Seismic Waveform Data Research Articles

Multi-Data Integration System to Capture Detailed Strong Ground Motion in the Tokyo Metropolitan Area

To accurately capture ground motion in the Tokyo metropolitan area, we have developed a multi-data integration system that combines a large amount of ground motion data gathered from nationwide strong-motion seismograph networks (K-NET and KiK-net); Metropolitan Seismic Observation network (MeSO-net), which covers the Tokyo metropolitan area with a high density of about 300 observation stations; observation equipment held by private companies; and smartphone-based seismographs. K-NET, KiK-net, and MeSO-net are operated by National Research Institute for Earth Science and Disaster Resilience. The seismic waveform data recorded by MeSO-net, which are based on borehole observations, are one of the most important data sets for this system. To ensure collection of the waveform data, we strengthened the data center functions and made the collected data available to the public. In addition, to estimate the ground motion at the surface, which is important for disaster prevention, from the waveform data of MeSO-net, we carried out temporary seismic and microtremor array observations on the ground surface at each MeSO-net borehole station, and estimated ground amplification characteristics and the S-wave velocity structure. We also developed a smartphone-based seismograph with the aim of realizing seismic observations for tens of thousands of sites in the future. We recruited monitors to deploy the smartphone seismometers in the Tokyo metropolitan area, and developed a function to notify monitors of the results of a rough evaluation of the soundness of buildings based on observation data acquired during an earthquake. Furthermore, we have developed a Tokyo metropolitan area version of Kyoshin Monitor, the strong motion monitor system, with which the current ground motion in the Tokyo metropolitan area can be captured in real time by integrating and visualizing observation data from K-NET, KiK-net, and MeSO-net on a map on the website. We can capture the propagation of the ground motion in detail directly from the high-density data set integrated from these three networks. In addition, we also integrated data from Super-Dense Real-time Monitoring of Earthquakes (SUPREME) network of Tokyo Gas Co., Ltd., which operates about 4,000 observation stations in the Tokyo metropolitan area, after applying a time correction. We verified the integration method by reproducing the ground motion in the Tokyo metropolitan area during the 2011 Tohoku earthquake. The study findings have made it clear that the ground motion in the Tokyo metropolitan area can be captured in more detail by the integration of data produced by the public and private sectors.

Research on seismic wave first arrival Picking Based on improved U-Net

For the current problem of low accuracy of seismic wave phase pickup, this paper proposes a method based on the improved U-Net seismic wave P-wave and S-wave first-to-arrival pickup. Firstly, in order to reduce the impact of pre-processing operations such as filtering, the 20,000 three-component seismic waveform data will be normalized as input. Secondly, in order to reduce the loss of information during the seismic waveform extraction and increase the amount of information of effective features at the starting point, the Residual Unit is added to the encoder part of U-Net only, instead of adding it to the decoder part. In order to reduce the loss of high frequency information of seismic waveform, convolution operation is used instead of pooling operation. Finally, the probability distributions of P-wave, S-wave and noise are used as the output. The results show that the accuracy of the first-to-arrival pickup of P-wave is 93% and the accuracy of the first-to-arrival pickup of S-wave is 90% of the test set. The improved U-Net increased 30.9% and 6.2% in P-wave accuracy, 7.6% and 2.8% in S-wave accuracy, 39.6% and 4.0% in the P-wave recall, and 41.5% and 4.7% in the S-wave recall, respectively, compared with the conventional STA/LTA and U-Net. The improved U-Net outperforms the other two models in both evaluation metrics of recall and accuracy. The research results of this paper are of great practical significance in the localization of earthquakes, the interpretation of the earthquake gestation mechanism, and the prevention and mitigation of earthquakes.

Seismic Waveform Data from Greece and Cyprus: Integration, Archival, and Open Access

