First-arrival Times Research Articles

Estimating common whelk (Buccinum undatum) abundance using baited remote underwater video (BRUV)

Abstract Baited static gear fisheries targeting benthic invertebrates have expanded at a global scale. While improvements have been made in the monitoring and management of these fisheries, reliable survey methods for stock assessments of many key species are lacking. In this study we examine the viability of a baited remote underwater video (BRUV) system for obtaining abundance indices for a data-limited stock (common whelk, Buccinum undatum), and compare this method to catch per unit effort (CPUE) from commercial fishing. BRUVs capable of collecting 15 hours of timelapse footage were deployed on pot strings in two distinct whelk fishing sites, with replication over a spring-neap tidal cycle. Three potential BRUV metrics—the maximum (MaxN) and mean (MeanN) abundance and time of first arrival (T1)—were calculated for each deployment, and significant linear relationships were identified between MaxN, MeanN, and CPUE across sites. Temporal variability in BRUV indices driven by tidal dynamics was minimal, although results suggest high current speeds may reduce abundance estimates, and a potential method for predicting density on the seabed using arrival rates is also demonstrated. BRUVs are a valuable tool in developing stock assessment surveys for static gear fisheries.

First-passage and first-arrival problems in continuous-time random walks: Beyond the diffusion approximation.

Some exact solutions of the first-passage and first-arrival problems for the continuous-time random-walk model are obtained. On the basis of these exact solutions, the following has been revealed. First, for some jump-length distributions with a finite variance, the approximate solutions obtained in the diffusion approximation can differ significantly from the exact solutions. Second, for some waiting time distributions with a finite mean, the times of first passage and the times of first arrival can significantly depend on the ensemble under consideration. In particular, the mean first-passage time corresponding to the stationary ensemble can be significantly greater than the mean first-passage time corresponding to the nonaged ensemble. Third, for any continuous distribution of jump lengths, the probability of first arrival is zero for a point-like target. This last result is contrary to existing opinion, but it is consistent with the fact that a single point has a probability measure equal to zero in the probability space defined by a continuous distribution of jump lengths.

Effect of stochastic resettings on the counting of level crossings for inertial random processes.

We study the counting of level crossings for inertial random processes exposed to stochastic resetting events. We develop the general approach of stochastic resetting for inertial processes with sudden changes in the state characterized by position and velocity. We obtain the level-crossing intensity in terms of that of underlying reset-free process for resetting events with Poissonian statistics. We apply this result to the random acceleration process and the inertial Brownian motion. In both cases, we show that there is an optimal resetting rate that maximizes the crossing intensity, and we obtain the asymptotic behavior of the crossing intensity for large and small resetting rates. Finally, we discuss the stationary distribution and the mean first-arrival time in the presence of resettings.

Accurate Trace-Cut and Phase Alignment of Active Ocean-Bottom Seismometer Data

Abstract Accurate positions of ocean-bottom seismometers (OBSs) on the seafloor are critical parameters and can only be obtained by inversion modeling of first-arrival travel times of overhead cross-line airgun shootings. With an increased sampling interval of ≤20 ms for long-term earthquake studies, apparent artifacts affect the phase alignment of first arrivals on the seismic sections of trace-cut airgun shots. Our analysis shows that these apparent misalignments are caused by timing inconsistencies and inaccuracies during the trace-cut, which are so-called rounding errors. To eliminate these rounding errors, a simple interpolation is used to resample traces. Further analysis shows the simple interpolation satisfactorily retains the original waveform. The improved timing accuracy significantly reduces the uncertainty of seafloor locations as shown by Hadal OBS data.

