Near-surface Structures Research Articles

Shallow Structure and Seismic Amplification Effects in the Weifang Segment of the Tanlu Fault Zone Based on the Spectral Ratio Method

Abstract The Weifang segment of the Tanlu fault zone (TLFZ) is located in the central section of the TLFZ, eastern China, and has been identified as an earthquake gap zone. Previous studies in the region have mainly focused on the crustal velocity structure and anisotropy, with limited attention to the shallow near-surface structures. In this study, we analyzed the distribution of sediment thickness and evaluated the seismic amplification effects in the Weifang segment of the TLFZ using the horizontal-to-vertical spectral ratio (HVSR) method and the standard spectral ratio (SSR) method. The data we used are from a dense array of 302 three-component seismometers deployed in 2017 for three months. The lowest peak frequency of HVSR indicates that the northwestern part of the study area exhibits relatively thicker sedimentary deposits, estimated to be 800–1200 m in thickness, consistent with both tomographic and geological studies. The SSRs are calculated from 43 regional and teleseismic earthquakes with respect to 12 reference stations. The results from SSR show strong amplification in the 0.2–2 Hz frequency range for sites on the northwestern part, and the amplitude can be up to 15 times larger than that of the bedrock site. We also find significant amplification effects as well as thick sedimentary layers at specific stations along the eastern branch of the TLFZ, suggesting a localized low-velocity zone along the fault. Our results also demonstrate that using the single-site seismic method can provide new constraints on the fine structure and site responses of the fault zone, which are important for seismic hazard assessment.

Polarization analysis and filtering of undersampled 3C seismic data in the frequency-slowness domain

Polarization filters have emerged as a useful tool for coherent noise denoising in multicomponent seismic data. By exploiting the polarization properties of seismic waves, these filters can significantly improve wave-type separation, which is a crucial step in seismic data processing. However, their effectiveness in land seismic data in complex areas can be impacted by the presence of scattering noise, and their applicability is limited by irregular spatial sampling, which hinders velocity information for wave-type discrimination. To address these limitations, we introduce a novel two-step method for distinguishing and separating polarized wave types in shot gathers captured from a spatially undersampled linear array of 3C vector sensors. The first step involves conducting a polarization analysis in the frequency-slowness domain using elliptical elements derived from the linear Radon transform of the seismic data. The second step isolates specific polarized waves using the 3C frequency-slowness polarization filter (3C-FSPF), a technique that uses tailored taper functions based on polarization analysis. Our method stands out from single-station filters that cannot use velocity information for wave-type discrimination and existing multistation filters that require regularly sampled data. To evaluate our method, comprehensive tests are conducted on synthetic and real data. The results consistently demonstrate the effectiveness of 3C-FSPF in separating diverse polarized wave types under conditions of irregular and sparse spatial sampling and noise. Our findings underscore the potential of this method for advancing exploration geophysics by enhancing the quality of seismic data, particularly in land regions with complex near-surface structures.

Study on the influencing factors of combined processing of active and passive surface-wave data on dispersion imaging

Active and passive surface-wave methods have garnered significant attention in the near-surface geophysical community for their non-destructive, non-invasive, low-cost, and accurate advantages in delineating subsurface shear (S)-wave velocity structures. They are increasingly being utilized to address numerous engineering and environmental problems. Surface waves obtained from actively excited sources such as a hammer and a harmonic shaker, however, lack low-frequency components, resulting in limited investigation depth. Conversely, passive surface waves such as microseisms (< 0.4 Hz, associated with natural ocean wave activity) and microtremor (>1 Hz, generated by cultural and wind sources) retrieved from ambient seismic noise typically lack high-frequency components, which is not conductive to characterizing fine near-surface structures. To overcome these frequency limitations, we employ a “mixed-source data” strategy, imposing active shot gathers into ambient noise data, to widen the frequency range of dispersion images and depth of investigative capabilities. We simulate both active and passive surface-wave data based on a two-layer model, noting that their dispersion images suffer from a mode kissing phenomenon at lower frequencies. By analyzing influencing factors such as the amplitude intensity, the signal-to-noise ratio and the excitation locations of active shot gathers, as well as the length of passive surface-wave data, we better understand their impacts on dispersion images from mixed-source surface-wave data. Simulation tests demonstrate that processing mixed-source data can effectively distinguish the mode kissing phenomenon. Moreover, the effectiveness of this strategy in enhancing the quality of dispersion image is verified, especially when surface-wave dispersion images perform poorly in either the low- or high-frequency bands. Additionally, a real-world example further demonstrates that processing mixed-source data offers significant advantages in improving the quality of dispersion images. This way provides a convenient and efficient measurement strategy for delineating shear-wave velocity profiles in finer shallow layers and deeper penetration depths.