Abstract The National Observatory of Athens data center for the European Integrated Data Archive (EIDA@NOA) is the national and regional node that supports International Federation of Digital Seismograph Networks and related webservices for seismic waveform data coming from the southeastern Mediterranean and the Balkans. At present, it serves data from eight permanent broadband and strong-motion networks from Greece and Cyprus, individual stations from the Balkans, temporary networks and aftershock deployments, and earthquake engineering experimental facilities. EIDA@NOA provides open and unlimited access from redundant node end points, intended mainly for research purposes (see Data and Resources). Analysis and quality control of the complete seismic data archive is performed initially by calculating waveform metrics and data availability. Seismic ambient noise metrics are estimated based on power spectral densities, and an assessment of each station’s statistical mode is achieved within each network and across networks. Moreover, the minimum ambient noise level expected for strong-motion installations is defined. Sensor orientation is estimated using surface-wave polarization methods to detect stations with misalignment on particular epochs. A single data center that hosts the complete seismic data archives with their respective metadata from networks covering similar geographical areas allows coordination between network operators and facilitates the adhesion to widely used best practices regarding station installation, data curation, and metadata definition. The overall achievement is harmonization among all contributing networks and a wider usage of all data archives, ultimately strengthening seismological research efforts in the region.

Batch seismic inversion using the iterative ensemble Kalman smoother

An ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and efficient estimation. The adaptivity relies on an analysis of the update direction used in the iterative procedure, and an interpretation of contributions from prior and likelihood to this update. The idea is that these must balance; if the prior dominates, the estimation process is inefficient while the estimation is likely to overfit and diverge if data dominates. Two approaches to meet this balance are formulated and evaluated. One is based on an interpretation of eigenvalue distributions and how this enters and affects weighting of prior and likelihood contributions. The other is based on balancing the norm magnitude of prior and likelihood vector components in the update. Only the latter is found to sufficiently regularize the data window. Although no guarantees for avoiding ensemble divergence are provided in the paper, the results of the adaptive procedure indicate that robust estimation performance can be achieved for ensemble-based inversion of seismic waveform data.

From National to Transnational Seismic Monitoring Products and Services in the Republic of Bulgaria, Republic of Moldova, Romania, and Ukraine

Abstract The aim of this article is to analyze the background, current status, and outlook of seismic monitoring products and services in Bulgaria, Moldova, Romania, and Ukraine. These countries manage seismic networks that contribute to the European Integrated Data Archive node in the framework of the Observatories and Research Facilities for European Seismology, which represents a collaborative effort in coordinating observational seismology across the European region through the collection, archiving, and dissemination of seismic waveform data, metadata, and related products. All of the aforementioned countries share a common threat: strong earthquakes occurring in the Vrancea area located in central-eastern Romania at intermediate depths (usually in the 60–180 km interval). Events such as the ones on 10 November 1940 and 4 March 1977 generated high damage in Romania, northern Bulgaria, and Moldova. In addition to Vrancea, crustal earthquakes in areas such as Shabla or Dulovo can lead to cross-border damage. Therefore, understanding the way national seismic networks are distributed, how they cooperate, and the products and services that they provide in (near) real time and their terms is of significant interest in the context of necessary hazard harmonization and joint emergency intervention and risk mitigation actions.

Historical earthquakes, tsunamis and real-time earthquake monitoring for tsunami advisory in the South China Sea region

The South China Sea Tsunami Advisory Center (SCSTAC) established by China, under the aegis of UNESCO's Intergovernmental Oceanographic Commission (UNESCO/IOC), had inaugurated commencing its full operation on November 5, 2019. This center is operating 24 × 7 h and round-the-clock shift to monitor tsunami hazard that pose a serious threat to countries which bordering the South China Sea (SCS) region. Prior to the official operation, SCSTAC had taken action for the last 10 years in upgrading their technology capability which are real-time earthquake monitoring and the processing system that is crucial to be able providing the international standard of tsunami warning services in the SCS and its adjacent areas. This paper briefly reviewed on the initiation steps and its developments of the South China Sea region Tsunami Warning and Mitigation Systems, tectonic setting as well as the characteristics of historical earthquakes and tsunamis in the region. In addition, we highlighted the structure and basic functions of the earthquake monitoring and processing system, earthquake location, source mechanism solution and finite fault model inversion using the real-time seismic waveform data from regional and global seismographic networks that will result in the rapid source parameters estimation for a larger earthquake in tsunami warning. Numerous simulations and hands-on events have shown that the preliminary earthquake parameters could be determined less than 8 min after earthquake. The W phase method is used and be able to produce rapid and reliable estimation of the moment magnitude and source mechanism for larger events within 10–15 min from earthquake origin time. A finite fault model can be acquired just after the earthquake event via computing teleseismic body-wave inversion program. The earthquake monitoring and processing system provide accurate and reliable information in contributing to tsunami warning services, thus promoting the development of tsunami warning technologies, which enhancing the tsunami warning capability and tsunami emergency responses. These high-end technology can be used in facilitating others such as marine disaster prevention, mitigation and its risk reduction.