Seismic Characterization of Subsurface Structures at Rock Valley, Nevada

ABSTRACT The Source Physics Experiment (SPE) aims at improving explosion monitoring techniques by investigating source characteristics of chemical explosions in geologic formations. One of the critical tasks in Rock Valley Direct Comparison (RV/DC), SPE phase III, is to prepare for the main experiment by characterizing the subsurface structures at the test site. Based on the seismic data acquired during an accelerated-weight-drop (AWD) seismic survey at Rock Valley, we first pick the P-wave first-arrival travel times and derive a P-wave velocity model using the adjoint-state first-arrival travel-time tomography. We then apply reverse-time migration to the processed seismic data and obtain high-resolution images of the subsurface structures along the two main survey lines. Our migration results show several reflectors corresponding to major geologic formation boundaries. We employ a multitask machine learning model to enhance the reverse-time migration images and identify faults from these images. We find that our automatically picked faults correlate well with the locations of known faults in the region in addition to many geologically undetected faults. Our subsurface characterization results refine our understanding of the geology in this region and provide valuable velocity and structural information for RV/DC geologic model building and fault identification.

Vegetation changes through stadial and interstadial stages of MIS 4 and MIS 3 based on a palynological analysis of the Girraween Lagoon sediments of Darwin, Australia

A palynological record from Girraween Lagoon sediments (Darwin region of the Northern Territory, Australia) provides detailed long-term insight into tropical savanna vegetation community patterns, climatic and fire relationships, through Marine Isotope Stage 4 (MIS 4: 71–57 thousand years ago, ka) and Marine Isotope Stage 3 (MIS 3: 57–29 ka). Owing to a lack of data in reconstructing northern Australian environments, this paper looks to define and describe to a greater degree the nature and scope of these stadial and interstadial stages for the region. Girraween Lagoon simultaneously provides proximal palaeoecological data for the time and region of Aboriginal people's first arrival into Australia, also encompassing the late Pleistocene continental decline of megafauna. This study provides a dataset enabling full exploration of long-term people-landscape and faunal-floral interactions. Sea levels and associated variations imposed on the transportation of moisture and heat, held implications for MIS 4 and MIS 3 monsoon strength, which was particularly consequential for Girraween regional ecology. Results reveal a prolonged transition from wooded- to grassy-savanna, into a cool drier semi-arid savanna. Increasingly episodic delivery of moisture influenced the permanency of freshwater in the landscape.

Description of the Cattle and Small Ruminants Trade Network in Senegal and Implication for the Surveillance of Animal Diseases

Livestock mobility, particularly that of small and large ruminants, is one of the main pillars of production and trade in West Africa: livestock is moved around in search of better grazing or sold in markets for domestic consumption and for festival-related activities. These movements cover several thousand kilometers and have the capability of connecting the whole West African region, thus facilitating the diffusion of many animal and zoonotic diseases. Several factors shape mobility patterns even in normal years and surveillance systems need to account for such changes. In this paper, we present an approach based on temporal network theory to identify possible sentinel locations, i.e., locations where pathogens circulation can be detected in the early phase of the epidemic (before the peak), using two indicators: vulnerability (i.e., the probability of being reached by the disease) and time of infection (i.e., the time of first arrival of the disease). Using these indicators in our structural analysis of the changing network enabled us to identify a set of nodes that could be used in an early warning system. As a case study, we simulated the introduction of transboundary animal diseases in Senegal and used data taken from 2020 Sanitary certificates (laissez-passer sanitaire (LPS)) issued by the Senegalese Veterinary Services to reconstruct the national mobility network. Our analysis showed that a static approach can significantly overestimate the speed and the extent of disease propagation, whereas temporal analysis revealed that the reachability and vulnerability of the different administrative departments (used as nodes of the mobility network) change over the course of the year. For this reason, several sets of sentinel nodes were identified in different periods of the year, underlining the role of temporality in shaping patterns of disease diffusion.

Application of Yu’s wavelet and supervirtual seismic refraction interferometry to enhance first arrivals: A case study from Kuche Mountain

The signal-to-noise ratio (S/N) of the 3D seismic data in Kuche Mountain is poor, where the first-arrival events are overlapped with random and coherent noises due to the data set collected within a challenging environment with abrupt changes in the topographical elevation. Therefore, it is difficult to carry out first-arrival picking, which in turn affects the application of tomographic inversion and static correction in this area. To face this challenge, first-arrival filtering using Yu’s wavelet and first-arrival enhancement with supervirtual seismic refraction interferometry are applied in 3D seismic data processing on the data set collected from the Kuche Mountain area. The result finds that, by using these two techniques together, the first-arrival events can be recognized easily, the first-arrival times can be picked accurately, and the amount of picked events increases greatly.

