The near-surface velocity reversal and its detection via unsupervised machine learning

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. U55-U63
Author(s):  
Mengyao Sun ◽  
Jie Zhang
Keyword(s):  
Machine Learning ◽  
Real Data ◽  
Automated Detection ◽  
Surface Velocity ◽  
Unsupervised Machine Learning ◽  
Detection Scheme ◽  
Near Surface ◽  
Waveform Modeling ◽  
Full Waveform ◽  
Velocity Reversal

In land seismic data processing, picking the first arrivals and imaging the near-surface velocity structures are important tasks. However, in many areas, the near-surface weathering layer includes high-velocity reversals, causing the first arrivals to exhibit shingling effects, which are difficult for picking at the far offset. We have used an acoustic full-waveform modeling method in a multilayered half-space to simulate first arrivals with the velocity reversal. Numerical tests indicate that under certain conditions, shingling occurs if the seismic wave propagates through a thin velocity reversal layer embedded in the shallow structures. Detection of shingling is essential for the selection of valid near-surface imaging solutions, such as first-arrival refraction, or waveform solutions for the appropriate areas. We find that an automated detection scheme that uses unsupervised machine learning can help identify the velocity reversal. We test the method on synthetic and real data, and the testing shows that the automated detection result matches our visual judgment well. After the automated detection, appropriate inversion approaches can be applied to corresponding areas.

FULL-WAVEFORM INVERSION WITH SOURCE AND RECEIVER COUPLING EFFECTS CORRECTION

Geophysics ◽  
2021 ◽  
pp. 1-37
Author(s):  
Xinhai Hu ◽  
Wei Guoqi ◽  
Jianyong Song ◽  
Zhifang Yang ◽  
Minghui Lu ◽  
...  
Keyword(s):  
Waveform Inversion ◽  
Nonlinear Least Squares ◽  
Real Data ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Linear Inversion ◽  
Near Surface ◽  
Model Parameter ◽  
Full Waveform ◽  
Coupling Factors

Coupling factors of sources and receivers vary dramatically due to the strong heterogeneity of near surface, which are as important as the model parameters for the inversion success. We propose a full waveform inversion (FWI) scheme that corrects for variable coupling factors while updating the model parameter. A linear inversion is embedded into the scheme to estimate the source and receiver factors and compute the amplitude weights according to the acquisition geometry. After the weights are introduced in the objective function, the inversion falls into the category of separable nonlinear least-squares problems. Hence, we could use the variable projection technique widely used in source estimation problem to invert the model parameter without the knowledge of source and receiver factors. The efficacy of the inversion scheme is demonstrated with two synthetic examples and one real data test.

Full waveform inversion: early start of model building process to resolve the complex near surface model in the Exmouth sub-basin, North West Shelf Australia

2018 ◽  
Vol 58 (2) ◽  
pp. 884
Author(s):  
Lianping Zhang ◽  
Haryo Trihutomo ◽  
Yuelian Gong ◽  
Bee Jik Lim ◽  
Alexander Karvelas
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Surface Velocity ◽  
Image Point ◽  
Surface Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
North West ◽  
Full Waveform

The Schlumberger Multiclient Exmouth 3D survey was acquired over the Exmouth sub-basin, North West Shelf Australia and covers 12 600 km2. One of the primary objectives of this survey was to produce a wide coverage of high quality imaging with advanced processing technology within an agreed turnaround time. The complexity of the overburden was one of the imaging challenges that impacted the structuration and image quality at the reservoir level. Unlike traditional full-waveform inversion (FWI) workflow, here, FWI was introduced early in the workflow in parallel with acquisition and preprocessing to produce a reliable near surface velocity model from a smooth starting model. FWI derived an accurate and detailed near surface model, which subsequently benefitted the common image point (CIP) tomography model updates through to the deeper intervals. The objective was to complete the FWI model update for the overburden concurrently with the demultiple stages hence reflection time CIP tomography could start with a reasonably good velocity model upon completion of the demultiple process.

