Bradley C. Wallet

• Phd
• Founder and Chief Scientist at Rho Earth

Compaction effects can obscure the impedance separation between hydrocarbon-bearing and fully brine-saturated sandstones. We improve discrimination by removing depth-related trends from inverted seismic impedance. Although the ratio of compressional-to-shear wave velocity versus seismic compressional-wave impedance cross plots show differences betw...

Seismic attributes are a well-established method for highlighting subtle features in seismic data to improve interpretability and suitability for quantitative analysis. Seismic attributes are an enabling technology in such areas as thin-bed analysis, geobody extraction, and seismic geomorphology. Seismic attributes are mathematical functions of the...

In a machine learning workflow, data normalization is a crucial step that compensates for the large variation in data ranges and averages associated with different types of input measured with different units. However, most machine learning implementations do not provide data normalization beyond the z-score algorithm which subtracts the mean from...

Recent developments in attribute analysis and machine learning have significantly enhanced interpretation workflows of 3D seismic surveys. Nevertheless, even in 2018, many sedimentary basins are only covered by grids of 2D seismic lines. These 2D surveys are suitable for regional feature mapping and often identify targets in areas not covered by 3D...

As the use of seismic attributes becomes more widespread, multivariate seismic analysis has become more commonplace for seismic facies analysis. Unsupervised machine-learning techniques provide methods of automatically finding patterns in data with minimal user interaction. When using unsupervised machine-learning techniques, such as [Formula: see...

Seismic attributes are a well-established method for highlighting subtle features buried in seismic data in order to improve interpretability and suitability for quantitative analysis. Seismic attributes are a critical enabling technology in such areas thin bed analysis, 3D geobody extraction, and seismic geomorphology. When it comes to seismic att...

Qualitative interpretation of spectral decomposition attributes has proven its value in interpreting many types of geologies. Historically, this has focused upon 2D interpretation upon a time slice, stratal slice, or phantom horizon slice. More recently, work has been done using spectral decomposition attributes for 3D geomodeling and extraction of...

Much of the world’s conventional oil and gas production comes from turbidite systems. Interpreting them in three dimensions using commercially available software generally requires seismic attributes. Hybrid carbonate turbidite systems are an interesting phenomenon that is not fully understood. I have examined the attribute expression of channel fo...

The E&P community, both in the industry and academia, is painfully aware of the challenges and complexity of performing seismic interpretation and reservoir characterization in increasingly larger, more intricate, and more heterogeneous data sets. This increase in size is coupled with an emphasis on

Much of the world's conventional oil and gas production comes from fluvial-deltaic reservoirs. The ability to accurately interpret the architectural elements comprising these systems greatly reduces the risk in exploration and development in these environments. We have evaluated methods for using spectral decomposition attributes to improve the vis...

Many thin beds in the subsurface are associated with hydrocarbons or other minerals. The seismic response of thin beds is complex due to interference between reflections from the different thin-layered interfaces. The conventional method of determining lithology, thickness, and other properties may

3D seismic data are rich with detailed geologic information. But despite our best seismic processing efforts, much of that geologic information is hidden within a cloud of noise. If the important information could be isolated and removed from this clutter, the accuracy of the geologic analysis could be dramatically increased. The method of structur...

A huge increase in the incidence and prevalence of chronic diseases has been reported in the United States (US) over the last 20 years. Similar increases have been seen globally. The herbicide glyphosate was introduced in 1974 and its use is accelerating with the advent of herbicide-tolerant genetically engineered (GE) crops. Evidence is mounting t...

Spectral decomposition can produce dozens of attributes for a single data set, far exceeding the ability for direct visualization. Some solutions have been proposed. The state-of-the-art approach is via the use of principal component analysis. However, this approach has significant inherent weaknesses, such as a lack of inclusion of spatial informa...

In this work we present a new method for latent space mapping based upon inter-point similarities. This method, diffusion maps, has a number of nice qualities compared with previous methods including the fact that it is based upon inter-point similarities rather than a Euclidean space. We then demonstrate application of this approach to mapping an...

The Chicontepec Formation is a deepwater reservoir of particular strategic importance to Mexico. Despite a large volume of proven reservoirs, development has been hampered by low permeability and uncertainty about controls upon production. In order to better understand the system and how geology may impact production, we have been doing detailed in...

This paper demonstrates the use of the hue and saturation components of a HSV color map to display dip. This concept was first demonstrated by Rijks and Jauffred (1991), but it still not readily available for seismic interpretation in commercial packages. We apply this technique to visualize dewatering patterns in mudstone in the North Sea offshore...

Constructing crustal models requires the aggregation of considerable amounts of data from multiple data sources. Unfortunately, such data are often noisy, and this uncertainty may produce large and unknown errors in the resulting models. Our previous work has focused upon identifying and removing outliers that might be expected to adversely impact...

An overview of latent space modeling of seismic data is presented. An approach was introduced that involved modeling the data as a lower-dimensional manifold representation of a latent space embedded in attribute space. A latent space was a lower-dimensional manifold embedded in attribute space that approximately contained the majority of the proba...

Constructing crustal models requires the aggregation of considerable amounts of data from multiple data sources. Unfortunately, such data often are noisy and outliers within the data may produce large errors in the resulting model. However, despite what are clearly large errors, verification and cleaning of the data is complicated by the fact that...