A Bayesian inference framework for fault slip distributions based on ensemble modelling of the uncertainty of underground structure: with a focus on uncertain fault dip

SUMMARYThe model prediction errors that originate from the uncertainty of underground structure are often a major contributor of the errors between the data and the model predictions in fault slip estimation using geodetic or seismic waveform data. However, most studies on slip inversions either neglect the model prediction errors or do not distinguish them from observation errors. Several methods that explicitly incorporated the model prediction errors in slip estimation, which has been proposed in the past decade, commonly assumed a Gaussian distribution for the stochastic property of the model prediction errors to simplify the formulation. Moreover, the information on both the slip distribution and the underground structure is expected to be successfully extracted from the data by incorporating the stochastic property of the model prediction errors. In this study, we propose a novel flexible Bayesian inference framework for estimating fault slips that can accurately incorporate non-Gaussian model prediction errors. This method considers the uncertainty of the underground structure, including fault geometry, based on the ensemble modelling of the uncertainty of Green’s functions. Furthermore, the framework allows the estimation of the posterior probability density function (PDF) of the parameters of the underground structure by calculating the likelihood of each sample in the ensemble. We performed numerical experiments for estimating the slip deficit rate (SDR) distribution on a 2-D thrust fault using synthetic data of surface displacement rates, which is the simplest problem setting that can essentially demonstrate the fundamental idea and validate the advantage of the proposed method. In the experiments, the dip angle of the fault plane was the parameter used to characterize the underground structure. The proposed method succeeded in estimating a posterior PDF of SDR that is consistent with the true one, despite the uncertain and inaccurate information of the dip angle. In addition, the method could estimate a posterior PDF of the dip angle that has a strong peak near the true angle. In contrast, the estimation results obtained using a conventional approach, which introduces regularization based on smoothing constraints and does not explicitly distinguish the prediction and observation errors, included a significant amount of bias, which was not noted in the results obtained using the proposed method. The estimation results obtained using different settings of the parameters suggested that inaccurate prior information of the underground structure with a small variance possibly results in significant bias in the estimated PDFs, particularly the posterior PDFs for SDR, those for the underground structure, and the posterior predicted PDF of the displacement rates. The distribution shapes of the model prediction errors for the representative model parameters in certain observation points are significantly asymmetric with large absolute values of the sample skewness, suggesting that they would not be well-modelled by Gaussian approximations.

Seismic features of the Permian Basin Region from receiver function analysis

We used receiver function (RF) analysis to derive new seismic models of Moho depth, crustal Vp/Vs ratio, depth of sedimentary velocity discontinuity, and corresponding sedimentary Vp/Vs ratio in the Permian Basin region from the seismic waveform data of 260 moment magnitude (Mw) ≥ 6 teleseismic events. We obtained the optimal models by searching one- and two-layer models and comparing similarity between the synthetic and observed RFs. We find the first-order velocity discontinuity in sedimentary basins is shallower than the basin basement. The Delaware Basin (DB) exhibits a bowl-shaped sedimentary velocity discontinuity, with depths ranging from 1.7 km to 5.8 km, whereas the Central Basin Platform (CBP) has a shallow sedimentary velocity discontinuity, with 0.2 km to 2.7 km depths. Moho depth ranges from 36 km to 46 km in the Permian Basin region. The median Vp/Vs ratios of the crust are 1.75, 1.79, and 1.78 in the DB, CBP, and the Diablo Platform (DP), respectively. Our interpretation of the different Vp/Vs ratio in the three subregions is that the crust in the DB includes more low-Vp/Vs-ratio felsic rocks, and the crust in the CBP and DP contains more high-Vp/Vs-ratio gabbroic and basaltic rocks from the Proterozoic and Phanerozoic mafic intrusion.

