Max Deffenbaugh

Efficient seismic forward modeling using simultaneous random sources and sparsity

The high cost of simulating densely sampled seismic forward modeling data arises from activating sources one at a time in sequence.Toincreaseefficiency,onecouldleveragerecentinnovations in seismic field-data acquisition and activate several e.g., 2‐6 sources simultaneously during modeling. However, such approaches would suffer from degraded data quality because of the interference between the model’s responses to the simultaneoussources.Twonewefficientsimultaneous-sourcemodeling approachesareproposedthatrelyonthenoveltandemuseofrandomness and sparsity to construct almost noise-free model response to individual sources. In each approach, thefirst step is to measure the model’s cumulative response with all sources activated simultaneously using randomly scaled band-limited impulses or continuous band-limited random-noise waveforms. In the second step, the model response to each individual source is estimated from the cumulative receiver measurement by exploiting knowledge of the random source waveforms and the sparsity of the model response to individual sources in a known transformdomaine.g.,curveletdomain.Theefficiencyachievable by the approaches is primarily governed by the sparsity of the model response. By invoking results from the field of compressive sensing, theoretical bounds are provided that assert that the approaches would need less modeling time for sparser i.e., simpler or more structured model responses.Asimulated modelingexampleisillustratedthatshowsthatdatacollectedwithas manyas8192sourcesactivatedsimultaneouslycanbeseparated into the 8192 individual source gathers with data quality comparable to that obtained when the sources were activated sequentially.Theproposedapproachescouldalsodramaticallyimprove seismic field-data acquisition efficiency if the source signatures actuallyprobingtheearthcanbemeasuredaccurately.

Sensitivity of the anterior lateral line to natural stimuli in the oyster toadfish, Opsanus tau (Linnaeus).

SUMMARY Inductive neural telemetry was used to record from microwire electrodes chronically implanted into the anterior lateral line nerve of the oyster toadfish, Opsanus tau (L.). The lateral lines of free-ranging toadfish were stimulated by the swimming movements of a prey fish (Fundulus heteroclitus), and the corresponding neural activity was quantified. Both spontaneously active and silent afferent fibers experienced an increase in neural firing as the prey approached the lateral line. Activity was evoked when the prey fish approached to within 8-12 cm of the neuromast, with increases in nerve firing rates directly correlated with diminishing distance. Thus, adult toadfish (28 cm standard length; 33 cm total length) were only able to detect mobile prey that approached within approximately 40% of their body length. Both spontaneously active and silent afferent fibers also experienced a dramatic increase in firing during predatory strikes, indicating that the fibers were not inhibited during rapid body movement. This study investigates, for the first time, the neural response of the anterior lateral line to prey stimuli in free-ranging fish.

Stack‐and‐Denoise: A new method to stack seismic datasets

Stacking combines a collection of noisy seismic gathers, such as NMO-corrected CMP gathers or migrated gathers, into a single less noisy seismic section. Existing techniques stack each gather independently, and in the process, ignore the tremendous structure of seismic signals. We propose a novel technique called Stack-andDenoise (SAD) that exploits the structure of seismic signals to obtain an enhanced stack. SAD comprises the following steps: (1) Estimate the signal-to-noise ratios (SNRs) for each trace in the gather using a new iterative algorithm called Leave-Me-Out; (2) Use the SNRs to perform a weighted stack of the noisy gathers (3) Attenuate the residual noise in the weighted stack using denoising in the wavelet, Fourier, or curvelet transform domain, for example. Though simple, SAD enjoys some desirable optimality properties. During synthetic and real data experiments, SAD significantly attenuated both multiples and random noise in the stacked estimate.

Texas Two-Step: A Framework for Optimal Multi-Input Single-Output Deconvolution

Multi-input single-output deconvolution (MISO-D) aims to extract a deblurred estimate of a target signal from several blurred and noisy observations. This paper develops a new two step framework-Texas two-step-to solve MISO-D problems with known blurs. Texas two-step first reduces the MISO-D problem to a related single-input single-output deconvolution (SISO-D) problem by invoking the concept of sufficient statistics (SSs) and then solves the simpler SISO-D problem using an appropriate technique. The two-step framework enables new MISO-D techniques (both optimal and suboptimal) based on the rich suite of existing SISO-D techniques. In fact, the properties of SSs imply that a MISO-D algorithm is mean-squared-error optimal if and only if it can be rearranged to conform to the Texas two-step framework. Using this insight, we construct new wavelet- and curvelet-based MISO-D algorithms with asymptotically optimal performance. Simulated and real data experiments verify that the framework is indeed effective.

Resolution of Converted Waves In Attenuating Media

PS waves offer better resolution than PP waves at shallow targets before attenuation becomes significant, but when QP > QS , there is a “resolution crossover depth,” i.e., a depth below which PP waves offer better resolution than PS waves. An equation is given for calculating this crossover depth at near offsets, where the crossover depth is the same for both horizontal and vertical resolution. Seismic data from two 2D OBS lines supports the predicted resolution behavior. The scaled-to-depth PS images show better resolution than the PP images in the shallow section, and the resolution crossover depth can be determined.

MITIGATING SEISMIC IMPACT ON MARINE LIFE: CURRENT PRACTICE AND FUTURE TECHNOLOGY

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Optimizing Seismic Survey Design and Processing Flows: A Fast Tree Search Using the Van Trees Inequality

The goal in designing a seismic survey is to achieve an optimal tradeoff between cost and information accuracy. There are many options for seismic data acquisition and multiple algorithms to choose from at each processing step. Each choice affects the cost as well as the accuracy of reservoir property estimates. The Van Trees inequality is a lower bound on the mean squared error of any estimate of a parameter from a data set. It can be used in conjunction with a fast tree search algorithm to rapidly identify the combination of acquisition and processing options that minimizes an objective function of both cost and estimate mean squared error. The bound can also identify for future research those steps in acquisition and processing where the most information is being lost. These applications are illustrated in assembling an acquisition and processing plan to estimate oil sand thickness.

Geophysical parameter estimation

Seismic exploration for oil and gas is a parameter estimation problem. Geological properties and fluid content of subsurface reservoirs are sensed from the earths surface. Seismic data are acquired by generating elastic waves at the surface and recording the reflections off subsurface targets using large receiver arrays. At present, it is not practical to determine earth parameters by iteratively refining an earth model until simulated seismic data match recorded field data. The computational demands of accurately simulating viscoelastic wave propagation in a 3-D heterogeneous earth are prohibitive. Instead, seismic data are processed to remove many complexities of wave propagation in the real environment, like multipath, attenuation, dispersion, shear waves, and interface waves. With these complexities removed, the processed data conform to simpler propagation models which can be solved in reasonable computer time. The cost of each simplification, however, is the loss of some information about the subsu...