Summary Near-surface structures and conditions have a large impact on the quality of land seismic data and the final subsurface image. However, the mechanism that causes the variations in this data quality is complex. This potentially indicates that the understanding of near surface could be useful to improve the quality of active seismic data in terms of not only seismic processing but also seismic acquisition. This study discusses the relationship between source performance of active seismic acquisition, which represents the magnitude of energy for seismic waves with early arrivals propagated from sources to receivers, and near-surface structures of S-wave velocity reconstructed by ambient noise. We used ambient noise on two seismic datasets in this study. While we investigated this relationship using an active 2D seismic dataset, we also utilized another passive seismic dataset at relatively sparse locations to estimate source performance with limited cost before active seismic acquisition. We provide a case study about onshore Japan to examine this relationship. This investigation compares the S-wave velocity profile estimated by applying seismic interferometry and multi-channel analysis of surface waves to the ambient noise in active/passive seismic data with the source performance analyzed active seismic data and borehole data.
In downhole microseismic measurement, head waves (or refracted waves) instead of direct waves can be recorded as first arrivals under limited conditions. The head waves have been generally regarded as a nuisance in microseismic analysis including microseismic event localization, because standard methods to determine the locations exploit the first arrivals of direct waves. Hence, if the first arrivals of head waves are wrongly used in such methods, they cause a large error on the event localization and significant economic repercussion. In this paper, I formulate an analytical criterion for measuring head waves as first arrivals and derive an analytical source location by utilizing the first arrival of head waves as well as direct waves. Then, numerical examples are shown to discuss the validity of this method. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 4:20 PM Location: 302B Presentation Type: Oral
Summary A complex near surface is a large challenge in land seismic imaging due to its strong lateral heterogeneity. Furthermore, since the reflectors within the near-surface area are not always well measured, it is difficult to accurately estimate its velocity distribution of the subsurface. In this paper, we present a method to effectively exploit internal multiples in Joint Migration Inversion (JMI) for near-surface imaging. JMI is an inversion algorithm to automatically provide both velocity and reflectivity of the subsurface by utilizing primaries and all higher-order scattering. Our proposed method aims to improve the inverted velocity and reflectivity models through JMI by partially enhancing the residual between observed data and forward modeled data and suppressing the influence of diving waves and the insufficiently measured reflectors directly originating from the near-surface region. We give two numerical examples for synthetic models including a complex near-surface model to show the effectiveness of the proposed method.
Joint Migration Inversion (JMI) automatically provides a structual image and velocity model of the subsurface by exploiting primaries and all higher order scattering. It has been shown that JMI is a robust algorithm to estimate reflectivity and velocity models, avoiding local minima. However, the estimated velocity models are relatively smooth and lack detail, as they only describe the propagation of waves. Moreover, some improvement in JMI seems to be needed to generate rapid velocity variations like salt structures. In this paper we present a velocity estimation procedure including a reflectivity constraint in JMI in order to improve the accuracy of the velocity model. The residual between the estimated velocity by JMI and the approximate velocity that is derived from the estimated reflectivity is minimized through an additional constraint in the objective function. Synthetic examples are shown to demonstrate the validity of the proposed method. Presentation Date: Tuesday, October 18, 2016 Start Time: 10:45:00 AM Location: Lobby D/C Presentation Type: POSTER
We develop signal processing and imaging techniques for characterizing subsurface structure with utilizing raw vibroseis shot data before sweep correction. Vibroseis shots are often used for a land seismic survey, and the raw data of observation are typically converted to impulse-shot records with correlation of shot sweep and stacking. However, as we demonstrated here, the raw data are useful for providing additional valuable information. One opportunity we have is to clean up the vibroseis shot gathers by removing traffic noise in the raw data. Traffic noise is one of the strongest kinds of noise in the frequency range overlapped to the vibroseismic shots, and this noise contaminates the shot gathers. Diversity stacking helps to improve the signal-to-noise ratio (SNR), but we develop a more advanced, and traffic-noise-oriented, filter with a machine-learning approach. This filter improves the SNR of shot gathers. Another opportunity is for near-surface velocity structure modeling. Near surface is important to know for topography correction of imaging (static collection) and shot and/or receiver couplings. However, the conventional vibroseismic shot geometry is not suitable to estimate these structures. We use ambient noise data recorded during the vibroseismic shot survey. Each sweep shot is 38 seconds and more than 10 sweep shots are repeated at each shot location. This means that we have 380 seconds of continuous data at each location. We use these data to extract surface waves using seismic interferometry, and estimate near-surface S-wave velocities from the surface waves. The velocity image provides detailed information of the structure at top 50 m along the entire survey.