Solving Geophysical Inversion Problems with Intractable Likelihoods: Linearized Gaussian Approximations Versus the Correlated Pseudo-marginal Method

A geophysical Bayesian inversion problem may target the posterior distribution of geological or hydrogeological parameters given geophysical data. To account for the scatter in the petrophysical relationship linking the target parameters to the geophysical properties, this study treats the intermediate geophysical properties as latent (unobservable) variables. To perform inversion in such a latent variable model, the intractable likelihood function of the (hydro)geological parameters given the geophysical data needs to be estimated. This can be achieved by approximation with a Gaussian probability density function based on local linearization of the geophysical forward operator, thereby, accounting for the noise in the petrophysical relationship by a corresponding addition to the data covariance matrix. The new approximate method is compared against the general correlated pseudo-marginal method, which estimates the likelihood by Monte Carlo averaging over samples of the latent variable. First, the performances of the two methods are tested on a synthetic test example, in which a multivariate Gaussian porosity field is inferred using crosshole ground-penetrating radar first-arrival travel times. For this example with rather small petrophysical uncertainty, the two methods provide near-identical estimates, while an inversion that ignores petrophysical uncertainty leads to biased estimates. The results of a sensitivity analysis are then used to suggest that the linearized Gaussian approach, while attractive due to its relative computational speed, suffers from a decreasing accuracy with increasing scatter in the petrophysical relationship. The computationally more expensive correlated pseudo-marginal method performs very well even for settings with high petrophysical uncertainty.

Correlation between RF signal first-arrival and minimum spectral transit time in cancellous bone

Measurement of the singular ultrasound velocity of a homogeneous sample may be calculated by division of its thickness and propagation transit time, generally utilizing a detection criterion of an RF signal’s first-arrival. A porous composite sample composed of two materials of differing ultrasound velocity will however exhibit a range of transit times. Ultrasound transit time spectroscopy (UTTS) considers propagation of an array of sonic-rays, with minima and maxima values ranging from entire higher and lower velocity materials, respectively. The aim of this Technical Note is to test the hypothesis that conventional RF signal transit time measurement correlates with the minimum spectral transit time, utilizing previously obtained experimental data from human cancellous bone samples. Linear regression yielded a coefficient of determination (R2) of 94%. The authors consider a practical consequence of the hypothesis is that information on the variability of transit time within a heterogeneous test sample may be better assessed by UTTS than conventional RF first-arrival transit time measurement. Noting the fundamental relationships between transit time, velocity, and elasticity of a test sample, there is further potential for UTTS to describe both velocity and elasticity spectroscopies within a wide range of structurally complex composites.

Incorporating geotechnical and geophysical investigations for underground obstruction detection: A case study

Determining the location and boundary of underground obstructions and/or anomalies is a common problem and often a great challenge for tunneling and underground construction. In this study, geotechnical investigations (penetration tests and borehole drilling/sampling) and geophysical investigations (surface wave method and cross-hole seismic method) were conducted with the aim of identifying the location and boundary of rock obstructions in Changi East, Singapore. The surface wave method is frequently used in the sites with lateral homogeneity in previous studies, but its application in the sites with rock obstructions is rare. The experimental results of this study indicate that the surface wave method is also able to determine the upper surface of rock obstructions, but difficult to identify the lateral and bottom boundaries of rock obstructions. To improve the precision of detection, the full waveform inversion (FWI) method was used to process the data from the cross-hole seismic survey. The results indicate that the inversion precision of P-wave is higher than that of S-wave. The horizontal and vertical ranges of rock obstructions in the P-wave inversion results are 14–26 m and 7.5–11.0 m respectively, roughly consistent with the results of penetration tests (about 15–25 m) and borehole log (8.85–10.80 m). This result proves that the sequential application of first-arrival time analysis and FWI can effectively delineate the boundary of rock obstructions. Finally, the results of various detection methods were analyzed and compared in this study. Considering the advantages of various methods, we propose a cost-effective and high-precision workflow containing both geotechnical and geophysical investigations.

