Subspace Gaussian Mixture Models for Language Identification and Dysarthric Speech Intelligibility Assessment

En esta Tesis se ha investigado la aplicación de técnicas de modelado de subespacios de mezclas de Gaussianas en dos problemas relacionados con las tecnologías del habla, como son la identificación automática de idioma (LID, por sus siglas en inglés) y la evaluación automática de inteligibilidad en el habla de personas con disartria. Una de las técnicas más importantes estudiadas es el análisis factorial conjunto (JFA, por sus siglas en inglés). JFA es, en esencia, un modelo de mezclas de Gaussianas en el que la media de cada componente se expresa como una suma de factores de dimensión reducida, y donde cada factor representa una contribución diferente a la señal de audio. Esta factorización nos permite compensar nuestros modelos frente a contribuciones indeseadas presentes en la señal, como la información de canal. JFA se ha investigado como clasficador y como extractor de parámetros. En esta última aproximación se modela un solo factor que representa todas las contribuciones presentes en la señal. Los puntos en este subespacio se denominan i-Vectors. Así, un i-Vector es un vector de baja dimensión que representa una grabación de audio. Los i-Vectors han resultado ser muy útiles como vector de características para representar señales en diferentes problemas relacionados con el aprendizaje de máquinas. En relación al problema de LID, se han investigado dos sistemas diferentes de acuerdo al tipo de información extraída de la señal. En el primero, la señal se parametriza en vectores acústicos con información espectral a corto plazo. En este caso, observamos mejoras de hasta un 50% con el sistema basado en i-Vectors respecto al sistema que utilizaba JFA como clasificador. Se comprobó que el subespacio de canal del modelo JFA también contenía información del idioma, mientras que con los i-Vectors no se descarta ningún tipo de información, y además, son útiles para mitigar diferencias entre los datos de entrenamiento y de evaluación. En la fase de clasificación, los i-Vectors de cada idioma se modelaron con una distribución Gaussiana en la que la matriz de covarianza era común para todos. Este método es simple y rápido, y no requiere de ningún post-procesado de los i-Vectors. En el segundo sistema, se introdujo el uso de información prosódica y formántica en un sistema de LID basado en i-Vectors. La precisión de éste estaba por debajo de la del sistema acústico. Sin embargo, los dos sistemas son complementarios, y se obtuvo hasta un 20% de mejora con la fusión de los dos respecto al sistema acústico solo. Tras los buenos resultados obtenidos para LID, y dado que, teóricamente, los i-Vectors capturan toda la información presente en la señal, decidimos usarlos para la evaluar de manera automática la inteligibilidad en el habla de personas con disartria. Los logopedas están muy interesados en esta tecnología porque permitiría evaluar a sus pacientes de una manera objetiva y consistente. En este caso, los i-Vectors se obtuvieron a partir de información espectral a corto plazo de la señal, y la inteligibilidad se calculó a partir de los i-Vectors obtenidos para un conjunto de palabras dichas por el locutor evaluado. Comprobamos que los resultados eran mucho mejores si en el entrenamiento del sistema se incorporaban datos de la persona que iba a ser evaluada. No obstante, esta limitación podría aliviarse utilizando una mayor cantidad de datos para entrenar el sistema.In this Thesis, we investigated how to effciently apply subspace Gaussian mixture modeling techniques onto two speech technology problems, namely automatic spoken language identification (LID) and automatic intelligibility assessment of dysarthric speech. One of the most important of such techniques in this Thesis was joint factor analysis (JFA). JFA is essentially a Gaussian mixture model where the mean of the components is expressed as a sum of low-dimension factors that represent different contributions to the speech signal. This factorization makes it possible to compensate for undesired sources of variability, like the channel. JFA was investigated as final classiffer and as feature extractor. In the latter approach, a single subspace including all sources of variability is trained, and points in this subspace are known as i-Vectors. Thus, one i-Vector is defined as a low-dimension representation of a single utterance, and they are a very powerful feature for different machine learning problems. We have investigated two different LID systems according to the type of features extracted from speech. First, we extracted acoustic features representing short-time spectral information. In this case, we observed relative improvements with i-Vectors with respect to JFA of up to 50%. We realized that the channel subspace in a JFA model also contains language information whereas i-Vectors do not discard any language information, and moreover, they help to reduce mismatches between training and testing data. For classification, we modeled the i-Vectors of each language with a Gaussian distribution with covariance matrix shared among languages. This method is simple and fast, and it worked well without any post-processing. Second, we introduced the use of prosodic and formant information with the i-Vectors system. The performance was below the acoustic system but both were found to be complementary and we obtained up to a 20% relative improvement with the fusion with respect to the acoustic system alone. Given the success in LID and the fact that i-Vectors capture all the information that is present in the data, we decided to use i-Vectors for other tasks, specifically, the assessment of speech intelligibility in speakers with different types of dysarthria. Speech therapists are very interested in this technology because it would allow them to objectively and consistently rate the intelligibility of their patients. In this case, the input features were extracted from short-term spectral information, and the intelligibility was assessed from the i-Vectors calculated from a set of words uttered by the tested speaker. We found that the performance was clearly much better if we had available data for training of the person that would use the application. We think that this limitation could be relaxed if we had larger databases for training. However, the recording process is not easy for people with disabilities, and it is difficult to obtain large datasets of dysarthric speakers open to the research community. Finally, the same system architecture for intelligibility assessment based on i-Vectors was used for predicting the accuracy that an automatic speech recognizer (ASR) system would obtain with dysarthric speakers. The only difference between both was the ground truth label set used for training. Predicting the performance response of an ASR system would increase the confidence of speech therapists in these systems and would diminish health related costs. The results were not as satisfactory as in the previous case, probably because an ASR is a complex system whose accuracy can be very difficult to be predicted only with acoustic information. Nonetheless, we think that we opened a door to an interesting research direction for the two problems

