A 108km2 Compressive Sensing Processing Trial

Summary Compressive sensing (CS) of seismic data is a new style of seismic acquisition whereby the data are recorded on a pseudorandom grid rather than along densely sampled lines in a conventional design. A CS design with a similar station density will generally yield better quality data at a similar cost compared to a conventional design, whereas a CS design with a lower station density will reduce costs while retaining quality. Previous authors (Mosher, 2014) have shown good results from CS surveys using proprietary methods for the design and processing. In this paper we show results obtained using commercially available services based on published algorithms (Lopez, 2016). This is a necessary requirement for adoption of CS by our industry. This report documents the results of a 108km2 CS acquisition and processing trial. The acquisition and processing were specifically designed to establish whether CS can be used for suppression of backscattered, low velocity, high frequency surface waves. We demonstrate that CS data can be reconstructed by a commercial contractor and that the suppression of backscattered surface waves is improved by using CS receiver gathers reconstructed to a dense shot grid. We also show that CS acquisition is a reliable alternative to conventional acquisition from which high-quality subsurface images can be formed.

Cellular coverage probability is independent of base station density under stochastic geometric models

We prove that under stochastic geometric modelling of cellular networks, the coverage probability is <i>not</i> a function of base stations density, contrary to widespread belief. That is, we reveal that the base station density, $\lambda$, that is appears in a plethora of published cellular coverage probability expressions is superfluous.<br>

Cellular coverage probability is independent of base station density under stochastic geometric models

We prove that under stochastic geometric modelling of cellular networks, the coverage probability is <i>not</i> a function of base stations density, contrary to widespread belief. That is, we reveal that the base station density, $\lambda$, that is appears in a plethora of published cellular coverage probability expressions is superfluous.<br>

Cellular coverage probability is independent of base station density under stochastic geometric models

Stochastic geometry (SG) has been extensively used to model cellular communications, under the assumption that the base stations (BS) are deployed as a Poisson point process in the Euclidean plane. This has spawned a huge number of articles over the past years for different scenarios, culminating in an equally huge number of expressions for the coverage probability in both the uplink (UL) and downink (DL) directions. The trouble is that those expressions include the BS density, $\lambda$, which we prove irrelevant in this article. We start by developing a SG model for a baseline cellular scenario, then prove that the coverage probability is independent of $\lambda$, contrary to popular belief.

Small Catchment Runoff Sensitivity to Station Density and Spatial Interpolation: Hydrological Modeling of Heavy Rainfall Using a Dense Rain Gauge Network

Precipitation is the most important input to hydrological models, and its spatial variability can strongly influence modeled runoff. The highly dense station network WegenerNet (0.5 stations per km2) in southeastern Austria offers the opportunity to study the sensitivity of modeled runoff to precipitation input. We performed a large set of runoff simulations (WaSiM model) using 16 subnetworks with varying station densities and two interpolation schemes (inverse distance weighting, Thiessen polygons). Six representative heavy precipitation events were analyzed, placing a focus on small subcatchments (10–30 km2) and different event durations. We found that the modeling performance generally improved when the station density was increased up to a certain resolution: a mean nearest neighbor distance of around 6 km for long-duration events and about 2.5 km for short-duration events. However, this is not always true for small subcatchments. The sufficient station density is clearly dependent on the catchment area, event type, and station distribution. When the network is very dense (mean distance < 1.7 km), any reasonable interpolation choice is suitable. Overall, the station density is much more important than the interpolation scheme. Our findings highlight the need to study extreme precipitation characteristics in combination with runoff modeling to decompose precipitation uncertainties more comprehensively.

Impact of Rainfall Spatiotemporal Variability and Model Structures on Flood Simulation in Semi-Arid Regions

The spatiotemporal heterogeneity in precipitation and underlying surfaces and hybrid runoff generation mechanism make hydrological modelling and forecasting in semi-arid regions becoming a challenge work. Therefore, to provide a reference for the development of hydrological models in such regions, two nested hydrological experimental watersheds in semi-arid regions were selected for attribution analysis. Based on the concept of large-sample hydrology, Large-scale numerical simulation experiments were performed by constructing different spatial and temporal scale rainfall schemes and combining three hydrological models with different runoff generation mechanisms. Finally, the influences of the time step, station density, and model structure on the flood simulations in semi-arid regions were evaluated. The spatial interpolation technique was used simultaneously to describe the high-dimensional complicated nonlinear relationships between the influencing factors and simulation results. The results show the following: ① the flood simulation accuracy was more sensitive to the time step than the spatial station density of the rainfall schemes and was highly dependent on the time step of the original observation data; and ② compared with the accuracy of the rainfall schemes, the model structure plays a dominant role in flood simulation accuracy. Thus, the hybrid model has significant potential for flood forecasting in semi-arid regions by combining different runoff generation mechanisms. ③ The spatial interpolation technique based on the k-nearest neighbour algorithm can construct a high-dimensional distribution of the influencing factors, yield high model simulation accuracy, and describe the complicated relationships among the time step, station density, model structure, and simulations.

Hydrometeorological - hydrometric station network design using multicriteria decision analysis and GIS techniques

The design of an optimum hydrometeorological and hydrometric station network constitutes a key factor for the collection of comprehensive and reliable hydrometeorological and flow data that are necessary both for decision making in water resources policy and management and for the hydrometeorological risk assessment. This article describes a methodology developed in a geographic information system (GIS) assisted by a multicriteria decision making (MCDM) approach, which combines a set of spatial criteria in order to propose suitable locations for such a station network installation. Through the design for two networks that meet different requirements, various aspects concerning this methodology are illustrated, such as, the methods regarding criteria classification and weighting, as well as, the effect of weighting itself on the location rating. In particular, the implementation is performed for the Sarantapotamos river basin, an area located in the western part of Attica region, Greece, which is characterized by a great diversity of economic activities, mainly industrial, rural and urban fabric in the lowlands. Finally, the analysis indicates that, in terms of station density as proposed by the World Meteorological Organization (WMO), the optimum hydrometeorological station network consists of three stations and the hydrometric one of two stations.

Competition and Coordination in the Mexican Retail Market for Gasoline

We document the following stylized facts for the Mexican retail market for gasoline using data for 2018-2019: (1) consumer prices adjust slower than wholesale prices; (2) more competition, in the form of a higher density of stations, implies lower markups and lower prices; and (3) more competition implies faster pass-through. However, we find geographical differences in the speed of pass-through that cannot be explained by differences in station density. We conjecture that coordination on high prices could be offsetting competitive pressure in some locations. We build a classifier that separates municipalities into two categories depending on whether the relative price concentration is on “high” prices or “low” prices. In the first type of municipalities, the price concentration is correlated positively with the price level and negatively with pass-through. For concentration in “low” prices the signs of the correlations are reversed.