Another old paper is added to the collection of reproducible documents:
Seismic AVO analysis of methane hydrate structures

Marine seismic data from the Blake Outer Ridge offshore Florida show strong “bottom simulating reflections” (BSR) associated with methane hydrate occurence in deep marine sediments. We use a detailed amplitude versus offset (AVO) analysis of these data to explore the validity of models which might explain the origin of the bottom simulating reflector. After careful preprocessing steps, we determine a BSR model which can successfully reproduce the observed AVO responses. The P- and S-velocity behavior predicted by the forward modeling is further investigated by estimating the P- and S-impedance contrasts at all subsurface positions. Our results indicate that the Blake Outer Ridge BSR is compatible with a model of methane hydrate in sediment, overlaying a layer of free methane gas-saturated sediment. The hydrate-bearing sediments seem to be characterized by a high P-wave velocity of approximately 2.5 km/s, an anomalously low S-wave velocity of approximately 0.5 km/s, and a thickness of around 190 meters. The underlaying gas-saturated sediments have a P-wave velocity of 1.6 km/s, an S-wave velocity of 1.1 km/s, and a thickness of approximately 250 meters.

A new paper is added to the collection of reproducible documents:
Acoustic staggered grid modeling in IWAVE

IWAVE is a framework for time-domain regular grid finite difference and finite element methods. The IWAVE package includes source code for infrastructure component, and implementations of several wave physics modeling categories. This paper presents two sets of examples using IWAVE acoustic staggered grid modeling. The first set illustrates the effectiveness of a simple version of Perfectly Matched Layer absorbing boundary conditions. The second set reproduce illustrations from a recent paper on error propagation for heterogeneous medium simulation using finite differences, and demostrate the interface error effect which renders all FD methods effectively first-order accurate. The source code for these examples is packaged with the paper source, and supports the user in duplicating the results presented here and using IWAVE in other settings.

Another old paper is added to the collection of reproducible documents:
A prospect for super resolution

Wouldn’t it be great if I could take signals of 10-30 Hz bandwidth from 100 different offsets and construct a zero-offset trace with 5-100 Hz bandwidth? This would not violate Shannon’s sampling theorem which theoretically allows us to have a transform from 100 signals of 20 Hz bandwidth to one signal at 2000 Hz bandwidth. The trouble is that simple NMO is not such a transformation. Never-the-less, if the different offsets really did give us any extra information, we should be able to put the information into extra bandwidth. Let us consider noise free synthetic data and see if we can come up with a model where this could happen.

Another old paper is added to the collection of reproducible documents:
Earthquake stacks at constant offset

I show Shearer’s earthquake stacks over all source-receiver locations at constant offset and compare them to exploration seismic data. This electronic document simply reads the stacks and plots them.

A new paper is added to the collection of reproducible documents:
Adaptive prediction filtering in t-x-y domain for random noise attenuation using regularized nonstationary autoregression

Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary. They may exhibit stationarity on a short timescale but eventually alter their behavior in time and space. We propose a 2D t-x adaptive prediction filter (APF) and further extend this to a 3D t-x-y version for random noise attenuation based on regularized nonstationary autoregression (RNA). Instead of using patching, a popular method for handling nonstationarity, we obtain smoothly nonstationary APF coefficients by solving a global regularized least-squares problem. We use shaping regularization to control the smoothness of the coefficients of APF. 3D space-noncausal t-x-y APF uses neighboring traces around the target traces in the 3D seismic cube to predict noise-free signal, so it provides more accurate prediction results than the 2D version. In comparison with other denoising methods, such as frequency-space deconvolution, time-space prediction filter, and frequency-space RNA, we test the feasibility of our method in reducing seismic random noise on three synthetic datasets. Results of applying the proposed method to seismic field data demonstrate that nonstationary t-x-y APF is effective in practice.

This reproducible paper is the first direct contribution from Jilin University, China.

