ABSTRACT Path-integral migration is a method for creating a migrated image without previous knowledge of the true velocity model by summing the migrated images from a representative set of velocity models. This concept can be expanded to automatically extract a velocity model, a technic called Migration Velocity Analysis by Double Path-Integral Migration (MVA by DPIM). In MVA by DPIM, a second, weighted image is created, with the weight containing the velocities used in the individual migrations. Division of the two images provides the velocity model. Here we discuss several practical aspects of implementing MVA by DPIM, ranging from the parametrization of the weight function to the stabilization of the division and the selection of only meaningful velocities. By means of tests on a wide range of velocity models, we find a robust implementation of the method. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Start Time: 2:40 PM Location: Poster Station 11 Presentation Type: Poster

ABSTRACT In recent years, 4D DAS VSP has matured rapidly as a tool for frequent or on demand monitoring in Deepwater. We share results from recent tests, the latest of many, that establish the robust 4D performance of DAS and deem it ready for use in Deepwater. We show early examples of how additional time steps from DAS between OBN surveys could aid production optimization in waterflood fields. The value realization process requires further maturation. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 8:30 AM Presentation Start Time: 10:35 AM Location: 225B Presentation Type: Oral

ABSTRACT Distributed Acoustic Sensing (DAS) is a rapidly growing novel sensing method for seismic data acquisition. DAS arrays are particularly well-suited for dense recording of low-frequency ambient surface waves on long (>5 km) linear sections of deployed optical fiber cable. Applying multi-channel analysis of surface waves (MASW) to ambient wavefield DAS recordings characterized by a large number of sensing points and long recording times may enable 1D characterization of the S-wave velocity profile to depths of 750 m or greater. We present a low-frequency ambient wavefield investigation using a DAS dataset acquired on an array deployed in suburban Perth, Australia. We extract storm-induced swell noise from the nearby Indian Ocean in a low-frequency band (0.1–1.8 Hz) and generate virtual shot gathers by applying cross-correlation and deconvolution seismic interferometric analyses. The resulting gathers are transformed into dispersion images through two different methods: phase shift and high-resolution linear Radon transform. To recover the near-surface S-wave velocity model, we first pick and then invert the recovered 1D Rayleigh-wave dispersion curves using a particle-swarm optimization algorithm. Inversion results show that low-frequency ambient-wavefield DAS data can constrain the Vs model to 750 m depth, which helps validate the potential of DAS technology as a tool for large-scale surface-wave investigation. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: 221C Presentation Type: Oral

ABSTRACT Recently, deep learning has been a popular approach for noise attenuation in the image processing industry. We apply a complex-valued residual convolutional neural network (ResNet) to attenuate random noise in the frequency - space (f-x) domain. Random noise attenuation in the f-x domain has the advantage in that the spectra often exhibit a few dominant harmonics. We generalize the ResNet algorithm to treat complex-valued data with the goal of suppressing noise. The resulting time - space (t-x) displays of denoised signals show the noise is significantly alleviated while preserving the amplitude of the signal. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 10:10 AM Location: Poster Station 2 Presentation Type: Poster

ABSTRACT Deblending of simultaneous source data is usually considered as an under-determined inverse problem. However, when it comes to abnormal noise that follows non-Gaussian distribution and possesses high-amplitude feature, the ?2-norm is a non-robust statistic that can easily lead to sub-optimal deblended results. Although abnormal noise can be attenuated in the common source domain at first, it is still challenging to apply a coherency-based filter due to the sparse receiver or crossline sampling, e.g., commonly found in the OBN acquisition. We propose a normalized shaping regularization using the confidence interval of normal distribution, making the inversion-based deblending approach robust for the separation of blended data when abnormal noise exists. Experimental results on both synthetic and field data show that the proposed method can effectively obtain better deblending performance and less energy leakage when abnormal noise exists. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Start Time: 2:15 PM Location: 214D Presentation Type: Oral

ABSTRACT As full-waveform inversion (FWI) is a nonunique and typically ill-posed inversion problem, simultaneous-source noisy data, which suffers from crosstalk noise caused by shots overlap and environmental noise, indeed requires a proper regularization to avoid cycle skipping and accelerate convergence. We propose normalized low-rank regularization to achieve simultaneous-source noisy data waveform inversion, in which the crosstalk noise caused by shots overlap is suppressed by nonlocal low-rank regularization on the updated velocity while normalized shaping operator is to shape the noisy misfit between recorded and simulated data into the least-squares sense. We test the performance of proposed method using synthetic simultaneous-source noisy data, and use the results of conventional full-waveform inversion with Tikhonov regularization as comparisons. Results show that the proposed normalized lowrank regularization can obtain clearer and smoother waveform inversion results. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Time: 3:05 PM Location: Poster Station 8 Presentation Type: Poster