Determination of wind speed and associated loads over the sports facility collapsed during the severe windstorm of 24 January 2009 in Sant Boi de Llobregat (Barcelona)

The severe windstorm of 24 January 2009, caused by an explosive cyclogenesis, affected coastal and precoastal areas of the northeast of the Iberian Peninsula, where damages were numerous and significant, both in urban areas and in forests. One of the most important effects was the collapse of a sports facility in Sant Boi de Llobregat (10 km southwest of Barcelona), killing four children. The objective of this study is to estimate the wind speed over the sports facility and calculate the suction of the wind on the roof of the building, and the consequent collapse of the walls. To get a first approximation, a simulation of the episode around the time of maximum wind gust was inspected using the mesoscale model MM5. In the second part, the damage around the collapsed facility was analyzed, with which we note the fact that a truck was dragged and knocked over by the wind. This analysis allows for the conclusion that, in conjunction with the maximum wind gust, there was a sudden and very local shift in the wind, which caused the gust to hit the building head on. Based on this observation, the wind speed on surface and at 7 m (roof of the building) was estimated, and the suction of the wind was calculatedPeer ReviewedPostprint (published version

Los yesos del Mioceno superior de Campo Coy (Cordillera Bética oriental, España)

The Campo Coy basin contains an important evaporite succession, up to 350 meters thick of gypsum, including two gypsum units: lower and upper gypsum units. These are characterized by fine-grain laminated and selenitic primary gypsums and by nodular-laminated and meganodular secondary gypsums. The geochemical study based on sulfate isotope compositions (δ34S and δ18O) and strontium isotope ratios (87Sr/86Sr) point to the chemical recycling of Triassic marine evaporites. Isotope compositions (δ18O and δD) of the hydration water of gypsum point to continental waters for primary gypsum precipitation. These results are consistent with a shallow lacustrine environment for the Campo Coy gypsum deposit.La cuenca de Campo Coy registra una sucesión evaporítica de más de 350 metros de potencia de yeso, dividida en dos unidades de yesos: unidad inferior y unidad superior. Estas unidades están formadas por litofacies de yeso primario laminado y yeso selenítico junto con litofacies de yeso secundario laminado-nodular y meganodular. El estudio geoquímico de la composición isotópica del sulfato (δ34S y δ18O) y de la relación isotópica del estroncio (87Sr/86Sr) muestra valores indicativos del reciclaje de evaporitas marinas triásicas. Los valores isotópicos del agua de hidratación del yeso (δ18O y δD) indican aguas de origen continental para el yeso primario. Estos resultados reflejan un ambiente lacustre somero durante la formación del depósito evaporítico de Campo Coy.This study was supported by the projects CGL-2013-42689 and CGL2016-79458 of the Spanish Government

