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CWP Seminars - 2016 Spring

CWP seminars discuss topics pertaining to our broad areas of research interests. These seminars are led by CWP faculty, students and, on occasion, by guest presenters. CWP seminars are held every Monday at 4 p.m. in the Green Center on the Colorado School of Mines campus. Click here to see previous CWP Seminars.

Note: To view weekly seminar schedules of individual CWP teams, click a link below:

A-Team seminars

C-Team seminars

iTeam seminars

Steam seminars

 

Spring 2016 CWP seminars

Date Speaker(s) Title Abstract
4/11 - 5/9

2016 Project Review Meeting preparations - no CWP Seminars

4/4

Alex Jia

Alex Jia

Marchenko imaging: synthetic and field data examples

Marchenko equations enables the retrieval of the Green's response to a virtual source in the subsurface from reflection measurements at the earth's surface. The Green's function retrieved contains accurate internal multiples of the inhomogeneous subsurface. However, the theories for acoustic Marchenko imaging is currently at the stage of synthetic experiments. In this talk, I will present one of the first studies to apply Marchenko imaging method to marine towed streamer data acquired at Gulf of Mexico. I will discuss the work flow and problems with field data, and compare the field data Marchenko image with RTM and least-square RTM image.

3/28

Oscar Jarillo Michel

Oscar Jarillo Michel

Anisotropic waveform inversion for microseismic velocity analysis and event location

Waveform inversion (WI) has been extensively used in reflection seismology and it can also provide improved velocity models and event locations for microseismic surveys. First, I will briefly introduce finite-difference modeling for SH-waves in VTI (transversely isotropic with a vertical symmetry axis) media, which may help constrain inversion for different parameters. Then, I will talk about an elastic WI algorithm for anisotropic media designed to estimate the 2D velocity field along with the source parameters (location, origin time, and moment tensor). The gradient of the objective function is obtained with the adjoint-state method, which requires two modeling simulations at each iteration. In the current implementation either the source parameters or the velocity model are fixed to minimize parameter trade-offs. Synthetic examples illustrate the accuracy of the inversion for layered VTI media and the sensitivity of the results to perturbations in the initial parameters.

3/21

Yuting Duan

Yuting Duan

Elastic least-squares reverse time migration

Least-squares migration (LSM) is an improved imaging algorithm that reduces migration artifacts and also improves the resolution of migration images. We propose four elastic least-squares migration reverse time migration (LSRTM) algorithms based on four types of imaging conditions. First, we formulate LSRTM using a new perturbation imaging condition, and compute perturbation images for squared P and S velocity models. Next, we construct an LSRTM algorithm using the displacement imaging condition that crosscorrelates components of the source and receiver displacement wavefields. Finally we derive LSRTM algorithms using conventional and scalar imaging conditions, which provide PP, PS, SP, and SS images. Among the four types of images, the perturbation image and scalar image do not suffer from polarity changes, and they can be stacked over experiments without an additional polarity correction. Using the perturbation imaging condition, we demonstrate that we are able to obtain images with higher resolution and reduced migration artifacts.

3/14

CSM Spring Break - no CWP Seminar

3/7

Thomas Planes

Thomas Plan├Ęs

Computing focusing functions for model-based redatuming: An inverse filter approach

Model-based redatuming aims at creating virtual sources and/or receivers below partially known upper layers. Such techniques usually consist in applying time shifts and amplitude corrections to the surface responses. These methods allow a correct focus of the direct waves but do not handle multiples correctly. I will present an approach to compute exact focusing functions based on the inversion of the transmission matrix of the upper layers. In theory, this method allows to achieve a "perfect" redatuming, in the sense that it completely removes the effects of the upper layers (primaries and multiples). It does require however a full knowledge of these layers.

Aaron Prunty

Aaron Prunty

Little arrows, BIG ARROWS, and this thing called the memory effect

A very interesting property of wave transmission through strongly disordered media, termed "the memory effect", was originally predicted and experimentally verified in the optics community back in the 1980's. I will discuss and illustrate what the memory effect is, and preview some of the extremely powerful imaging applications it has since been applied to in various fields that rely on optical imaging methods. I will then illustrate the theory of how this transmission property works, using simple language and intuitive ideas, and finally conclude with some preliminary results of my research on understanding this transmission property in acoustic media.

2/29

Tong Bai

Tong Bai

Waveform inversion for attenuation estimation in anisotropic media

In this talk, I will focus on models with VTI symmetry for both velocity and attenuation. Time-domain finite difference method will be serving as the forward engine, with the Standard Linear Solid (SLS) model accounting for the attenuation effects. With the accurate velocity parameters, I will only invert for Thomsen-style attenuation parameters Qp0, Qs0, epsQ, and delQ. I will show the derivations of gradients of corresponding attenuation parameters, on the basis of Tarantola's scheme (1988). Some transmission experiments with Gaussian model will be provided to illustrate the feasibility of recovering anisotropic attenuation parameters.

2/22

Daniel Rocha, Jr.

Daniel Rocha, Jr.

Elastic imaging applications using the energy norm

The elastic imaging condition based on the energy norm is applicable not only to isotropic media, as seen in my previous presentations, but also to anisotropic media with any symmetry system. For reverse time migration, the proposed imaging condition does not suffer from polarity reversal and backscattering artifacts. For the anisotropic case, the main advantage of this imaging condition is that no wave-mode decomposition is required. Wave-mode decomposition substantially increases the cost of elastic wavefield imaging in anisotropic media. Furthermore, we can apply this imaging condition to a passive seismic experiment. For instance, one can use wavefield imaging to locate microseismic sources in areas subject to hydraulic fracturing. The correlation between the P- and S- decomposed modes from the backpropagated wavefield is the most used imaging condition for a passive experiment using elastic extrapolated wavefields. Alternatively, for passive imaging, we suggest to use the energy imaging condition, which does not require decomposed wave modes. We show an elastic TTI Marmousi example and a simple tilted orthorhombic experiment to highlight the benefits of the energy-norm application for migration, and a simple 2D passive experiment using a double-couple source for the microseismic application.

2/15

President's Day holiday - no CWP Seminar

2/8

John Stockwell

John Stockwell

Data resources and hazards

John will talk about the data that is available to CWP and the new CWP data library, Athena.

 

2/1

Satyan Singh

Satyan Singh

Beyond Marchenko - obtaining virtual receivers and virtual sources in the subsurface to retrieve the virtual Green's function

 

By solving the Marchenko equations, the Green's function can be retrieved between a virtual receiver in the subsurface to points at the surface (no physical receiver is required at the virtual location). We extend the idea of these equations to retrieve the Green's function between any two points in the subsurface; i.e, between a virtual source and a virtual receiver (no physical source or physical receiver is required at either of these locations). This Green's function is called the virtual Green's function and includes all the primaries, internal and free-surface multiples. Similar to the Marchenko Green's function, we require the reflection response at the surface (single-sided illumination) and an estimate of the first arrival travel time from the virtual location to the surface.

1/25

Nishant Kamath

Nishant Kamath

Full waveform inversion for elastic 2D VTI media

When we perform Full-waveform Inversion (FWI) for multiple parameters, the manner in which the model is parameterized influences the results. In the case of FWI for elastic 2D VTI media, we invert for four model parameters. I will present results of tests performed on a synthetic model for three parameterizations, and explain them in terms of radiation patterns.

1/18

Thomas Rapstine

Thomas Rapstine

Recovering sound from video

I will present on the final project work for a class I took last semester. The topic of the work involves recovering monochromatic sound from video

Ivan Chen Ning Lim

Ivan Lim

Distributed acoustic sensing

My research area is on Distributed Acoustic Sensing (DAS). DAS system uses optical fiber cables to perform strain measurements along the cable with a special "interrogator unit" IU. My current research involves using the measured strain along three opticl fiber cables to calculate three-component displacement. The three different strain measurements provide information on curvature and torsion of the cable which allows the determination of the shape of the cable. Curvature, torsion and shape can be related through the Frenet-Serret framework. The determination of the shape shows the displacement at the measurement points of the optical fiber.

Lucas Andrade de Almeida

Lucas Andrade de Almeida

Compressive sensing and reconstruction

Compressive sensing is a new way of understanding how to solve underdetermined problems when the signal of interest is sparse. Using this approach, one can recover high dimensional signals by measuring only a fraction of the samples that would be necessary. In this talk, I will briefly present the basics of compressive sensing with some examples of reconstruction and talk about my current research in the field.

1/11

1st CWP seminar of the Spring 2016 semester (CWP administrative topics)

 

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2013

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2012

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