Анализ эквивалентности трехмерных моделей при 3D-интерпретации данных аэроэлектроразведки

The article is devoted to the problem of the equivalence of three-dimensional geoelectric models obtained as a result of 3D-interpretation of aerial electrical survey data in medium where local conductive bodies are covered by a heterogeneous near-surface layer. To carry out the research, we used data from aerial electrical prospecting carried out in the Creighton area (Canada) when searching for polymetallic ores. A two-stage 3D-inversion was used to build the geoelectric model. The purpose of the first stage of 3D-inversion was to reconstruct near-surface structures based on survey data obtained at the early times. The second stage of 3D-inversion was based on the residual map constructed from the practical signals and the signals obtained after the first stage of 3D-inversion at later times. Using the residual map, the starting positions of local bodies under the overlying layer were specified, and their geometry and properties were obtained during local 3D-inversions. Based on the results of 3D-inversion, a local conductive body was identified and confirmed by drilling data. An equivalence analysis was carried out for a three-dimensional model of this body. Various options for initial models differing in the number of blocks describing the body were considered, as well as options for changing the size of blocks in the model obtained as a result of 3D-inversion. It was found that the main equivalence is related to the large distance between flight lines, which is several times greater than the distance between positions on the profile. Therefore, most equivalent models are characterized by a decrease in the size of blocks in the direction orthogonal to the profiles, due to an increase in their size along the profile and a change in resistivity. At the same time, according to the criterion for drilling wells, the resulting equivalent models were identical, i.e. from a geological point of view, no fundamental difference was found between the resulting equivalent models.

Detection of the low-velocity layer using a convolutional neural network on passive surface-wave data: An application in Hangzhou, China

Passive surface-wave methods using dense seismic arrays have gained growing attention in near-surface high-resolution imaging in urban environments. Deep learning (DL) in the extraction of dispersion curves and inversion can release a tremendous workload brought by dense seismic arrays. We presented a case study of imaging shear-wave velocity (Vs) structure and detecting low-velocity layer (LVL) in the Hangzhou urban area (eastern China). We used traffic-induced passive surface-wave data recorded by dense linear arrays. We extracted phase-velocity dispersion curves from noise recordings using seismic interferometry and multichannel analysis of surface waves. We adopted a convolutional neural network to estimate near-surface Vs models by inverting Rayleigh-wave fundamental-mode phase velocities. To improve the accuracy of the inversion, we utilized the sensitivities to weight the loss function. The average root mean square error from the weighted inversion is 46% lower than that from the unweighted DL inversion. The estimated pseudo-2D Vs profiles correspond to the velocities obtained from downhole seismic measurements. Compared with an investigation on the same survey area, our inversion results are more consistent with the Vs provided by downhole seismic measurements within 50–60 m where the LVL exists. The trained neural network successfully identified that the LVL is located at 50–60 m deep. To check the applicability of the trained neural network, we applied it to a nearby passive surface-wave survey and the inversion results agree with the existing investigation results. The two applications demonstrate the accuracy and efficiency of delineating near-surface Vs structures with the LVL from traffic-induced noise using the DL technique. The DL inversion has great potential for monitoring subsurface medium changes in urban areas.

Crustal Structures Inferred from Combined Terrestrial and Earth Gravity Data beneath the Babouri-Figuil and Mayo Oulo-Lere Basins, North Cameroon and South Chad

In this work, the study of the crustal structure of the Babouri-Figuil and Mayo Oulo-Lere sedimentary basins was carried out through the interpretation of gravity data. These data were obtained by combining the terrestrial gravity data obtained from the Earth Gravitational Model 2008. The analysis of the terrestrial Bouguer anomaly maps reveals both negative and positive anomalies. Negative anomalies, i.e., low-density signatures, are interpreted as specific rock types on the basis of the geological knowledge of the region while the positive anomalies are attributed to basaltic rocks underlying a generally granitic environment. The empirical method was used to distinguish anomalies due to deep structures from those due to near-surface structures. This method testifies that the residual map of degree 4 is appropriate. Six profiles are drawn on this residual Bouguer anomaly map and are interpreted using spectral analysis and 2D modeling methods. The results indicate that the mean depths of mass sources at the near-surface of the Babouri-Figuil and Mayo Oulo-Lere sedimentary basins are located at 1.50 km and 1.55 km, respectively. Moreover, the Babouri-Figuil Basin is constituted of two formations while the Mayo Oulo-Lere Basin exhibits three distinct formations. These models also help clarify the geological structure of the study area as well as the thicknesses of the sedimentary basins.