A Geometric Deep Learning Approach to Sound Source Localization and Tracking

La localización y el tracking de fuentes sonoras mediante agrupaciones de micrófonos es un problema que, pese a llevar décadas siendo estudiado, permanece abierto. En los últimos años, modelos basados en deep learning han superado el estado del arte que había sido establecido por las técnicas clásicas de procesado de señal, pero estos modelos todavía presentan problemas para trabajar en espacios con alta reverberación o para realizar el tracking de varias fuentes sonoras, especialmente cuando no es posible aplicar ningún criterio para clasificarlas u ordenarlas. En esta tesis, se proponen nuevos modelos que, basados en las ideas del Geometric Deep Learning, suponen un avance en el estado del arte para las situaciones mencionadas previamente.Los modelos propuestos utilizan como entrada mapas de potencia acústica calculados con el algoritmo SRP-PHAT, una técnica clásica de procesado de señal que permite estimar la energía acústica recibida desde cualquier dirección del espacio. Además, también proponemos una nueva técnica para suprimir analíticamente el efecto de una fuente en las funciones de correlación cruzada usadas para calcular los mapas SRP-PHAT. Basándonos en técnicas de banda estrecha, se demuestra que es posible proyectar las funciones de correlación cruzada de las señales capturadas por una agrupación de micrófonos a un espacio ortogonal a una dirección dada simplemente usando una combinación lineal de las funciones originales con retardos temporales. La técnica propuesta puede usarse para diseñar sistemas iterativos de localización de múltiples fuentes que, tras localizar la fuente con mayor energía en las funciones de correlación cruzada o en los mapas SRP-PHAT, la cancelen para poder encontrar otras fuentes que estuvieran enmascaradas por ella.Antes de poder entrenar modelos de deep learning necesitamos datos. Esto, en el caso de seguir un esquema de aprendizaje supervisado, supone un dataset de grabaciones de audio multicanal con la posición de las fuentes etiquetada con precisión. Pese a que existen algunos datasets con estas características, estos no son lo suficientemente extensos para entrenar una red neuronal y los entornos acústicos que incluyen no son suficientemente variados. Para solventar el problema de la falta de datos, presentamos una técnica para simular escenas acústicas con una o varias fuentes en movimiento y, para realizar estas simulaciones conforme son necesarias durante el entrenamiento de la red, presentamos la que es, que sepamos, la primera librería de software libre para la simulación de acústica de salas con aceleración por GPU. Tal y como queda demostrado en esta tesis, esta librería es más de dos órdenes de magnitud más rápida que otras librerías del estado del arte.La idea principal del Geometric Deep Learning es que los modelos deberían compartir las simetrías (i.e. las invarianzas y equivarianzas) de los datos y el problema que se quiere resolver. Para la estimación de la dirección de llegada de una única fuente, el uso de mapas SRP-PHAT como entrada de nuestros modelos hace que la equivarianza a las rotaciones sea obvia y, tras presentar una primera aproximación usando redes convolucionales tridimensionales, presentamos un modelo basado en convoluciones icosaédricas que son capaces de aproximar la equivarianza al grupo continuo de rotaciones esféricas por la equivarianza al grupo discreto de las 60 simetrías del icosaedro. En la tesis se demuestra que los mapas SRP-PHAT son una característica de entrada mucho más robusta que los espectrogramas que se usan típicamente en muchos modelos del estado del arte y que el uso de las convoluciones icosaédricas, combinado con una nueva función softargmax que obtiene una salida de regresión a partir del resultado de una red convolucional interpretándolo como una distribución de probabilidad y calculando su valor esperado, permite reducir enormemente el número de parámetros entrenables de los modelos sin reducir la precisión de sus estimaciones.Cuando queremos realizar el tracking de varias fuentes en movimiento y no podemos aplicar ningún criterio para ordenarlas o clasificarlas, el problema se vuelve invariante a las permutaciones de las estimaciones, por lo que no podemos compararlas directamente con las etiquetas de referencia dado que no podemos esperar que sigan el mismo orden. Este tipo de modelos se han entrenado típicamente usando estrategias de entrenamiento invariantes a las permutaciones, pero estas normalmente no penalizan los cambios de identidad por lo que los modelos entrenados con ellas no mantienen la identidad de cada fuente de forma consistente. Para resolver este problema, en esta tesis proponemos una nueva estrategia de entrenamiento, a la que llamamos sliding permutation invariant training (sPIT), que es capaz de optimizar todas las características que podemos esperar de un sistema de tracking de múltiples fuentes: la precisión de sus estimaciones de dirección de llegada, la exactitud de sus detecciones y la consistencia de las identidades asignadas a cada fuente.Finalmente, proponemos un nuevo tipo de red recursiva que usa conjuntos de vectores en lugar de vectores para representar su entrada y su estado y que es invariante a las permutaciones de los elementos del conjunto de entrada y equivariante a las del conjunto de estado. En esta tesis se muestra como este es el comportamiento que deberíamos esperar de un sistema de tracking que toma como