An old paper is added to the collection of reproducible documents: Probabilistic moveout analysis by time warping

Parameter estimation from reflection moveout analysis represents one of the most fundamental problems in subsurface model building. We propose an efficient moveout inversion method that is based on the process of automatic flattening of common-midpoint (CMP) gathers using local slopes. We show that as a byproduct of this flattening process, we can also estimate reflection traveltimes corresponding to the flattened CMP gathers. This traveltime information allows us to construct a highly overdetermined system and subsequently invert for moveout parameters including normal-moveout (NMO) velocities and quartic coefficients related to anisotropy. We utilize the 3D generalized moveout approximation (GMA) that can accurately capture the effects of complex anisotropy on reflection traveltimes as the basis for our moveout inversion. Due to the cheap forward traveltime computations by GMA, we employ a Monte Carlo inversion scheme for an improved handling of the non-linearity between reflection traveltimes and moveout parameters. This choice also allows us to set up a probabilistic inversion workflow within a Bayesian framework, where we can obtain the posterior probability distributions that contain valuable statistical information on estimated parameters such as uncertainty and correlations. We use synthetic and real-data examples including the data from the SEAM Phase II unconventional reservoir model to demonstrate the performance of our proposed method and discuss insights into the problem of moveout inversion gained from analyzing the posterior probability distributions. Our results suggest that the solutions to the problem of traveltime-only moveout inversion from 2D CMP gathers are relatively well-constrained by the data. However, parameter estimation from 3D CMP gathers associated with more moveout parameters and complex anisotropic models are generally non-unique and that there are trade-offs among inverted parameters, especially the quartic coefficients.

An old paper is added to the collection of reproducible documents: Relative time seislet transform

The seislet transform utilizes the wavelet-lifting scheme and local slopes to analyze the seismic data. In its definition, the designing of prediction operators specifically for seismic images and data is an important issue. We propose a new formulation of the seislet transform based on the relative time (RT) attribute. This method uses RT volume to construct multiscale prediction operators. With the new prediction operators, the seislet transform gets accelerated since distant traces get predicted directly. We apply the proposed method to synthetic and real data to demonstrate that the new approach reduces computational cost and obtains excellent sparse representation on test datasets.

An old paper is added to the collection of reproducible documents: High-resolution recursive stacking using plane-wave construction

We propose an approach to normal moveout (NMO) stacking, which eliminates the effects of “NMO stretch” and restores a wider frequency band by replacing conventional stacking with a regularized inversion to zero offset. The resulting stack is a model that best fits the data using additional constraints imposed by shaping regularization. We introduce a recursive stacking scheme using plane-wave construction in the backward operator of shaping regularization to achieve a higher resolution stack. The advantage of using recursive stacking along local slopes in the application to NMO and stack is that it avoids “stretching effects” caused by NMO correction and is insensitive to non-hyperbolic moveout in the data. Numerical tests demonstrate the algorithm’s ability to attain a higher frequency stack with a denser temporal sampling interval compared to those of the conventional stack and to minimize stretching effects caused by NMO correction. We apply this method to a 2-D field dataset from the North Sea and achieve noticeable resolution improvements in the stacked section compared with that of conventional NMO and stack.

An old paper is added to the collection of reproducible documents: Microseismic source localization using time-domain path-integral migration

Localization of passive seismic sources is crucial for real-time monitoring of hydraulic fracturing. Using the similarity of diffraction imaging and passive seismic imaging, we propose a method that uses path-integral formulation to apply diffraction-type migration on time-shifted microseismic records and to focus seismic energy focuses at correct onsets and accurate locations in time coordinates. An efficient workflow is applied to do path-integral migration based on analytical integration. Passive seismic sources can be additionally highlighted by envelope stacking. Numerical experiments with synthetic data verify the effectiveness of the proposed method.