MIAKF: Motion Inertia Estimated Adaptive Kalman Filtering for Underground Mine Tunnel Positioning

Coal mine location-based services are vital fundamental facilities for achieving intelligence. Ultra-wide band (UWB) is widely used for underground mine positioning because of its powerful anti-interference capability. However, UWB suffers from range errors caused by first-arrival time (FAT) delay, non-line of sight (NLOS) as well as trilateral positioning deformation in narrow and long tunnels. Inertial Measurement Unit (IMU) sensor can effective solve the interference issue by tracing moving targets without external data, while it also needs precise initial alignment and may cause accumulative error. To efficiently increase positioning accuracy in underground tunnels, Motion Inertia Estimated Adaptive Kalman Filtering (MIAKF) is proposed combining the advantages of IMU and UWB. Firstly, heading memory is designed to store target motion historical state and estimate its forth coming state. Secondly, an dynamically updated inertial confidence parameter pair is introduced as the error weights of two sensors to measure the inertia motion deviation. Then, by converting the vector differences into angles and parameterizing these angles, the bias of each sensor can be measured. Finally, Kalman filtering error matrix is automatically adjusted by parameterized angles to obtain accurate position. Extensive experiments in factory and real scenarios show MIAKF can achieve 19% higher accuracy than benchmark methods.

Concrete Pile Defect Identification: Insights from Cross-Hole Sonic Logging and High Strain Dynamic Pile Test

This paper aims to identify the types of defects, location of defects, size of defects, and capacity of five concrete piles using a High strain dynamic pile test and Cross-hole sonic logging test. In this study, Cross-Hole Sonic Logging method (CSL) is a potent quality-control tool that exclusively detects much smaller flaws with high accuracy with utilizing velocity and first arrival time (FAT) from ultrasonic waves to find out the integrity of the drill shaft. From CSL test, the bearing capacity needs to be calculated manually using a waterfall diagram and soil profile. Moreover, the high strain dynamic pile test is used to identify integrity and bearing capacity of concrete pile via resistance analysis down to the pile and split of the base to shaft resistance when only pile head measurements are taken then using Case Method and CAPWAP analysis. Through this analysis, the bearing capacity can be interpreted; the Belta (damage factor) is applied to estimate location and size of the defect in the concrete pile. Based on the result of CLS test for three concrete piles, P01, P04, and P10, indicated that P01 and P04 are “Good” pile integrity with the bearing capacity of 4583kN and 6600kN, while P10 showed as “Potential defect at depth 9.25m to 10.25m” of pile integrity with bearing capacity of 6600kN. Furthermore, High strain dynamic pile test of two concrete piles, P2E and P2P, showed that the integrity category is “Ok” with bearing capacity 9896kN.

Velocity Structure and Cu-Au Mineralization of the Duobaoshan Ore District, NE China: Constrained by First-Arrival Seismic Tomography

The genesis of deeply buried deposits in the Duobaoshan ore district, the largest porphyry-related Cu-Mo-Au ore field in northeastern China, is not well understood and their exploration is lacking because the fine velocity structure of this region is not comprehensively understood. Herein, first-arrival seismic travel times were picked along a deep seismic reflection profile and inverted using the tomographic method to obtain a detailed velocity profile of the upper 2900 m of the crust beneath this region. The profile showed that the velocity varied from 1900 to 6100 m/s and that the crust was subdivided into five parts by two low-velocity (2500–4000 m/s) blocks. Based on previous studies, the boundaries between the high-speed and low-speed bodies were interpreted as hidden fractures, and the 5000–6100 m/s parts were interpreted as concealed granite bodies in these sections. Porphyry copper deposits in the Duobaoshan ore district were related to the occulted granite bodies, and epithermal Au deposits were associated with the occulted fracture zones. Comprehensive evaluation of hydrothermal activity, regional magnetic anomalies, and deposit distribution indicated that the hidden fractures served as channels for ore-related magmas. Combining previous research on the Duobaoshan ore district with our results of the high-velocity interface, we infer that the prospecting range of the Tongshan deposit is below the depth of 1000 m.