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.

Birefringence in Injection-Molded Cyclic Olefin Copolymer Substrates and Its Impact on Integrated Photonic Structures.

This contribution quantifies the birefringence within injection-molded cyclic olefin copolymer plates and discusses its impact on the mechanical properties of the plates. It also focuses on the impact of birefringence on integrated waveguides and Bragg gratings and provides fabrication guidelines for such structures. The anisotropy in all three dimensions of the workpiece is examined by means of polarimetry and a prism coupler. It is found that the birefringence is inhomogenously distributed within the workpieces, whereas the maximum birefringence not only varies locally, but also depends on the observation direction. Overall, a maximum birefringence of 10 × 10-4 is found at the plate's surface near the injection gate. The anisotropy then reduces exponentially towards the center of the workpiece and saturates at 1.8 × 10-4, in a depth of 0.4 mm. Thus, the birefringence strongly affects near-surface photonic structures. It is found that, depending on their orientation and the local birefringence of the substrate, waveguides and Bragg gratings fabricated with comparable parameters behave completely differently in terms of polarization-dependent optical attenuation, cross-sectional intensity distribution and Bragg reflection signal. For example, the support of the TM mode can vary between total loss and an optical attenuation of 0.9 dB × cm-1. In consequence, this study underlines the importance of quantifying the birefringent state of an injection-molded cyclic olefin copolymer workpiece if it is supposed to serve as a substrate for integrated photonic structures. The study furthermore demonstrates that birefringence effects can be omitted by burying the photonic structures deeper into the volume of the thermoplastic.

Characterizing Near-Surface Velocity Structures via Deep Learning

Characterizing Near-Surface Velocity Structures via Deep Learning

Detection of Low-Velocity Layer Using Passive Surface-Wave Data and Deep Learning: An Application in The Urban Area of Hangzhou, China

Passive surface-wave methods using dense seismic arrays have gained growing attention in near-surface high-resolution imaging in urban environments. Deep learning (DL) can release a tremendous workload brought by dense seismic arrays. We presented a case study of shear-wave velocity (Vs) structure imaging in the Hangzhou urban area (eastern China) using DL inversion. Noise data were recorded by dense linear arrays with approximately 5 m spacing deployed along two crossing roads for investigating the top 80 m of the subsurface. Phase-velocity dispersion curves are extracted from virtual shot gathers using multichannel analysis of surface waves. We divided the area where the low-velocity layer (LVL) may exist into three layers with a thickness of 5 m. We gave the four layers weak constraints to generate training dataset and adopted a convolutional neural network to directly invert fundamental-mode Rayleigh-wave phase velocity for 1D Vs models. To improve the accuracy, we further applied the sensitivities to weight the loss function in DL inversion. The obtained pseudo-2D Vs profiles correspond to the velocities estimated from logging data and previous survey. The well-trained neural network successfully identified that the LVL is located at 50-60 m deep. And this network was also achieved accurately the inversion of a dense seismic network nearby. The results of this survey demonstrate the accuracy and efficiency of delineating near-surface structures from traffic-induced noise using the DL technique, which has great potential for monitoring subsurface changes in urban areas.

Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications

Abstract In Cameroon, the drying up of wells and boreholes during the dry season is characterized by a lack of knowledge about the surface structures of the subsoil and the spatial distribution of groundwater. In this study, we propose to use gravity data to detect and characterize the surface structures of the south-west (SW) Cameroon and to analyze their hydrogeological implications. The application of the normalized standard deviation method on the data obtained from the stable downward continuation of the Bouguer anomalies led to the detection of 71 lineaments that characterize the tectonic events of the study area. The statistical analysis of the lineament directions shows that the near-surface structures were mainly affected by a tectonic of direction east–west followed by that oriented SW-north-west. These two directions are consistent with the megastructures in the region like the Ngoro-Belabo Shear Zone and the Sanaga Fault stretching from the Gulf of Guinea to the Central African Republic. In addition, the application of the Euler deconvolution method permits us to evaluate the interval in which lies the depth of each lineament hosting Euler solutions. The analyses showed that 12 lineaments, whose roof depths are between zero and 250 m, are very favorable for hydrogeological prospecting.

