Earthquake-based Full-Waveform Inversion at the Exploration Scale from Dense Broadband Array Data

2020 ◽  
Author(s):  
Qiancheng Liu ◽  
Frederik Simons ◽  
Fuchun Gao ◽  
Paul Williamson ◽  
Jeroen Tromp
Keyword(s):  
Moment Tensor ◽  
Regional Scale ◽  
Waveform Inversion ◽  
Three Dimensional ◽  
Velocity Model ◽  
Hydrocarbon Exploration ◽  
Full Waveform Inversion ◽  
Centroid Moment Tensor ◽  
Shear Wave Speed ◽  
Full Waveform

<p>In challenging environments with natural seismicity and where active source acquisition is expensive and dangerous, the question arises whether naturally occurring earthquakes offer useful information for hydrocarbon exploration. Here, we report on an experiment that installed 252/247 receivers to acquire data for two periods of 7 months, in the presence of significant rugged topography (elevations from 500 m to 3500 m). The station density is about 1 per 25 km^2 (compared to, e.g., USArray, where the average station spacing was 70 km). Data were recorded in a frequency band from 0.2 Hz to 50 Hz. Several thousand seismic events originating within the array bounds were identified in these data. A compressional-speed tomographic velocity model was derived using first-arriving phases. Centroid moment tensor (CMT) solutions have been obtained for about 4% of the identified events using Green’s function-based multicomponent waveform inversion, assuming a layered velocity model. We are now working to improve that model by performing elastic full-waveform inversion for three-dimensional compressional and shear-wave speed perturbations, honoring the topography, after a prior full-wavefield-based reassessment of the earthquake source mechanisms. We are also aiming to increase the number of events considered in the inversion while weighting the data based on estimates of data quality. This is assessed with a flexible automated procedure that considers a variety of data attributes over a range of frequencies. We run simulations using the spectral-element package SPECFEM3D on a cluster that employs 4 GPU cards per simulation. We identify the promising areas of good initial fit from the highest-quality seismic traces and gradually bring the predictions in line with the observations via LBFGS model optimization. We review the results of our work so far, discuss how to continue to bring best practices from global seismology down to the regional scale, and consider the implications for using such passive experiments to complement or replace active exploration in such challenging zones.</p>

Imaging the Deep Galicia margin using three-dimensional full waveform inversion

2020 ◽  
Author(s):  
Bhargav Boddupalli ◽  
Tim Minshull ◽  
Joanna Morgan ◽  
Gaye Bayrakci
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
Waveform Inversion ◽  
Three Dimensional ◽  
Velocity Model ◽  
Normal Faults ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Time Model ◽  
Full Waveform

<p>Imaging of hyperextended zone and exhumed continental mantle rocks can improve our understanding of the tectonics of the final stages of rifting. In the Deep Galicia margin, the upper and lower crust are coupled allowing the normal faults to cut through the brittle crust and penetrate to the mantle leading to serpentinization of the mantle. Localized extensional forces caused extreme thinning and elongation of crystalline continental crust causing the continental blocks to slip over a lithospheric-scale detachment fault called the S-reflector.  </p><p>A high-resolution velocity model obtained using seismic full waveform inversion gives us deeper insights into the rifting process. In this study, we present results from three dimensional acoustic full waveform inversion performed using wide-angle seismic data acquired in the deep water environments of the Deep Galicia margin using ocean bottom seismometers. We performed full waveform inversion in the time domain, starting with a velocity model obtained using travel-time tomography, of dimensions 78.5 km x 22.1 km and depth 12 km. The high-resolution modelling shows short-wavelength variations in the velocity, adding details to the travel-time model. We superimposed our final model, converted to two-way time, on pre-stack time-migrated three-dimensional reflection data from the same survey. Compared to the starting model, our model shows improved alignment of the velocity variations along the steeply dipping normal faults and a sharp velocity contrast across the S-reflector. We validated our result using checkerboard tests, by tracking changes in phases of the first arrivals during the inversion and by comparing the observed and the synthetic waveforms. We observe a clear evidence for preferential serpentinization (45 %) of the mantle with lower velocities in the mantle correlating with the fault intersections with the S-reflector.</p>

scholarly journals Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

2021 ◽  
Author(s):  
Ehsan Jamali Hondori ◽  
Chen Guo ◽  
Hitoshi Mikada ◽  
Jin-Oh Park
Keyword(s):  
Seismic Data ◽  
Initial Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Control Measures ◽  
Japan Trench ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Time Migration ◽  
Shallow Sediments

