Enhanced P-wave velocity imaging by marine controlled-source seismic surveys with Distributed Acoustic Sensing

Enhanced P-wave velocity imaging by marine controlled-source seismic surveys with Distributed Acoustic Sensing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhanced P-wave velocity imaging by marine controlled-source seismic surveys with Distributed Acoustic Sensing SHUN Fukushima, Masanao Shinohara, Tomoaki Yamada, Ryota Hino, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5344756/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 May, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract P-wave velocity (Vp) structures in marine areas have been estimated using marine controlled-source seismic surveys and refraction methods. The spatial resolution of Vp structures depends on the number of receivers and shots. In marine environments, Vp resolution is often limited due to the sparse distribution of ocean bottom seismometers (OBSs). Recently, the use of distributed acoustic sensing (DAS) has enabled high-resolution strain measurements over long distances, with station density along DAS cables being much higher than that of OBSs. Applying seismic control surveys and refraction methods to DAS data can enhance the horizontal resolution of Vp structures. This study demonstrates that seismic refraction using DAS data is effective for imaging shallow Vp structures with high spatial resolution. A seismic survey was conducted using R/V Hakuho-maru, controlled sources, and DAS and OBS measurements along a seafloor optical fiber cable off Sanriku, Japan. Strong lateral heterogeneities in the Vp structure were observed, due to dense DAS data. The 2-D Vp structure obtained from the DAS data showed good agreement with the seismic reflection profile. This experiment highlights the unique advantages of DAS data over OBSs for seismic refraction. Earth and environmental sciences/Solid earth sciences/Seismology Earth and environmental sciences/Solid earth sciences/Geophysics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction P-wave velocity (Vp) structures in marine areas are generally estimated using seismic refraction surveys with controlled seismic sources, ocean-bottom seismometers (OBS), and/or hydrophone streamers. Analytical methods for seismic refraction, such as t -sum inversion, are often used to estimate shallow Vp structures 1 , 2 . The t -sum inversion method estimates the one-dimensional (1-D) Vp structure beneath a receiver or source. An analysis of t (intercept time)– p (slowness) domain has advantages over that of T (time)– X (distance) domain for shallow Vp structures because the travel time of refracted P-waves from shallow layers is generally longer than that of direct water waves from a seismic source in marine refraction surveys. Another advantage of analysis in the t-p domain is the high vertical resolution of the obtained shallow seismic structures 2 . However, the spatial resolution of Vp structures estimated using the seismic refraction method is limited by the spacing of the receivers or seismic sources. In seismic surveys using OBSs, the typical interval between OBSs is a few tens of kilometers. Even with a spatially dense seismic source, the resolution is determined by the receiver spacing. By contrast, the spatial horizontal resolution is higher in seismic reflection surveys because the intervals between both the source and receiver can be small. Seismic reflection surveys are effective for obtaining subsurface reflectivity images, with high sensitivity to impedance contrasts in the subsurface 3 – 5 . The spatial resolution of seismic reflection images has reached less than ten meters. Therefore, these high-resolution images have revealed detailed lateral heterogeneity in horizontal reflectivity across various study regions 4 – 6 . However, accurately estimating Vp structures from seismic reflection analysis is challenging because these analyses are optimized to produce clearer reflection images 3 . To estimate the lateral heterogeneity of Vp structures and fully understand the subsurface properties in marine areas, improving the spatial resolution of Vp structures in seismic refraction surveys is essential. In seismic refraction analysis, spatially dense observations have enabled improvements in the spatial resolution of Vp structure. Applying t -sum inversion to dense data over a large area can produce numerous 1-D Vp profiles at short intervals. When receivers and sources are aligned linearly and dense observations are performed, high-resolution two-dimensional (2-D) Vp structures can be estimated by aligning 1-D velocity structures. However, deploying a large number of OBSs is challenging due to their high costs. OBSs are typically installed at intervals of several kilometers because of these limitations. In contrast, hydrophone streamers have a much larger number of receivers, with spatial intervals typically just a few meters. However, using hydrophone streamer data for Vp structure estimation in seismic refraction surveys has several drawbacks. Estimating deep structures is difficult with hydrophone streamers because their length is limited 7 . This restriction prevents the collection of deep-penetrating seismic refraction waves, which are necessary for gathering velocity information from deeper regions. Moreover, the signal-to-noise ratio of seismic waves is generally lower because hydrophone streamers are towed near the sea surface, where noise levels are higher compared to the seafloor 8 . Recently, Distributed Acoustic Sensing (DAS) technology has emerged, offering high-resolution strain or strain rate measurements (at spatial intervals of several meters) over long distances (up to tens of kilometers) 9 . DAS cables have a higher station density than traditional seismic observation methods using individual seismic sensors, making DAS especially useful in environments where deploying large numbers of seismometers, such as in offshore areas, is difficult. For instance, DAS data have been used for seismic observations of natural earthquakes in marine areas 10 . In seismic refraction surveys, DAS offers clear advantages over both OBS and hydrophone streamers. The receiver density along a DAS cable is significantly higher than the typical receiver density in marine controlled-source surveys using OBSs. As a result, the horizontal resolution of the Vp structure is expected to improve with the dense spatial data provided by DAS. Although the receiver density of DAS is similar to that of hydrophone streamers, DAS has an added advantage: its stations are fixed to the seafloor, allowing for the observation of seismic sources with long offset distances. Consequently, DAS data from seafloor cables are well-suited for seismic refraction surveys with controlled sources, enabling the acquisition of detailed Vp structures with high-resolution in both the vertical and horizontal directions. To estimate shallow Vp structures with high spatial resolution, this study conducted seismic refraction surveys using a marine-controlled seismic source and DAS measurements. Data were collected from DAS measurements connected to a seafloor cable, along with OBS data, off the coast of Sanriku, Japan (Fig. 1 ). A common receiver gather (CRG) of the DAS data was obtained after processing. The seismic signals of refracted, reflected, and direct water waves from the controlled source were identified in the CRG records. These records were converted from the T-X domain to the t-p domain using the slant stack 1 method, with a spatial interval of 125 m. The dense DAS measurements allowed us to observe gradual and/or sudden spatial changes in the trajectories of refracted P-waves in the t-p domain. This high-density data enabled us to trace spatial changes in P-wave trajectories at intervals of 125 m. The t -sum inversion method was applied to determine 1-D Vp structures below each observation point, which were spaced at approximately 125 m, covering distances from 20 km to 85 km from the coast. By aligning the 1-D velocity structures, a high-resolution 2-D Vp structure was constructed. The resulting 2-D Vp structure revealed three distinct units with different vertical gradients of Vp. Detailed lateral heterogeneities in Vp, as well as variations in the thickness of each unit, were also identified. The 2-D Vp structure obtained from the DAS data showed good agreement with the seismic reflection profile. In conclusion, our study demonstrated that spatially dense DAS data significantly enhanced the spatial resolution of Vp structures in seismic refraction surveys. This experiment highlights the unique advantages of DAS data over OBSs. Result The CRG records of the DAS data in the T-X domain clearly showed water, refraction, and reflection waves from the airguns at distances of 20–60 km from the coast (Figs. 2-A-1, 2-B-1, and 2-C-1). In contrast, seismic signals from the airguns were not clearly observed in the CRG records of the DAS data at distances greater than 60 km (Fig. 2-D-1). In the CRG record of the DAS data at distances of 20–60 km, direct water waves were recognized as the first arrivals at offset distances of up to 5 km (Fig. 2). Following the direct water waves, refraction waves with slow apparent velocities (1.5-2.5 km/s) were observed. At offset distances from 3 km to 15 km, the first arrivals of refracted waves with faster apparent velocities (3.0–5.0 km/s) were identified (Fig. 2). By comparing the CRG records of the vertical component data from the pop-up OBS installed along the cable (Fig. 1), these refracted waves were interpreted as penetrating P-waves (Fig. S1). The refracted waves recorded by the OBS had the same apparent velocities as those recorded by the DAS measurements. Furthermore, the travel times in the OBS records were consistent with those in the DAS records (Fig. S1). Although the DAS measurements clearly showed refracted waves up to an offset distance of 60 km, refracted waves could not be identified in the DAS records at distances greater than 60 km (Fig. 2-D-1). The attenuation of laser light over long travel distances in DAS observations is thought to be the cause of the low signal-to-noise ratio (SNR) of seismic signals in offshore DAS data. The transformation of the CRG data from the T-X domain to the t-p domain was performed using a slant stack (see Methods). The strain data obtained from the DAS measurements for the opposite directions of the receiver were processed separately, and the CRG data with offsets of up to 15 km were used for each transformation. Stacking techniques, including slant stacks for DAS data, effectively improve the SNR of seismic signals. The very short receiver intervals in DAS measurements, which are shorter than the wavelength of the P-wave, allow us to stack a large amount of data without spatial aliasing. For the t-p transformation, data were gathered from 51 adjacent DAS stations (approximately 125 m apart) and transformed this gathered DAS data from the T-X domain into the t-p domain (hereafter referred to as “gathered t-p transformation”). Since the gathered t-p transformation enhanced the P-wave refraction arrivals, the trajectory of the refracted P-waves can be clearly seen in the τ-p domain (Fig. 2 ). When the τ-p transformation was applied to the CRG data from only one DAS station at a distance of 76.7 km from the coast, it was difficult to identify the trajectory of P-wave refraction (Figs. S2-A and C). In contrast, the trajectories of the refracted P-waves can be clearly obtained using the gathered t-p transformations (Fig. 2 and Figs. S2-B and D). In particular, the improvement in the SNR of the seismic signal by the gathered t-p transformation is effective for data in offshore areas more than approximately 60 km from the coast (Fig. S2) because the decrease in SNR due to laser light attenuation was more significant in offshore areas than in nearshore areas. The