An old paper is added to the collection of reproducible documents: Full waveform inversion of passive seismic data for sources and velocities

From the seismic imaging point of view, the difficulty in locating passive seismic sources lies in their unknown start times. In other words, the source model has an additional dimension of time, which leads to an extended model space. Without proper preconditioning, the computational cost of directly inverting for the source functions can be intractable. Using the recently proposed cross-correlation time-reversal imaging condition, we formulate the imaging task as an inverse problem, and use a sparse weighting function calculated from the cross-correlation of back-propagated events to constrain the model space. We demonstrate that the proposed approach can effectively reduce the number of model parameters, leading to a rapid convergence rate using preconditioned conjugate-gradient iterations. The least-squares imaging of passive seismic sources can be further incorporated into full waveform inversion for Earth properties using the variable projection method. Synthetic examples verify the proposed method.

An old paper is added to the collection of reproducible documents: Using well-seismic mistie to update the velocity model

We propose a method to aid in velocity model building based on misties between modeled synthetic seismograms from well log data and the seismic image. The method is based on the fact that when the migration velocity is inconsistent with the true migration velocity, there is a mistie between a modeled synthetic seismogram from well log data and the seismic image. The proposed approach uses local similarity to estimate the mistie at every sample along the synthetic seismogram and uses the result to update the migration velocity at the well location. The updated velocity information is interpolated along seismic structure using predictive painting to generate a new geologically consistent velocity model. We iteratively update the migration velocity model using only the seismic-well tie mistie. The results of our experiments with a simple layered model and an isotropic synthetic model indicate that the proposed workflow provides an effective method for integrating well log data in conventional velocity model building workflows.

An old paper is added to the collection of reproducible documents: Investigating the possibility of locating microseismic sources using distributed sensor network

Distributed sensor networks are designed to provide computation in-situ and in real-time. The conventional time-reversal imaging approach for microseismic event location may not be optimal for such an environment. To address this challenge, we develop a methodology of locating multiple microseismic events with unknown start times based on the cross-correlation imaging condition borrowed from active-source seismic imaging. The imaging principle states that a true microseismic source must correspond to the location where all the backward-propagated events coincide in both space and time. Instead of simply stacking the backward-propagated seismic wavefields, as suggested by time-reversal imaging, we perform multiplication reduction to compute a high-resolution microseismicity map. The map has an extra dimension of time, indicating the start times of different events. Combined with a distributed sensor network, our method is designed for monitoring microseismic activities and mapping fracture development during hydraulic fracturing in-situ and in real-time. We use numerical examples to test the ability of the proposed technique to produce high-resolution images of microseismic locations.

An old paper is added to the collection of reproducible documents: Least-squares non-stationary triangle smoothing

We propose a fast and accurate method to estimate the radius of non-stationary triangle smoothing for matching two seismic datasets. The smoothing radius is estimated by non-linear least-squares inversion using an iterative Gauss-Newton approach. We derive and implement the derivative of the smoothing operator to compute the gradient for inversion. The proposed method is useful for implementing non-stationary triangle smoothing as a low-cost edge-preserving filter. The efficiency of the proposed method is also confirmed in several field data examples of seismic data matching applications in non-stationary local signal and noise orthogonalization, non-stationary local slope estimation, and matching low-resolution and high resolution seismic images from the same exploration area.

An old paper is added to the collection of reproducible documents: Estimation of timeshifts in time-lapse seismic images using spectral decomposition

Time-lapse timeshifts are difficult to measure from seismic data in the presense of low frequencies or thin beds causing tuning effects. We propose to decompose time-lapse seismic images into discrete frequency components using the local time-frequency transform before estimating timeshifts. Use of high frequency components mitigate problems associated with sidelobe interference. We use amplitude-adjusted plane-wave destruction (APWD) filters to invert for both timeshifts and amplitude changes between the time-lapse seismic images at each frequency. Plane-wave destruction can efficiently measure small shifts between seismic traces, making this algorithm particularly effective. The effectiveness of the proposed workflow is confirmed using a 1D synthetic example and a field data example from the Cranfield CO2 sequestration project.