AbstractFull-waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model in a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to ensure that the observed and simulated waveforms kinematically fit within an error of less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer-stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM), to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data are acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model that could improve the depth images down to almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature that could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.

scholarly journals Three-dimensional full waveform inversion of short-period teleseismic wavefields based upon the SEM–DSM hybrid method

2015 ◽  
Vol 202 (2) ◽  
pp. 811-827 ◽  
Author(s):  
Vadim Monteiller ◽  
Sébastien Chevrot ◽  
Dimitri Komatitsch ◽  
Yi Wang
Keyword(s):  
Hybrid Method ◽  
Waveform Inversion ◽  
Three Dimensional ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Short Period

Pre-Stack Full Waveform Inversion for Velocity Model Building

2011 ◽  
Author(s):  
S. Jerry Kapoor ◽  
Denes Vigh ◽  
Timothy John Bunting
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Velocity Model Building

Full-waveform inversion for full-wavefield imaging: Decades in the making

2021 ◽  
Vol 40 (5) ◽  
pp. 324-334
Author(s):  
Rongxin Huang ◽  
Zhigang Zhang ◽  
Zedong Wu ◽  
Zhiyuan Wei ◽  
Jiawei Mei ◽  
...  
Keyword(s):  
Model Building ◽  
Seismic Imaging ◽  
Structural Information ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Data Sets ◽  
Full Waveform Inversion ◽  
Data Types ◽  
Velocity Models ◽  
Full Waveform

Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward-modeling process to generate full-wavefield data, an inversion scheme to minimize the difference between modeled and recorded data, and, more importantly, an accurate velocity model to correctly propagate and collapse energy of different wave modes. All of these elements have been embedded in the framework of full-waveform inversion (FWI) since it was proposed three decades ago. However, for a long time, the application of FWI did not find its way into the domain of full-wavefield imaging, mostly owing to the lack of data sets with good constraints to ensure the convergence of inversion, the required compute power to handle large data sets and extend the inversion frequency to the bandwidth needed for imaging, and, most significantly, stable FWI algorithms that could work with different data types in different geologic settings. Recently, with the advancement of high-performance computing and progress in FWI algorithms at tackling issues such as cycle skipping and amplitude mismatch, FWI has found success using different data types in a variety of geologic settings, providing some of the most accurate velocity models for generating significantly improved migration images. Here, we take a step further to modify the FWI workflow to output the subsurface image or reflectivity directly, potentially eliminating the need to go through the time-consuming conventional seismic imaging process that involves preprocessing, velocity model building, and migration. Compared with a conventional migration image, the reflectivity image directly output from FWI often provides additional structural information with better illumination and higher signal-to-noise ratio naturally as a result of many iterations of least-squares fitting of the full-wavefield data.

Multi-parameter model building for the Qiuyue structure using four-component ocean-bottom seismometer data

Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Yuzhu Liu ◽  
Xinquan Huang ◽  
Jizhong Yang ◽  
Xueyi Liu ◽  
Bin Li ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Velocity Analysis ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Full Waveform ◽  
P Wave Velocity

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multi-channel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom seismometer (OBS) acquisition program acquired a four-component dataset in East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we applied a full-waveform inversion (FWI) workflow to this dense four-component OBS dataset. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from 3D to 2D, a preconditioned first-arrival traveltime tomography based on an improved scattering integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at 2.0 km and 4.7 km depth. Initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic full-waveform inversion to refine both velocity models. Compared to a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single-component, the workflow presented in this study represents a good approach for inverting the four-component OBS dataset to characterize sub-seafloor velocity structures.

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