In recent years, underground hydrogen storage (UHS) has garnered significant attention as a concept for large-scale energy storage. Developing hydrogen characterization and monitoring methods specific to each target geologic formation (e.g., salt caverns, reservoirs in depleted oil/gas fields, and saline aquifers in porous media) is crucial for the safe operation of hydrogen storage. However, due to the limited number of actual operations and laboratory experiments (e.g., rock physics for hydrogen) related to UHS in porous media, the specifications required for seismic monitoring (e.g., P-/S-wave velocity and density changes generated by hydrogen injection) are not yet fully understood. A recent study showed the results of laboratory experiments on cyclic UHS in porous media. The experiments demonstrated that P-wave velocity decreases nonlinearly with increasing hydrogen saturation in sandstone specimens, with a reduction of up to 3.5% observed at 36% hydrogen saturation. This study presents hydrogen characterization in porous media with conditions similar to the experiment using elastic full-waveform inversion (FWI) for time-lapse surface seismic data. The shot data are generated by seismic forward modeling for several synthetic subsurface models that incorporate a hydrogen plume. The hydrogen plume assumes four velocity-density scenarios, partially based on the laboratory study results. Numerical experiments are conducted to explore the validity and challenges of hydrogen characterization using elastic FWI.
We present a seismic modeling process based on one-way propagators to handle both reflection and refraction. In this process, vertical reflectivity, horizontal reflectivity, and velocity models are independently defined by their own coordinate positions in a so-called staggered grid. We introduce the concept of intermediate propagation, such as down-rightgoing and up-leftgoing wavefields, which are propagation modes between the horizontal and vertical reflectivity grid. In addition, internal multiples can also be generated by this process. This modeling method can serve as the engine for a controlled full wavefield inversion process. A numerical example is shown to demonstrate the validity of the proposed method. Presentation Date: Thursday, September 28, 2017 Start Time: 11:00 AM Location: 381A Presentation Type: ORAL
Seismic imaging is a significant technology to provide the image of the subsurface in several fields such as hydrocarbon exploration/production and civil engineering. A fundamental problem in seismic imaging is that both the depth reflectivity and velocity distribution of the subsurface have to be predicted by only seismic events observed at the surface, and it still remains a challenging research topic. Joint migration inversion (JMI) is one of the seismic waveform imaging algorithms that were recently proposed. JMI is capable of simultaneously estimating velocity and reflectivity models of the subsurface by exploiting reflected waves including internal multiples. The seismic modeling algorithm in the JMI process is a method termed full wavefield modeling (FWMod), which is a one-way propagator-based reflection modeling algorithm, including higher-order scattering and transmission effects. In this thesis, two directions to improve the accuracy of seismic imaging based on JMI are discussed. On one hand, an extension of FWMod is proposed to correctly deal with not only reflected waves but refracted/diving waves via one-way propagators in the horizontal direction, and this method is extended to a new JMI algorithm. On the other hand, we assume that only reflected waves including internal multiples are utilized in the imaging based on JMI and present two novel methods for the inversion and pre-processing: 1) iterative reflectivity-constrained velocity estimation, 2) surface amplitude correction via learning from synthetic models for land seismic data. The reflectivity-constrained velocity estimation is employed to improve the accuracy of the estimated velocity by exploiting the estimated reflectivity in the JMI process. The surface amplitude correction process is introduced to mitigate the influence of the amplitude variations caused by source/receiver response sensitivities and the difference of the features between observed land seismic data and the simulated data by the used imaging scheme. The numerical and field data examples for both land and marine cases demonstrate that the proposed approach is capable of effectively estimating reflectivity and velocity model, even though the low frequency components of the observed data are absent.