entradas las estimaciones de un modelo de localización multifuente y se compara el rendimiento de estas redes recursivas invariantes a las permutaciones con redes recursivas GRU convencionales para aplicaciones de tracking de fuentes sonoras.The localization and tracking of sound sources using microphone arrays is a problem that, even if it has attracted attention from the signal processing research community for decades, remains open. In recent years, deep learning models have surpassed the state-of-the-art that had been established by classic signal processing techniques, but these models still struggle with handling rooms with strong reverberations or tracking multiple sources that dynamically appear and disappear, especially when we cannot apply any criteria to classify or order them. In this thesis, we follow the ideas of the Geometric Deep Learning framework to propose new models and techniques that mean an advance of the state-of-the-art in the aforementioned scenarios. As the input of our models, we use acoustic power maps computed using the SRP-PHAT algorithm, a classic signal processing technique that allows us to estimate the acoustic energy received from any direction of the space and, therefore, compute arbitrary-shaped power maps. In addition, we also propose a new technique to analytically cancel a source from the generalized cross-correlations used to compute the SRP-PHAT maps. Based on previous narrowband cancellation techniques, we prove that we can project the cross-correlation functions of the signals captured by a microphone array into a space orthogonal to a given direction by just computing a linear combination of time-shifted versions of the original cross-correlations. The proposed cancellation technique can be used to design iterative multi-source localization systems where, after having found the strongest source in the generalized cross-correlation functions or in the SRP-PHAT maps, we can cancel it and find new sources that were previously masked by thefirst source. Before being able to train deep learning models we need data, which, in the case of following a supervised learning approach, means a dataset of multichannel recordings with the position of the sources accurately labeled. Although there exist some datasets like this, they are not large enough to train a neural network and the acoustic environments they include are not diverse enough. To overcome this lack of real data, we present a technique to simulate acoustic scenes with one or several moving sound sources and, to be able to perform these simulations as they are needed during the training, we present what is, to the best of our knowledge, the first free and open source room acoustics simulation library with GPU acceleration. As we prove in this thesis, the presented library is more than two orders of magnitude faster than other state-of-the-art CPU libraries. The main idea of the Geometric Deep Learning philosophy is that the models should fit the symmetries (i.e. the invariances and equivariances) of the data and the problem we want to solve. For single-source direction of arrival estimation, the use of SRP-PHAT maps as inputs of our models makes the rotational equivariance of the problem undeniably clear and, after a first approach using 3D convolutional neural networks, we present a model using icosahedral convolutions that approximate the equivariance to the continuous group of spherical rotations by the discrete group of the 60 icosahedral symmetries. We prove that the SRP-PHAT maps are a much more robust input feature than the spectrograms typically used in many state-of-the-art models and that the use of the icosahedral convolutions, combined with a new soft-argmax function that obtains a regression output from the output of the convolutional neural network by interpreting it as a probability distribution and computing its expected value, allows us to dramatically reduce the number of trainable parameters of the models without losing accuracy in their estimations. When we want to track multiple moving sources and we cannot use any criteria to order or classify them, the problem becomes invariant to the permutations of the estimates, so we cannot directly compare them with the ground truth labels since we cannot expect them to be in the same order. This kind of models has typically been trained using permutation invariant training strategies, but these strategies usually do not penalize the identity switches and the models trained with them do not keep the identity of every source consistent during the tracking. To solve this issue, we propose a new training strategy, which we call sliding permutation invariant training, that is able to optimize all the features that we could expect from a multi-source tracking system: the precision of the direction of arrival estimates, the accuracy of the source detections, and the consistency of the assigned identities. Finally, we propose a new kind of recursive neural network that, instead of using vectors as their input and their state, uses sets of vectors and is invariant to the permutation of the elements of the input set and equivariant to the permutations of the elements of the state set. We show how this is the behavior that we should expect from a tracking model which takes as inputs the estimates of a multi-source localization model and compare these permutation-invariant recursive neural networks with the conventional gated recurrent units for sound source tracking applications.<br /

