Acquiring Sustainable, Efficient High-Resolution Seismic Data for Geothermal Exploration in an Urban Environment

The overall conditions under which geophysical data are being acquired have changed over the past five years due to the global economy combined with an increased emphasis on low environmental impact sustainability and safety. For land seismic acquisition, minimizing land disturbance, reducing CO2 emissions and increasing crew safety are key motivators to use innovations that drastically change conventional land seismic acquisition methods. One of the sources proven to do this is the eVibe developed by Seismic Mechatronics B V. They were recently contracted to undertake an urban seismic program utilizing their proprietary eVibe source in combination with Stryde Nodes. The seismic survey was acquired in one of the largest cities in the Netherlands, without the need for permits. Being able to minimize environmental impact, to reach a high safety standard and to acquire high-quality data in a noisy urban environment with the used technology made this project a success. This paper compares the results achieved by the Storm10 eVibe in combination with Stryde nodes to results previously obtained by an explosive survey. We show that the results are technically superior, with the eVibe and the Stryde Nodes proving far better suited to acquiring seismic data within this challenging and restrictive urban environment.Applied Geophysics and Petrophysic

Shear Waves in Streamer Data via Nongeometrical Conversions

In this paper we show that when an airgun source and a hydrophone streamer are situated in the vicinity of the water bottom, shear-related events are generated via the evanescent part of the P wave in the water. The shear-related events are not only the often-used surface/Scholte waves but also body Swaves which can be reflected/refracted in the subsurface. The gain of this approach that neither source nor receiver is making any contact with the water bottom itself, making it possible to do shear-wave surveys on water in an efficient manner. We show that such shear-wave events are observed in real data, and can be reproduced by modelling. The results are further validated via real measurements in a borehole.Geoscience & EngineeringCivil Engineering and Geoscience

On the mechanical vibrator-earth contact geometry and its dynamics

The geometry of the contact between a vibrator and the earth underneath influences the dynamics of the vibrator. Although a vibrator might appear to be well-coupled with the earth on a macroscale, perfect coupling certainly does not occur on the microscale. With the aid of contact mechanical modeling and concepts, it can be shown that this lack of contact at the microscale, or rather the change thereof during a sweep, can have a significant effect on the dynamics of the vibrator-earth system. Modeling of such changing contact predicts that the dynamic behavior varies considerably with the vibrator drive level. The most significant effect predicted by the model is a decrease in the base-plate resonance frequency with an increasing drive level. Extensive drive-level tests carried out in a field experiment confirm this change of resonance behavior with drive level.Applied Geophysics and Petrophysic

The spatial data-adaptive minimum-variance distortionless-response beamformer on seismic single-sensor data

Coherent noise generated by surface waves or ground roll within a heterogeneous near surface is a major problem in land seismic data. Array forming based on single-sensor recordings might reduce such noise more robustly than conventional hardwired arrays. We use the minimum-variance distortionless-response (MVDR) beamformer to remove (aliased) surface-wave energy from single-sensor data. This beamformer is data adaptive and robust when the presumed and actual desired signals are mismatched. We compute the intertrace covariance for the desired signal, and then for the total signal (desired signal+noise) to obtain optimal weights. We use the raw data of only one array for the covariance of the total signal, and the wavenumber-filtered version of a full seismic single-sensor record for the covariance of the desired signal. In the determination of optimal weights, a parameter that controls the robustness of the beamformer against an arbitrary desired signal mismatch has to be chosen so that the results are optimal. This is similar to stabilization in deconvolution problems. This parameter needs to be smaller than the largest eigenvalue provided by the singular value decomposition of the presumed desired signal covariance. We compare results of MVDR beamforming with standard array forming on single-sensor synthetic and field seismic data. We apply 2D and 3D beamforming and show prestack and poststack results. MVDR beamformers are superior to conventional hardwired arrays for all examples.GeotechnologyCivil Engineering and Geoscience

Parametrization for 2-D SH full waveform inversion

With single-parameter full waveform inversion, estimating the inverse of the Hessian matrix will accelerate the convergence, but is computationally expensive. Therefore, an approximate Hessian, which is easier to compute, is often used. Similarly, in the case of multi-parameter full waveform inversion, the computation of the Hessian terms that contain derivatives with respect to more than one type of parameter, called cross-parameter Hessian terms, is not usually feasible. If the nonlin- ear inverse problem is well-posed, then the result should be independent of the parametrization choice provided we start close to the global minimum. However, the choice of parametrization will affect the rate of convergence to the exact solution and the “best” choice of parametrization is the one with the fastest rate. If the inverse problem is ill-posed the choice of parametrization introduces a bias towards a particular solution among the non-unique ones that explain the data. This obfuscates the search for the “best” parametrization. We investigated parametrization choices for a 2-D SH experiment where only the reflected wavefield is recorded. Our numerical examples suggest that certain type of scatterers are better inverted by one parametrization choice than another due to the parametrization bias. Therefore, there is nothing like a “best” parametrization in these single-component SH examples.Geoscience & EngineeringCivil Engineering and Geoscience