shapes of the trajectories for the refracted P-wave in the t-p domain are related to the Vp structure beneath each receiver. As only one datum in the t-p domain is obtained from each CRG record at each station, gradual and/or sudden spatial changes in the shapes of trajectories of refracted P-waves within a few kilometers cannot be captured using conventional marine controlled-source seismic surveys that rely on data from sparsely deployed OBSs. In contrast, the spatially dense dataset from DAS measurements offers a new opportunity to observe spatial changes in the shapes of the trajectories for refracted P-waves in the t-p domain at an interval of 125 m (Figs. 3 -A and B). For example, intercept times on trajectories at velocities of 2.0 km/s (i.e., p of 0.5 s/km) and 3.2 km/s (i.e., p of 0.3125 s/km) change gradually and suddenly at different distances from the coast, respectively. The intercept times at a velocity of 2.0 km/s along the trajectories at each receiver gradually increased seawards, with a horizontal gradient of approximately 0.018 s/km (Fig. 3 -C). In contrast, the increase in t at a velocity of 3.2 km/s with distance from the coast was not monotonic. At distances ranging from 25 km to 45 km, t gradually increased from 0.6 s to 1.6 s. However, t suddenly increased from 1.6 s to 2.4 s within the distance range of 45 to 55 km. Beyond 55 km, t gradually increased from 2.4 s to 2.6 s. These sudden spatial changes in t at each velocity suggest lateral heterogeneity in the Vp structures. Estimation of the 1-D Vp structure using the τ -sum inversion with an ultra-short horizontal interval (approximately 125 m in this study) was conducted using the spatially dense DAS data. Moreover, the vertical gradient of Vp was calculated for each 1-D profile. Two 1-D Vp structures and the vertical gradient of Vp below a single observation point were obtained through the t -sum inversion using the t-p data from the east side and the west side of a linear profile of controlled sources passing through the observation point. Based on the advantages of the spatially dense DAS data, a 2-D Vp structure was obtained by aligning 1-D profiles from both the east and west sides at an ultra-short interval of 125 m. No smoothing for the horizontal direction was applied. The horizontal position of each Vp, vertical gradient of Vp, and depth as discrete points are defined by Eq. ( 2 ) in the Methods section, resulting in a 2-D Vp structure obtained at distances between 25 and 85 km from the coast (Figs. 4 -A and B). Using the 2-D profile of the Vp vertical gradient, the obtained 2-D Vp structure was divided into three units with different vertical gradients (Fig. 4 -B). The vertical Vp gradients changed at Vp of 2.0 km/s and 3.5 km/s. The spatial averages of Vp and its vertical gradient for the uppermost unit (unit-1) are 1.79 km/s and 0.03 s − 1 , respectively. The unit underlying unit-1 (unit-2) has a spatial average Vp of 2.62 km/s and a vertical gradient of 0.07 s − 1 . For unit-3, the spatial Vp and its vertical gradient are 4.27 km/s and 0.45 s − 1 , respectively. The advantage of t -sum inversion is its high resolution in the vertical direction for the obtained shallow Vp structures 2 , and DAS measurements enable us to conduct analyses in the t-p domain with an ultra-short interval of 125 m over long distances between 25 and 85 km from the coast. Due to both the advantages of t -sum inversion and DAS measurements, the t -sum inversion using DAS data with a short horizontal interval of 125 m revealed remarkable features of spatial variation in the thicknesses of unit-1 and unit-2. The thickness of unit-1 was 0.25 km at a horizontal distance of 25 km from the coast and gradually increased seaward (Fig. 4 -B), reaching 1.2 km at a horizontal distance of 85 km from the coast. Although the thickness of unit-1 increases gradually with distance from the coast to offshore, the thickness of unit-2 varies over short horizontal distances. Unit-2 has a uniform thickness from 25 km to 48 km, measuring 0.50 km. However, the thickness of unit-2 rapidly increases from 0.5 km to 1.5 km between distances of 48 km and 50 km. The thickness of unit-2 becomes constant again at horizontal distances greater than 50 km. The t -sum inversion with DAS data revealed significant spatial variation in the thickness of unit-2 within a horizontal distance of 5 km. The Vp of the uppermost region of unit-1 at a distance of 20–45 km ranges from 1.65 to 1.80 km/s (Fig. 4 -D). The Vp values in the lowermost regions of unit-1 became approximately 1.9 km/s. The depths of the seafloor were approximately 0.6 km at a distance of 25 km, gradually increasing to approximately 0.8 km at a distance of 45 km. The thicknesses of the uppermost region of unit-1 were 0.4 km and 0.6 km at horizontal distances of 25 km and 45 km, respectively. The thickness of these regions increased monotonically (Fig. 4 -D). In contrast, the Vp of the uppermost region of unit-1 at horizontal distances of 45–85 km was slower than 1.65 km/s. Below this uppermost region, the Vp values gradually increased from 1.65 km/s to 1.9 km/s (Fig. 4 -D). The depths of the seafloor gradually increased from 0.8 km to 1.6 km at distances ranging from 45 km to 85 km. The thicknesses of the uppermost regions were 0.6 km, 0.4 km, and 1.0 km at distances of 45 km, 60 km, and 85 km, respectively, with thicknesses gradually varying. In unit-2, the uppermost regions at horizontal distances of 25–85 km have a Vp of approximately 2.2 km/s (Fig. 4 -E). The Vp of the bottom layer of unit-2 became approximately 3.2 km/s. The thicknesses at horizontal distances of 25–50 km ranged from 0.4 to 0.6 km. At distances of 50–85 km, the thickness of unit-2 is approximately 1.4 km. The layers with a Vp of 2.6–2.8 km/s at distances ranging from 55 to 85 km have thicknesses of approximately 0.8 km. In contrast, the thicknesses of the layer with Vp of 2.6–2.8 km/s at distances ranging from 25 to 55 km can be recognized as smaller than 0.1 km. Due to the spatial density of the DAS data, spatial variation in the thickness of the layers can be obtained sequentially. Additionally, sudden spatial changes in the structures of unit-1 at a distance of 45 km and unit-2 at a distance of 50 km can be clearly recognized. The 2-D Vp structures, obtained with a spatial horizontal interval of 125 m through τ -sum inversion with DAS data, revealed lateral heterogeneity with a horizontal resolution of a few kilometers in the Vp structure. Discussion In estimating the Vp structure by τ -sum inversion, evaluating the horizontal resolution of the 2-D Vp model is difficult because CRG data were used in the T-X domain with offsets smaller than 15 km for the \(\:\tau\:\) - \(\:p\) transformation. Although the 2-D velocity model was obtained by aligning the 1-D velocity models to an ultra-short interval of 125 m, the horizontal resolution was not the same as the 125 m interval. Generally, the horizontal resolution of seismic reflection images is high. However, evaluating the horizontal resolution of seismic reflection images using multichannel streamers is challenging due to spatial stacking techniques, such as common midpoint gathering 8 . It is well known that the horizontal resolution of high-resolution seismic reflection images, particularly when using a single-channel streamer, can reach a few tens of meters. Comparing the lateral variation of the velocity discontinuities in the Vp model with the horizontal reflectors in a seismic reflection profile obtained using a single-channel streamer is effective for evaluating the spatial resolution of the 2-D Vp model. Therefore, our 2-D Vp structure was compared with the seismic reflection profile obtained using a single-channel hydrophone streamer (see Methods). The depths to two-way travel times used in this 2-D velocity model were converted and compared with the seismic reflection profile. Figure 5 -A shows the continuous lateral reflectors in the seismic reflection profile. Several continuous lateral reflectors were observed over a distance range of 25–85 km (Fig. 5 -A and 5 -B). The interfaces of unit-1, unit-2, and unit-3 corresponded to the interpretation of lateral continuous reflectors (lines A and B in Figs. 5 -A and 5 -B). In unit-1, several lateral continuous reflectors can be seen at distances of 45–80 km (Fig. 5 -C). The lateral continuous reflectors and iso-velocity curves for the two-way travel times showed good consistency. At distances of 48 km and 66 km, the two-way travel times of the lateral continuous reflectors rapidly increased (areas A and B in Figs. 5 -C and 5 -D). In these regions, the layer with a Vp slower than 1.65 km/s became suddenly thicker during the two-way travel times. From the comparison between the seismic reflection image and our 2-D Vp structure, the velocity variation in unit-1 is consistent with the shape of the reflectors in the reflection profile (Fig. 5 -B). The two-dimensional Vp structure obtained by the \(\:\tau\:\) - \(\:p\) method using DAS data can express lateral heterogeneities of less than a few kilometers (Fig. 5 ). Consequently, it can be concluded that the spatial resolution of the 2-D Vp structure was higher than a few kilometers. It is known that the velocity profile obtained by the \(\:\tau\:\) - \(\:p\) method has high spatial resolution in the vertical direction 1 . The two-dimensional velocity structure obtained by the method proposed in this study has high spatial resolution in both vertical and horizontal directions, comparable to the reflection profiles obtained by controlled sources and hydrophone streamers. Before DAS technology became available, the \(\:\tau\:\) - \(\:p\) method for deep structures in marine areas was applied to data from single OBSs, which were usually installed at sparse intervals of several kilometers. Therefore, revealing detailed lateral variations in the Vp structures using data from conventional OBS surveys with marine-controlled sources is difficult. In contrast, DAS measurements can capture seismic data from controlled sources at large lateral intervals (tens of meters). Ultra-dense DAS measurement data enabled us to obtain a 1-D Vp structure at ultra-short intervals of tens or hundreds of meters using the \(\:\tau\:\) - \(\:p\) method, revealing the lateral heterogeneities of the velocity structure (Fig. 4 ). Since the trajectory of P-wave arrivals in the \(\:\tau\:\) - \(\:p\) domain was obtained at very short intervals of 125 m, the Vp structures from the seafloor to a depth of 5 km can be estimated every 125 m in the horizontal direction through τ -sum inversion using DAS data. The obtained 2-D Vp structure exhibited three velocity units, with lateral heterogeneities in each unit revealed using spatially dense DAS data (Fig. 4 ). Although seismic reflection surveys using hydrophone streamers provide seismic reflection images with high spatial resolution in the horizontal direction (several tens of meters) 4 , obtaining a precise distribution of Vp with large spatial intervals using seismic reflection methods is challenging, especially in deep regions. The spatial sampling interval of the Vp structure from conventional seismic refraction surveys using pop-up OBSs is much smaller than that from seismic reflection surveys, although precise Vp can be estimated using seismic refraction methods. In contrast, our new approach for estimating seismic structures using DAS data enabled us to obtain precise Vp structures with high spatial sampling intervals in both vertical and horizontal directions. The ratio of P- and S-wave velocities (Vp/Vs) is essential for estimating pore fluid pressure and rock properties such as porosity 11 – 13 . This information on pore fluid pressure or porosity aids in understanding faults properties 14 – 16 and monitoring subsurface oil or CO 2 reservoirs 17 , 18 . In recent years, several studies have applied ambient noise surface