Simulation of the impact of sea level rise groundwater flooding along the south-eastern coast of India

Sea-level rise affects low-lying coastal regions that jeopardize superficial infrastructure through groundwater flooding, which also impacts near-surface artificial structures, inhumed networks, and near-shore ecosystems. In the present study, groundwater flow modeling has been utilized to simulate different scenarios of sea level rise to evaluate the groundwater level changes and predict possible vulnerable zones to groundwater inundation along the coast of the Nagapattinam district. The model has been simulated for ten years (2010–2019) under the transient condition with MODFLOW-2000 engine in Visual MODFLOW. Calibration and validation of the simulated model have been performed by trial-and-error method considering groundwater heads of selected observations well data, which results in a root mean square value of 1.765 m, 0.941 as correlation coefficient, and a value of 0.687 m as the standard error. Groundwater flooding due to a 0.6 m sea level rise is forecasted to cause significant flooding, covering 23.83% of the study area. A 1.0 m rise in sea level impacts 26.2% of the study region. The sea level rise scenarios evaluate that the coastal groundwater heads will reduce as they are restricted by topography from rising landward. The present study suggests groundwater in the coastal regions is influenced by sea level rise due to climate change impacting the coastal ecosystems.

Fireworks: A Potential Artificial Source for Imaging Near-Surface Structures

Abstract Seismic waves induced by incident acoustic waves from air disturbances can be used to image near-surface structures. In this article, we analyze seismic waveforms recorded by a dense array on the Xishancun landside in Li County, Sichuan Province, southwest China during the Lunar New Year’s Eve (27 January 2017). A total of eight event clusters have been identified as a result of firework explosions. For each cluster, which comprises dozens of individual events with high similarity, we manually pick arrival times of the first event recorded by the array and locate it with a grid-search method. We then rotate three-component waveforms of all events from the east, north, and vertical coordinate system to the local LQT coordinates (L, positive direction perpendicular to the landslide surface and pointing downwards; Q, positive direction is from the launch location of firework to the station along the landslide surface; T, perpendicular to the plane formed by the L and Q directions, and the selected positive direction of the T axis makes LQT form the left-hand coordinate system), and stack the LQT components for those events with cross-correlation values CC ≥ 0.8 with respect to the first event. Characteristics of the stacked LQT components are also examined. The particle motions at each station are retrograde ellipse in the frequency range of ∼5–50 Hz, suggesting air-coupled Rayleigh waves generated by the firework explosions. Spectrograms of the Rayleigh waves also show clear dispersions, which might be used to image near-surface velocity structures. Although we cannot directly extract the phase velocities due to the limitation of the seismic array, our study shows that the fireworks might provide a low-cost and easy-to-use seismic source for imaging near-surface structures.

Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture

The inversion of the Rayleigh wave dispersion curve is a crucial step in obtaining the shear wave velocity (VS) of near-surface structures. Due to the characteristics of being ill-posed and nonlinear, the existing inversion methods presented low efficiency and ambiguity. To address these challenges, we describe a six-layer deep neural network algorithm for the inversion of 1D VS from dispersion curves of the fundamental mode Rayleigh surface waves. Our method encompasses several key advancements: (1) we use a finer layer to construct the 1-D VS model of the subsurface, which can describe a more complex near-surface geology structure; (2) considering the ergodicity and orderliness of strata evolution, the constrained Markov Chain was employed to reconstruct the complex velocity model; (3) we build a practical and complete dispersion curve inversion process. Our model tested the performance using a random synthetic dataset and the influence of different factors, including the number of training samples, learning rate, and the selection of optimal artificial neural network architecture. Finally, the field test dispersion data were used to further verify the method’s effectiveness. Our synthetic dataset proved the diversity and rationality of the random VS model. The results of training and predicting showed higher accuracy and could speed the inversion process (only ~15 s), and we proved the important effect of different factors. The outcomes derived from the application of this technique to the measured dispersion data in the Yellow River Delta exhibit a strong correlation with the outcomes obtained from the integration of the very fast simulated annealing method and the downhill simplex method, as well as the statistically derived shear wave velocity data of the sedimentary layers in the Yellow River Delta. From a long-term perspective, our method can provide an alternative for deriving VS models for complex near-surface structures.

