a deep reflection profile of the Yuanmou area of Yunnan province, China

Deep reflection seismic data contain abundant surface wave information for which the velocity structure in the near-surface shallow underground can be obtained using multichannel analysis of surface waves (MASW). This paper uses the MASW method to process deep reflection data in the Yuanmou area in Yunnan province, China. The MASW method uses the phase velocity spectral transformation of Rayleigh waves to obtain its dispersion curve. The initial model for different lithology classifications is established to invert the underground velocity structure. This study obtains a two-dimensional shear wave velocity structure profile to investigate the shear wave velocity structure. Generally, the study results have shown that the shear wave velocity structure obtained by the MASW method corresponds well with the surface conditions and accurately reflects faults' changes, folds, and lithology. Moreover, the formation sand content calculation shows a large difference in Dongshan sag's formation sand content. The sand contents of Cretaceous and Middle Jurassic strata are over 30% similar, and the sandstone content of underlying Early Jurassic strata is mostly 30% lower. The stratum buried depth of Early Jurassic strata is between 0– 800 m. The dip angle of LZJF at this location is 90°. The west side of the LZJF is based on Proterozoic uplift, and the Quaternary sedimentary thickness exceeds 300 m above the uplift stratum. Whereas on the east side of the LZJF, the stratum of the Dongshan sag section is mainly a synclinal structure.

Metamorphic bedrock geometry of Santorini using HVSR information and geophysical modeling of ambient noise and active-source surface-wave data

Santorini is the most active volcano of the Southern Aegean Volcanic Arc, with volcanic material that has accumulated during the last 600+ Kyrs on top of the pre-volcanic Santorini island. The geometry of the pre-volcanic basement not only provides constraints on the volcanic history, but is also fundamental for seismic hazard assessment, as it partly controls site-effects on strong ground motions. We investigate the geometry of the metamorphic (Cycladic) basement of Santorini using information from both passive (noise) and active source data. We performed a large number (∼280) of single-station ambient noise measurements and collected additional data from 42 previous studies to compute the Horizontal-to-Vertical Spectral Ratio (HVSR) curves. The majority of the recovered HVSR curves show prominent spectral ratio peaks (locally larger than 7–8) indicating a large impedance contrast between pyroclastic volcanic strata and bedrock metamorphic formations. We also performed active (MASW) and passive (Noise Array) surface wave measurements at 4 selected sites to determine local dispersion curves. To find the 1D Vs structure from the surface down to the metamorphic bedrock we performed a joint Monte Carlo inversion of the dispersion curves and the fundamental frequency (f0HVSR) values from the HVSR curves. These models, as well as deep borehole information and HVSR data along the caldera cliff, where the thickness and stratigraphy of the volcanic formations above the bedrock can be directly observed, allowed us to determine a linear relationship between the thickness of the pyroclastic formations and their fundamental resonance period for central-southern Santorini. The resulting geometry of pre-volcanic island shows deep basins around the pre-Alpine bedrock outcrop (Profitis Ilias) and generally agrees with recent results from larger-scale tomographic and gravity data but with some significant local differences. The basins are 200+ meters thick in the Kamari and Perissa area (southeastern Santorini) and >400 m thick in the central (Fira-Imerovigli) area, while there is a thick (∼70-130 m) pyroclastic layer on top of a metamorphic basement “plateau” in the Megalochori area.

Combined analysis of active and passive surface waves for 2D characterization of a landfill

Active and passive surface wave methods have been effective tools for near-surface investigation. For the cases both the near-surface resolution and investigation depth are required, the incorporation of active and passive surface wave methods is necessary to measure the dispersion curve over a broad frequency band. In this work, we carry out the active and passive surface wave methods to characterize a landfill, mainly for delineating its base. Both active and passive surface wave data are collected using a linear array and the roll-along acquisition mode. To analyze the passive data, we use the seismic interferometry technique to generate the virtual source gather. According to the dispersion analysis results, the frequency bands for available phase velocity estimations of active and passive data are different but overlapped. We then employ the multiscale window analysis of surface waves (MWASW) method to extract several dispersion curves from active shot gather and passive virtual source gather. The use of the MWASW method is based on the consideration of heterogeneity in landfills. Once the dispersion curves are extracted, the two kinds of phase velocity data are combined to produce composite dispersion curves. By simultaneously inverting the composite dispersion curves of multisize spatial windows, a 2D shear-wave velocity (Vs) model is generated that shows a clear velocity contrast between the refuse and the underlying stratum at a depth around 5 m consistent with the result of five in-line boreholes. The successful investigation of refuse depth demonstrates that the combination of active and passive surface waves has a good ability to resolve the Vs model of landfills from surface to deep formations.

Simultaneous Inversion for Surface Wave Phase Velocity and Earthquake Centroid Parameters: Methodology and Application

AbstractBoth the regional earthquake surface wave and seismic ambient noise provide important constraints on the Earth's structure, and yet no study satisfactorily combined them for the best imaging of subsurface seismic structure. In this study, we address this issue by developing a new method to simultaneously determine surface wave phase velocity and earthquake centroid parameters in three steps: (a) preliminary phase velocity inversion based on seismic ambient noise, (b) preliminary earthquake relocation based on earthquake surface wave data, and (c) simultaneous inversion for phase velocity and earthquake centroid parameters with constraints of interstation phase velocity measurements based on seismic ambient noise and event‐station phase velocity measurements based on earthquake surface wave data. Application of the method in the North South Seismic Belt region in China results in high‐resolution Rayleigh wave phase velocity maps and accurate earthquake centroid parameters. The additional earthquake data notably improve resolution of the inverted phase velocity model in west Yunnan and central Tibetan blocks, the regions with sparse seismic station coverage. The inverted phase velocity model exhibits high‐velocity anomalies in cratonic regions and the Emeishan Large Igneous Province, and low‐velocity anomalies in the interior and surrounding regions of the Tibetan Plateau. Relocation places earthquakes in shallow depths with geotherm above the crustal rock's brittle‐ductile transition temperature of , revealing thermal control on thickness of the seismogenic zone. With earthquake centroid parameters constrained, earthquake data are expected to provide further constraints on the deep seismic structure that is beyond the sampling limit of seismic ambient noise.