Imported fire ant colonies were quantified in 1,000-m(2) circular subplots spaced approximately 125 m apart on a sheep and goat farm in Oklahoma. Social form (percent polygyny), mound density, cumulative above-ground mound volume, and average mound volume were subjected to multiple regression analyses to examine trends related to landscape metrics...

Considerable effort has been invested in developing methods for classifying and identifying facies from well logs. However, identifying facies at individual measurement points tells us little about the processes that gave rise to these individual facies. In this paper, we propose a method for clustering and analyzing bed sets from well logs. The ma...

Currently, many large geoscientific efforts (e.g., EarthScope, Continental Dynamics, and GeoSwath) have emphasized that a crucial need in advancing our understanding of the structure and evolution of the continents is high-resolution, 3-D models of lithospheric structure. In addition, the geoscience community recognizes that our ultimate goal is th...

A study was undertaken to characterize surface temperatures of mounds of imported fire ant, Solenopsis invicta Buren (Hymenoptera: Formicidae) and S. richteri Forel, and their hybrid, as it relates to sun position and shape of the mounds, to better understand factors that affect absorption of solar radiation by the nest mound and to test feasibilit...

The human interpreter is perhaps the best pattern-recognition engine currently available. Often, interpreting seismic data involves inspection and analysis of multiple seismic attributes. While interpreters are skilled at visualizing complex geologic features in 3D, they are less adept at visualizing a great many attributes at once. Recent software...

The nests of some mound-building ants are thought to serve an important function as passive solar collectors. To test this hypothesis, imported fire ant (Solenopsis invicta Buren, S. richteri Forel, and their hybrid) mound shape characteristics (south facing slope angle and area, mound height, and basal elongation in the plane of the ground) were q...

Spectral decomposition can produce dozens of attributes for a single data set, far exceeding the ability for direct visualization. Wallet and Marfurt (2008) proposed the use of the Image Grand Tour (IGT) for the construction of lower dimensional views of spectral information, and they demonstrated the ability to construct multiple images, each show...

Imported fire ants construct earthen nests (mounds) that exhibit many characteristics which make them potentially good targets for remote sensing programs, including geographical orientation, topography, and bare soil surrounded by actively growing vegetation. Template-based features and object-based features extracted from aerial multispectral ima...

The stinging habits of imported fire ants, their abundance, and their mound-building habits combine to make them serious pests in agricultural and urban areas. Quantifying fire ant mounds is time-consuming and expensive. Automated Decisions, LLC is partnered with the USDA, ARS Biological Control of Pests Research Unit under Cooperative Research and...

Previous work by the authors has studied the problem of searching for projections to low dimensional spaces which are optimal for classification based upon some training data set. In our earlier work, we relied upon using separate training and test data to attempt to estimate and correct the problem of overfitting. In this paper, we examine the pro...

Details work in our group on the use of low-level features for the identification of man-made regions in unmanned aerial vehicle (UAV) imagery. The feature sets that we have examined include classical statistical features such as the coefficient of variation in a window about a pixel, locally computed fractal dimension, and fractal dimension comput...

In this paper the use of a Genetic Algorithm to solve a constrained vehicle routing problem is explored. The problem is two-dimensional with obstacles represented as ellipses of uncertainty surrounding each obstacle point. A route is defined as a series of points through which the vehicle sequentially travels from the starting point to the ending p...

This paper focuses on our recent work in the application of mixture based density estimation methods to high-dimensional data. We discuss the use of parallel coordinates and the grand tour for the identification of cluster structure within high-dimensional data sets In addition we will discuss some of our latest efforts in the development of visual...

Microcalcification clusters are often an important indicator for the detection of malignancy in mammograms. In many cases, microcalcifications are the only indication of a malignancy. However, the detection of microcalcifications can be a difficult process. They are small and can be embedded in dense tissue. This paper presents a method for automat...

Difficulties in performing density estimation in high dimensional space require dimensionality reduction in many automatic target recognition applications. This paper presents a method using a genetic algorithm to determine projections which are near optimal according to an arbitrary criteria function. An example is presented which optimizes a disc...

This paper introduces a new adaptive mixtures type estima- tor. This paper provides details on the new estimator along with some examples of its performance on several different types of data sets. In addition we provide a comparison between the performance of this estimator and the standard recursive adaptive mixtures estimator for one of the data...

Many problems have a structure with an inherently two (or higher) dimensional nature. Unfortunately, the classical method of representing problems when using Genetic Algorithms (GAs) is of a linear nature. We develop a genome representation with a related crossover mechanism which preserves spatial relationships for two dimensional problems. We the...

Many problems have a structure with an inherently two (or higher) dimensional nature. Unfortunately, the classical method of representing problems when using Genetic Algorithms (GAs) is of a linear nature. We develop a genome representation with a related crossover mechanism which preserves spatial relationships for 2D problems. We then explore how...

The image grand tour (IGT) is a method for visualizing multi– spectral images or multiple registered images. The IGT projects a linear combination of the pixel vectors into one– dimensional space (for each pixel) and renders these projected values as a gray–scale image. We will present a JAVA–based version of the IGT with additional software enhanc...

Given a data set consisting of a large number of pre- dictors plus a response, the problem addressed in this work is to select a minimal model which correctly predicts the response. Methods for achieving this subsetting of the predictors have been the topic of a considerable amount of study within the statistics community. Unfortunately, current me...