Hydrological Evaluation of the Groundwater Potential in the Fractured Karoo Aquifer Using Magnetic and Electrical Resistivity Methods: Case Study of the Balfour Formation, Alice, South Africa

The study is aimed at evaluating the groundwater accumulations present in Alice using magnetic and electrical resistivity measurements to examine the trends of structural elements and characterize the groundwater resource for borehole drilling. The magnetic maps show a low magnetic linear structure moving northwest to southeast direction, which may be caused by fractures. The linear high intensities were probably caused by dolerite dykes, while dolerite sills caused broader high-intensity areas. The depth slices show that the near-surface magnetic structures are visible to a depth of about 19 m, and the deep-seated structures are found at a depth of about 31 m, possibly deeper. Twenty-five vertical electrical soundings (VES) of the Schlumberger array were measured with AB/2 varying between 1.5 m and 250 m across the study area. The VES interpretation showed four geoelectric layers composed of HK and HA curve types. The geoelectric layer’s thicknesses are (1) topsoil from 0.4 to 1.8 m, (2) weathered layer from 0.8 to 17.5 m, and (3) weathered/fractured layer from 9.9 to 143.9 m; the third layer could be the productive water-bearing zones, and (4) bedrock layer has an infinite thickness. The layers have resistivity values of 20-5752 Ωm, 3-51 Ωm, 136-352 Ωm, and 44-60428 Ωm, respectively. A correlation of the VES with the borehole log indicated a well-matched result. The magnetic and electrical resistivity surveys provided a detailed subsurface structure and helped identify possible fractures that could act as a passage for groundwater.

Characteristic evaluation of passive seismic ellipticity method in urban shield tunnel

In recent years, engineering examples have shown that traditional surface-wave detection methods are easily limited in areas with small space and heavy traffic. The horizontal-to-vertical spectral ratio survey method has become increasingly prevalent in site measurements and is favored by geophysicists for its ability to detect near-surface velocity structures with greater sensitivity than phase velocities. Based on the phase difference method and polarization filtering, we proposed a passive seismic ellipticity method. The objective of this method is to extract the Rayleigh waves from ambient noise recordings and the analysis of ellipticity dispersion characteristic. This method was applied to an actual case study. Specifically, the velocity dispersion profile of the site is obtained using the active-source surface-wave method. Then, we compared the results obtained by the passive seismic ellipticity method with those obtained by the active-source surface-wave method and the field data, to evaluate the accuracy of results. The results demonstrate that the proposed method can effectively detect the location of subsurface shield tunnels and has the characteristics of simple data processing steps, less interference, and high stability of results. This demonstrates the necessity of using the phase difference method to select segments dominated by Rayleigh waves in ambient noise recordings and applying polarization filtering before calculating ellipticity. The paper provides an actual case study for the application and promotion of the passive seismic ellipticity method and has significant practical value and reference significance.

Mapping near-surface structures in a geophysical test site using magnetic and electromagnetic induction gradients

We applied Magnetic Gradiometry (MG) and Electromagnetic Induction (EMI) techniques to map and differentiate the geophysical response of 17 buried structures at <2.50 m deep in the Teoloyucan Geophysical Test Site (TGTS). To enhance the response of near-surface structures, we include the Electromagnetic Induction Gradient (EMIG) and complement MG and EMI acquisition and processing methodologies. This includes, acquiring and adding together orthogonal-transect surveys, computing the EMIG and sectioning area maps into close-up maps. The orthogonal-transect surveys clearly show distinct distributions of the physical properties resulting from the acquisition direction, the orientation of the transects and from the orientation and heading of the sensors. Compared with EMI, EMIG provides remarkable results for apparent conductivity although limited for in-phase data. For MG and EMIG, close-up maps yield better results than area maps. Also, adding the orthogonal-transect survey grids gives better results, followed by the E−W transect surveys and then by the S−N transect surveys. Most of the mapped structures present low-medium MG values and medium-high EMIG values. The proposed methodology is consistent for concrete and air-cavity structures, and for structures made with various materials and complex geometries. In some cases, the structures do not have enough contrast with their surrounding terrain and in others, the anomalies are shifted in <1.50 m. In this study, we demonstrate the convenience of computing the EMIG and the benefits of applying the used methodology to retrieve near-surface features that would be overlook with common-practice methodologies.

