ALBERT

Description: ABSTRACT We present a structural smoothing regularization scheme in the context of inversion of marine controlled-source electromagnetic data. The regularizing hypothesis is that the electrical parameters have a structure similar to that of the elastic parameters observed from seismic data. The regularization is split into three steps. First, we ensure that our inversion grid conforms with the geometry derived from seismic. Second, we use a seismic stratigraphic attribute to define a spatially varying regularization strength. Third, we use an indexing strategy on the inversion grid to define smoothing along the seismic geometry. Enforcing such regularization in the inversion will encourage an inversion result that is more intuitive for the interpreter to deal with. However, the interpreter should also be aware of the bias introduced by using seismic data for regularization. We illustrate the method using one synthetic example and one field data example. The results show how the regularization works and that it clearly enforces the structure derived from seismic data. From the field data example we find that the inversion result improves when the structural smoothing regularization is employed. Including the broadside data improves the inversion results even more, due to a better balancing between the sensitivities for the horizontal and vertical resistivities.

Description: We analyze the shapes of twenty eight hypothesized scoria cones in three regions on Mars, i.e. Ulysses and Hydraotes Colles and Coprates Chasma. Using available HiRISE and CTX Digital Elevation Models, we determine the basic morphometric characteristics of the cones and estimate from ballistic modelling the physical parameters of volcanic eruptions that could have formed them. When compared to terrestrial scoria cones, most of the studied cones show larger volumes (up to 4.2 × 10 9  m 3 ), larger heights (up to 573 m) and smaller average slopes. The average slopes of the Ulysses, Hydraotes and Coprates cones range between 7° and 25°, and the maximum slopes only rarely exceed 30°, which suggests only a minor role of scoria redistribution by avalanching. Ballistic analysis indicates that all cones were formed in a similar way and their shapes are consistent with an ejection velocity about two times larger and a particle size about twenty times smaller than on Earth. Our results support the hypothesis that the investigated edifices were formed by low energy Strombolian volcanic eruptions and hence are equivalent to terrestrial scoria cones. The cones in Hydraotes Colles and Coprates Chasma are on average smaller and steeper than the cones in Ulysses Colles, which is likely due to the difference in topographic elevation and the associated difference in atmospheric pressure. This study provides the expected morphometric characteristics of Martian scoria cones, which can be used to identify landforms consistent with this type of activity elsewhere on Mars and distinguish them from other conical edifices.

Description: ABSTRACT A marine source generates both a direct wavefield and a ghost wavefield. This is caused by the strong surface reflectivity, resulting in a blended source array, the blending process being natural. The two unblended response wavefields correspond to the real source at the actual location below the water level and to the ghost source at the mirrored location above the water level. As a consequence, deghosting becomes deblending (‘echo-deblending’) and can be carried out with a deblending algorithm. In this paper we present source deghosting by an iterative deblending algorithm that properly includes the angle dependence of the ghost: It represents a closed-loop, non-causal solution. The proposed echo-deblending algorithm is also applied to the detector deghosting problem. The detector cable may be slanted, and shot records may be generated by blended source arrays, the blending being created by simultaneous sources. Similar to surface-related multiple elimination the method is independent of the complexity of the subsurface; only what happens at and near the surface is relevant. This means that the actual sea state may cause the reflection coefficient to become frequency dependent, and the water velocity may not be constant due to temporal and lateral variations in the pressure, temperature, and salinity. As a consequence, we propose that estimation of the actual ghost model should be part of the echo-deblending algorithm. This is particularly true for source deghosting, where interaction of the source wavefield with the surface may be far from linear. The echo-deblending theory also shows how multi-level source acquisition and multi-level streamer acquisition can be numerically simulated from standard acquisition data. The simulated multi-level measurements increase the performance of the echo-deblending process. The output of the echo-deblending algorithm on the source side consists of two ghost-free records: one generated by the real source at the actual location below the water level and one generated by the ghost source at the mirrored location above the water level. If we apply our algorithm at the detector side as well, we end up with four ghost-free shot records. All these records are input to migration. Finally, we demonstrate that the proposed echo-deblending algorithm is robust for background noise.

Description: ABSTRACT The recent use of marine electromagnetic technology for exploration geophysics has primarily focused on applying the controlled source electromagnetic method for hydrocarbon mapping. However, this technology also has potential for structural mapping applications, particularly when the relative higher frequency controlled source electromagnetic data are combined with the lower frequencies of naturally occurring magnetotelluric data. This paper reports on an extensive test using data from 84 marine controlled source electromagnetic and magnetotelluric stations for imaging volcanic sections and underlying sediments on a 128-km-long profile. The profile extends across the trough between the Faroe and Shetland Islands in the North Sea. Here, we focus on how 2.5D inversion can best recover the volcanic and sedimentary sections. A synthetic test carried out with 3D anisotropic model responses shows that vertically transverse isotropy 2.5D inversion using controlled source electromagnetic and magnetotelluric data provides the most accurate prediction of the resistivity in both volcanic and sedimentary sections. We find the 2.5D inversion works well despite moderate 3D structure in the synthetic model. Triaxial inversion using the combination of controlled source electromagnetic and magnetotelluric data provided a constant resistivity contour that most closely matched the true base of the volcanic flows. For the field survey data, triaxial inversion of controlled source electromagnetic and magnetotelluric data provides the best overall tie to well logs with vertically transverse isotropy inversion of controlled source electromagnetic and magnetotelluric data a close second. Vertical transverse isotropy inversion of controlled source electromagnetic and magnetotelluric data provided the best interpreted base of the volcanic horizon when compared with our best seismic interpretation. The structural boundaries estimated by the 20-Ω·m contour of the vertical resistivity obtained by vertical transverse isotropy inversion of controlled source electromagnetic and magnetotelluric data gives a maximum geometric location error of 11% with a mean error of 1.2% compared with the interpreted base of the volcanic horizon. Both the model study and field data interpretation indicate that marine electromagnetic technology has the potential to discriminate between low-resistivity prospective siliciclastic sediments and higher resistivity non-prospective volcaniclastic sediments beneath the volcanic section.