ABSTRACT We present the modeling of blended acquisition for a node survey acquiring long offsets (50 km) with a focus on the low-frequency diving waves. The synthetic data simulates six sources, with shots fired both simultaneously and in flip-flop formation. The diving waves are recovered using an iterative inversion-based deblending methodology, which would subsequently be used for refraction FWI. Acquisition options are studied using different shot firing orders, source vessel spacing, dither time, and background noise. We show that this deblending methodology successfully recovers useful signal across a wide range of acquisition geometries and firing patterns. The success of deblending in recovering the diving waves is quantitatively established. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Time: 4:45 PM Location: 221A Presentation Type: Oral

ABSTRACT Mathematical morphological filtering (MMF) is a powerful tool for image processing based on the shape of the structure element (SE). It was introduced into seismic data processing to suppress noise and enhance signal quality. We explain the basic mathematic morphology concepts with set theory and define the basic and advanced morphological operations in seismic data processing. Unlike conventional seismic filtering techniques, MMF is a nonlinear operator so that it can more effectively isolate and attenuate seismic noise based on their shape differences from the signal. We apply different types of morphological filter on field (and various stages of processed) data to demonstrate their effectiveness for suppression of both coherent and incoherent noise that results in an improvement of signal-to-noise ratio. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Start Time: 1:50 PM Location: 305 Presentation Type: Oral

ABSTRACT Robust Principal Component Analysis (RPCA) based seismic denoising approaches yield promising results in separating useful seismic events from noise. However, current RPCA-based methods suffer from expensive computational costs, which hinders their wide applications in seismic data denoising and preprocessing. In this work, we develop a cost effective denoising technique based on RPCA. Instead of solving for the clean data and noise simultaneously, we alternatively update them. This approach admits a large stepsize and increases the speed. In addition, we improve the model by incorporating a non convex term. To verify the effectiveness of our technique, we applied our denoising technique to both synthetic and field reflection seismic data. From the numerical results, we observe that our denoising methods not only produce comparable or better denoising results but also yield efficient computational cost. Through comparison to other RPCA-based denoising methods, our method is at least 4–10× faster. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 11:00 AM Location: Poster Station 1 Presentation Type: Poster

ABSTRACT Recently the Reflection Full waveform Inversion method has been proposed as an alternative to classical ray-based velocity model building methods. This method considers a scale separation between the reflectivity and the velocity models. In the original RFWI approach, the reflectivity and the velocity models are inverted by sequentially solving two optimization sub-problems. During the velocity model estimation subproblem, the reflectivity model is keep fixed therefore ignoring reflectivity-velocity coupling effects. This may lead to a slow convergence toward the final velocity model and also generate some in-consistencies between the reflectivity and the velocity model. We propose, here, to reformulate the conventional RFWI approach in order to better account for the coupling between the velocity and reflectivity models. Compared to the conventional RFWI this formulation induces several changes. First, the gradient expression contains the gradient of the conventional RFWI plus an additional term. We show that under particular circumstances, the variable projection method applies allowing to ignore this supplementary gradient term. Another consequence of this formulation is that the reflectivity model has to be re-migrated for every perturbation of the velocity models. Finally, we verify with a synthetic experiment that the reflectivity-velocity consistent RFWI approach has a faster convergence than the conventional approach. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 3:55 PM Location: 302B Presentation Type: Oral

ABSTRACT Accurate identification of the spatial distribution of various pore types in carbonate reservoirs by seismic inversion is challenging due to the diverse pore structures resulting from deposition and diagenetic modification and low-resolution of seismic data. We use a bulk-frame flexibility factor from a rock-physics model as an indicator to analyze the effect of pore structure variations on acoustic velocity and impedance. A good correlation is found between acoustic impedance (AI) and the product of porosity and γ representing different pore types. It is found that when <4.5, the dominant pore type is moldic with relatively high AI value; when 5.5<<15, intercrystalline pores are prevalent and the AI is lower than moldic pores in a given porosity range. Model-based post-stack inversion built by strata models and seismic data with entirely different resolution are then applied to estimate the spatial variation of carbonate pore shapes. Inversion results based on high-resolution strata model and high-resolution seismic data have the highest resolution and could help delineate diversified pore structures precisely. The resolution of seismic data is the most significant factor for the final resolution of model-based inversion. These methods can be applied to estimate the spatial variation of carbonate reservoir pore types more accurately. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 10:35 AM Location: Poster Station 3 Presentation Type: Poster