wave analysis to seafloor DAS data, estimating S-wave velocity (Vs) structures with a spatial resolution of hundreds of meters 19 – 21 . Our method also provides another physical property, Vp. Combining the Vp and Vs structures allow for the estimation of the Vp/Vs structure in a marine area at a spatial interval of hundreds of meters. This will be important for both scientific and engineering fields. The experiment on seismic refraction using DAS data highlights the unique advantages of DAS data over OBSs. Methods Seismic data acquisition In 1996, the Earthquake Research Institute of the University of Tokyo installed a seafloor seismic and tsunami observation system (Sanriku cable system) that used an optical fiber cable to transmit geophysical data offshore Sanriku (Fig. 1 ). This seafloor cable system has three seismic stations (SOB1-3), with spare fibers (dark fibers) available for DAS observations. The three seismic stations were equipped with accelerometers, collecting acceleration data at a sampling frequency of 100 Hz in real time. A seismic survey was conducted using controlled seismic sources: DAS and OBSs (Fig. 1 ) 22 . The survey took place from November 3 to November 8, 2020. The Hakuho-maru R/V, belonging to the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), utilized four large airguns (Bolt 1500 LL) as controlled seismic sources. Each airgun had a chamber capacity of 1,500 in³ and was shot along a profile of approximately 200 km. The shooting interval was 40 s, corresponding to a distance of about 100 m. A short hydrophone streamer with two channels was used to acquire the reflection data, resulting in seismic reflection records of 15 s in length with a sampling frequency of 1 kHz. DAS measurements using the Sanriku cable system were performed with two identical DAS interrogators made by AP Sensing GmBH 23 . Strain data along the cable were recorded at a temporal sampling frequency of 500 Hz. The sensing range, spatial sampling interval, and gauge length were set to 100 km, 5 m, and 40 m, respectively. In addition to the DAS and cabled seismic stations, nine free-fall pop-up OBSs were deployed along the seafloor cables (Fig. 1 ). The pop-up OBSs were installed before the shooting of the airguns and recovered after the shooting was completed. In this study, the pop-up OBSs and SOB within the range of DAS observations were used to evaluate the effectiveness of applying the seismic refraction method to DAS data. Thus, data from the four pop-up OBSs and SOB3 were utilized. The pop-up OBSs employed in this study have a three-component velocity-sensitive sensor with a natural frequency of 4.5 Hz. Although the sampling frequency of the data from the pop-up OBSs was 200 Hz, it was set to 50 Hz to unify the sampling frequencies for all datasets. CRG data were created from the DAS and OBS data. The time window started 5 s before the shot time, and the duration was set to 35 s. The seafloor positions of all DAS stations and OBSs were estimated from the travel times of the direct water waves from the airguns. The DAS stations were positioned every 100 m, approximately 500 m apart. The seafloor positions of the DAS stations between the station positions determined by the airguns were estimated using linear interpolation. t -sum inversion The CRG data were transformed in the T-X domain into t − p domain data to obtain the Vp structure of the shallow layers. The slant stacking technique 1 , 2 transforms seismic data in the T-X domain into the t-p domain. The t -sum inversion using discretely sampled t-p data can be performed when seismic sources are positioned at the sea surface and receivers are located on the seafloor as follows 2 : $$\:{h}_{1}=\:\frac{\frac{\left\{\tau\:\left({p}_{2}\right)-{\left({c}_{0}^{-2}-{p}_{2}^{2}\right)}^{\frac{1}{2}}{h}_{0}\right\}}{2}}{{\left({p}_{1}^{2}-{p}_{2}^{2}\right)}^{\frac{1}{2}}}\:\left(i=1\right),{h}_{i}=\:\frac{\frac{\left\{\tau\:\left({p}_{i}\right)-{\left({c}_{0}^{-2}-{p}_{i}^{2}\right)}^{\frac{1}{2}}{h}_{0}\right\}}{2}-{\sum\:}_{k=1}^{i-1}{\left({p}_{k}^{2}-{p}_{k+1}^{2}\right)}^{\frac{1}{2}}{h}_{k}}{{\left({p}_{i}^{2}-{p}_{i+1}^{2}\right)}^{\frac{1}{2}}}\:\left(i\ge\:2\right),\:$$ 1 where \(\:{h}_{0}\) and \(\:{c}_{0}\) are the thickness of the seawater layer and Vp in seawater, respectively. The value of \(\:{c}_{0}\) was set to 1.5 km/s. \(\:{h}_{i}\) and \(\:{p}_{i}\) are the thickness and slowness of the P-wave in the i th layer, respectively. In this study, a bandpass filter was applied to all CRG data from the DAS and OBSs in the frequency range of 8–15 Hz. The CRG data were transformed from the T-X domain to the \(\:\tau\:\) - \(\:p\) domain for eastward and westward shooting from the receiver, respectively. Data with offsets smaller than 15 km were used for the transformation. For a straight fiber cable laid horizontally, the DAS measurement closely approximates that of a linear strain meter 9 . Therefore, the horizontally axial strain (i.e., the DAS data) has low sensitivity to vertically incident P-waves 24 . To increase the signal-to-noise ratio (SNR) of P-waves, data from 51 adjacent DAS stations were gathered and transformed into CRG data, making the T-X domain into the \(\:\tau\:\) - \(\:p\) domain. The data mapped into the \(\:\tau\:\) - \(\:p\) domain clearly showed the trajectories of refracted P-waves from the shallow layers for both DAS data (Fig. 2 ) and the vertical component data of the OBS (Fig. S1 ). The trajectories of the refracted waves were manually selected, and the τ -sum inversion was performed. One-dimensional Vp structures were obtained at spatial intervals of approximately 125 m. Additionally, the vertical gradient of Vp was calculated for each 1-D profile. The analysis range was from 25 km to 80 km from the coast. Assuming a laterally homogeneous medium, the ray parameter measured by the slant stack technique (i.e., p ) reflects the velocity in the horizontal direction at the deepest point of a seismic ray. The offset distance between the receiver and the deepest point on the ray path is described as follows: $$\:{x}_{\text{b}}=\:-\:\frac{1}{2}\frac{d\tau\:}{dp},$$ 2 where, \(\:\tau\:\) and \(\:p\) are intercept time and slowness, respectively, and x b is the offset distance between the receiver and the deepest point on the ray path. Velocity-depth and vertical Vp gradient-depth profiles from the τ -sum inversion methods have already been obtained. From this equation and the obtained profile, the horizontal offset from the receiver at a given depth can be calculated. In other words, the velocities, vertical gradients, and depths were plotted in two dimensions. Consequently, a 2-D Vp structure and a 2-D vertical Vp gradient profile were obtained at offsets between 25 km and 80 km from the coast (Fig. 4 ). Using the 2-D profile of the Vp vertical gradient, the obtained 2-D Vp structure was divided into three units with different vertical Vp gradients. The vertical Vp gradients changed at Vp values of 2.0 km/s and 3.5 km/s. Polynomial curve fitting was applied to the interface data points of each 1-D velocity structure. The degree of the polynomial curve was determined by minimizing the Akaike Information Criterion. The degrees for the interfaces between unit-1 and unit-2, and unit-2 and unit-3 were 7 and 9, respectively. Seismic reflection image The seismic reflection profile was obtained using single-channel seismic (SCS) data (Fig. 5 -A). Additionally, the prediction error filtering method with a maximum lag time of 0.1 s to remove repeated signals and a bandpass filter between 20 Hz and 40 Hz to the seismic data, were applied. Automatic gain control with a time-gate length of 1.5 s was also utilized. Declarations Code availability The codes used in this study are available upon request from the authors. Competing interests The authors declare no competing interests. Author Contribution SF played a leading role in this study, including data processing, analysis, and completion of the manuscript. MS contributed to the development of the analytical method and interpretation of the results. HT contributed to the estimation of the seafloor positions at all DAS stations. MS, TY, RH, RA, YI, and YY led the temporal DAS observations and interpreted the results. All the authors have read and approved the final version of the manuscript. Acknowledgement The authors thank Drs. M. Masuda, S. Tanaka, Messrs. T. Hashimoto, K. Miyakawa, and T. Yagi of the Earthquake Research Institute, University of Tokyo, for their technical support with the DAS observations. We thank the captains, ship crew, and onboard technicians of the R/V Hakuho-maru for their dedicated efforts in data acquisition during the KH20-11 cruises. Comments by Prof. N. Hirata, Prof. K. Mochizuki and Dr. Akiko Takeo inspired our research. This study was supported by the Ministry of Education, Culture, Sports, Science, and Technology of Japan under the Earthquake and Volcano Hazards Observation and Research Program (Earthquake and Volcano Hazard Reduction Research). A part of this study was funded by the Earthquake Research Institute at the University of Tokyo. This study was supported by JSPS KAKENHI (grant number 24K22892). Some figures were created using Matplotlib and Generic Mapping Tools. Data Availability The DAS observations were conducted as part of the Earthquake and Volcano Hazards Observation and Research Program (Earthquake and Volcano Hazard Reduction Research) by the Ministry of Education, Culture, Sports, Science and Technology of Japan. The raw data supporting the conclusions of this study are available from the corresponding authors upon request. References Stoffa, P. L., Buhl, P., Diebold, J. B. & Wenzel, F. Direct mapping of seismic data to the domain of intercept time and ray parameter; a plane-wave decomposition. Geophysics . 46 , 255–267 (1981). Shinohara, M., Hirata, N. & Takahashi, N. High Resolution Velocity Analysis of Ocean Bottom Seismometer Data by the τ-p Method. Mar. Geophys. Res. 16 , 185–199 (1994). Kamei, R., Pratt, R. G. & Tsuji, T. Waveform tomography imaging of a megasplay fault system in the seismogenic Nankai subduction zone. Earth Planet. Sci. Lett. 317–318 , 343–353 (2012). Fujie, G. et al. The nature of the Pacific plate as subduction inputs to the northeastern Japan arc and its implication for subduction zone processes. Prog Earth Planet. Sci. 10 , 1–28 (2023). Tsuru, T. et al. Tectonic features of the Japan Trench convergent margin off Sanriku, northeastern Japan, revealed by multichannel seismic reflection data. J. Geophys. Res. 105 , 16403–16413 (2000). Park, J. O., Tsuru, T., Kodaira, S., Cummins, P. R. & Kaneda, Y. Splay fault branching along the Nankai subduction zone. Science . 297 , 1157–1160 (2002). Jamali Hondori, E., Guo, C., Mikada, H. & Park, J. O. Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope. Pure Appl. Geophys. 178 , 1609–1630 (2021). Jones, E. J. W. Marine Geophysics (Wiley, 1999). Zhan, Z. Distributed Acoustic Sensing Turns Fiber-Optic Cables into Sensitive Seismic Antennas. Seismol. Res. Lett. 91 , 1–15 (2020). Shinohara, M. et al. Distributed Acoustic Sensing measurement by using seafloor optical fiber cable system off Sanriku for seismic observation. OCEANS 2019 MTS/IEEE SEATTLE Preprint at (2019). https://doi.org/10.23919/oceans40490.2019.8962757 Dvorkin, J., Mavko, G. & Nur, A. Overpressure detection from compressional- and shear-wave data. Geophys. Res. Lett. 26 , 3417–3420 (1999). Akuhara, T., Tsuji, T. & Tonegawa, T. Overpressured underthrust sediment in the Nankai trough forearc inferred from transdimensional inversion of high-frequency teleseismic waveforms. Geophys. Res. Lett. 47 , (2020). Buckingham, M. J. Compressional and shear wave properties of marine sediments: comparisons between theory and data. J. Acoust. Soc. Am. 117 , 137–152 (2005). Tsuji, T. et al. VP/VS ratio and shear-wave splitting in the Nankai Trough seismogenic zone: Insights into effective stress, pore pressure, and sediment consolidation. Geophysics . 