Description: ABSTRACT Presence of noise in the acquisition of surface nuclear magnetic resonance data is inevitable. There are various types of noise, including Gaussian noise, spiky events, and harmonic noise that affect the signal quality of surface nuclear magnetic resonance measurements. In this paper, we describe an application of a two-step noise suppression approach based on a non-linear adaptive decomposition technique called complete ensemble empirical mode decomposition in conjunction with a statistical optimization process for enhancing the signal-to-noise ratio of the surface nuclear magnetic resonance signal. The filtering procedure starts with applying the complete ensemble empirical mode decomposition method to decompose the noisy surface nuclear magnetic resonance signal into a finite number of intrinsic mode functions. Afterwards, a threshold region based on de-trended fluctuation analysis is defined to identify the noisy intrinsic mode functions, and then the no-noise intrinsic mode functions are used to recover the partially de-noised signal. In the second stage, we applied a statistical method based on the variance criterion to the signal obtained from the initial phase to mitigate the remaining noise. To demonstrate the functionality of the proposed strategy, the method was evaluated on an added-noise synthetic surface nuclear magnetic resonance signal and on field data. The results show that the proposed procedure allows us to improve the signal-to-noise ratio significantly and, consequently, extract the signal parameters (i.e., and V 0 ) from noisy surface nuclear magnetic resonance data efficiently.

Description: No abstract is available for this article.

Description: NASA's New Horizons spacecraft flies past Pluto on July 14, 2015, carrying two instruments that detect charged particles. Pluto has a tenuous, extended atmosphere that is escaping the planet's weak gravity. The interaction of the solar wind with Pluto's escaping atmosphere depends on solar wind conditions as well as the vertical structure of Pluto's atmosphere. We have analyzed Voyager 2 particles and fields measurements between 25 and 39 AU and present their statistical variations. We have adjusted these predictions to allow for the Sun's declining activity and solar wind output. We summarize the range of SW conditions that can be expected at 33 AU and survey the range of scales of interaction that New Horizons might experience. Model estimates for the solar wind stand-off distance vary from ~7 to ~1000 R P with our best estimate being around 40 R P (where we take Pluto's radius to be R P  = 1184 km).

Description: No abstract is available for this article.

Description: ABSTRACT Modern airborne transient electromagnetic surveys typically produce datasets of thousands of line kilometres, requiring careful data processing in order to extract as much and as reliable information as possible. When surveys are flown in populated areas, data processing becomes particularly time consuming since the acquired data are contaminated by couplings to man-made conductors (power lines, fences, pipes, etc.). Coupled soundings must be removed from the dataset prior to inversion, and this is a process that is difficult to automate. The signature of couplings can be both subtle and difficult to describe in mathematical terms, rendering removal of couplings mostly an expensive manual task for an experienced geophysicist. Here, we try to automate the process of removing couplings by means of an artificial neural network. We train an artificial neural network to recognize coupled soundings in manually processed reference data, and we use this network to identify couplings in other data. The approach provides a significant reduction in the time required for data processing since one can directly apply the network to the raw data. We describe the neural network put to use and present the inputs and normalizations required for maximizing its effectiveness. We further demonstrate and assess the training state and performance of the network before finally comparing inversions based on unprocessed data, manually processed data, and artificial neural network automatically processed data. The results show that a well-trained network can produce high-quality processing of airborne transient electromagnetic data, which is either ready for inversion or in need of minimal manual processing. We conclude that the use of artificial neural network scan significantly reduce the processing time and its costs by as much as 50%.

Description: ABSTRACT Radial-trace time–frequency peak filtering filters a seismic record along the radial-trace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial-trace time–frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often random noise whose energy is concentrated in certain directions; the noise in these directions is correlative. We refer to this kind of random noise (that is distributed randomly in time but correlative in the space) as directional random noise. Under radial-trace time–frequency peak filtering, the directional random noise will be treated as signal and enhanced when this noise has same direction as the signal. Therefore, we need to identify the directional random noise before the filtering. In this paper, we test the linearity of signal and directional random noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional random noise is a random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional random noise by the Hurst exponent values. The directional random noise can then be suppressed by using a long filtering window length during the radial-trace time–frequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional random noise and can effectively recover the reflected events.