The African Humid Period (AHP) between ∼15 and 5.5 cal. kyr BP caused major environmental change in East Africa, including filling of the Suguta Valley in the northern Kenya Rift with an extensive (∼2150 km 2 ), deep (∼300 m) lake. Interfingering fluvio-lacustrine deposits of the Baragoi paleo-delta provide insights into the lake-level history and how erosion rates changed during this time, as revealed by delta-volume estimates and the concentration of cosmogenic 10 Be in fluvial sand. Erosion rates derived from delta-volume estimates range from 0.019 to 0.03 mm yr −1 . 10 Be-derived paleo-erosion rates at ∼11.8 cal. kyr BP ranged from 0.035 to 0.086 mm yr −1 , and were 2.7 to 6.6 times faster than at present. In contrast, at ∼8.7 cal. kyr BP, erosion rates were only 1.8 times faster than at present. Because 10 Bederived erosion rates integrate over several millennia, we modeled the erosion-rate history that best explains the 10 Be data using established non-linear equations that describe in situ cosmogenic isotope production and decay. Two models with different temporal constraints (15-6.7 and 12-6.7 kyr) suggest erosion rates that were ∼25 to ∼300 times higher than the initial erosion rate (pre-delta formation). That pulse of high erosion rates was short (∼4 kyr or less) and must have been followed by a rapid decrease in rates while climate remained humid to reach the modern 10 Be-based erosion rate of ∼0.013 mm yr −1 .Our simulations also flag the two highest 10 Be-derived erosion rates at ∼11.8 kyr BP related to nonuniform catchment erosion. These changes in erosion rates and processes during the AHP may reflect a strong increase in precipitation, runoff, and erosivity at the arid-to-humid transition either at ∼15 or ∼12 cal. kyr BP, before the landscape stabilized again, possibly due to increased soil production and denser vegetation.

Summary The analysis of surface wave dispersion curves is a way to infer the vertical distribution of shear-wave velocity. The range of applicability is extremely wide: going, for example, from seismological studies to geotechnical characterizations and exploration geophysics. However, the inversion of the dispersion curves is severely ill-posed and only limited efforts have been put in the development of effective regularization strategies. In particular, relatively simple smoothing regularization terms are commonly used, even when this is in contrast with the expected features of the investigated targets. To tackle this problem, stochastic approaches can be utilized, but they are too computationally expensive to be practical, at least, in case of large surveys. Instead, within a deterministic framework, we evaluate the applicability of a regularizer capable of providing reconstructions characterized by tunable levels of sparsity. This adjustable stabilizer is based on the minimum support regularization, applied before on other kinds of geophysical measurements, but never on surface wave data. We demonstrate the effectiveness of this stabilizer on: i) two benchmark—publicly available— datasets at crustal and near-surface scales; ii) an experimental dataset collected on a well-characterized site. In addition, we discuss a possible strategy for the estimation of the depth of investigation. This strategy relies on the integrated sensitivity kernel used for the inversion and calculated for each individual propagation mode. Moreover, we discuss the reliability, and possible caveats, of the direct interpretation of this particular estimation of the depth of investigation, especially in the presence of sharp boundary reconstructions.

Given the range of geological conditions under which airborne EM surveys are conducted, there is an expectation that the 2D and 3D methods used to extract models that are geologically meaningful would be favoured over 1D inversion and transforms. We do after all deal with an Earth that constantly undergoes, faulting, intrusions, and erosive processes that yield a subsurface morphology, which is, for most parts, dissimilar to a horizontal layered earth. We analyse data from a survey collected in the Musgrave province, South Australia. It is of particular interest since it has been used for mineral prospecting and for a regional hydro-geological assessment. The survey comprises abrupt lateral variations, more-subtle lateral continuous sedimentary sequences and filled palaeovalleys. As consequence, we deal with several geophysical targets of contrasting conductivities, varying geometries and at different depths. We invert the observations by using several algorithms characterised by the different dimensionality of the forward operator. Inversion of airborne EM data is known to be an ill-posed problem. We can generate a variety of models that numerically adequately fit the measured data, which makes the solution non-unique. The application of different deterministic inversion codes or transforms to the same dataset can give dissimilar results, as shown in this paper. This ambiguity suggests the choice of processes and algorithms used to interpret AEM data cannot be resolved as a matter of personal choice and preference. The degree to which models generated by a 1D algorithm replicate/or not measured data, can be an indicator of the data's dimensionality, which perse does not imply that data that can be fitted with a 1D model cannot be multidimensional. On the other hand, it is crucial that codes that can generate 2D and 3D models do reproduce the measured data in order for them to be considered as a plausible solution. In the absence of ancillary information, it could be argued that the simplest model with the simplest physics might be preferred

(2017). "Fast 3D multichannel deconvolution of electromagnetic induction loop-loop apparent conductivity data sets acquired at low induction numbers." GEOPHYSICS, 82(6), E357-E369.

We present an algorithm that performs sequentially one‐dimensional inversion of subsurface magnetic permeability and electrical conductivity by using multi‐configuration electromagnetic induction sensor data. The presented method is based on the conversion of the in‐phase and out‐of‐phase data into effective magnetic permeability and electrical conductivity of the equivalent homogeneous half‐space. In the case of small‐offset systems, such as portable electromagnetic induction sensors, for which in‐phase and out‐of‐phase data are moderately coupled, the effective half‐space magnetic permeability and electrical conductivity can be inverted sequentially within an iterative scheme. We test and evaluate the proposed inversion strategy using synthetic and field examples. First, we apply it to synthetic data for some highly magnetic environments. Then, the method is tested on real field data acquired in a basaltic environment to image a formation of archaeological interest. These examples demonstrate that a joint interpretation of in‐phase and out‐of‐phase data leads to a better characterisation of the subsurface in magnetic environments such as volcanic areas.