Rupture Process of theM6.5 Stanley, Idaho, Earthquake Inferred from Seismic Waveform and Geodetic Data

AbstractThe 2020 M 6.5 Stanley, Idaho, earthquake produced rupture in the north of the active Sawtooth fault in the northern basin and range at depth, without any observable surface rupture. Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data yield several millimeters of static offsets out to ∼100 km from the rupture and up to ∼0.1 m of near-field crustal deformation. We combine the GPS and InSAR data with long-period regional seismic waveforms to derive models of kinematic slip and afterslip. We find that the coseismic rupture is complex, likely involving up to 2 m combined left-lateral strike slip and normal slip on a previously unidentified ∼south-southeast-striking fault. This slip is predominantly left-lateral strike slip, different from the dominant east-northeast–west-northwest normal faulting of the region. At least one ∼northeast-trending fault, likely associated with the Trans-Challis fault system, is inferred to have accommodated a few decimeters of right-lateral afterslip, consistent with vigorous aftershock activity at depth along northeast-trending lineations.

Visualization of hydraulic fracture using physics-informed clustering to process ultrasonic shear waves

Ultrasonic transmission is sensitive to the spatial variation in mechanical properties of materials due to the presence of cracks/fractures. Wave propagation through fractured media introduces changes in the frequency content, travel time and transmission coefficient of the wave. A workflow based on physics-informed unsupervised learning is developed to process the transmitted ultrasonic-shear waveforms to non-invasively visualize the geomechanical alterations due to hydraulic fracturing. Novelty of the work involves the assignment of both statistically consistent and physically consistent clusters to the measurements of shear waveforms acquired across the one axial and two frontal planes. Physically consistent/relevant information is incorporated by considering the travel time of the peak of spectral energy and transmission coefficient of the transmitted waveform. The proposed workflow generates maps of geomechanical alterations across the frontal and axial planes of the sample. The outputs of the workflow are in good agreement with independent techniques viz. acoustic emission and X-ray computed tomography. The proposed workflow can be adapted for improved fracture characterization in the subsurface when processing sonic-logging, cross-wellbore seismic or surface seismic waveform data.

Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization

Full-waveform inversion is an important and widely used method to reconstruct subsurface velocity images. Waveform inversion is a typical nonlinear and ill-posed inverse problem. Existing physics-driven computational methods for solving waveform inversion suffer from the cycle-skipping and local-minima issues, and do not mention that solving waveform inversion is computationally expensive. In recent years, data-driven methods become a promising way to solve the waveform-inversion problem. However, most deep-learning frameworks suffer from the generalization and overfitting issue. In this article, we developed a real-time data-driven technique and we call it VelocityGAN, to reconstruct accurately the subsurface velocities. Our VelocityGAN is built on a generative adversarial network (GAN) and trained end to end to learn a mapping function from the raw seismic waveform data to the velocity image. Different from other encoder–decoder-based data-driven seismic waveform-inversion approaches, our VelocityGAN learns regularization from data and further imposes the regularization to the generator so that inversion accuracy is improved. We further develop a transfer-learning strategy based on VelocityGAN to alleviate the generalization issue. A series of experiments is conducted on the synthetic seismic reflection data to evaluate the effectiveness, efficiency, and generalization of VelocityGAN. We not only compare it with the existing physics-driven approaches and data-driven frameworks but also conduct several transfer-learning experiments. The experimental results show that VelocityGAN achieves the state-of-the-art performance among the baselines and can improve the generalization results to some extent.

Repeating Earthquakes Along the Colombian Subduction Zone

Colombia is tectonically active, and several large earthquakes have ruptured the Colombia-Ecuador subduction zone (CESZ) during the last century. Among them, the Colombia-Ecuador earthquake in 1906 (Mw8.4) and the Tumaco earthquake in 1979 (Mw8.3) generated destructive tsunamis. Therefore, it is important to characterize the seismic rupture processes and their relation with interplate coupling along the CESZ. We searched for repeating earthquakes by performing waveform similarity analysis. Cross correlation (CC) values were computed between earthquake pairs with hypocenter differences of less than 50 km that were located in the northern CESZ (1°–4°N) and that occurred from June 1993 to February 2018. We used broadband and short-period seismic waveform data from the Servicio Geológico Colombiano (SGC) seismic network. A CC threshold value of 0.90 was used to identify the waveform similarity and select repeating earthquakes. We found repeating earthquakes distributed near the trench and the coast. Our estimated repeating earthquakes near the trench suggest that the interplate coupling in this region is low. This is in clear constrast to the occurrence of a large slip in the 1906 Colombia-Ecuador earthquake along the trench in the southern part of the CESZ, and suggests that rupture modes are different between the northern and southern parts of CESZ near the trench.