76 , WA71–WA82 (2011). Takemura, S. et al. A review of shallow slow earthquakes along the Nankai Trough. Earth Planet Space . 75 , 164 (2023). Scholz, C. H. Earthquakes and friction laws. Nature . 391 , 37–42 (1998). Ajayi, T., Gomes, J. S. & Bera, A. A review of CO2 storage in geological formations emphasizing modeling, monitoring and capacity estimation approaches. Pet. Sci. 16 , 1028–1063 (2019). Medina, C. R., Mastalerz, M. & Rupp, J. A. Characterization of porosity and pore-size distribution using multiple analytical tools: Implications for carbonate reservoir characterization in geologic storage of CO2. Environ. Geosci. 24 , 51–72 (2017). Fukushima, S. et al. Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry. Earth Planet Space . 74 , 1–11 (2022). Spica, Z. J. et al. Marine sediment characterized by ocean-bottom fiber‐optic seismology. Geophys. Res. Lett. 47 , (2020). Viens, L. et al. Understanding surface wave modal content for high-resolution imaging of submarine sediments with distributed acoustic sensing. Geophys. J. Int. 232 , 1668–1683 (2022). Shinohara, M., Yamada, T., Akuhara, T., Mochizuki, K. & Sakai, S. Performance of seismic observation by distributed acoustic sensing technology using a seafloor cable off Sanriku, Japan. Front. Mar. Sci. 9 , 844506 (2022). Cedilnik, G., Lees, G., Schmidt, P., Herstrøm, S. & Geisler, T. Ultra-long reach fiber distributed acoustic sensing for power cable monitoring. in Proceedings of the JICABLE’19 10th International Conference on Power Insulated Cables, Versailles, France (2019). Benioff, H. A linear strain seismograph. Bull. Seismol. Soc. Am. 25 , 283–309 (1935). Hunter & Matplotlib A 2D Graphics Environment. 9 , 90–95 (2007). Wessel, P. et al. The generic mapping tools version 6. Geochem. Geophys. Geosyst. 20 , 5556–5564 (2019). Additional Declarations No competing interests reported. Supplementary Files SupplementaryfileFukushimaetalScientificreports.docx Cite Share Download PDF Status: Published Journal Publication published 24 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Dec, 2024 Reviews received at journal 24 Dec, 2024 Reviews received at journal 20 Dec, 2024 Reviews received at journal 20 Dec, 2024 Reviewers agreed at journal 11 Dec, 2024 Reviewers agreed at journal 09 Dec, 2024 Reviewers agreed at journal 09 Dec, 2024 Reviewers invited by journal 29 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Editor invited by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 28 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5344756","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":383342035,"identity":"de5ec73f-7f27-402a-a959-761b0f3ac124","order_by":0,"name":"SHUN Fukushima","email":"data:image/png;base64,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","orcid":"","institution":"Tohoku University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"SHUN","middleName":"","lastName":"Fukushima","suffix":""},{"id":383342036,"identity":"81a74b60-97d8-424c-bd49-e0b76eb09642","order_by":1,"name":"Masanao Shinohara","email":"","orcid":"","institution":"University of Tokyo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Masanao","middleName":"","lastName":"Shinohara","suffix":""},{"id":383342037,"identity":"dff19e58-f6e9-43c1-a32c-88a4fa0c5f01","order_by":2,"name":"Tomoaki Yamada","email":"","orcid":"","institution":"University of Tokyo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tomoaki","middleName":"","lastName":"Yamada","suffix":""},{"id":383342038,"identity":"7fa88765-c6e2-4b6b-92fc-1e80faead92c","order_by":3,"name":"Ryota Hino","email":"","orcid":"","institution":"Graduate School of Science, Tohoku University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ryota","middleName":"","lastName":"Hino","suffix":""},{"id":383342039,"identity":"3f345c50-0e05-4350-8aa0-76fd1546fa35","order_by":4,"name":"Ryosuke Azuma","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ryosuke","middleName":"","lastName":"Azuma","suffix":""},{"id":383342040,"identity":"6265761d-29ed-4168-a210-3da41146b9d1","order_by":5,"name":"Yoshihiro Ito","email":"","orcid":"","institution":"Disaster Prevention Research Institute, Kyoto University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yoshihiro","middleName":"","lastName":"Ito","suffix":""},{"id":383342041,"identity":"5d76868f-7292-47e3-a0e6-5ac39f5322e3","order_by":6,"name":"Yusuke Yamashita","email":"","orcid":"","institution":"Kyoto Univ.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Yamashita","suffix":""},{"id":383342042,"identity":"55481cdc-1b84-464b-bfd8-650ee0014924","order_by":7,"name":"Hiroki Takano","email":"","orcid":"","institution":"INPEX CORPORATION","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hiroki","middleName":"","lastName":"Takano","suffix":""}],"badges":[],"createdAt":"2024-10-28 07:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5344756/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5344756/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-01190-0","type":"published","date":"2025-05-24T15:58:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71628736,"identity":"9dc89a4f-27c9-4740-9594-a06c0666e5c7","added_by":"auto","created_at":"2024-12-17 09:15:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":221762,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the seismic experiment described in this study. The black line represents the route of the seafloor cable installed by the Earthquake Research Institute, University of Tokyo. The blue line marks the segment where DAS data was recorded, covering a total length of 100 km. The red line indicates the seismic refraction and reflection profile from this study. The green line shows the along-profile shooting range of approximately 200 km, where 1,500 LL airguns were fired at 40-second intervals, every 100 m. Purple triangles mark the positions of pop-up type OBSs used in the analysis, while white triangles indicate OBSs not used for analysis. The purple square (SOB3) and white squares (SOB2 and SOB3) represent in-line OBSs connected to the seafloor cable system. Only the SOB3 data was used to estimate the Vp structure．\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5344756/v1/f17bb1c909e7f9d8c48304ab.png"},{"id":71628740,"identity":"15f5a2dc-0d53-4a60-a014-d9a43c5bee5b","added_by":"auto","created_at":"2024-12-17 09:15:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4011573,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of CRG records in the T-X domain and τ-p domain at distances of 26.0 km (station No. 5228, A-1–3), 40.0 km (No. 8135, B-1–3), 50.0 km (No. 10226, C-1–3), and 70.2 km (No. 14306, D-1–3). In the T-X domain, a bandpass filter from 8 Hz to 15 Hz was applied to each trace in the CRG records. Each trace was normalized by its amplitude, and all travel times were reduced by 3.5 km/s. Within a distance of 5 km, the refracted P-wave can be identified following the arrival of the direct water wave. At distances between 5 km and 15 km, the refracted P-wave is clearly seen as the first arrival. Transformation of CRG data from the T-X domain to the τ-p domain was performed separately for westward and eastward shooting from the receiver. Panels A–D-2 and A–D-3 display the τ-p domain for westward and eastward shooting, respectively. Black arrows indicate the trajectories of refracted waves in the τ-p domain.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5344756/v1/61c4764ea013686022860ec4.png"},{"id":71628738,"identity":"95db72bb-907e-4705-822f-8ae302113193","added_by":"auto","created_at":"2024-12-17 09:15:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2977658,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial changes in the trajectories of refracted P-waves for westward shooting (A) and eastward shooting (B) from a receiver in the τ-p domain. The numbers in the upper left of (A) and (B) indicate distances from the coast. Small black arrows denote the trajectories of refracted waves in the τ-p domain. (C) shows the spatial changes in intercept time for westward (left) and eastward (right) data. Blue and red circles in (C) mark intercept times at velocities of 2.0 km/s and 3.2 km/s, respectively. Spatially dense DAS measurements provide new opportunities to observe gradual and sudden changes in the shape of P-wave trajectories in the τ-p domain.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5344756/v1/8138ce0185975c0bca0025d6.png"},{"id":71628737,"identity":"a3944ab7-b92f-48b4-b4fc-da72fbb4a096","added_by":"auto","created_at":"2024-12-17 09:15:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170350,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Two-dimensional Vp structure estimated using the τ-sum inversion method. (B) Same as (A), but black lines indicate gaps in the vertical Vp gradient from each 1-D velocity structure. Vertical Vp gradients change at Vp values of 2.0 km/s and 3.5 km/s. Gray lines show the result of polynomial curve fitting to the gaps in the vertical Vp gradient from each 1-D structure, with the blue-filled region indicating a 95% confidence interval for the fitting. (C) Profile of the vertical gradient of Vp with depth. The 2-D Vp structure is divided into three units with different vertical gradients of Vp. (D) and (E) are similar to (A), but focus on the Vp structures of unit-1 and unit-2, respectively. To analyze the spatial changes in the Vp structures of unit-1 and unit-2, the Vp scale in (D) and (E) differs from that in (A).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5344756/v1/d8556fee5dbb9113cda420c0.png"},{"id":71628739,"identity":"486994a5-a86a-41a0-8336-ec2723a77b4b","added_by":"auto","created_at":"2024-12-17 09:15:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2495252,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Seismic reflection profile obtained from SCS data. (B) Same as (A), but gray, blue, green, and orange lines highlight laterally continuous reflectors. (C) Two-dimensional Vp structure in two-way travel time. (D) Two-way travel time range corresponding only to unit-1. (E) 2-D Vp structure of unit-1 obtained by τ-sum inversion. In (B–E), the blue and green lines show the interface between unit-1 and unit-2 (Line A) and unit-2 and unit-3 (Line B), respectively. The orange line indicates a laterally continuous reflector from the seafloor multiple. In regions A and B in (C) and (D), two-way travel times of the continuous reflectors increase rapidly, and the layer with a Vp slower than 1.65 km/s thickens abruptly in two-way travel times. The vertical axis represents two-way travel time from the sea surface.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5344756/v1/ed0a42a5b41529d27a428db6.png"},{"id":83460717,"identity":"bc222cd3-4a87-4f97-8d66-d798457b1cd7","added_by":"auto","created_at":"2025-05-26 16:13:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9820896,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5344756/v1/7a1d7b91-c213-45e6-a1ea-ab572ee1cef6.pdf"},{"id":71628741,"identity":"f21b1637-3b76-4d63-9f34-3200c50f74d3","added_by":"auto","created_at":"2024-12-17 09:15:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5716220,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileFukushimaetalScientificreports.docx","url":"https://assets-eu.researchsquare.com/files/rs-5344756/v1/8a7e7b78d5176dd55302e193.