The effects of magnetic and radiometric responses on hydrothermal zones and its implication in minerals characterization over part of Nasarawa, North Central Nigeria

Airborne magnetic and radiometric data set over part of Nasarawa, North central Nigeria have been enhanced and interpreted qualitatively. Attributes maps (first and second vertical derivative; and analytic signal maps) from the total magnetic intensity map (TMI) were used to delineate near-surface structures hosting sedimentary rocks forming minerals and differentiate regions based on amplitude responses. The structure trends NE-SW, NW-SE, E-W and N-S directions and the area is predominantly underlain by basement and sedimentary outcrops in the northeastern/western, central portion and southern parts respectively. The total cout map showed the low, moderate and high levels ranged from 6.40 to 14.70 ?R/h, 13.31 to 16.66 ?R/h and 18.02 to 41.23 ?R/h respectively. The Potassium concentration revealed the lowest concentration level (0.02-0.0.16%), moderate level (0.17-0.47%) and highest concentration of (18–27%). The lowest concentration level in the eTh map is related to siltstones and sandstones (Ansh), shale and mudstones (Nsh) and sandstone. The highest level reaches 31.35 ppm in volcanics rocks. The equivalent Uranium concentration map (eU) indicates that the high level of uranium concentration is associated with the black shales, siltstones and sandstones (Ansh) while shale and mudstones (Nsh), sandstones, shale and sandy clay (LaSS), sandstones (Ess), shale and limestones (Esh) and alluvium deposition has the lowest one. The potassium thorium ratio map shows zones characterized by the high K/eTh ratio values that are strong indicators of hydrothermal alteration. From the K/eTh ratio map, it can be observed that the areas affected by the hydrothermal process are featured in pink colour and have a high value of 0.37 in the K/eTh ratio which is an indication of gold mineralisation.

Near-Surface Structure Investigation Using Ambient Noise in the Water Environment Recorded by Fiber-Optic Distributed Acoustic Sensing

Near-surface structure investigation plays an important role in studying shallow active faults and has various engineering applications. Therefore, we developed a near-surface structure investigation method using ambient noise in a water environment. This newly developed seismic acquisition technology, fiber-optic distributed acoustic sensing (DAS), was used to acquire ambient noise from the Yangtze River. The recorded data were processed to reconstruct surface waves based on the theory of seismic interferometry. The fundamental-mode dispersion curves were extracted and inverted to obtain a shear-wave velocity model below the DAS line. We compared the inverted velocity model with the subsurface geological information from near the study area. The results from the inverted model were consistent with the prior geological information. Therefore, ambient noise in the water environment can be combined with DAS technology to effectively investigate near-surface structures.

Near-Surface Dose Correlates With Moist Desquamation and Unplanned Reconstructive Surgery in Patients With Implant-Based Reconstruction Receiving Postmastectomy Radiation Therapy

Postmastectomy radiation therapy (PMRT) reduces disease recurrence in appropriately selected patients but may compromise implant-based reconstruction. We investigated whether near-surface dose correlates with radiation-related toxic effects in these patients. Patients receiving PMRT at a single institution from 2016 to 2019 were retrospectively reviewed. Patient demographics and treatment information were collected. Three near-surface structures were retrospectively generated, bound by the chest wall tangent beam as well as the skin surface and the skin-3 mm contour (SR3), skin surface and skin-5 mm contour (SR5), or skin-5 and skin-10 mm contours. Dosimetric analysis of these near-surface contours was performed in 2 Gy intervals. Univariate and multivariate analyses were used to identify predictors of moist desquamation, grade 2+ chest wall pain, use of opiate pain medication, unplanned reconstructive surgery, and implant failure. Logistic regression for each outcome and near-surface contour was performed for receiver-operator area under the curve (AUC) analysis and the Youden J Statistic was used to determine the optimal threshold for each dosimetric parameter. Of 126 patients reviewed, 109 met the study's eligibility criteria. Median follow-up was 2.3 years. Twenty-five patients (23%) underwent unplanned reconstructive surgery, and 10 (9.2%) experienced implant failure. Among clinical variables, low body mass index and history of smoking predicted unplanned surgery on univariate and multivariate analyses, and moist desquamation predicted grade 2+ chest wall pain. The top dosimetric parameters by AUC for moist desquamation, grade 2+ chest wall pain, use of opiates, unplanned reconstructive surgery, and implant failure were SR5 D10 cc (AUC=0.701, optimal threshold 57.8 Gy, P < .001), SR3 D10 cc (AUC=0.600, optimal threshold 56.8 Gy, P=.079), SR5 D10 cc (AUC=0.642, optimal threshold 57.3 Gy, P=.041), SR3 V44 Gy (AUC=0.711, optimal threshold 81%, P=.001), and SR3 V44 Gy (AUC=0.688, optimal threshold 82%, P=.052), respectively. Near-surface dose correlates with moist desquamation and unplanned reconstructive surgery after PMRT. Further evaluation of prospective optimization of dosimetric parameters related to SR3 and SR5 should be considered.