Imaging sedimentary basins from high-resolution aeromagnetics and texture analysis

Nathan, David; Aitken, Alan; Holden, Eun‐Jung; Wong, Jason C.

doi:10.1016/j.cageo.2019.104396

Cited by 9 publications

(3 citation statements)

References 69 publications

(91 reference statements)

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“…These methods are used for a variety of purposes, ranging from the visual enhancement of structural features, to determining source parameters such as the depth to the top of a source body. Many of these methods are still being built upon, and entirely new approaches are also being developed for edge detection/structure mapping (e.g., some examples from the past 10 years include Cascone et al, 2012;Holden et al, 2012;Lee et al, 2012;Ferreira et al, 2013;Hidalgo-Gato and Barbosa, 2015;Tschirhart and Morris, 2015;Foks and Li, 2016;Sun et al, 2016;Oliveira et al, 2017;Nathan et al, 2020) and source parameter estimation (e.g., some examples from the past 10 years include Ulla et al, 2010;Cooper, 2012Cooper, , 2014Cooper, , 2015Curto et al, 2015;Tontini et al, 2018;De Souza et al, 2020). Many of these involve high-order derivatives, which may be susceptible to noise in the data.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks applied to the interpretation of lineaments in aeromagnetic data

Naprstek

Smith

2022

GEOPHYSICS

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Parameter estimation in aeromagnetics is an important tool for geological interpretation. Due to aeromagnetic data being highly prevalent around the world, it can often be used to assist in understanding the geology of an area as a whole, or for locating potential areas of further investigation for mineral exploration. Methods that automatically provide information such as the location and depth to the source of anomalies are useful to the interpretation, particularly in areas where a large number of anomalies exist. Unfortunately, many of the current methods rely on high-order derivatives, and are therefore susceptible to noise in the data. Convolution neural networks (CNNs) are a subset of machine learning methods that are well-suited to image processing tasks, and have been shown to be effective at interpreting other geophysical data, such as seismic sections. Following several similar successful approaches, we have developed a CNN methodology for estimating the location and depth of lineament-type anomalies in aeromagnetic maps. To train the CNN model, we utilized a synthetic aeromagnetic data modeler to vary relevant physical parameters, and developed a representative dataset of approximately 1.4 million images. These were then used for training classification CNNs, with each class representing a small range of depth values. We first applied the model to a series of difficult synthetic datasets with varying amounts of noise, comparing the results against the tilt-depth method. We then applied the CNN model to a dataset from north-eastern Ontario, Canada, that contained a dyke with known depth, which was correctly estimated. This method is shown to be robust to noise, and can easily be applied to new datasets using the trained model, which has been made publicly available.

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Section: Introductionmentioning

confidence: 99%

Convolutional neural networks applied to the interpretation of lineaments in aeromagnetic data

Naprstek

Smith

2022

GEOPHYSICS

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“…Automatic characterization of sedimentary basin and its surrounding basement was demonstrated using amplitude-normalized return to the pole (RTP) magnetic data to improve edge detection; a tilt-angle filter was applied to the RTP aeromagnetic survey data [11]. Other approaches for structural characterization of sedimentary basins and lineaments that have been widely published are discussed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of aeromagnetic filtering techniques in locating the primary target in sedimentary terrain: A review

2021

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This article analyzes some aeromagnetic filtering techniques for mitigating deceptive geophysical conceptions that may result in a distorted range of geological information from aeromagnetic data. The implication of using the aeromagnetic method, data processing, and enhancement to distinguish sediment-produced anomalies was considered. Two methods to locate buried faults in aeromagnetic data were compared: Edge and fault detection were considered using the magnetic contrast and horizontal gradient methods, whereas rapid depth estimation was considered using the Euler deconvolution method and Signum method. The general challenge to find the magnetic anomaly depth and delineate edges relies on geophysical filtering techniques discussed in order to maintain its geological relevance. The magnetic-contrast layer model signatures help clarify the existence of intra-sedimentary faults. The horizontal gradient approach relative to other derivative methods has better noise stability and fast adaptation to grids without modifying parameters. However, the Signum transform (ST) approach offers a more special solution in depth estimation than the Euler’s deconvolution approach whose solution relies on the required choice of default shape parameters and windows. The Euler deconvolution procedure may not be able to detect structures found by the ST approach and vice versa. As a result, these techniques may be used in conjunction with one another during analysis, as complementary interpretation tools. This review will however aid in the analysis of information used as a criterion for determining faults using various analytical techniques like ST or Euler deconvolution.

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“…Texture recognition has been one of the most important tasks in computer vision for many years. Examples of applications are found in medicine [24], botany [36], geology [32], surveillance [1], etc.…”

Section: Introductionmentioning

confidence: 99%

Reorganizing local image features with chaotic maps: an application to texture recognition

Florindo¹

2020

Preprint

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Despite the recent success of convolutional neural networks in texture recognition, model-based descriptors are still competitive, especially when we do not have access to large amounts of annotated data for training and the interpretation of the model is an important issue. Among the model-based approaches, fractal geometry has been one of the most popular, especially in biological applications. Nevertheless, fractals are part of a much broader family of models, which are the non-linear operators, studied in chaos theory. In this context, we propose here a chaos-based local descriptor for texture recognition. More specifically, we map the image into the three-dimensional Euclidean space, iterate a chaotic map over this three-dimensional structure and convert it back to the original image. From such chaos-transformed image at each iteration we collect local descriptors (here we use local binary patters) and those descriptors compose the feature representation of the texture. The performance of our method was verified on the classification of benchmark databases and in the identification of Brazilian plant species based on the texture of the leaf surface. The achieved results confirmed our expectation of a competitive performance, even when compared with some learning-based modern approaches in the literature.

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Imaging sedimentary basins from high-resolution aeromagnetics and texture analysis

Cited by 9 publications

References 69 publications

Convolutional neural networks applied to the interpretation of lineaments in aeromagnetic data

Convolutional neural networks applied to the interpretation of lineaments in aeromagnetic data

Analysis of aeromagnetic filtering techniques in locating the primary target in sedimentary terrain: A review

Reorganizing local image features with chaotic maps: an application to texture recognition

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