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced P-wave velocity imaging by marine controlled-source seismic surveys with Distributed Acoustic Sensing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eP-wave velocity (Vp) structures in marine areas are generally estimated using seismic refraction surveys with controlled seismic sources, ocean-bottom seismometers (OBS), and/or hydrophone streamers. Analytical methods for seismic refraction, such as \u003cem\u003et\u003c/em\u003e-sum inversion, are often used to estimate shallow Vp structures\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003et\u003c/em\u003e-sum inversion method estimates the one-dimensional (1-D) Vp structure beneath a receiver or source. An analysis of \u003cem\u003et\u003c/em\u003e (intercept time)\u0026ndash;\u003cem\u003ep\u003c/em\u003e (slowness) domain has advantages over that of \u003cem\u003eT\u003c/em\u003e (time)\u0026ndash;\u003cem\u003eX\u003c/em\u003e (distance) domain for shallow Vp structures because the travel time of refracted P-waves from shallow layers is generally longer than that of direct water waves from a seismic source in marine refraction surveys. Another advantage of analysis in the \u003cem\u003et-p\u003c/em\u003e domain is the high vertical resolution of the obtained shallow seismic structures\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the spatial resolution of Vp structures estimated using the seismic refraction method is limited by the spacing of the receivers or seismic sources. In seismic surveys using OBSs, the typical interval between OBSs is a few tens of kilometers. Even with a spatially dense seismic source, the resolution is determined by the receiver spacing.\u003c/p\u003e \u003cp\u003eBy contrast, the spatial horizontal resolution is higher in seismic reflection surveys because the intervals between both the source and receiver can be small. Seismic reflection surveys are effective for obtaining subsurface reflectivity images, with high sensitivity to impedance contrasts in the subsurface\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The spatial resolution of seismic reflection images has reached less than ten meters. Therefore, these high-resolution images have revealed detailed lateral heterogeneity in horizontal reflectivity across various study regions\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, accurately estimating Vp structures from seismic reflection analysis is challenging because these analyses are optimized to produce clearer reflection images\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. To estimate the lateral heterogeneity of Vp structures and fully understand the subsurface properties in marine areas, improving the spatial resolution of Vp structures in seismic refraction surveys is essential.\u003c/p\u003e \u003cp\u003eIn seismic refraction analysis, spatially dense observations have enabled improvements in the spatial resolution of Vp structure. Applying \u003cem\u003et\u003c/em\u003e-sum inversion to dense data over a large area can produce numerous 1-D Vp profiles at short intervals. When receivers and sources are aligned linearly and dense observations are performed, high-resolution two-dimensional (2-D) Vp structures can be estimated by aligning 1-D velocity structures. However, deploying a large number of OBSs is challenging due to their high costs. OBSs are typically installed at intervals of several kilometers because of these limitations. In contrast, hydrophone streamers have a much larger number of receivers, with spatial intervals typically just a few meters. However, using hydrophone streamer data for Vp structure estimation in seismic refraction surveys has several drawbacks. Estimating deep structures is difficult with hydrophone streamers because their length is limited\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This restriction prevents the collection of deep-penetrating seismic refraction waves, which are necessary for gathering velocity information from deeper regions. Moreover, the signal-to-noise ratio of seismic waves is generally lower because hydrophone streamers are towed near the sea surface, where noise levels are higher compared to the seafloor\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecently, Distributed Acoustic Sensing (DAS) technology has emerged, offering high-resolution strain or strain rate measurements (at spatial intervals of several meters) over long distances (up to tens of kilometers)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. DAS cables have a higher station density than traditional seismic observation methods using individual seismic sensors, making DAS especially useful in environments where deploying large numbers of seismometers, such as in offshore areas, is difficult. For instance, DAS data have been used for seismic observations of natural earthquakes in marine areas\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In seismic refraction surveys, DAS offers clear advantages over both OBS and hydrophone streamers. The receiver density along a DAS cable is significantly higher than the typical receiver density in marine controlled-source surveys using OBSs. As a result, the horizontal resolution of the Vp structure is expected to improve with the dense spatial data provided by DAS. Although the receiver density of DAS is similar to that of hydrophone streamers, DAS has an added advantage: its stations are fixed to the seafloor, allowing for the observation of seismic sources with long offset distances. Consequently, DAS data from seafloor cables are well-suited for seismic refraction surveys with controlled sources, enabling the acquisition of detailed Vp structures with high-resolution in both the vertical and horizontal directions.\u003c/p\u003e \u003cp\u003eTo estimate shallow Vp structures with high spatial resolution, this study conducted seismic refraction surveys using a marine-controlled seismic source and DAS measurements. Data were collected from DAS measurements connected to a seafloor cable, along with OBS data, off the coast of Sanriku, Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A common receiver gather (CRG) of the DAS data was obtained after processing. The seismic signals of refracted, reflected, and direct water waves from the controlled source were identified in the CRG records. These records were converted from the \u003cem\u003eT-X\u003c/em\u003e domain to the \u003cem\u003et-p\u003c/em\u003e domain using the slant stack\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e method, with a spatial interval of 125 m. The dense DAS measurements allowed us to observe gradual and/or sudden spatial changes in the trajectories of refracted P-waves in the \u003cem\u003et-p\u003c/em\u003e domain. This high-density data enabled us to trace spatial changes in P-wave trajectories at intervals of 125 m. The \u003cem\u003et\u003c/em\u003e-sum inversion method was applied to determine 1-D Vp structures below each observation point, which were spaced at approximately 125 m, covering distances from 20 km to 85 km from the coast. By aligning the 1-D velocity structures, a high-resolution 2-D Vp structure was constructed. The resulting 2-D Vp structure revealed three distinct units with different vertical gradients of Vp. Detailed lateral heterogeneities in Vp, as well as variations in the thickness of each unit, were also identified. The 2-D Vp structure obtained from the DAS data showed good agreement with the seismic reflection profile. In conclusion, our study demonstrated that spatially dense DAS data significantly enhanced the spatial resolution of Vp structures in seismic refraction surveys. This experiment highlights the unique advantages of DAS data over OBSs.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eThe CRG records of the DAS data in the \u003cem\u003eT-X\u003c/em\u003e domain clearly showed water, refraction, and reflection waves from the airguns at distances of 20\u0026ndash;60 km from the coast (Figs. 2-A-1, 2-B-1, and 2-C-1). In contrast, seismic signals from the airguns were not clearly observed in the CRG records of the DAS data at distances greater than 60 km (Fig. 2-D-1). In the CRG record of the DAS data at distances of 20\u0026ndash;60 km, direct water waves were recognized as the first arrivals at offset distances of up to 5 km (Fig. 2). Following the direct water waves, refraction waves with slow apparent velocities (1.5-2.5 km/s) were observed. At offset distances from 3 km to 15 km, the first arrivals of refracted waves with faster apparent velocities (3.0\u0026ndash;5.0 km/s) were identified (Fig. 2). By comparing the CRG records of the vertical component data from the pop-up OBS installed along the cable (Fig. 1), these refracted waves were interpreted as penetrating P-waves (Fig. S1). The refracted waves recorded by the OBS had the same apparent velocities as those recorded by the DAS measurements. Furthermore, the travel times in the OBS records were consistent with those in the DAS records (Fig. S1). Although the DAS measurements clearly showed refracted waves up to an offset distance of 60 km, refracted waves could not be identified in the DAS records at distances greater than 60 km (Fig. 2-D-1). The attenuation of laser light over long travel distances in DAS observations is thought to be the cause of the low signal-to-noise ratio (SNR) of seismic signals in offshore DAS data.\u003c/p\u003e\u003cp\u003eThe transformation of the CRG data from the \u003cem\u003eT-X\u003c/em\u003e domain to the \u003cem\u003et-p\u003c/em\u003e domain was performed using a slant stack (see Methods). The strain data obtained from the DAS measurements for the opposite directions of the receiver were processed separately, and the CRG data with offsets of up to 15 km were used for each transformation. Stacking techniques, including slant stacks for DAS data, effectively improve the SNR of seismic signals. The very short receiver intervals in DAS measurements, which are shorter than the wavelength of the P-wave, allow us to stack a large amount of data without spatial aliasing. For the \u003cem\u003et-p\u003c/em\u003e transformation, data were gathered from 51 adjacent DAS stations (approximately 125 m apart) and transformed this gathered DAS data from the \u003cem\u003eT-X\u003c/em\u003e domain into the \u003cem\u003et-p\u003c/em\u003e domain (hereafter referred to as \u0026ldquo;gathered \u003cem\u003et-p\u003c/em\u003e transformation\u0026rdquo;). Since the gathered \u003cem\u003et-p\u003c/em\u003e transformation enhanced the P-wave refraction arrivals, the trajectory of the refracted P-waves can be clearly seen in the \u003cem\u003eτ-p\u003c/em\u003e domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When the \u003cem\u003eτ-p\u003c/em\u003e transformation was applied to the CRG data from only one DAS station at a distance of 76.7 km from the coast, it was difficult to identify the trajectory of P-wave refraction (Figs. S2-A and C). In contrast, the trajectories of the refracted P-waves can be clearly obtained using the gathered \u003cem\u003et-p\u003c/em\u003e transformations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figs. S2-B and D). In particular, the improvement in the SNR of the seismic signal by the gathered \u003cem\u003et-p\u003c/em\u003e transformation is effective for data in offshore areas more than approximately 60 km from the coast (Fig. S2) because the decrease in SNR due to laser light attenuation was more significant in offshore areas than in nearshore areas.