Turning‐ray tomography

Geophysics ◽  
1995 ◽  
Vol 60 (6) ◽  
pp. 1917-1929 ◽  
Author(s):  
Joseph P. Stefani
Keyword(s):  
Velocity Structure ◽  
Initial Point ◽  
Real Data ◽  
Point Sources ◽  
Surface Velocity ◽  
Inversion Algorithm ◽  
Low Velocity ◽  
Linear Inversion ◽  
Near Surface ◽  
Velocity Inversion

Turning‐ray tomography is useful for estimating near‐surface velocity structure in areas where conventional refraction statics techniques fail because of poor data or lack of smooth refractor/velocity structure. This paper explores the accuracy and inherent smoothing of turning‐ray tomography in its capacity to estimate absolute near‐surface velocity and the statics times derived from these velocities, and the fidelity with which wavefields collapse to point diffractors when migrated through these estimated velocities. The method comprises nonlinear iterations of forward ray tracing through triangular cells linear in slowness squared, coupled with the LSQR linear inversion algorithm. It is applied to two synthetic finite‐ difference data sets of types that usually foil conventional refraction statics techniques. These models represent a complex hard‐rock overthrust structure with a low‐velocity zone and pinchouts, and a contemporaneous near‐shore marine trench filled with low‐ velocity unconsolidated deposits exhibiting no seismically apparent internal structure. In both cases velocities are estimated accurately to a depth of one‐ fifth the maximum offset, as are the associated statics times. Of equal importance, the velocities are sufficiently accurate to correctly focus synthetic wavefields back to their initial point sources, so migration/datuming applications can also use these velocities. The method is applied to a real data example from the Timbalier Trench in the Gulf of Mexico, which exhibits the same essential features as the marine trench synthetic model. The Timbalier velocity inversion is geologically reasonable and yields long and short wavelength statics that improve the CMP gathers and stack and that correctly align reflections to known well markers. Turning‐ray tomography estimates near‐surface velocities accurately enough for the three purposes of lithology interpretation, statics calculations, and wavefield focusing for shallow migration and datuming.

Near-surface full-waveform inversion in a transmission surface-consistent scheme

Geophysics ◽  
2020 ◽  
pp. 1-57
Author(s):  
Daniele Colombo ◽  
Ernesto Sandoval ◽  
Diego Rovetta ◽  
Apostolos Kontakis
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Surface Velocity ◽  
Full Waveform Inversion ◽  
Velocity Mapping ◽  
Signal To Noise ◽  
Near Surface ◽  
Velocity Modeling ◽  
Full Waveform

Land seismic velocity modeling is a difficult task largely related to the description of the near surface complexities. Full waveform inversion is the method of choice for achieving high-resolution velocity mapping but its application to land seismic data faces difficulties related to complex physics, unknown and spatially varying source signatures, and low signal-to-noise ratio in the data. Large parameter variations occur in the near surface at various scales causing severe kinematic and dynamic distortions of the recorded wavefield. Some of the parameters can be incorporated in the inversion model while others, due to sub-resolution dimensions or unmodeled physics, need to be corrected through data preconditioning; a topic not well described for land data full waveform inversion applications. We have developed novel algorithms and workflows for surface-consistent data preconditioning utilizing the transmitted portion of the wavefield, signal-to-noise enhancement by generation of CMP-based virtual super shot gathers, and robust 1.5D Laplace-Fourier full waveform inversion. Our surface-consistent scheme solves residual kinematic corrections and amplitude anomalies via scalar compensation or deconvolution of the near surface response. Signal-to-noise enhancement is obtained through the statistical evaluation of volumetric prestack responses at the CMP position, or virtual super (shot) gathers. These are inverted via a novel 1.5D acoustic Laplace-Fourier full waveform inversion scheme using the Helmholtz wave equation and Hankel domain forward modeling. Inversion is performed with nonlinear conjugate gradients. The method is applied to a complex structure-controlled wadi area exhibiting faults, dissolution, collapse, and subsidence where the high resolution FWI velocity modeling helps clarifying the geological interpretation. The developed algorithms and automated workflows provide an effective solution for massive full waveform inversion of land seismic data that can be embedded in typical near surface velocity analysis procedures.