Multi-objective full waveform inversion in the absence of low frequencies

Least-squares inversion of seismic reflection waveforms can reconstruct remarkably detailed models of the Earth’s subsurface. However, the cycle-skipping associated with the highfrequency waveforms are responsible for spurious local minima in its objective function. Therefore, it is often difficult for descent methods to converge to the true model without starting from an accurate large-scale velocity estimate. To partially overcome this difficulty, we propose to use multiple objective functions for inversion. An additional constraint based on cross-correlation is added to the conventional least-squares (LS) inversion. Observations suggest this will result in a model with an accurate background velocity and reflectivity that corresponds to the global minimum of the least-squares objective function. Optimization of a cross-correlation based function (CC) in the data domain appears to pull the trapped solution out of the local minima associated with the least-squares objective function, and vice versa. Some 2-D numerical tests confirm the validity of the approach in the absence of low temporal data frequencies, starting from a constant initial velocity model.Geoscience & EngineeringCivil Engineering and Geoscience

Acoustic directional snapshot wavefield decomposition

Up–down wavefield decomposition is effectuated by a scaled addition or subtraction of the pressure and vertical particle velocity, generally on horizontal or vertical surfaces, and works well for data given on such surfaces. The method, however, is not applicable to decomposing a wavefield when it is given at one instance in time, i.e. on snapshots. Such situations occur when a wavefield is modelled with methods like finite-difference techniques, for the purpose of, for example, reverse time migration, where the entire wavefield is determined per time instance. We present an alternative decomposition method that is exact when working on snapshots of an acoustic wavefield in a homogeneous medium, but can easily be approximated to heterogeneous media, and allows the wavefield to be decomposed in arbitrary directions. Such a directional snapshot wavefield decomposition is achieved by recasting the acoustic system in terms of the time derivative of the pressure and the vertical particle velocity, as opposed to the vertical derivative in up–down decomposition for data given on a horizontal surface. As in up–down decomposition of data given at a horizontal surface, the system can be eigenvalue decomposed and the inverse of the eigenvector matrix decomposes the wavefield snapshot into fields of opposite directions, including up–down decomposition. As the vertical particle velocity can be rotated at will, this allows for decomposition of the wavefield into any spatial direction; even spatially varying directions are possible. We show the power and effectiveness of the method by synthetic examples and models of increasing complexity.Applied Geophysics and Petrophysic

Localizing microseismic events on field data using a U-Net based convolutional neural network trained on synthetic data

Hydraulic fracturing plays an important role when it comes to the extraction of resources in unconventional reservoirs. The microseismic activity arising during hydraulic fracturing operations needs to be monitored to both improve productivity and to make decisions about mitigation measures. Recently, deep learning methods have been investigated to localize earthquakes given field-data waveforms as input. For optimal results, these methods require large field data sets that cover the entire region of interest. In practice, such data sets are often scarce. To overcome this shortcoming, we propose initially to use a (large) synthetic data set with full waveforms to train a U-Net that reconstructs the source location as a 3D Gaussian distribution. As field data set for our study we use data recorded during hydraulic fracturing operations in Texas. Synthetic waveforms were modelled using a velocity model from the site that was also used for a conventional diffraction-stacking (DS) approach. To increase the U-Nets ability to localize seismic events, we augmented the synthetic data with different techniques, including the addition of field noise. We select the best performing U-Net using 22 events that have previously been identified to be confidently localized by DS and apply that U-Net to all 1245 events. We compare our predicted locations to DS and the DS locations refined by a relative location (DSRL) method. The U-Net based locations are better constrained in depth compared to DS and the mean hypocenter difference with respect to DSRL locations is 163 meters. This shows potential for the use of synthetic data to complement or replace field data for training. Furthermore, after training, the method returns the source locations in near real-time given the full waveforms, alleviating the need to pick arrival times.Applied Geophysics and PetrophysicsImPhys/Computational Imagin

An auxiliary bump functional to overcome cycle skipping in waveform inversion

To overcome the local minima problem in FWI, we propose to use an auxiliary data-domain objective function during inversion. It reduces the data to a simpler form by squaring, followed by blurring to ensure that events that are too far apart can still interact during the inversion. As it effectively replaces seismic arrivals by bumps, we call it the bump functional. This objective function is less sensitive to cycle skipping. Its rôle is to guide the inversion towards the global minimum by pulling the trapped solution out of the local minima associated with the least-squares functional whenever necessary. Waveform inversion cannot be performed with only the auxiliary objective function because it is insensitive to the polarity of the arrivals and the source signature. Therefore, we alternate between minimization with this and the classic least-squares functional. We confirm the validity of the approach using a simple numerical example with reflection data.Geoscience & EngineeringCivil Engineering and Geoscience