\u003c/p\u003e \u003cp\u003eThe shapes of the trajectories for the refracted P-wave in the \u003cem\u003et-p\u003c/em\u003e domain are related to the Vp structure beneath each receiver. As only one datum in the \u003cem\u003et-p\u003c/em\u003e domain is obtained from each CRG record at each station, gradual and/or sudden spatial changes in the shapes of trajectories of refracted P-waves within a few kilometers cannot be captured using conventional marine controlled-source seismic surveys that rely on data from sparsely deployed OBSs. In contrast, the spatially dense dataset from DAS measurements offers a new opportunity to observe spatial changes in the shapes of the trajectories for refracted P-waves in the \u003cem\u003et-p\u003c/em\u003e domain at an interval of 125 m (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A and B). For example, intercept times on trajectories at velocities of 2.0 km/s (i.e., \u003cem\u003ep\u003c/em\u003e of 0.5 s/km) and 3.2 km/s (i.e., \u003cem\u003ep\u003c/em\u003e of 0.3125 s/km) change gradually and suddenly at different distances from the coast, respectively. The intercept times at a velocity of 2.0 km/s along the trajectories at each receiver gradually increased seawards, with a horizontal gradient of approximately 0.018 s/km (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-C). In contrast, the increase in \u003cem\u003et\u003c/em\u003e at a velocity of 3.2 km/s with distance from the coast was not monotonic. At distances ranging from 25 km to 45 km, \u003cem\u003et\u003c/em\u003e gradually increased from 0.6 s to 1.6 s. However, \u003cem\u003et\u003c/em\u003e suddenly increased from 1.6 s to 2.4 s within the distance range of 45 to 55 km. Beyond 55 km, \u003cem\u003et\u003c/em\u003e gradually increased from 2.4 s to 2.6 s. These sudden spatial changes in \u003cem\u003et\u003c/em\u003e at each velocity suggest lateral heterogeneity in the Vp structures.\u003c/p\u003e \u003cp\u003eEstimation of the 1-D Vp structure using the \u003cem\u003eτ\u003c/em\u003e-sum inversion with an ultra-short horizontal interval (approximately 125 m in this study) was conducted using the spatially dense DAS data. Moreover, the vertical gradient of Vp was calculated for each 1-D profile. Two 1-D Vp structures and the vertical gradient of Vp below a single observation point were obtained through the \u003cem\u003et\u003c/em\u003e-sum inversion using the \u003cem\u003et-p\u003c/em\u003e data from the east side and the west side of a linear profile of controlled sources passing through the observation point. Based on the advantages of the spatially dense DAS data, a 2-D Vp structure was obtained by aligning 1-D profiles from both the east and west sides at an ultra-short interval of 125 m. No smoothing for the horizontal direction was applied. The horizontal position of each Vp, vertical gradient of Vp, and depth as discrete points are defined by Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in the \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section, resulting in a 2-D Vp structure obtained at distances between 25 and 85 km from the coast (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A and B). Using the 2-D profile of the Vp vertical gradient, the obtained 2-D Vp structure was divided into three units with different vertical gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B). The vertical Vp gradients changed at Vp of 2.0 km/s and 3.5 km/s. The spatial averages of Vp and its vertical gradient for the uppermost unit (unit-1) are 1.79 km/s and 0.03 s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. The unit underlying unit-1 (unit-2) has a spatial average Vp of 2.62 km/s and a vertical gradient of 0.07 s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. For unit-3, the spatial Vp and its vertical gradient are 4.27 km/s and 0.45 s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eThe advantage of \u003cem\u003et\u003c/em\u003e-sum inversion is its high resolution in the vertical direction for the obtained shallow Vp structures\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and DAS measurements enable us to conduct analyses in the \u003cem\u003et-p\u003c/em\u003e domain with an ultra-short interval of 125 m over long distances between 25 and 85 km from the coast. Due to both the advantages of \u003cem\u003et\u003c/em\u003e-sum inversion and DAS measurements, the \u003cem\u003et\u003c/em\u003e-sum inversion using DAS data with a short horizontal interval of 125 m revealed remarkable features of spatial variation in the thicknesses of unit-1 and unit-2. The thickness of unit-1 was 0.25 km at a horizontal distance of 25 km from the coast and gradually increased seaward (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B), reaching 1.2 km at a horizontal distance of 85 km from the coast. Although the thickness of unit-1 increases gradually with distance from the coast to offshore, the thickness of unit-2 varies over short horizontal distances. Unit-2 has a uniform thickness from 25 km to 48 km, measuring 0.50 km. However, the thickness of unit-2 rapidly increases from 0.5 km to 1.5 km between distances of 48 km and 50 km. The thickness of unit-2 becomes constant again at horizontal distances greater than 50 km. The \u003cem\u003et\u003c/em\u003e-sum inversion with DAS data revealed significant spatial variation in the thickness of unit-2 within a horizontal distance of 5 km.\u003c/p\u003e \u003cp\u003eThe Vp of the uppermost region of unit-1 at a distance of 20\u0026ndash;45 km ranges from 1.65 to 1.80 km/s (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-D). The Vp values in the lowermost regions of unit-1 became approximately 1.9 km/s. The depths of the seafloor were approximately 0.6 km at a distance of 25 km, gradually increasing to approximately 0.8 km at a distance of 45 km. The thicknesses of the uppermost region of unit-1 were 0.4 km and 0.6 km at horizontal distances of 25 km and 45 km, respectively. The thickness of these regions increased monotonically (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-D). In contrast, the Vp of the uppermost region of unit-1 at horizontal distances of 45\u0026ndash;85 km was slower than 1.65 km/s. Below this uppermost region, the Vp values gradually increased from 1.65 km/s to 1.9 km/s (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-D). The depths of the seafloor gradually increased from 0.8 km to 1.6 km at distances ranging from 45 km to 85 km. The thicknesses of the uppermost regions were 0.6 km, 0.4 km, and 1.0 km at distances of 45 km, 60 km, and 85 km, respectively, with thicknesses gradually varying. In unit-2, the uppermost regions at horizontal distances of 25\u0026ndash;85 km have a Vp of approximately 2.2 km/s (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-E). The Vp of the bottom layer of unit-2 became approximately 3.2 km/s. The thicknesses at horizontal distances of 25\u0026ndash;50 km ranged from 0.4 to 0.6 km. At distances of 50\u0026ndash;85 km, the thickness of unit-2 is approximately 1.4 km. The layers with a Vp of 2.6\u0026ndash;2.8 km/s at distances ranging from 55 to 85 km have thicknesses of approximately 0.8 km. In contrast, the thicknesses of the layer with Vp of 2.6\u0026ndash;2.8 km/s at distances ranging from 25 to 55 km can be recognized as smaller than 0.1 km. Due to the spatial density of the DAS data, spatial variation in the thickness of the layers can be obtained sequentially. Additionally, sudden spatial changes in the structures of unit-1 at a distance of 45 km and unit-2 at a distance of 50 km can be clearly recognized. The 2-D Vp structures, obtained with a spatial horizontal interval of 125 m through \u003cem\u003eτ\u003c/em\u003e-sum inversion with DAS data, revealed lateral heterogeneity with a horizontal resolution of a few kilometers in the Vp structure.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn estimating the Vp structure by \u003cem\u003eτ\u003c/em\u003e-sum inversion, evaluating the horizontal resolution of the 2-D Vp model is difficult because CRG data were used in the \u003cem\u003eT-X\u003c/em\u003e domain with offsets smaller than 15 km for the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e transformation. Although the 2-D velocity model was obtained by aligning the 1-D velocity models to an ultra-short interval of 125 m, the horizontal resolution was not the same as the 125 m interval. Generally, the horizontal resolution of seismic reflection images is high. However, evaluating the horizontal resolution of seismic reflection images using multichannel streamers is challenging due to spatial stacking techniques, such as common midpoint gathering\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It is well known that the horizontal resolution of high-resolution seismic reflection images, particularly when using a single-channel streamer, can reach a few tens of meters. Comparing the lateral variation of the velocity discontinuities in the Vp model with the horizontal reflectors in a seismic reflection profile obtained using a single-channel streamer is effective for evaluating the spatial resolution of the 2-D Vp model. Therefore, our 2-D Vp structure was compared with the seismic reflection profile obtained using a single-channel hydrophone streamer (see Methods). The depths to two-way travel times used in this 2-D velocity model were converted and compared with the seismic reflection profile.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-A shows the continuous lateral reflectors in the seismic reflection profile. Several continuous lateral reflectors were observed over a distance range of 25\u0026ndash;85 km (Fig.\u0026nbsp;\u0026lt;link rid=\"fig5\"\u0026gt;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u0026lt;/link\u0026gt;\u003c/span\u003e-A and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-B). The interfaces of unit-1, unit-2, and unit-3 corresponded to the interpretation of lateral continuous reflectors (lines A and B in Figs.\u0026nbsp;\u0026lt;link rid=\"fig5\"\u0026gt;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u0026lt;/link\u0026gt;\u003c/span\u003e-A and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-B). In unit-1, several lateral continuous reflectors can be seen at distances of 45\u0026ndash;80 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-C). The lateral continuous reflectors and iso-velocity curves for the two-way travel times showed good consistency. At distances of 48 km and 66 km, the two-way travel times of the lateral continuous reflectors rapidly increased (areas A and B in Figs.\u0026nbsp;\u0026lt;link rid=\"fig5\"\u0026gt;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u0026lt;/link\u0026gt;\u003c/span\u003e-C and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-D). In these regions, the layer with a Vp slower than 1.65 km/s became suddenly thicker during the two-way travel times. From the comparison between the seismic reflection image and our 2-D Vp structure, the velocity variation in unit-1 is consistent with the shape of the reflectors in the reflection profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-B). The two-dimensional Vp structure obtained by the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e method using DAS data can express lateral heterogeneities of less than a few kilometers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Consequently, it can be concluded that the spatial resolution of the 2-D Vp structure was higher than a few kilometers. It is known that the velocity profile obtained by the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e method has high spatial resolution in the vertical direction\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The two-dimensional velocity structure obtained by the method proposed in this study has high spatial resolution in both vertical and horizontal directions, comparable to the reflection profiles obtained by controlled sources and hydrophone streamers.