Composición isotópica del sulfato de las evaporitas Messinienses de la cuenca del Piamonte (Italia)

The Piedmont basin (NW Italy) records a Messinian Salinity Crisis (MSC) succession including a selenite gypsum deposit assigned to the Primary Lower Gypsum (PLG, MSC stage 1). Strontium isotope ratios are in the range of the PLG deposits of the Mediterranean area. Sulfate isotope compositions of vertically oriented selenite gypsum beds, in the lower part of the succession, are similar to those reported in other PLG deposits. However, flattened branching selenite cones in the upper part show higher isotope compositions, mainly in δ34S values, suggesting intense BSR conditions, stronger than reported in other PLG deposits. We interpret this chemical shift during deposition of the upper part of the PLG as the result of increased marine restriction assisted by the marginal position of this basin in the Adriatic Gulf during the Apennine and Alpine upliftsLa cuenca del Piamonte (NW Italia) contiene una serie Messiniense que incluye una unidad de yeso selenítico atribuida al PLG (MSC estadio 1). La isotopía del estroncio confirma esta asignación. La isotopía del sulfato de los yesos seleníticos de desarrollo vertical de la parte inferior de la serie es comparable a la de otras series PLG del Mediterráneo. Sin embargo, los conos de desarrollo horizontal de la parte superior de la serie muestran composiciones isotópicas mayores, especialmente en δ34S, sugiriendo una intensa actividad bacteriana (BSR) no observada en otros depósitos PLG. Interpretamos esta diferencia como resultado de mayores condiciones de restricción marina de la cuenca del Piamonte debidas a la posición marginal de esta cuenca en el Golfo Adriático durante el levantamiento de los Apeninos y los Alpe

New constraints on the closure of the Betic Seaway and the western Mediterranean palaeoclimate during the Messinian Salinity Crisis from the Campo Coy Basin (SE Spain)

The Campo Coy Basin (SE Spain) exposes >1 km of sedimentary succession with a variety of rocks including a thick evaporitic succession previously associated with the Messinian time. These evaporites were supposedly deposited in a restricted Mediterranean-Atlantic seaway connecting the Lorca and Guadix-Baza basins, although no chronological or geochemical data existed. Here we use palaeomagnetism together with vertebrate and foraminifera biostratigraphy to constrain the age of the Campo Coy succession between <9 Ma and 4.7 Ma. We use geochemistry (δ34S, δ18O and 87Sr/86Sr values) of the gypsum deposits to evaluate their marine or continental origin. In addition, we describe the underlying and overlying lithostratigraphic units to reconstruct the palaeogeographic evolution of this region. Our results show that the sediments were deposited in a continental environment, indicating that the Betic Seaway was already closed in this region during the late Tortonian and that the neighbouring marine basins of Guadix-Baza and Lorca were disconnected during that time. The δ34S, δ18O and 87Sr/86Sr values of the gypsum indicate recycling from the Triassic sulphates. Sedimentary facies of the evaporites point to an environment dominated by a saline lake with continental sabkha episodes developed during the driest periods. Well-defined and laterally continuous evaporitic cyclicity suggests an orbital forcing and high sedimentation rates preceding the Messinian Salinity Crisis (MSC). Alluvial deposits are contemporaneous with the MSC indicating a dry continental environment in this region during the Mediterranean restriction. Overlaying lacustrine carbonates are rich in small vertebrate fauna including African species that migrated to Europe during the MSC. These carbonates have low δ18O and δ13C values characteristic for freshwater input in an open lake just after the Zanclean flood, suggesting that a wet climate followed the MSC.Funding was provided by the grants CGL-2016-79458 and PID2020-118999GB-I00 from the Spanish Ministry of Science and Innovation (MCIN)/ State Research agency of Spain (AEI)/10.13039/501100011033, and by the Catalonian Government Actions 21-SGR-829 and PGC2018-094122-B-100