Teleseismic Tomography for Imaging the Upper Mantle Beneath Northeast China

Tomographic imaging technology is a geophysical inversion method. According to the ray scanning, this method carries on the inversion calculation to the obtained information, and reconstructs the image of the parameter distribution rule of elastic wave and electromagnetic wave in the measured range, so as to delineate the structure of the geological body. In this paper, teleseismic tomography is applied by using seismic travel time data to constrain layered crustal structure where Fast Marching Methods (FMM) and the subspace method are considered as forward and inverse methods, respectively. Based on the travel time data picked up from seismic waveform data in the study region, the P-wave velocity structure beneath Northeast China down to 750 km is obtained. It can be seen that there are low-velocity anomalies penetrating the mantle transition zone under the Changbai volcano group, Jingpohu Volcano, and Arshan Volcano, and these low-velocity anomalies extend to the shallow part. In this paper, it is suggested that the Cenozoic volcanoes in Northeast China were heated by the heat source provided by the dehydration of the subducted Pacific plate and the upwelling of geothermal matter in the lower mantle. The low-velocity anomaly in the north Songliao basin does not penetrate the mantle transition zone, which may be related to mantle convection and basin delamination. According to the low-velocity anomalies widely distributed in the upper mantle and the low-velocity bodies passing through the mantle transition zone beneath the volcanoes, this study suggests that the Cenozoic volcanoes in Northeast China are kindred and have a common formation mechanism.

3-D joint inversion of seismic waveform and airborne gravity gradiometry data

SUMMARYSeismic full waveform inversion (FWI) is a robust velocity model building technique for hydrocarbon exploration. However, the density reconstruction within the framework of multiparameter FWI leads to more degrees of freedom in the parametrization, and the sensitivity of the inversion change significantly from velocity to density, thereby increasing the ill-posedness of the inverse problem. Gravity gradiometry data inversion is an effective method for resolving density distribution. Combining gravity gradiometry data in FWI could alleviate the non-linearity of the inversion by contributing additional density information for the velocity model building. I develop a 3-D joint seismic waveform and gravity gradiometry inversion method for estimating the velocity and density distribution in the subsurface. The method alternatingly minimizes the waveform and gravity gradiometry misfit. The cross-gradient constraint is applied to enhance the structural similarity between the density and velocity models. The effectiveness of the joint inversion algorithm is demonstrated by a 3-D checkerboard model and 3-D SEAM model. Synthetic examples demonstrate that the joint inversion can improve the image quality in geologically complex areas. A case study from the South China Sea shows that the joint inversion improves the velocity and density solutions compared to a standalone seismic FWI. The joint inversion results are consistent with the pre-stack depth migration section and the shape of the salt body is well resolved.

A method to fuse multiphysics waveforms and improve predictive explosion detection: theory, experiment and performance

SUMMARYNatural and human-made sources of transient energy often emit multiple geophysical signatures that include mechanical and electromagnetic waveforms. We present a constructive method to fuse and evaluate statistics that we derive from such multiphysics waveforms that improves our capability to detect small, near-ground explosions over similar methods that consume single signature waveforms. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both hypotheses of a binary test on noisy data and provide researchers with the density functions required to forecast the ability of Fisher's Method to screen fused explosion signatures from noise. We apply this method against 12 d, multisignature explosion and noise records to show (1) that a fused multiphysics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions roughly 0.8 magnitude units lower than an acoustic emission, STA/LTA detector for the same detection probability and (2) that we can quantitatively predict how this fused, multiphysics statistic performs with Fisher's Method. Our work thereby offers a baseline method for predictive waveform fusion that supports multiphenomenological explosion monitoring (multiPEM) and is applicable to any binary testing problem in observational geophysics.

Regional Seismic Networks Operating along the West Coast of the United States of America

AbstractThe Pacific coast of the contiguous United States hosts the highest seismic risk in the country due to the intersection of high-seismic hazard and the high densities of population and infrastructure. The regional seismic networks in Washington, Oregon, Nevada, and California have operated for many years and have collected long catalogs and large amounts of seismic waveform data in a variety of formats, including digital records. These data are available for engineering purposes and research into earthquakes, other natural and man-made seismic sources, and the Earth’s structure. The West Coast networks are closely coordinating as they embark on the implementation of West Coast ShakeAlert, an earthquake early warning system.