\u003c/p\u003e \u003cp\u003eBefore DAS technology became available, the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e method for deep structures in marine areas was applied to data from single OBSs, which were usually installed at sparse intervals of several kilometers. Therefore, revealing detailed lateral variations in the Vp structures using data from conventional OBS surveys with marine-controlled sources is difficult. In contrast, DAS measurements can capture seismic data from controlled sources at large lateral intervals (tens of meters). Ultra-dense DAS measurement data enabled us to obtain a 1-D Vp structure at ultra-short intervals of tens or hundreds of meters using the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e method, revealing the lateral heterogeneities of the velocity structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Since the trajectory of P-wave arrivals in the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e domain was obtained at very short intervals of 125 m, the Vp structures from the seafloor to a depth of 5 km can be estimated every 125 m in the horizontal direction through \u003cem\u003eτ\u003c/em\u003e-sum inversion using DAS data. The obtained 2-D Vp structure exhibited three velocity units, with lateral heterogeneities in each unit revealed using spatially dense DAS data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough seismic reflection surveys using hydrophone streamers provide seismic reflection images with high spatial resolution in the horizontal direction (several tens of meters)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, obtaining a precise distribution of Vp with large spatial intervals using seismic reflection methods is challenging, especially in deep regions. The spatial sampling interval of the Vp structure from conventional seismic refraction surveys using pop-up OBSs is much smaller than that from seismic reflection surveys, although precise Vp can be estimated using seismic refraction methods. In contrast, our new approach for estimating seismic structures using DAS data enabled us to obtain precise Vp structures with high spatial sampling intervals in both vertical and horizontal directions.\u003c/p\u003e \u003cp\u003eThe ratio of P- and S-wave velocities (Vp/Vs) is essential for estimating pore fluid pressure and rock properties such as porosity\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This information on pore fluid pressure or porosity aids in understanding faults properties\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and monitoring subsurface oil or CO\u003csub\u003e2\u003c/sub\u003e reservoirs\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In recent years, several studies have applied ambient noise surface wave analysis to seafloor DAS data, estimating S-wave velocity (Vs) structures with a spatial resolution of hundreds of meters\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Our method also provides another physical property, Vp. Combining the Vp and Vs structures allow for the estimation of the Vp/Vs structure in a marine area at a spatial interval of hundreds of meters. This will be important for both scientific and engineering fields. The experiment on seismic refraction using DAS data highlights the unique advantages of DAS data over OBSs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSeismic data acquisition\u003c/p\u003e \u003cp\u003eIn 1996, the Earthquake Research Institute of the University of Tokyo installed a seafloor seismic and tsunami observation system (Sanriku cable system) that used an optical fiber cable to transmit geophysical data offshore Sanriku (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This seafloor cable system has three seismic stations (SOB1-3), with spare fibers (dark fibers) available for DAS observations. The three seismic stations were equipped with accelerometers, collecting acceleration data at a sampling frequency of 100 Hz in real time.\u003c/p\u003e \u003cp\u003eA seismic survey was conducted using controlled seismic sources: DAS and OBSs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The survey took place from November 3 to November 8, 2020. The Hakuho-maru R/V, belonging to the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), utilized four large airguns (Bolt 1500 LL) as controlled seismic sources. Each airgun had a chamber capacity of 1,500 in\u0026sup3; and was shot along a profile of approximately 200 km. The shooting interval was 40 s, corresponding to a distance of about 100 m. A short hydrophone streamer with two channels was used to acquire the reflection data, resulting in seismic reflection records of 15 s in length with a sampling frequency of 1 kHz.\u003c/p\u003e \u003cp\u003eDAS measurements using the Sanriku cable system were performed with two identical DAS interrogators made by AP Sensing GmBH\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Strain data along the cable were recorded at a temporal sampling frequency of 500 Hz. The sensing range, spatial sampling interval, and gauge length were set to 100 km, 5 m, and 40 m, respectively. In addition to the DAS and cabled seismic stations, nine free-fall pop-up OBSs were deployed along the seafloor cables (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The pop-up OBSs were installed before the shooting of the airguns and recovered after the shooting was completed. In this study, the pop-up OBSs and SOB within the range of DAS observations were used to evaluate the effectiveness of applying the seismic refraction method to DAS data. Thus, data from the four pop-up OBSs and SOB3 were utilized. The pop-up OBSs employed in this study have a three-component velocity-sensitive sensor with a natural frequency of 4.5 Hz. Although the sampling frequency of the data from the pop-up OBSs was 200 Hz, it was set to 50 Hz to unify the sampling frequencies for all datasets.\u003c/p\u003e \u003cp\u003eCRG data were created from the DAS and OBS data. The time window started 5 s before the shot time, and the duration was set to 35 s. The seafloor positions of all DAS stations and OBSs were estimated from the travel times of the direct water waves from the airguns. The DAS stations were positioned every 100 m, approximately 500 m apart. The seafloor positions of the DAS stations between the station positions determined by the airguns were estimated using linear interpolation.\u003c/p\u003e \u003cp\u003e \u003cb\u003et\u003c/b\u003e \u003cb\u003e-sum inversion\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe CRG data were transformed in the \u003cem\u003eT-X\u003c/em\u003e domain into \u003cem\u003et\u0026thinsp;\u0026minus;\u0026thinsp;p\u003c/em\u003e domain data to obtain the Vp structure of the shallow layers. The slant stacking technique\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e transforms seismic data in the \u003cem\u003eT-X\u003c/em\u003e domain into the \u003cem\u003et-p\u003c/em\u003e domain. The \u003cem\u003et\u003c/em\u003e-sum inversion using discretely sampled \u003cem\u003et-p\u003c/em\u003e data can be performed when seismic sources are positioned at the sea surface and receivers are located on the seafloor as follows\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{h}_{1}=\\:\\frac{\\frac{\\left\\{\\tau\\:\\left({p}_{2}\\right)-{\\left({c}_{0}^{-2}-{p}_{2}^{2}\\right)}^{\\frac{1}{2}}{h}_{0}\\right\\}}{2}}{{\\left({p}_{1}^{2}-{p}_{2}^{2}\\right)}^{\\frac{1}{2}}}\\:\\left(i=1\\right),{h}_{i}=\\:\\frac{\\frac{\\left\\{\\tau\\:\\left({p}_{i}\\right)-{\\left({c}_{0}^{-2}-{p}_{i}^{2}\\right)}^{\\frac{1}{2}}{h}_{0}\\right\\}}{2}-{\\sum\\:}_{k=1}^{i-1}{\\left({p}_{k}^{2}-{p}_{k+1}^{2}\\right)}^{\\frac{1}{2}}{h}_{k}}{{\\left({p}_{i}^{2}-{p}_{i+1}^{2}\\right)}^{\\frac{1}{2}}}\\:\\left(i\\ge\\:2\\right),\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{0}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{0}\\)\u003c/span\u003e\u003c/span\u003e are the thickness of the seawater layer and Vp in seawater, respectively. The value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{0}\\)\u003c/span\u003e\u003c/span\u003e was set to 1.5 km/s. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e are the thickness and slowness of the P-wave in the \u003cem\u003ei\u003c/em\u003eth layer, respectively.\u003c/p\u003e \u003cp\u003eIn this study, a bandpass filter was applied to all CRG data from the DAS and OBSs in the frequency range of 8\u0026ndash;15 Hz. The CRG data were transformed from the \u003cem\u003eT-X\u003c/em\u003e domain to the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e domain for eastward and westward shooting from the receiver, respectively. Data with offsets smaller than 15 km were used for the transformation. For a straight fiber cable laid horizontally, the DAS measurement closely approximates that of a linear strain meter\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Therefore, the horizontally axial strain (i.e., the DAS data) has low sensitivity to vertically incident P-waves\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. To increase the signal-to-noise ratio (SNR) of P-waves, data from 51 adjacent DAS stations were gathered and transformed into CRG data, making the \u003cem\u003eT-X\u003c/em\u003e domain into the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e domain.\u003c/p\u003e \u003cp\u003eThe data mapped into the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e domain clearly showed the trajectories of refracted P-waves from the shallow layers for both DAS data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and the vertical component data of the OBS (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The trajectories of the refracted waves were manually selected, and the \u003cem\u003eτ\u003c/em\u003e-sum inversion was performed. One-dimensional Vp structures were obtained at spatial intervals of approximately 125 m. Additionally, the vertical gradient of Vp was calculated for each 1-D profile. The analysis range was from 25 km to 80 km from the coast. Assuming a laterally homogeneous medium, the ray parameter measured by the slant stack technique (i.e., \u003cem\u003ep\u003c/em\u003e) reflects the velocity in the horizontal direction at the deepest point of a seismic ray. The offset distance between the receiver and the deepest point on the ray path is described as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{x}_{\\text{b}}=\\:-\\:\\frac{1}{2}\\frac{d\\tau\\:}{dp},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e are intercept time and slowness, respectively, and \u003cem\u003ex\u003c/em\u003e\u003csub\u003eb\u003c/sub\u003e is the offset distance between the receiver and the deepest point on the ray path. Velocity-depth and vertical Vp gradient-depth profiles from the \u003cem\u003eτ\u003c/em\u003e-sum inversion methods have already been obtained. From this equation and the obtained profile, the horizontal offset from the receiver at a given depth can be calculated. In other words, the velocities, vertical gradients, and depths were plotted in two dimensions. Consequently, a 2-D Vp structure and a 2-D vertical Vp gradient profile were obtained at offsets between 25 km and 80 km from the coast (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Using the 2-D profile of the Vp vertical gradient, the obtained 2-D Vp structure was divided into three units with different vertical Vp gradients. The vertical Vp gradients changed at Vp values of 2.0 km/s and 3.5 km/s. Polynomial curve fitting was applied to the interface data points of each 1-D velocity structure. The degree of the polynomial curve was determined by minimizing the Akaike Information Criterion. The degrees for the interfaces between unit-1 and unit-2, and unit-2 and unit-3 were 7 and 9, respectively.\u003c/p\u003e \u003cp\u003eSeismic reflection image\u003c/p\u003e \u003cp\u003eThe seismic reflection profile was obtained using single-channel seismic (SCS) data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-A). Additionally, the prediction error filtering method with a maximum lag time of 0.1 s to remove repeated signals and a bandpass filter between 20 Hz and 40 Hz to the seismic data, were applied. Automatic gain control with a time-gate length of 1.5 s was also utilized.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eThe codes used in this study are available upon request from the authors.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSF played a leading role in this study, including data processing, analysis, and completion of the manuscript. MS contributed to the development of the analytical method and interpretation of the results. HT contributed to the estimation of the seafloor positions at all DAS stations. MS, TY, RH, RA, YI, and YY led the temporal DAS observations and interpreted the results. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Drs. M. Masuda, S. Tanaka, Messrs. T. Hashimoto, K. Miyakawa, and T. Yagi of the Earthquake Research Institute, University of Tokyo, for their technical support with the DAS observations. We thank the captains, ship crew, and onboard technicians of the R/V Hakuho-maru for their dedicated efforts in data acquisition during the KH20-11 cruises. Comments by Prof. N. Hirata, Prof. K. Mochizuki and Dr. Akiko Takeo inspired our research. This study was supported by the Ministry of Education, Culture, Sports, Science, and Technology of Japan under the Earthquake and Volcano Hazards Observation and Research Program (Earthquake and Volcano Hazard Reduction Research). A part of this study was funded by the Earthquake Research Institute at the University of Tokyo. This study was supported by JSPS KAKENHI (grant number 24K22892). Some figures were created using Matplotlib and Generic Mapping Tools.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe DAS observations were conducted as part of the Earthquake and Volcano Hazards Observation and Research Program (Earthquake and Volcano Hazard Reduction Research) by the Ministry of Education, Culture, Sports, Science and Technology of Japan. The raw data supporting the conclusions of this study are available from the corresponding authors upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStoffa, P. L., Buhl, P., Diebold, J. B. \u0026amp; Wenzel, F. Direct mapping of seismic data to the domain of intercept time and ray parameter; a plane-wave decomposition. \u003cem\u003eGeophysics\u003c/em\u003e. \u003cb\u003e46\u003c/b\u003e, 255\u0026ndash;267 (1981).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShinohara, M., Hirata, N. \u0026amp; Takahashi, N. High Resolution Velocity Analysis of Ocean Bottom Seismometer Data by the τ-p Method. \u003cem\u003eMar. Geophys. Res.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 185\u0026ndash;199 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamei, R., Pratt, R. G. \u0026amp; Tsuji, T. Waveform tomography imaging of a megasplay fault system in the seismogenic Nankai subduction zone. \u003cem\u003eEarth Planet. Sci. Lett.\u003c/em\u003e \u003cb\u003e317\u0026ndash;318\u003c/b\u003e, 343\u0026ndash;353 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujie, G. et al. The nature of the Pacific plate as subduction inputs to the northeastern Japan arc and its implication for subduction zone processes. \u003cem\u003eProg Earth Planet. Sci.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1\u0026ndash;28 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsuru, T. et al. Tectonic features of the Japan Trench convergent margin off Sanriku, northeastern Japan, revealed by multichannel seismic reflection data. \u003cem\u003eJ. Geophys. Res.\u003c/em\u003e \u003cb\u003e105\u003c/b\u003e, 16403\u0026ndash;16413 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, J. O., Tsuru, T., Kodaira, S., Cummins, P. R. \u0026amp; Kaneda, Y. Splay fault branching along the Nankai subduction zone. \u003cem\u003eScience\u003c/em\u003e. \u003cb\u003e297\u003c/b\u003e, 1157\u0026ndash;1160 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamali Hondori, E., Guo, C., Mikada, H. \u0026amp; Park, J. O. Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope. \u003cem\u003ePure Appl. Geophys.\u003c/em\u003e \u003cb\u003e178\u003c/b\u003e, 1609\u0026ndash;1630 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones, E. J. W. \u003cem\u003eMarine Geophysics\u003c/em\u003e (Wiley, 1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhan, Z. Distributed Acoustic Sensing Turns Fiber-Optic Cables into Sensitive Seismic Antennas. \u003cem\u003eSeismol. Res. Lett.\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 1\u0026ndash;15 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShinohara, M. et al. Distributed Acoustic Sensing measurement by using seafloor optical fiber cable system off Sanriku for seismic observation. \u003cem\u003eOCEANS 2019 MTS/IEEE SEATTLE\u003c/em\u003e Preprint at (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.23919/oceans40490.2019.8962757\u003c/span\u003e\u003cspan address=\"10.23919/oceans40490.2019.8962757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDvorkin, J., Mavko, G. \u0026amp; Nur, A. Overpressure detection from compressional- and shear-wave data. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 3417\u0026ndash;3420 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkuhara, T., Tsuji, T. \u0026amp; Tonegawa, T. Overpressured underthrust sediment in the Nankai trough forearc inferred from transdimensional inversion of high-frequency teleseismic waveforms. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuckingham, M. J. Compressional and shear wave properties of marine sediments: comparisons between theory and data. \u003cem\u003eJ. Acoust. Soc. Am.\u003c/em\u003e \u003cb\u003e117\u003c/b\u003e, 137\u0026ndash;152 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsuji, T. et al. VP/VS ratio and shear-wave splitting in the Nankai Trough seismogenic zone: Insights into effective stress, pore pressure, and sediment consolidation. \u003cem\u003eGeophysics\u003c/em\u003e. \u003cb\u003e76\u003c/b\u003e, WA71\u0026ndash;WA82 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakemura, S. et al. A review of shallow slow earthquakes along the Nankai Trough. \u003cem\u003eEarth Planet Space\u003c/em\u003e. \u003cb\u003e75\u003c/b\u003e, 164 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScholz, C. H. Earthquakes and friction laws. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e391\u003c/b\u003e, 37\u0026ndash;42 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjayi, T., Gomes, J. S. \u0026amp; Bera, A. A review of CO2 storage in geological formations emphasizing modeling, monitoring and capacity estimation approaches. \u003cem\u003ePet. Sci.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1028\u0026ndash;1063 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedina, C. R., Mastalerz, M. \u0026amp; Rupp, J. A. Characterization of porosity and pore-size distribution using multiple analytical tools: Implications for carbonate reservoir characterization in geologic storage of CO2. \u003cem\u003eEnviron. Geosci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 51\u0026ndash;72 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukushima, S. et al. Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry. \u003cem\u003eEarth Planet Space\u003c/em\u003e. \u003cb\u003e74\u003c/b\u003e, 1\u0026ndash;11 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpica, Z. J. et al. Marine sediment characterized by ocean-bottom fiber‐optic seismology. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViens, L. et al. Understanding surface wave modal content for high-resolution imaging of submarine sediments with distributed acoustic sensing. \u003cem\u003eGeophys. J. Int.\u003c/em\u003e \u003cb\u003e232\u003c/b\u003e, 1668\u0026ndash;1683 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShinohara, M., Yamada, T., Akuhara, T., Mochizuki, K. \u0026amp; Sakai, S. Performance of seismic observation by distributed acoustic sensing technology using a seafloor cable off Sanriku, Japan. \u003cem\u003eFront. Mar. Sci.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 844506 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCedilnik, G., Lees, G., Schmidt, P., Herstr\u0026oslash;m, S. \u0026amp; Geisler, T. Ultra-long reach fiber distributed acoustic sensing for power cable monitoring. in \u003cem\u003eProceedings of the JICABLE\u0026rsquo;19 10th International Conference on Power Insulated Cables, Versailles, France\u003c/em\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenioff, H. A linear strain seismograph. \u003cem\u003eBull. Seismol. Soc. Am.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 283\u0026ndash;309 (1935).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter \u0026amp; Matplotlib A 2D Graphics Environment. \u003cb\u003e9\u003c/b\u003e, 90\u0026ndash;95 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWessel, P. et al. The generic mapping tools version 6. \u003cem\u003eGeochem. Geophys. Geosyst.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 5556\u0026ndash;5564 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5344756/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5344756/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eP-wave velocity (Vp) structures in marine areas have been estimated using marine controlled-source seismic surveys and refraction methods. The spatial resolution of Vp structures depends on the number of receivers and shots. In marine environments, Vp resolution is often limited due to the sparse distribution of ocean bottom seismometers (OBSs). Recently, the use of distributed acoustic sensing (DAS) has enabled high-resolution strain measurements over long distances, with station density along DAS cables being much higher than that of OBSs. Applying seismic control surveys and refraction methods to DAS data can enhance the horizontal resolution of Vp structures. This study demonstrates that seismic refraction using DAS data is effective for imaging shallow Vp structures with high spatial resolution. A seismic survey was conducted using R/V Hakuho-maru, controlled sources, and DAS and OBS measurements along a seafloor optical fiber cable off Sanriku, Japan. Strong lateral heterogeneities in the Vp structure were observed, due to dense DAS data. The 2-D Vp structure obtained from the DAS data showed good agreement with the seismic reflection profile. This experiment highlights the unique advantages of DAS data over OBSs for seismic refraction.\u003c/p\u003e","manuscriptTitle":"Enhanced P-wave velocity imaging by marine controlled-source seismic surveys with Distributed Acoustic Sensing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 09:15:29","doi":"10.21203/rs.3.rs-5344756/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-25T05:47:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-24T08:57:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-21T01:33:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-20T14:44:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219659852836251004184203488360127046057","date":"2024-12-11T13:39:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185217073852224611247789880807375112697","date":"2024-12-10T02:03:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114437516869268063757023288422358833991","date":"2024-12-09T19:19:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-29T21:03:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T05:03:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-13T04:44:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-12T06:16:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-28T07:01:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3aef0b26-f08e-46ed-a2a4-d09abb626355","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40831946,"name":"Earth and environmental sciences/Solid earth sciences/Seismology"},{"id":40831947,"name":"Earth and environmental sciences/Solid earth sciences/Geophysics"}],"tags":[],"updatedAt":"2025-05-26T16:10:55+00:00","versionOfRecord":{"articleIdentity":"rs-5344756","link":"https://doi.org/10.1038/s41598-025-01190-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-24 15:58:29","publishedOnDateReadable":"May 24th, 2025"},"versionCreatedAt":"2024-12-17 09:15:29","video":"","vorDoi":"10.1038/s41598-025-01190-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-01190-0","workflowStages":[]},"version":"v1","identity":"rs-5344756","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5344756","identity":"rs-5344756","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}