Introduction to this special section: Near-surface imaging and modeling

2020 ◽  
Vol 39 (5) ◽  
pp. 310-310
Author(s):  
Steve Sloan ◽  
Dan Feigenbaum
Keyword(s):  
Open Source Software ◽  
Special Section ◽  
Waveform Inversion ◽  
Radar Data ◽  
Surface Velocity ◽  
Surface Imaging ◽  
Near Surface ◽  
Velocity Models ◽  
Full Waveform ◽  
Ground Penetrating

This special section on near-surface imaging and modeling was intended originally to focus on improving deeper imaging for exploration purposes through more accurate representations of the near surface, the highly variable zone that energy must traverse through on the way down and back up again to be recorded at the surface. However, as proposed manuscript topics started coming in, it became clear that this section would cover a wider range, from kilometers down to meters. Papers in this section highlight a range of near-surface-related work that includes applying full-waveform inversion (FWI) to improve near-surface velocity models, identifying potential sinkhole hazards before they collapse, the potential of smartphones as geophysical sensors, and new open-source software for ground-penetrating radar data.

Characterizing the near-surface velocity structures by applying machine learning

2018 ◽  
Author(s):  
Ziang Li ◽  
Jie Zhang ◽  
Zhiyang Liu ◽  
Sen Liu ◽  
Zhibo Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Velocity ◽  
Near Surface

scholarly journals Full Waveform Inversion in generalized coordinates for zones of curved topography

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
César Augusto Arias- Chica ◽  
David Abreo ◽  
Sergio Abreo ◽  
Luis Fernando Duque- Gómez ◽  
Ana Beatríz Ramírez- Silva
Keyword(s):  
Computing Time ◽  
Waveform Inversion ◽  
Surface Velocity ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Velocity Models ◽  
Full Waveform ◽  
Wide Range ◽  
Rugged Topography ◽  
Uniform Rectangular Grid

Full waveform inversion (FWI) has been recently used to estimate subsurface parameters, such as velocity models. This method, however, has a number of drawbacks when applied to zones with rugged topography due to the forced application of a Cartesian mesh on a curved surface. In this work, we present a simple coordinate transformation that enables the construction of a curved mesh. The proposed transformation is more suitable for rugged surfaces and it allows mapping a physical curved domain into a uniform rectangular grid, where acoustic FWI can be applied in the traditional way by introducing a modified Laplacian. We prove that the proposed approximation can have a wide range of applications, producing precise near-surface velocity models without increasing the computing time of the FWI.

High-resolution Radon preconditioning for full-waveform inversion of land seismic data

2017 ◽  
Vol 5 (4) ◽  
pp. SR23-SR33 ◽  
Author(s):  
Xin Cheng ◽  
Kun Jiao ◽  
Dong Sun ◽  
Zhen Xu ◽  
Denes Vigh ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Velocity Fields ◽  
Surface Velocity ◽  
Surface Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Full Waveform

Over the past decade, acoustic full-waveform inversion (FWI) has become one of the standard methods in the industry to construct high-resolution velocity fields from the seismic data acquired. While most of the successful applications are for marine acquisition data with rich low-frequency diving or postcritical waves at large offsets, the application of acoustic FWI on land data remains a challenging topic. Land acoustic FWI application faces many severe difficulties, such as the presence of strong elastic effects, large near-surface velocity contrast, and heterogeneous, topography variations, etc. In addition, it is well-known that low-frequency transmitted seismic energy is crucial for the success of FWI to overcome sensitivity to starting velocity fields; unfortunately, those are the parts of the data that suffer the most from a low signal-to-noise ratio (S/N) in land acquisition. We have developed an acoustic FWI application on a land data set from North Kuwait, and demonstrated our solutions to mitigate some of the challenges posed by land data. More specifically, we have developed a semblance-based high-resolution Radon (HR-Radon) inversion approach to enhance the S/N of the low-frequency part of the FWI input data and to ultimately improve the convergence of the land FWI workflow. To mitigate the impact of elastic effects, we included only the diving and postcritical early arrivals in the waveform inversion. Our results show that, with the aid of HR-Radon preconditioning and a carefully designed workflow, acoustic FWI has the ability to derive a reliable high-resolution near-surface model that could not be otherwise recovered through traditional tomographic methods.

An application of full-waveform inversion to land data using the pseudo-Hessian matrix

2016 ◽  
Vol 4 (4) ◽  
pp. T627-T635
Author(s):  
Yikang Zheng ◽  
Wei Zhang ◽  
Yibo Wang ◽  
Qingfeng Xue ◽  
Xu Chang
Keyword(s):  
Hessian Matrix ◽  
Waveform Inversion ◽  
Computational Cost ◽  
Velocity Model ◽  
Surface Velocity ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Near Surface ◽  
Full Waveform ◽  
The Time Domain

Full-waveform inversion (FWI) is used to estimate the near-surface velocity field by minimizing the difference between synthetic and observed data iteratively. We apply this method to a data set collected on land. A multiscale strategy is used to overcome the local minima problem and the cycle-skipping phenomenon. Another obstacle in this application is the slow convergence rate. The inverse Hessian can enhance the poorly blurred gradient in FWI, but obtaining the full Hessian matrix needs intensive computation cost; thus, we have developed an efficient method aimed at the pseudo-Hessian in the time domain. The gradient in our FWI workflow is preconditioned with the obtained pseudo-Hessian and a synthetic example verifies its effectiveness in reducing computational cost. We then apply the workflow on the land data set, and the inverted velocity model is better resolved compared with traveltime tomography. The image and angle gathers we get from the inversion result indicate more detailed information of subsurface structures, which will contribute to the subsequent seismic interpretation.

Full-waveform static corrections using blind channel identification

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. U55-U66 ◽  
Author(s):  
Robbert van Vossen ◽  
Jeannot Trampert
Keyword(s):  
Synthetic Data ◽  
Depth Resolution ◽  
Surface Heterogeneity ◽  
Surface Velocity ◽  
Coherent Noise ◽  
Data Set ◽  
Channel Identification ◽  
Near Surface ◽  
Full Waveform ◽  
Static Corrections

Near-surface wavefield perturbations can be very complex and completely mask the target reflections. Despite this complexity, conventional methods rely on parameterizations characterized by simple time and amplitude anomalies to compensate for these perturbations. Determining and compensating for time shifts is generally referred to as (residual) static corrections, whereas surface-consistent deconvolution techniques deal with amplitude anomalies. We present an approach that uses the full waveform to parameterize near-surface perturbations. Therefore, we refer to this method as waveform statics. Important differences from conventional static corrections are that this approach allows time shifts to vary with frequency and takes amplitude variations directly into account. Furthermore, the procedure is fully automated and does not rely on near-surface velocity information. The waveform static corrections are obtained usingblind channel identification and applied to the recordings using multichannel deconvolution. As a result, the method implicitly incorporates array forming. The developed method is validated on synthetic data and applied to part of a field data set acquired in an area with significant near-surface heterogeneity. The source and receiver responses obtained are strongly correlated to the near-surface conditions and show changes, both in phase and frequency content, along the spread. The application of the waveform statics demonstrates that they not only correct for near-surface wavefield perturbations, but also strongly reduce coherent noise. This results in substantial improvements, both in trace-to-trace coherency and in depth resolution. In addition, the procedure delineates reflection events that are difficult to detect prior to our proposed correction. Based on these results, we conclude that complex near-surface perturbations can be successfully dealt with using the multichannel, full-waveform, static-correction procedure.

Sign in / Sign up

Export Citation Format

Share Document