3D Geophysical Predictive Modeling by Spectral Feature Subset Selection in Mineral Exploration

"Several technical challenges are related to data collection, inverse modeling, model fusion, and integrated interpretations in the exploration of geophysics. A fundamental problem in integrated geophysical interpretation is the proper geological understanding of multiple inverted physical property images. Tackling this problem requires high-dimensional techniques for extracting geological information from modeled physical property images. In this study, we developed a 3D statistical tool to extract geological features from inverted physical property models based on a synergy between independent component analysis and continuous wavelet transform. An automated interpretation of multiple 3D geophysical images is also presented through a hybrid spectral feature subset selection (SFSS) algorithm based on a generalized supervised neural network algorithm to rebuild limited geological targets from 3D geophysical images. Our self-proposed algorithm is tested on an Au/Ag epithermal system in British Columbia (Canada), where layered volcano-sedimentary sequences, particularly felsic volcanic rocks, are associated with mineralization. Geophysical images of the epithermal system were obtained from 3D cooperative inversion of aeromagnetic, direct current resistivity, and induced polarization data sets. The recovered cooperative susceptibilities allowed locating a magnetite destructive zone associated with porphyritic intrusions and felsic volcanoes (Au host rocks). The practical implementation of the SFSS algorithm in the study area shows that the proposed spectral learning scheme can efficiently learn the lithotypes and Au grade patterns and makes predictions based on 3D physical property inputs. The SFSS also minimizes the number of extracted spectral features and tries to pick the best representative features for each target learning case. This approach allows interpreters to understand the relevant and irrelevant spectral features in addition to the 3D predictive models. Compared to conventional 3D interpolation methods, the 3D lithology and Au grade models recovered with SFSS add predictive value to the geological understanding of the deposit in places without access to prior geological and borehole information.

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

Multi-label prediction method for lithology, lithofacies and fluid classes based on data augmentation by cascade forest

Predicting the lithology, lithofacies and reservoir fluid classes of igneous rocks holds significant value in the domains of CO2 storage and reservoir evaluation. However, no precedent exists for research on the multi-label identification of igneous rocks. This study proposes a multi-label data augmented cascade forest method for the prediction of multilabel lithology, lithofacies and fluid using 9 conventional logging data features of cores collected from the eastern depression of the Liaohe Basin in northeastern China. Data augmentation is performed on an unbalanced multi-label training set using the multi-label synthetic minority over-sampling technique. Sample training is achieved by a multi-label cascade forest consisting of predictive clustering trees. These cascade structures possess adaptive feature selection and layer growth mechanisms. Given the necessity to focus on all possible outcomes and the generalization ability of the method, a simulated well model is built and then compared with 6 typical multi-label learning methods. The outperformance of this method in the evaluation metrics validates its superiority in terms of accuracy and generalization ability. The consistency of the predicted results and geological data of actual wells verifies the reliability of our method. Furthermore, the results show that it can be used as a reliable means of multi-label prediction of igneous lithology, lithofacies and reservoir fluids.Document Type: Original articleCited as: Han, R., Wang, Z., Guo, Y., Wang, X., A, R., Zhong, G. Multi-label prediction method for lithology, lithofacies and fluid classes based on data augmentation by cascade forest. Advances in Geo-Energy Research, 2023, 9(1): 25-37. https://doi.org/10.46690/ager.2023.07.0

Wavelet decomposition and advanced denoising techniquesn for analysis and classification of seismic signals

This work describes an automatic classification procedure for seismic signals suitable for the analysis of complex, broad-band waveforms commonly associated with fluid-rock interaction in volcanic and hydrothermal systems. Based on Discrete Wavelet Transform, a set of significant seismic signal features that characterize the type of event is identified (e.g. noise, volcano tectonic, long period). These features are initially assessed for events whose category (class) can be previously determined by an expert analyst. A Bayesian Pattern Recognition supervised technique based on these features is adopted to classify a new ‘unlabelled pattern’, whose class is unknown. In this way values computed for known events are used to classify events of unknown identity ('supervised classification'). A test was performed on seismological data recorded at Campi Flegrei (Italy), which was divided into three classes. Automatic classification accuracy ranges from 82% to 100% over a broad range of datasets

A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) model for urban Earthquake vulnerability assessment

© 2018 by the authors. Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to aMultilayer Perceptron (MLP) neural network for producing an Earthquake VulnerabilityMap (EVM). Finally, an EVMwas produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city's vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management

Fluid injections in the subsurface: a multidisciplinary approach for better understanding their implications on induced seismicity and the environment.