ABSTRACT Undergraduate and graduate engineering and geology students investigate water storage and water yield from the Andean Puna, and design and construct downstream irrigation canals in the agrarian highland community of Zurite, Perú. This 2-year project, funded by Geoscientists Without Borders (GWB) relies on students to design research objectives, conduct hydrogeological field research, and produce tangible results, such as over 2 km. of irrigation canals, that benefit the community of Zurite. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Start Time: 2:40 PM Location: 303B Presentation Type: Oral

ABSTRACT P-P reflection amplitudes vary by offset and by azimuth in the presence of one set of vertical aligned fractures. At a boundary wherein the orthorhombic axes are misaligned by 70 at a shale-carbonate boundary, simple models of azimuthal (az’l) P-P amplitudes do not apply. This paper presents oil production data as evidence of aligned (or azimuthal or fracture) porosity connected to the borehole (fracture porosity) in an onshore naturally-fractured carbonate oil reservoir. The change with azimuth of the near-angle amplitudes (6-15) and the AVA gradient (amplitude variation with angle, AVAz) have been calculated and displayed and tied to oil production data. The industry standard az’l amplitude measurement, AVAz, ties the oil calibration data moderately. The az’l variation of the near-angle amplitude data ties the oil production data quite well. Aligned porosity (fracture porosity) causes az’l P impedance. The az’l variation of the near-angle amplitudes is the response to az’l P impedance. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 4:20 PM Location: 217D Presentation Type: Oral

ABSTRACT Recently, with the increase of dense seismic monitoring networks and the resultant massive seismic data all over the world (e.g., Groningen gas reservoir), the traditional geophysical algorithms have faced the "big data" and high dimensionality issues and become inefficient and costly. Deep learning, as a candidate to mitigate the "big data" and high dimensionality problems, has started to be applied to many geophysical problems; however, previous deep learning studies seldom consider the model uncertainty, which may seriously impair reservoir production estimates, ground motion predictions, and seismic early warnings. Bayesian neural networks provide a practical solution to solve the uncertainty quantification problems in deep learning, i.e., to make AI safe. In this paper, we construct a Bayesian convolutional neural network and implement a stochastic regularized technique "dropout" to quantify the uncertainty of seismic location. This method has been applied to induced seismicities in the Groningen gas field in the Netherlands. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 11:25 AM Location: Poster Station 1 Presentation Type: Poster

ABSTRACT Distributed acoustic sensing (DAS) is a newly developed technology, and it is getting more attention in the energy industry, civil engineering investigations and geo-hazard studies due to its dense sensor spacing, low cost of fiber cables and high repeatability for monitoring. Long-term monitoring by DAS brings us huge amount of data, processing of which is laborious and time consuming. We use the quarry blasts data recorded by the Stanford DAS array to demonstrate that reliable information can be extracted using extremely short recordings. We apply denoising, missing data interpolation, seismic interferometry and dispersion analysis to the DAS data. The average velocity changes along the DAS line are obtained using two segments of 20-second recordings. The average velocity changes are consistent with the nearby basement construction schedule. Using pieces of 100-second recording, the phase velocity changes with frequency have been obtained. The dispersion results are consistent with those obtained using month-long data recordings. These consistent results suggest that reliable information can be extracted using an extremely short period of DAS recordings due to anthropogenic activities. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 4:20 PM Location: 221C Presentation Type: Oral

ABSTRACT It is widely known that anisotropic inversion is a nonlinear ill-posed problem, which are usually simplified as a linear problem. However, the linear problem is still unstable which is usually avoided by adding a specific mathematical regularization item (suppose the parameters follow Gaussian distribution) on anisotropic inversion. The regularization item can only works for a simple model which limits the applications in subsurface complicated structure. We proposed a data-driven regularization method to improve the stability of anisotropic inversion. The method uses dictionary learning algorithm to obtain dictionary of anisotropic characteristics and elastic parameters from logs. The obtained dictionary is used to sparsely represent prior information which is eventually used to constrain the anisotropic inversion procedure. Requirement that the model conforms to a particular mathematical distribution is no longer needed in this sparse representation anisotropic inversion (SRAI) method. The only requirement is to provide sufficient logs in the same work area to learn the parameters’ characteristic. The stratigraphic characteristics in logs are contained in the inversion method which makes the inversed result approaching to the subsurface structures. The EAGE model and the field data are used to test the reliability of the algorithm. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 3:05 PM Location: 217D Presentation Type: Oral

ABSTRACT Anelastic attenuation causes energy dissipation of seismic waves propagating inside the earth, which leads to amplitude decay and phase distortion of migrated images. To correct for this dissipation effect, we use Gaussian beams as wavefield propagator to formulate an efficient and accurate linearized inversion scheme, denoted as least-squares Q Gaussian beam migration (LSQGBM), to iteratively solve for optimal images in viscoacoustic media. With this method, we can not only compensate for the amplitude loss and phase shift caused by attenuation loss during migration, but also produce images with better balanced amplitudes and higher resolution. Numerical tests on synthetic and field datasets demonstrate the validity and effectiveness of our proposed method. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Start Time: 2:15 PM Location: Poster Station 10 Presentation Type: Poster

ABSTRACT Reservoir characterization based on amplitude variation with offsets/angles (AVO/AVA) is the core of geophysics, which focuses on the quantitative prediction of lithology and pore geofluid properties. In this study, we have proposed one simultaneous stochastic inversion approach for discrete fluid-facies and continuous geofluid parameters based on differential evolution Markov Chain Monte Carlo (DE-MCMC) algorithm and Gaussian mixture model. The Gaussian mixture model influenced by reservoir pore fluid-facies is assumed to be the prior probability density distribution of elastic parameters in Bayesian inference. The likelihood probability function is first established with time-frequency joint domain seismic data. Then, the standard posterior probability distribution expressed with a multi-dimensional Gaussian mixture model is derived. Due to the superiority of multi-solution global optimization in DE-MCMC model, the DE-MCMC model is first introduced into the optimization of mixture posterior PDF. The fluid-facies is predicted effectively through the posterior probabilities of discrete facies. The main advantage of DE-MCMC AVO inversion is that the probability distributions of estimated parameters can be easily built based on the multiple-solutions optimization. Thus, the uncertainty and reliability of geofluid parameters and fluid-facies are assessed effectively. Model and field application examples demonstrate the feasibility and stability of the proposed methodology evidently. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 3:55 PM Location: Poster Station 8 Presentation Type: Poster

ABSTRACT Full waveform inversion (FWI) is a challenging data-fitting procedure based on full wave-field modeling to extract quantitative information from seismograms, and it has been widely knows as the most prospective quantitative seismic imaging technology. However, as an ill-posed and highly nonlinear inverse problem, there are still challenges for the application of FWI in land seismic data. In this paper, we briefly introduce a strategy of the FWI application to land seismic data: the geology structural information as the prior model to decrease the non-linear problem and the compensation attenuation coefficient to scale the gradient. Then we show an application of FWI to the land seismic data from Southwest Oil Filed of CNPC in Sichuan basin. The final results show the feasibility and validity in the land data application, and have the potential to the 3D land data. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Time: 1:50 PM Location: 302B Presentation Type: Oral

ABSTRACT We have conducted a high resolution vertical cable seismic (VCS) survey using a set of buoyed vertical array of receivers which are moored to the seafloor for shallow gas hydrate exploration. The downgoing receiver ghost reflections are separated from the entire wavefield and imaged for an extended subsurface coverage. Reverse time migration (RTM) method with a free surface top boundary condition is used to accurately model the receiver ghost during the wavefield simulation. A synthetic wavelet is propagated as the source wavefield and the separated ghost reflection is backpropagated as the receiver wavefield, followed by a crosscorrelation imaging condition. Because of the asymmetrical reflection point between the source and receiver, conventional velocity analysis methods are not applicable to the VCS survey. Therefore, we used seismic interferometry to develop a velocity model for imaging. A case history from Umitaka Spur in Sea of Japan is presented to delineate the corresponding gas hydrate system. High resolution resulting images suggest that a large gas chimney exists beneath a pockmark and is responsible for transferring methane gas from a deep hydrocarbon source to the shallow sediments through a nearly vertical faulting regime. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Start Time: 4:20 PM Location: 214C Presentation Type: Oral