Wavelet estimation from marine pressure measurements

Geophysics ◽  
1998 ◽  
Vol 63 (6) ◽  
pp. 2108-2119 ◽  
Author(s):  
Are Osen ◽  
Bruce G. Secrest ◽  
Lasse Amundsen ◽  
Arne Reitan

A new and alternative procedure for the deterministic estimation of the seismic source time function (wavelet) is proposed. This paper follows a series of reports on source signature estimation, requiring neither statistical assumptions on the signature nor any knowledge about the earth below the receivers. The proposed estimation method, which in principle is exact, uses conventional recordings of the pressure on a surface below the source and recordings of the pressure at one or more locations above the receiver surface. The derivation of the method is based on the Kirchhoff‐Helmholtz integral equation. The formulation ensures that the scattered energy is filtered from the wavelet estimation, enabling the wavelet to be detected from the direct pressure field. Along with the derivation, we present a numerical example.

2013 ◽  
Vol 5 (2) ◽  
pp. 1125-1162 ◽  
Author(s):  
S. C. Stähler ◽  
K. Sigloch

Abstract. Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves, but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that the parameter space can be chosen small enough to make Bayesian sampling computationally feasible. We propose a framework for PRobabilistic Inference of Source Mechanisms (PRISM) that parameterises and samples earthquake depth, moment tensor, and source time function efficiently by using information from previous non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of >1000 STFs by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits to propagate these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible.


Geophysics ◽  
1990 ◽  
Vol 55 (7) ◽  
pp. 902-913 ◽  
Author(s):  
Arthur B. Weglein ◽  
Bruce G. Secrest

A new and general wave theoretical wavelet estimation method is derived. Knowing the seismic wavelet is important both for processing seismic data and for modeling the seismic response. To obtain the wavelet, both statistical (e.g., Wiener‐Levinson) and deterministic (matching surface seismic to well‐log data) methods are generally used. In the marine case, a far‐field signature is often obtained with a deep‐towed hydrophone. The statistical methods do not allow obtaining the phase of the wavelet, whereas the deterministic method obviously requires data from a well. The deep‐towed hydrophone requires that the water be deep enough for the hydrophone to be in the far field and in addition that the reflections from the water bottom and structure do not corrupt the measured wavelet. None of the methods address the source array pattern, which is important for amplitude‐versus‐offset (AVO) studies. This paper presents a method of calculating the total wavelet, including the phase and source‐array pattern. When the source locations are specified, the method predicts the source spectrum. When the source is completely unknown (discrete and/or continuously distributed) the method predicts the wavefield due to this source. The method is in principle exact and yet no information about the properties of the earth is required. In addition, the theory allows either an acoustic wavelet (marine) or an elastic wavelet (land), so the wavelet is consistent with the earth model to be used in processing the data. To accomplish this, the method requires a new data collection procedure. It requires that the field and its normal derivative be measured on a surface. The procedure allows the multidimensional earth properties to be arbitrary and acts like a filter to eliminate the scattered energy from the wavelet calculation. The elastic wavelet estimation theory applied in this method may allow a true land wavelet to be obtained. Along with the derivation of the procedure, we present analytic and synthetic examples.


Solid Earth ◽  
2014 ◽  
Vol 5 (2) ◽  
pp. 1055-1069 ◽  
Author(s):  
S. C. Stähler ◽  
K. Sigloch

Abstract. Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that the parameter space can be chosen small enough to make Bayesian sampling computationally feasible. We propose a framework for PRobabilistic Inference of Seismic source Mechanisms (PRISM) that parameterises and samples earthquake depth, moment tensor, and source time function efficiently by using information from previous non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of >1000 source time functions (STFs) by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits propagating these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible.


1976 ◽  
Vol 66 (4) ◽  
pp. 1221-1232
Author(s):  
Robert B. Herrmann

abstract The shape of long-period teleseismic P-wave signals is a function of many factors, among which are focal depth, focal mechanism, the source time function, and the earth structures at both the source and receiver. The effect of focal depth is quite pronounced, so much so, that focal depths should be able to be determined to within 10 km on the basis of the long-period P-wave character. This resolution capability is demonstrated for events occurring in continental and oceanic crust as observed by seismographs in the 30° to 80° epicentral distance range.


Geophysics ◽  
1991 ◽  
Vol 56 (2) ◽  
pp. 190-201 ◽  
Author(s):  
A. Ziolkowski