Fluid injections in the subsurface are common operations in underground industrial activities such as oil and gas exploitation, geothermal energy development, and carbon capture and storage (CCS). In recent years, it became a focal point as new drilling technologies (e.g., hydraulic fracturing) enable the extraction of oil and gas in unconventional reservoirs and the development of CCS injection techniques became a key research topic in the context of the low-carbon energy transition. Fluid injections have drawn the attention also in the general public because of their main potential implications such as the induced seismicity phenomenon (Rubinstein and Mahani, 2015) and the environmental pollution (Burton et al., 2016, Pitchel et al., 2016). Considering the strong socioeconomic impact of fluid injection operations (National Research Council, 2013; Ellsworth, 2013; Grigoli et al., 2017) the current research in this field needs the integration of multidisciplinary studies, involving knowledge on geology, seismology, source physics, hydrogeology, fluid geochemistry, rocks geomechanics for a complete understanding of the phenomenon and to set-up the most effective and “best practice” protocols for the monitoring of areas where injection operation are performed. On this basis, this work applied a multidisciplinary approach integrating seismological methods, geochemical studies, and machine learning techniques. Two key-study areas characterized by high fluid-rock interaction and fluid-injection in the subsurface were analyzed: i) the High Agri Valley (hereinafter HAV), hosting the largest onshore oil field in West Europe, in which wastewater disposal operations have been carried out since 2006 at the Costa Molina 2 injection well and where both natural and induced seismicity clusters were recognized; ii) the Mefite d’Ansanto, the largest natural emission of CO2-rich gases with mantle-derived fluids (from non‐volcanic environment) ever measured on the Earth (Carcausi et al., 2013; Caracausi and Paternoster, 2015; Chiodini et al, 2010). Regarding the HAV study area, we reconstructed the preliminary catalogue of seismicity through accurate absolute locations in a 3D-velocity model (Serlenga and Stabile, 2019) of earthquakes detected from the local seismic INSIEME network managed by the CNR-IMAA. A total of 852 between local tectonic and induced earthquakes occurred in the HAV between September 2016 and March 2019. We tested the potential of the unsupervised machine-learning approach as an automated tool to make faster dataset exploratory analysis, founding the density-based approach (DBSCAN algorithm-Density-Based Spatial Clustering of Applications with Noise, Ester et al., 1996) particularly suitable for the fast identification of clusters in the catalogue resulting from both injection-induced events and tectonic local earthquake swarms. Moreover, we proposed a semi-automated workflow for earthquake detection and location with the aim to improve the current standard procedures, quite time-consuming and strictly related to human operators. The workflow, integrating manual, semi-automatic and automatic detection and location methods enabled us to characterize a low magnitude natural seismic sequence occurred in August 2020 in the southwestern area of the HAV (Castelsaraceno sequence) in a relatively short time with respect to the application of standard techniques, thus representing a starting point for the improvement of the efficiency of seismic monitoring techniques of both anthropogenic and natural seismicity in the HAV. Our multidisciplinary approach involved the geochemical study of the HAV groundwaters with the aim to: (1) determine the geochemical processes controlling the chemical composition; (2) define a geochemical conceptual model regarding fluid origin (deep vs shallow) and mixing processes by means isotopic data; (3) establish a geochemical baseline for the long-term environmental monitoring of the area. A total of 39 water samples were collected from springs and wells located at the main hydro-structures bordering the valley to determine chemical (major, minor and trace elements) and isotopic composition (e.g., dD, d18O, d13C-TDIC and noble gas). All investigated water samples have a meteoric origin, although some springs show long and deep flow than the other ones, and a bicarbonate alkaline-earth composition, thus suggesting the carbonate hydrolysis as the main water-rock interaction process. Our results demonstrated that HAV groundwater is chemically suitable for drinking use showing no criticalities for potentially toxic metals reported by the Italian and European legislation guidelines. Particular attention was given on thermal water of Tramutola well, built by Agip S.p.a. for oil & gas exploration, with the occurrence of bubbling gases. The geochemical study highlighted a substantial difference of these CH4-dominated thermal fluids with the rest of the dataset. Helium isotope (3He/4He) indicate a prevalent radiogenic component with a contribution of mantle-derived helium (~20%) and the average δ13C-CO2 value is of – 4.6 ‰ VPDB, consistent with a mantle origin. Methane isotope composition indicates a likely microbial isotopic signature (δ13C-CH4 =−63.1‰, −62.4‰, δD-CH4=−196‰, −212‰), probably due to biodegradation processes of thermogenic hydrocarbons. The methane output at the well, evaluated by means of anemometric measurement of the volume flow (m3/h) is of ~156 t/y, that represent about 1.5% of total national anthropogenic sources related to fossil fuel industry (Etiope et al., 2007). Our work highlighted that Tramutola well may represent a key natural laboratory to better understand the complex coupling effects between mechanical and fluid-dynamic processes in earthquake generation. Moreover, the integration of seismic and geochemical data in this work allowed us to identify the most suitable locations for the future installation of multiparametric stations for the long-term monitoring of the area and development of integrated research in the HAV. Regarding the Mefite d’Ansanto, we analyzed the background seismicity in the emission area recorded by a dense temporary seismic network deployed at the site between 30-10-2019 and 02-11-2019. First, we implemented and tested an automated detection algorithm based on non-parametric statistics of the recorded amplitudes at each station, collecting a total dataset of 8561 events. Then, both unsupervised (DBSCAN) and supervised (KNN-k-nearest neighbors classification, Fix & Hodges, 1951) machine learning techniques were applied, based on specific parameters (duration, RMS-amplitude and arrival slope) of the detected events. DBSCAN algorithm allowed to determine characteristic bivariate correlations among tremors parameters: a high linear correlation (r~0.6-0.7) between duration and RMS-amplitude and a lower one (r~0.5-0.6) between amplitude and arrival slope (first arrival parametrization). These relationships let us to define training samples for the KNN algorithm, which allowed to classify tremor signals at each station and to automatically discriminate between tremors and accidentally detected anthropogenic noise. Results allowed to extract new information on seismic tremor at Mefite d’Ansanto, previously poorly quantitively analyzed, and its discrimination, thus providing a starting workflow for monitoring the non-volcanic emission. Isotopic geochemistry (3He/4He, 4 He/20Ne, δ13CCO2) indicated a mixing of mantle (30%-40%) and crust-derived fluids. The source location of the emission related tremor would represent a step forward in its characterization, and for setting up more advanced automated detection and machine learning classification techniques to exploit the information provided by seismic tremor for an improved automatic monitoring of non-volcanic, CO2 -gas emissions