Another old paper is added to the collection of reproducible documents:
The time and space formulation of azimuth moveout

Azimuth moveout (AMO) transforms 3-D prestack seismic data from one common azimuth and offset to different azimuths and offsets. AMO in the time-space domain is represented by a three-dimensional integral operator. The operator components are the summation path, the weighting function, and the aperture. To determine the summation path and the weighting function, we derive the AMO operator by cascading dip moveout (DMO) and inverse DMO for different azimuths in the time-space domain. To evaluate the aperture, we apply a geometric approach, defining AMO as the result of cascading prestack migration (inversion) and modeling. The aperture limitations provide a consistent description of AMO for small azimuth rotations (including zero) and justify the economic efficiency of the method.

A new paper is added to the collection of reproducible documents:
Deblending using normal moveout and median filtering in common-midpoint gathers

The benefits of simultaneous source acquisition are compromised by the challenges of dealing with intense blending noise. In this paper, we propose a processing workflow for blended data. The incoherent property of blending noise in the common-midpoint (CMP) gathers is utilized for applying median filtering along the spatial direction after normal moveout (NMO) correction. The key step in the proposed workflow is that we need to obtain a precise velocity estimation which is required by the subsequent NMO correction. Because of the intense blending noise, the velocity scan can not be obtained in one step. We can recursively polish both deblended result and velocity estimation by deblending using the updated velocity estimation and velocity scanning using the updated deblended result. We use synthetic and field data examples to demonstrate the performance of the proposed approach. The migrated image of deblended data is cleaner than that of blended data, and is similar to that of unblended data.

A new paper is added to the collection of reproducible documents:
A robust approach to time-to-depth conversion and interval velocity estimation from time migration in the presence of lateral velocity variations

The problem of conversion from time-migration velocity to an interval velocity in depth in the presence of lateral velocity variations can be reduced to solving a system of partial differential equations. In this paper, we formulate the problem as a nonlinear least-squares optimization for seismic interval velocity and seek its solution iteratively. The input for inversion is the Dix velocity which also serves as an initial guess. The inversion gradually updates the interval velocity in order to account for lateral velocity variations that are neglected in the Dix inversion. The algorithm has a moderate cost thanks to regularization that speeds up convergence while ensuring a smooth output. The proposed method should be numerically robust compared to the previous approaches, which amount to extrapolation in depth monotonically. For a successful time-to-depth conversion, image-ray caustics should be either nonexistent or excluded from the computational domain. The resulting velocity can be used in subsequent depth-imaging model building. Both synthetic and field data examples demonstrate the applicability of the proposed approach.

A new paper is added to the collection of reproducible documents:
Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization

We introduce a novel iterative estimation scheme for separation of blended seismic data from simultaneous sources. The scheme is based on an augmented estimation problem, which can be solved by iteratively constraining the deblended data using shaping regularization in the seislet domain. We formulate the forward modeling operator in the common receiver domain, where two sources are assumed to be blended using a random time-shift dithering approach. The nonlinear shaping-regularization framework offers some freedom in designing a shaping operator to constrain the model in an underdetermined inverse problem. We design the backward operator and the shaping operator for the shaping regularization framework. The backward operator can be optimally chosen as a half of the identity operator in the two-source case, and the shaping operator can be chosen as coherency-promoting operator. Three numerically blended synthetic datasets and one numerically blended field dataset demonstrate the high-performance deblending effect of the proposed iterative framework. Compared with alternative f-k domain thresholding and f-x predictive filtering, seislet-domain soft thresholding exhibits the most robust behavior.

A new paper is added to the collection of reproducible documents:
Fast algorithms for elastic-wave-mode separation and vector decomposition using low-rank approximation for anisotropic media

Wave mode separation and vector decomposition are significantly more expensive than wavefield extrapolation and are the computational bottleneck for elastic reverse-time migration (ERTM) in heterogeneous anisotropic media. We express elastic wave mode separation and vector decomposition for anisotropic media as space-wavenumber-domain operations in the form of Fourier integral operators, and develop fast algorithms for their implementation using their low-rank approximations. Synthetic data generated from 2D and 3D models demonstrate that these methods are accurate and efficient.