Expulsion of fluids from tight rocks is an important process in sedimentary basins for phenomena such as distribution of overpressure and primary migration of hydrocarbons. This paper shows that the “Toggle Switch” (TS) algorithm of Miller and Nur is well-suited to model expulsion of brine by hydraulic fracturing from tight rocks undergoing compaction. Local random overpressure is generated in the TS algorithm using a void ratio function with random compaction length. The TS algorithm equilibrates critical fluid pressure by local exchange of excess fluid. We show that the algorithm gives net migration of fluid upwards, because the least compressive stress is decreasing towards the surface. The random void ratio makes the total expulsion rate from the layer intermittent. In the limit of zero cell size the expulsion rate approaches a mean value. When the rock has a random strength, it is shown that critically pressured clusters remain after the TS algorithm has been applied. The implementation of the algorithm is mass conservative, which ensures that the expelled mass is exactly the mass of pore fluid lost by compaction. Expressions are derived for the rate of fluid expulsion and for an effective fracture permeability.

Hyperspectral classification using artificial neural networks is commonly applied on camera dependent interpolated data, or on the results of a dimensionality reduction algorithm. While these methods usually produce satisfactory results, they have severe limitations when part of the spectrum is missing, for example when parts of the image are overexposed or affected by bad pixels. This article presents an input layer based on the Haar transform for artificial neural networks used for hyperspectral data classification. This input layer is designed to perform efficiently with incomplete data and is independent of the specific bands used by the camera. This could enable providing pre-trained neural networks, which can be used with a camera with different specifications than the one used for training. This paper shows that a classifier for mineral identification built using this approach performs better than standard normalization on incomplete spectra, and similarly on complete spectra. Additionally, it shows that such a classifier matches local spectral features, and therefore that the artificial neural network is matching the spectrum shape.

A regional bedrock map provides a foundation from which to build geological interpretations. However, rapid and accurate bedrock mapping in an area that lacks outcrop is a common problem, especially in regions with sparse data. A historic bedrock map from an Au and base metal project in the Kerkasha district, Eritrea, is significantly improved by predicting bedrock distribution in areas previously mapped as transported overburden. Publicly-available remote sensing data (DTM and ASTER) were combined with airborne geophysical data (magnetics and radiometrics) to provide features for bedrock prediction. Remote sensing data were pre-processed using Principal Components Analysis (PCA) to yield an equal number of principal components (PC) as input features. Four iterations were trialled, using different combinations of remote sensing PC features. The two initial trials used all available remote sensing data but compared results when feature ranking and selection is applied to reduce the number of PCs used for training and classification. The subsequent two trials used subsets of available remote-sensing data, selected based on domain expertise (i.e., the domain-specific knowledge of a geologist), with all respective PCs were retained. Five-fold cross-validation scores were highest when a DTM, magnetics, and radiometrics data were included as input features. However, qualitative visual appraisal of predicted results across trials, complemented by maps of class membership uncertainty (using a measure of entropy), indicate that geologically-meaningful results are also produced when radiometrics are omitted and only the DTM and magnetics are used. The study concludes with a generalised workflow to assist geologists who are seeking to improve the bedrock interpretation of areas under cover in a single area of interest. Domain expertise is shown to be critical for the selection of appropriate input features and validation of results during predictive lithologic mapping.