Asymmetric Lévy Flights Are More Efficient in Random Search

We study the first-arrival (first-hitting) dynamics and efficiency of a one-dimensional random search model performing asymmetric Lévy flights by leveraging the Fokker–Planck equation with a δ-sink and an asymmetric space-fractional derivative operator with stable index α and asymmetry (skewness) parameter β. We find exact analytical results for the probability density of first-arrival times and the search efficiency, and we analyse their behaviour within the limits of short and long times. We find that when the starting point of the searcher is to the right of the target, random search by Brownian motion is more efficient than Lévy flights with β≤0 (with a rightward bias) for short initial distances, while for β>0 (with a leftward bias) Lévy flights with α→1 are more efficient. When increasing the initial distance of the searcher to the target, Lévy flight search (except for α=1 with β=0) is more efficient than the Brownian search. Moreover, the asymmetry in jumps leads to essentially higher efficiency of the Lévy search compared to symmetric Lévy flights at both short and long distances, and the effect is more pronounced for stable indices α close to unity.

Geophysical Imaging of the Critical Zone along the Eastern Betic Shear Zone (EBSZ), SE Iberian Peninsula

The critical zone (CZ) represents the most-shallow subsurface, where the bio-, hydro-, and geospheres interact with anthropogenic activity. To characterize the thickness and lateral variations of the CZ, here we focus on the Eastern Betic Shear Zone (EBSZ), one of the most tectonically active regions in the Iberian Peninsula. Within the EBSZ, the Guadalentín Depression is a highly populated area with intensive agricultural activity, where the characterization of the CZ would provide valuable assets for land use management and seismic hazard assessments. To achieve this, we have conducted an interdisciplinary geophysical study along the eastern border of the Guadalentín Depression to characterize the CZ and the architecture of the shallow subsurface. The datasets used include Electrical Resistivity Tomography (ERT), first-arrival travel time seismic tomography, and multichannel analysis of surface waves (MASW). The geophysical datasets combined help to constrain the high-resolution structure of the subsurface and image active fault systems along four transects. The resulting geophysical models have allowed us to interpret the first ~150 m of the subsurface and has revealed: (i) the variable thickness of the CZ; (ii) the CZ relationship between the fault zone and topographic slope; and (iii) the differences in CZ thickness associated with the geological units. Our results provide a method for studying the shallow subsurface of active faults, complementing previous geological models based on paleo-seismological trenches, and can be used to improve the CZ assessment of tectonically active regions.

Analysis of the shallow crustal structure of the Bam Fault Zone, Iran, using the surface wave and first-arrival P-wave tomography approaches

The crustal structure around the Bam fault located southeast of Iran was investigated by applying surface and P-wave tomography to locally recorded aftershocks of the destructive Bam earthquake (Mw 6.6) on December 26, 2003. The aftershock dataset was recorded for 1 month after the main shock by 23 temporary seismic stations operated by the International Institute of Earthquake Engineering and Seismology (IIEES). After applying the selection criteria, 331 well-located events, including more than 1700 waveforms, remained. By calculating the surface wave dispersion measurements in the period range of 0.6–5 s, a tomographic procedure was then performed, and the local dispersion curves were extracted for each geographic grid point (1.8 km × 1.8 km). We applied an iterative damped least-squares inversion procedure to obtain the VSV and VSH models at depths of up to 6.5 km. The discrepancy between these VSV and VSH models also resulted in the radial anisotropy model. Moreover, the P-wave first-arrival times were inverted by the computer program SIMULPS to calculate the VP model for a depth range of 6.5–14 km, while the calculated VP model does not have sufficient resolution at depths of up to 6 km. The tomographic results indicate that a ~ 2 km thick upper sedimentary layer with a shear wave velocity of less than 2.4 km/s and relatively negative radial anisotropy covers the study area (except Bam City, with a thin 500 m positive radial anisotropy layer), which overlay on relatively positive radial anisotropy. Our results indicate that the main shock occurred on the Bam fault escarpment; then the rupturing was shifted to the co-seismic surface rupture (approximately 5 km west of the escarpment) at a depth of ~8–10 km. This dividing of energy on the fault plane (an oblique Y shape) leads to damping most of the energy as a deformation in the lower sedimentary layer.

Method of Automatically Detecting the Abnormal First Arrivals Using Delay Time (December 2020)

Abnormal first-arrival times in the automatic first-arrival picking significantly affect the structural inversion and static correction. After implementing an automatic picking, the manual intervention is still required to correct or eliminate false first-arrival times, which seriously affects the efficiency of the first-arrival picking. Therefore, it is necessary to develop automatic removal methods for abnormal first-arrival times. Here, we propose a distance-based outlier detection algorithm to automatically and effectively eliminate the abnormal first-arrival times. In order to identify the abnormal first-arrival times, each first-arrival time is decomposed into a receiver delay time and a shot delay time. Then, according to offset and azimuth information, we eliminate the influences of offset and shot delay times on the first-arrival times in a single shot to get the receiver delay times. We further rearrange the delay times in polar coordinates. Finally, we apply a distance-based outlier detection algorithm to the delay times on different azimuths and delete the first-arrival times that correspond to the detected outliers. Our study indicates that the distance-based outlier detection algorithm can effectively eliminate the abnormal first-arrival times. The results from real data processing demonstrate that the proposed automatic removal approach of the abnormal first-arrival times can effectively improve the quality of the first-arrival times. In addition, we find that our proposed algorithm has a better performance than a least-squares regression method for low signal-to-noise ratio (SNR) seismic data.

Machine learning-enabled traveltime inversion based on the horizontal source-location perturbation

Gradient-based traveltime tomography, which aims to minimize the difference between modeled and observed first-arrival times, is a highly nonlinear optimization problem. Stabilization of this inverse problem often requires using regularization. Although regularization helps avoid local minima solutions, it might cause low-resolution tomograms because of its inherent smoothing property. However, although conventional ray-based tomography can be robust in terms of the uniqueness of the solution, it suffers from the limitations inherent in ray tracing, which limits its use in complex media. To mitigate the aforementioned drawbacks of gradient and ray-based tomography, we have approached the problem in a novel way leveraging data-driven inversion techniques based on training deep convolutional neural networks (DCNN). Because DCNN often face challenges in detecting high-level features from the relatively smooth traveltime data, we use this type of network to map horizontal changes in observed first-arrival traveltimes caused by a source shift to lateral velocity variations. The relationship between them is explained by a linearized eikonal equation. Construction of the velocity models from this predicted lateral variation requires information from, for example, a vertical well log in the area. This vertical profile is then used to build a tomogram from the output of the network. The synthetic and field data results verify that the suggested approach reliably estimates the velocity models. Because of the limited depth penetration of first-arrival traveltimes, the method is particularly favorable for near-surface applications.

Stochastic resetting and first arrival subjected to Gaussian noise and Poisson white noise.

We study the dynamics of an overdamped Brownian particle subjected to Poissonian stochastic resetting in a nonthermal bath, characterized by a Poisson white noise and a Gaussian noise. Applying the renewal theory we find an exact analytical expression for the spatial distribution at the steady state. Unlike the single exponential distribution as observed in the case of a purely thermal bath, the distribution is double exponential. Relaxation of the transient spatial distributions to the stationary one, for the limiting cases of Poissonian rate, is investigated carefully. In addition, we study the first-arrival properties of the system in the presence of a delta-function sink with strength κ, where κ=0 and κ=∞ correspond to fully nonreactive and fully reactive sinks, respectively. We explore the effect of two competitive mechanisms: the diffusive spread in the presence of two noises and the increase in probability density around the initial position due to stochastic resetting. We show that there exists an optimal resetting rate, which minimizes the mean first-arrival time (MFAT) to the sink for a given value of the sink strength. We also explore the effect of the strength of the Poissonian noise on MFAT, in addition to sink strength. Our formalism generalizes the diffusion-limited reaction under resetting in a nonequilibrium bath and provides an efficient search strategy for a reactant to find a target site, relevant in a range of biophysical processes.