Mineralogy and distribution of critical elements in the Sn–W–Pb–Ag–Zn Huanuni deposit, Bolivia

The polymetallic Huanuni deposit, a world-class tin deposit, is part of the Bolivian tin belt. As a likely case for a “mesothermal” or transitional deposit between epithermal and porphyry Sn types (or shallow porphyry Sn), it represents a case that contributes significantly to the systematic study of the distribution of critical elements within the “family” of Bolivian tin deposits. In addition to Sn, Zn and Ag, further economic interest in the area resides in its potential in critical elements such as In, Ga and Ge. This paper provides the first systematic characterisation of the complex mineralogy and mineral chemistry of the Huanuni deposit with the twofold aim of identifying the mineral carriers of critical elements and endeavouring plausible metallogenic processes for the formation of this deposit, by means of a multi-methodological approach. With In concentrations consistently over 2000 ppm, the highest potential for relevant concentrations in this metal resides in widespread tin minerals (cassiterite and stannite) and sphalerite. Hypogene alteration assemblages are hardly developed due to the metasedimentary nature of host rocks, but the occurrence of potassium feldspar, schorl, pyrophyllite and dickite as vein material stand for potassic to phyllic or advanced argillic alteration assemblages and relatively high-temperature (and low pH) mineralising fluids. District-scale mineralogical zonation suggests a thermal zonation with decreasing temperatures from the central to the peripheral areas. A district-scale zonation has been also determined for d34SVCDT values, which range -7.2‰ to 0.2‰ (mostly -7‰ to -5‰) in the central area and -4.2‰ to 1.0‰ (mainly constrained between -2‰ and 1‰) in peripheral areas. Such values stand for magmatic and metasedimentary sources for sulfur, and their spatial zoning may be related to differential reactivity between mineralising fluids and host rocks, outwardly decreasing from the central to the peripheral areasPeer ReviewedPostprint (published version

Distribution of indium in the Ánimas - Chocaya - Siete Suyos District

The Ánimas - Chocaya - Siete Suyos district in SW Bolivia hosts a Bolivian-type polymetallic vein mineralization composed mostly of cassiterite, sphalerite, pyrite, galena, stannite, lead sulfosalts, tin sulfosalts and silver sulfosalts. In addition to base (Zn, Sn, Pb) and precious (Ag) metals, important concentrations of In have been described. Systematic EPMA analyses have revealed that the highest concentrations are found in an early generation of sphalerite (up to 9.66 wt% In) and in stannite (up to 4.11 wt% In). Although In-bearing sphalerites are relatively Fe-rich (mostly between 6.0 and 18.1 mol % FeS), the atomic concentrations of these two elements do not yield any correlation. In contrast, In is positively correlated with Cu mostly along a Cu/In = 1 proportion pointing to a (In3+ + Cu+ ) ¿ 2Zn2+ coupled substitution. A relatively high activity of Cu during the crystallization of In-rich sphalerite is also supported by exsolutions of chalcopyrite and stannite.Peer ReviewedPostprint (author's final draft

Indium Mineralization in the Volcanic Dome-Hosted Ánimas-Chocaya-Siete Suyos Polymetallic Deposit, Potosí, Bolivia