There are three related problems with our approach to signature deconvolution. First, there is a confusion among geophysicists about the basis of the convolutional model itself, which leads to doubts about the value of measurements of the source signature. Secondly, it is not generally recognized that statistical methods of wavelet estimation are unreliable. Thirdly, many explorationists are unaware that it is practical in many cases to make meaningful measurements of the source signature. The convolutional model of the reflection seismogram applies only for a point source, and is the convolution of the source signature with the impulse response of the earth, of Green’s function, which contains all possible arrivals, including reflections, refractions, multiples and diffractions. Stabilized deconvolution of the data with a known band‐limited signature is straightforward. The signature can be obtained by independent measurements, as described in the literature. The recovery of the elastic layer parameters from the band‐limited impulse response of the earth, after removal of the source signature by deconvolution, is the problem of inversion, and is not discussed in this paper. The theory of wave propagation does not support the commonly held view that a reflection seismogram can be regarded as a convolution of a wavelet with the series of normal‐incidence primary reflection coefficients. This is true of both prestack and poststack data. Poststack seismic inversion schemes, based on this model, that use well logs to extract the wavelet for predicting lateral variations in lithology away from the wells, rely on the wavelet to be laterally invariant. Even if there is perfect shot‐to‐shot repeatability, this model must yield a different wavelet at every well, and therefore the extracted wavelet does vary laterally. These schemes are therefore self‐contradictory and, in the worst cases, their results are likely to be worthless. Published methods for determining the source signature from measurements for the land vibrator, marine seismic source arrays, and dynamite on land are summarized. None of these methods appears to be in use. A Vibroseis example is included to show that the signal transmitted into the ground by the vibrators does not closely resemble the predetermined sweep, as is normally assumed. The transmitted signal could be determined in processing from measurements of the vibrator behaviour that are made in production for vibrator control, if only these measurements were recorded. Normally they are not. Instead of using measurements to determine the signature, the exploration industry relies on wavelet estimation methods that depend on both a model and statistical assumptions that have no theoretical justification.


2005 ◽  
Vol 32 (14) ◽  
pp. n/a-n/a ◽  
Author(s):  
Olivier Sèbe ◽  
Pierre-Yves Bard ◽  
Jocelyn Guilbert

2021 ◽  
Vol 62 (4) ◽  
Author(s):  
Ulrich Mießner ◽  
Thorben Helmers ◽  
Ralph Lindken ◽  
Jerry Westerweel

Abstract In this study, we reconstruct the 3D pressure field and derive the 3D contributions of the energy dissipation from a 3D3C velocity field measurement of Taylor droplets moving in a horizontal microchannel ($$\rm Ca_c=0.0050$$ Ca c = 0.0050 , $$\rm Re_c=0.0519$$ Re c = 0.0519 , $$\rm Bo=0.0043$$ Bo = 0.0043 , $$\lambda =\tfrac{\eta _{d}}{\eta _{c}}=2.625$$ λ = η d η c = 2.625 ). We divide the pressure field in a wall-proximate part and a core-flow to describe the phenomenology. At the wall, the pressure decreases expectedly in downstream direction. In contrast, we find a reversed pressure gradient in the core of the flow that drives the bypass flow of continuous phase through the corners (gutters) and causes the Taylor droplet’s relative velocity between the faster droplet flow and the slower mean flow. Based on the pressure field, we quantify the driving pressure gradient of the bypass flow and verify a simple estimation method: the geometry of the gutter entrances delivers a Laplace pressure difference. As a direct measure for the viscous dissipation, we calculate the 3D distribution of work done on the flow elements, that is necessary to maintain the stationarity of the Taylor flow. The spatial integration of this distribution provides the overall dissipated energy and allows to identify and quantify different contributions from the individual fluid phases, from the wall-proximate layer and from the flow redirection due to presence of the droplet interface. For the first time, we provide deep insight into the 3D pressure field and the distribution of the energy dissipation in the Taylor flow based on experimentally acquired 3D3C velocity data. We provide the 3D pressure field of and the 3D distribution of work as supplementary material to enable a benchmark for CFD and numerical simulations. Graphical abstract


Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 854-886 ◽  
Author(s):  
Ken Larner ◽  
Ron Chambers ◽  
Mai Yang ◽  
Walt Lynn ◽  
Willon Wai

Despite significant advances in marine streamer design, seismic data are often plagued by coherent noise having approximately linear moveout across stacked sections. With an understanding of the characteristics that distinguish such noise from signal, we can decide which noise‐suppression techniques to use and at what stages to apply them in acquisition and processing. Three general mechanisms that might produce such noise patterns on stacked sections are examined: direct and trapped waves that propagate outward from the seismic source, cable motion caused by the tugging action of the boat and tail buoy, and scattered energy from irregularities in the water bottom and sub‐bottom. Depending upon the mechanism, entirely different noise patterns can be observed on shot profiles and common‐midpoint (CMP) gathers; these patterns can be diagnostic of the dominant mechanism in a given set of data. Field data from Canada and Alaska suggest that the dominant noise is from waves scattered within the shallow sub‐buttom. This type of noise, while not obvious on the shot records, is actually enhanced by CMP stacking. Moreover, this noise is not confined to marine data; it can be as strong as surface wave noise on stacked land seismic data as well. Of the many processing tools available, moveout filtering is best for suppressing the noise while preserving signal. Since the scattered noise does not exhibit a linear moveout pattern on CMP‐sorted gathers, moveout filtering must be applied either to traces within shot records and common‐receiver gathers or to stacked traces. Our data example demonstrates that although it is more costly, moveout filtering of the unstacked data is particularly effective because it conditions the data for the critical data‐dependent processing steps of predictive deconvolution and velocity analysis.


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