Summary. We present a binary inversion algorithm for inverting gravity data in salt imaging. The density contrast is restricted to being one of two possibiliti

Landslides are considered a natural process, with hundreds of events occurring every year in many regions of the world. However, human activities can significantly affect how stable a slope or cliff is, increasing the chances of slope collapses. Moreover, agricultural irrigation has potential to saturate subsurface materials well below ground level and is known to be an important factor that can trigger landslides in many countries. A macroregional literature review of irrigation-induced landslides was developed in this investigation, considering what has been published in Chinese, English, and Spanish. A total of 115 peer-reviewed papers, books and book chapters, graduate and undergraduate theses, and government reports were found, including 82 case studies (23 in Chinese, 26 in English, and 33 in Spanish). Results from this analysis indicate that studies focusing on this important topic have increased exponentially since the 1960s, with most irrigation-induced landslides occurring in dry climates (precipitation less than 600 mm/year), with rainfall concentrated during summer months. The majority of studies have been done in the loess region of China (Asian region), followed by Peru (Latin American region), though cases were found from other macroregions (African, Indian, Russian, Angloamerican, and Indonesian). Based on this global review, new agricultural irrigation projects located in collapsible areas must include a landslide risk analysis. Cultivated areas can follow a series of measures to minimize the chances of triggering a landslide, which would put human lives, ecosystems, food production, and infrastructure at risk.

Majes is one of the largest agricultural areas in the Arequipa region (southern Peru). Low seasonal precipitation and increasing water demands for agricultural irrigation, industry, and human consumption have made water supply projections a major concern. Agricultural development is becoming more extensive in this dry, sunny climate where crops can be grown year-round. However, because this type of project usually involves significant perturbations to the regional water cycle, understanding the effects of irrigation on local hydrology is crucial. Based on the watershed-scale Soil and Water Assessment Tool (SWAT), this investigation focuses on the impacts of intensive irrigation on hydrological responses in the Majes region. This study is unique because we allow for crop-field scale input within our regional-scale model to provide information at this smaller scale, which is important to inform local stakeholders and decision makers. Each hydrologic response unit (HRU) was generated to represent an individual crop field, so that management practices could be applied according to real-world scenarios. The management file of each HRU was modified to include different operation schedules for crop rotation, irrigation, harvest, and tillage. The model was calibrated and validated against monthly observed stream discharge during the 2009–2020 period. Additionally, evapotranspiration, irrigation water volume, and daily stream discharge downstream of the local river (Siguas) were used to verify the model performance. A total of 49 sub-basins and 4222 HRUs were created, with 3000 HRUs designated to represent individual crop fields. The simulation results revealed that infiltration from agricultural activities in Majes represents the majority of annual groundwater return flow, which makes a substantial contribution to streamflow downstream of the Siguas River. Simulations also suggested that groundwater flow processes and the interactions between surface and groundwater have a major impact on the water balance of the study area. Additionally, climate variability had a higher impact on surface runoff than on groundwater return flow, illustrating that the groundwater component in the study area is important for future water resources resiliency under expected climate change scenarios. Finally, there is a need to perform a follow-up implementation to provide a guideline for decision-makers to assess future sustainable water resources management under varying climatic conditions for this arid irrigated system.

We investigate self-demagnetization effects on magnetic data and develop a comparison of two existing inversion methods as they apply to quantitative interpretation of such data in highly magnetic environments. We begin by evaluating the effect on magnetization direction when susceptibility is a scalar and increases from low values into the realm of self-demagnetization. We show through numerical experiments that susceptibility values of greater than 0.1 SI lead to significant self-demagnetization effects. Second, we show that conventional inversion can perform well for interpretation of self-demagnetization problems with simple source geometries. However, as the geometry becomes more complex in realistically complex problems, this approach can produce poor results and a more robust technique is required. Our numerical experiments indicate that directly inverting amplitude data, which can be derived from total-field magnetic anomaly data and are weakly dependent on magnetization direction, produces superior results when interpreting data from areas with complex geology and high magnetic susceptibilities. We conclude by evaluating the application of our preferred approach on a large field data set exhibiting strong self-demagnetization, multiple source bodies, and complex structures.

The light weight deflectometer (LWD) is a portable, nondestructive testing device that can estimate pavement layer parameters, namely moduli. Conventional backcalculation of layer parameters from LWD deflections is formulated as an inverse problem where predicted vertical deflections are matched to observed vertical deflections using a gradient search algorithm. In this paper, we present an LWD backcalculation scheme to recover layer parameters, including top-layer thickness, of a two-layer earthwork system. Our approach resolves the problem using a dynamic finite-element (FE) model for the forward calculation of LWD deflection data and implements a genetic algorithm (GA) as the inverse solver. The objective function we minimize is formulated as a measure of the data misfit between predicted and observed data, normalized by the peak deflections, and it includes 180 data points from the dynamic deflection time history. The objective function contains multiple local minima that can potentially trap ...

Insufficient access to safe drinking water is one of the most challenging global humanitarian issues. The development of low-cost microcontrollers and the widespread availability of cheap electronic components raise the possibility of developing and using low-cost geophysical instrumentation with open-source designs and software solutions to circumvent geophysical instrumentation capital cost issues. To these ends, we alter an existing low-cost direct current (DC) resistivity meter design and develop an optional modular Raspberry Pi data-logging system to improve the unit’s functionality and usability and to ensure data integrity. Numerical modeling and physical testing demonstrate that the system is more robust than previously published low-cost designs and works in a more diverse range of geologic scenarios — especially conductive environments. Our instrument was tested in a Geoscientists Without Borders project jointly run between researchers from Colorado School of Mines (CSM) and Université d’Abomey-Calavi (UAC), Cotonou, Benin. A key project component involved CSM and UAC students constructing and validating two low-cost DC resistivity meters and then using these instruments for fieldwork aimed at better characterizing and monitoring the health of a local aquifer used as a groundwater source for communities in the Cotonou region. The low-cost instruments were successfully used alongside a commercial resistivity meter to acquire data for 2D inversion of aquifer hydrostratigraphy, indicating the presence of a clay-sand contact. The costs of the redesigned instrument and data logger, respectively, are $177 and $108 (in 2021 USD) with future cost reductions possible, which are fractions of the price of commercial resistivity meters.