A volcanic dome complex of Miocene age hosts the In-bearing Ánimas-Chocaya-Siete Suyos district in SW Bolivia. Ore mineralization occurs as banded and massive infillings in sub-vertical, NE-SW striking veins. In this article, a detailed petrographic study is combined with in situ mineral geochemistry determinations in ore from the Arturo, Chorro and Diez veins in the Siete Suyos mine, the Ánimas, Burton, Colorada, and Rosario veins in the Ánimas mine and the Nueva vein in the Chocaya mine. A three-stage paragenetic sequence is roughly determined for all of them, and includes (1) an early low-sulfidation stage that is dominated by cassiterite, pyrrhotite, arsenopyrite, and high-Fe sphalerite (FeS > 21 mol. %); (2) a second intermediate-sulfidation stage dominated by pyrite + marcasite ± intermediate product, sphalerite (FeS < 21 mol. %), stannite, and local famatinite; and, (3) a late intermediate-sulfidation stage dominated by galena and Ag-Pb-Sn sulfosalts. Electron-probe microanalyses reveal high indium enrichment in stage-2 sphalerite (up to 9.66 wt.% In) and stannite (up to 4.11 wt.% In), and a moderate enrichment in rare wurtzite (up to 1.61 wt.% In), stage-1 sphalerite (0.35 wt.% In), cassiterite (up to 0.25 wt.% In2O3), and ramdohrite (up to 0.24 wt.% In). Therefore, the main indium mineralization in the district can be associated to the second, intermediate-sulfidation stage, chiefly in those veins in which sphalerite and stannite are more abundant. Atomic concentrations of In and Cu in sphalerite yield a positive correlation at Cu/In = 1 that agrees with a (Cu+ + In3+) ↔ 2Zn2+ coupled substitution. The availability of Cu in the mineralizing fluids during the crystallization of sphalerite is, in consequence, essential for the incorporation of indium in its crystal lattice and would control the distribution of indium enrichment at different scales. The highest concentrations of indium in sphalerite, which is found in the Diez vein in the Siete Suyos mine, occur in crustiform bands of sphalerite with local "chalcopyrite disease" texture, which has not been observed in the other studied veins. In stannite, the atomic concentrations of In are negatively correlated with those of Cu and Sn at Cu + In = 2 and Sn + In = 1. Thus, atomic proportions and correlations suggest the contextualization of the main indium mineralization in the sphalerite-stannite-roquesite pseudoternary system

Spatial and Temporal Controls on the Distribution of Indium in Xenothermal Vein-Deposits: The Huari Huari District, Potosí, Bolivia

The Huari Huari deposit, Potosí Department in SW Bolivia, hosts polymetallic stratiform and vein mineralization of Miocene age with significant concentrations of the critical metal indium (In). Vein mineralization records document early crystallization of quartz and cassiterite followed by prominent associations of sulfides and sulfosalts. The earliest sulfide was arsenopyrite, followed by pyrrhotite, and progressively giving way to pyrite as the main iron sulfide, whereas Cu-Ag-Pb sulfosalts constitute late hypogene associations. Sphalerite is the chief ore mineral, and its crystallization is extended during most of the mineralization lifespan as evidenced by its initial cocrystallization with pyrrhotine, then with pyrite, and finally with Ag-Pb sulfosalts. The composition of sphalerite varies from early to late generations with a continuous decrease in FeS that attests to a decrease in temperature, which is constrained to vary from ~450 to <200 °C, and/or an increase in f(S2), both congruent with the described paragenetic sequence. Indium concentrated mostly in the structure of Fe-rich sphalerite (up to 3.49 wt. %) and stannite (up to 2.64 wt. %) as limited solid solutions with roquesite in the (Zn,Fe)S-Cu2FeSnS4-CuInS2 pseudoternary system. In sphalerite, In shows a strong positive correlation with Cu at Cu/In = 1, suggesting its incorporation via a (Cu+ + In3+) ↔ 2Zn2+ coupled substitution, and it does not correlate with Fe. In stannite, In shows a moderate, negative correlation with Cu and Sn, and an In3+ ↔ (Cu+ + ½ Sn4+) coupled substitution is suggested. Coexisting sphalerite and stannite yielded the highest In concentrations and crystallized at temperatures between 350 and 250 °C. Copper activity probably played a major role in the accumulation of In in the structure of sphalerite since In-bearing sphalerite coexisted with the deposition of stannite, shows high concentrations of Cu (up to 0.13 atoms per formula unit (a.p.f.u.)) in its structure, and hosts exsolutions of stannite and chalcopyrite. Distribution on the district scale of In suggests an input of hydrothermal fluids richer in Cu in the central position of the mineralizing system, represented by the Antón Bravo vein