Source Characteristics of the 2017 Ms 6.6 (Mw 6.3) Jinghe Earthquake in the Northeastern Tien Shan

Abstract On 8 August 2017, an Ms 6.6 earthquake occurred in the northeastern Tien Shan orogenic belt. To reveal the source characteristics of this earthquake completely, the teleseismic and near-field seismic waveform data were collected as well as the coseismic Interferometric Synthetic Aperture Radar displacement data, and the methods of the backprojection and the finite-fault joint inversion were adopted. The backprojection of the teleseismic recordings indicates a unilateral rupture propagating 15 km westward. Two stages of the rupture were recognized from the backprojection results: in the first ∼5 s, the rupture took place near the hypocenter, with an accelerating energy release but a small rupture velocity; then the rupture extended to the west, with a decelerating energy release but a relatively fast rupture velocity. The joint inversion of the multiple datasets shows a major slip asperity of about 24 km × 18 km. The asperity extended mainly to the west, with a duration of approximately 10 s. The average rupture velocity over the asperity was estimated to be approximately 2.0 km/s, which is close to that 1.9 km/s estimated by the backprojection. It is interesting that the high-frequency sources were aligned almost on the margin of the slip asperity. Moreover, the occurrence of the earthquake sequence is found to relate with the low-VP/VS zone, implying a tectonic property, which controls the nucleation and rupture of earthquakes.

Research on a Distributed Processing Model Based on Kafka for Large-Scale Seismic Waveform Data

For storage and recovery requirements on large-scale seismic waveform data of the National Earthquake Data Backup Center (NEDBC), a distributed cluster processing model based on Kafka message queues is designed to optimize the inbound efficiency of seismic waveform data stored in HBase at NEDBC. Firstly, compare the characteristics of big data storage architectures with that of traditional disk array storage architectures. Secondly, realize seismic waveform data analysis and periodic truncation, and write HBase in NoSQL record form through Spark Streaming cluster. Finally, compare and test the read/write performance of the data processing process of the proposed big data platform with that of traditional storage architectures. Results show that the seismic waveform data processing architecture based on Kafka designed and implemented in this paper has a higher read/write speed than the traditional architecture on the basis of the redundancy capability of NEDBC data backup, which verifies the validity and practicability of the proposed approach.

Adjoint Hamiltonian Monte Carlo algorithm for the estimation of elastic modulus through the inversion of elastic wave propagation data

SUMMARYEfficient inversion of noisy seismic waveform data produced due to elastic wave propagation for the estimation of a high‐dimensional elastic modulus vector is achieved. Estimation is carried out in a Bayesian framework using Hamiltonian Monte Carlo (HMC) that enables efficient statistical estimation over high‐dimensional parameters. The truncated Karhunen‐Loève (K‐L) expansion is introduced to reduce the dimensionality of the elastic modulus vector. Expensive computations of the gradient of the state vector with respect to the parameter vector at every step are also eliminated through the adjoint method, which is developed from a general one‐step discretization of the governing second‐order ordinary differential equations (ODEs). An Adjoint HMC algorithm that employs a truncated K‐L expansion of the elastic modulus vector is presented. The efficacy of the algorithm is investigated with respect to two representative problems with varying geometric complexity. Adjoint HMC offers a significant speed up in gradient calculation time over the direct differentiation counterpart as the number of terms in the K‐L expansion increases. The algorithm is able to estimate the true elastic modulus within the credible intervals for both cases.

First-arrival picking with a U-net convolutional network

In exploration geophysics, the first arrivals on data acquired under complicated near-surface conditions are often characterized by significant static corrections, weak energy, low signal-to-noise ratio, and dramatic phase change, and they are difficult to pick accurately with traditional automatic procedures. We have approached this problem by using a U-shaped fully convolutional network (U-net) to first-arrival picking, which is formulated as a binary segmentation problem. U-net has the ability to recognize inherent patterns of the first arrivals by combining attributes of arrivals in space and time on data of varying quality. An effective workflow based on U-net is presented for fast and accurate picking. A set of seismic waveform data and their corresponding first-arrival times are used to train the network in a supervised learning approach, then the trained model is used to detect the first arrivals for other seismic data. Our method is applied on one synthetic data set and three field data sets of low quality to identify the first arrivals. Results indicate that U-net only needs a few annotated samples for learning and is able to efficiently detect first-arrival times with high precision on complicated seismic data from a large survey. With the increasing training data of various first arrivals, a trained U-net has the potential to directly identify the first arrivals on new seismic data.