Imagerie intégrée par inversion géophysique 3D, extraction de caractéristique multivariée et sélection de caractéristique spectrale

Il existe plusieurs défis techniques liés à la collecte de données, à la modélisation inverse et la fusion de modèles ainsi qu’aux interprétations intégrées en géophysique d'exploration minérale. Dans cette étude de doctorat, de nouvelles méthodologies sont testées et évaluées pour l'interprétation intégrée 3D de plusieurs images géophysiques ciblant les gisements minéraux. La modélisation 3D inverse de multiples données géophysiques, de statistiques multivariées et de méthodes d'apprentissage automatique est utilisée pour développer une méthodologie robuste d'interprétation intégrée 3D. Une inversion coopérative de plusieurs données géophysiques est proposée pour l'imagerie 3D d'un gisement épithermal Au-Ag en Colombie-Britannique (Canada). Les données géophysiques multiples sont interprétées et évaluées par des observations pétrophysiques, géochimiques et géologiques de forage afin d'améliorer notre compréhension du système épithermal de Newton en termes de signatures géophysiques. L'interprétation intégrée a identifié quatre domaines pétrophysiques basés sur les trois propriétés physiques inversées en coopération, y compris la résistivité électrique, la chargeabilité PP et la susceptibilité magnétique. Nous avons également développé un outil statistique 3D pour extraire des informations géologiques à partir de modèles de propriétés physiques inversés basés sur l’analyse de composants indépendants à travers la maximisation de la néguentropie. Une interprétation automatisée de plusieurs images géophysiques 3D est également présentée par un algorithme de sélection de sous-ensembles spectraux hybrides (SSSH), basé sur un algorithme d'intelligence artificielle supervisée généralisée qui cherche à reconstruire des cibles géologiques limitées à partir d’images géophysiques 3D. Le présent algorithme SSSH prend les caractéristiques spectrales extraites de la décomposition en ondelettes des composants indépendants des propriétés physiques et essaye simultanément de minimiser la fonction de coûts du réseau neuronal et le nombre de caractéristiques spectrales d'entrée grâce à une optimisation algorithmique multi-objectif. Par rapport aux méthodes d'interpolation 3D conventionnelles, telles que le maillage direct 3D et le krigeage, les modèles géologiques 3D récupérés avec SFSS ajoutent une valeur prédictive à nos connaissances géologiques dans des endroits sans information de forage

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia

© 2019 China University of Geosciences (Beijing) and Peking University Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration of the extent of probability and vulnerability of assets. This study develops an integrated model by using the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of various zones are obtained. The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city fall under low to very low earthquake risk classifications. The findings of this research are useful for government agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh